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Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Achievement Tests: Types, Interpretations and Uses : Types, Interpretations and Uses, Nova Science Publishers, Incorporated,

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Achievement Tests: Types, Interpretations and Uses : Types, Interpretations and Uses, Nova Science Publishers, Incorporated,

EDUCATION IN A COMPETITIVE AND GLOBALIZING WORLD

ACHIEVEMENT TESTS

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

TYPES, INTERPRETATIONS AND USES

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EDUCATION IN A COMPETITIVE AND GLOBALIZING WORLD

ACHIEVEMENT TESTS

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TYPES, INTERPRETATIONS AND USES

LUCAS E. MADSEN EDITOR

Nova Science Publishers, Inc. New York

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Copyright © 2011 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Madsen, Lucas E. Achievement tests : types, interpretations, and uses / Lucas E. Madsen. p. cm. Includes index. ISBN H%RRN 1. Educational tests and measurements. 2. Achievement tests. I. Title. LB3060.3.M33 2010 371.26'2--dc22 2010037905

Published by Nova Science Publishers, Inc. † New York

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CONTENTS Preface

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Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

vii Visual Motor Assessment Tests and Assistive Technologies: Description and Application in Post-Secondary Educational Achievement of Adults with Learning Disabilities Line Tremblay, Elizabeth Emptage, Cynthia Whissell, Brahim Chebbi and Stephane Bouchard The Use of Teacher Recommendations and Achievement Tests as Valid Instruments for Selecting Ethnic Minority Students into Secondary School Tracks Geert Driessen and Ed Smeets Construct-Irrelevant Variance in Achievement Test Scores: A Social Cognitive Perspective David E. Ferrier and Benjamin J. Lovett

1

57

89

Achievement Tests in the Norwegian School System: Types and Uses Eyvind Elstad, Are Turmo and Øystein Guttersrud

109

Mathematics Teachers Growing Professionally through Distance and Distributed Learning Opportunities Elizabeth Jakubowski, Darryl Corey and Hasan Unal

125

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vi Chapter 6

Contents Neuropsychological Parameters Affecting the Academic Aptitude Test (AAT) Achievement at the End of High School in 1996 and their Impact on Job Status in 2002: A Multifactorial Approach in a Follow-Up Study Daniza M. Ivanovic, Hernán T. Pérez, Boris P. Leiva, Nora S. Díaz, Bárbara D. Leyton, Atilio Aldo F. Almagià, María Soledad C. Urrutia, Cristián G. Larraín, Paulina E. Olave, Nélida B. Inzunza and Rodolfo M. Ivanovic

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Index

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191

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PREFACE Standardized achievement testing is increasingly common in educational and industrial settings. K-12 students take state assessments to comply with federal education laws. Many colleges administer assessments to place incoming students in initial courses and ensure that graduates have benefited from instruction. Professions such as law and medicine give assessments for certification and licensure. This book presents research in the study of achievement tests, including visual motor assessment tests and assistive technologies as applied to adults with learning disabilities; using teacher's recommendations and achievement tests for promoting ethnic minority students into secondary schools; as well as test anxiety and test motivation in achievement test performance. Chapter 1 - Motor skills are defined in terms of the ability to manipulate body parts and objects physically (Branningan and Decker, 2003, Brown, Unsworth, and Lyons, 2009a), whereas visual-motor skills (or visual motor integration or coordination) reflect the degree to which visual perception and finger-hand movement are well coordinated (Beery and Beery, 2006). For almost a century, tests have been developed to measure gross and fine motor skills and visual-motor coordination. Research findings using these tests show that poor motor performance and poor coordination were found in children with emotional, behavioral and pervasive developmental disorders (Emck, Bosscher, Beek, and Doreleijers, 2009) and in children with learning disabilities (LDs) (Getchell, McMenamin, and Whitall, 2005). Chapter 2 - In the Netherlands, the transition from primary school to the tracked secondary school system is the most decisive moment in a student’s school career. This transition is based upon the teacher’s recommendation as to the most appropriate school track for a student and supported by the results

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viii

Lucas E. Madsen

of achievement tests. For some time now there has been a vigorous discussion as to whether the use of the teacher’s recommendations and achievement tests are valid instruments for ethnic minority students. In the past, evidence was found for ‘over-recommending’ or ethnic minority students receiving higher educational recommendations than were warranted on the basis of their achievement test results. More recently it was suggested that the opposite or ‘under-recommending’ was becoming common practice among teachers or ethnic minority students receiving lower recommendations than justified by their school performance. Both under- and over-recommending signify a deviation from the meritocratic principle according to which such recommendations should only be given on the basis of demonstrated talent and ability. Using recent large-scale data from the Dutch national PRIMA cohort study, which includes more than 10,000 students from almost 500 primary schools, analysis of variance and multi-level analyses were performed to examine differences in the levels of recommendation provided. The aim was to better understand the influence of achievement test performance, student background characteristics (e.g., ethnicity, social milieu, sex), and school characteristics (e.g., ethnic and social class composition, degree of urbanization) on the transition from primary to secondary education in the Netherlands. The results showed the phenomena of over- or underrecommending or, in other words, groups of students receiving an educational recommendation which is not in line with their school performance, to no longer exist. Student achievement appeared to be the most important factor for the explanation of the level of recommendation, which clearly provides support for the meritocratic principle. Chapter 3 - Standardized achievement testing is increasingly common in educational and industrial settings. K-12 students take state assessments to comply with federal education laws. Many colleges administer assessments to place incoming students in initial courses and ensure that graduates have benefited from instruction. Professions such as law and medicine give assessments for certification and licensure. Even many employers assess job applicants’ levels of literacy and mathematical skills. Although many of these tests have shown substantial evidence of reliability and validity, there are a host of factors affecting scores on the tests that do not derive from examinees’ actual achievement levels. In this chapter, the authors consider three such factors, reviewing the research on their effects and discussing implications for achievement testing in various settings. First, the authors examine the influence of test-taking motivation on performance. Examinees’ motivation to do well on a test varies widely, with predictable

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Preface

ix

effects on resulting scores. The authors then move on to the impact of test anxiety, including anxiety caused by knowledge of stereotypes concerning one’s own group (stereotype threat). Although anxiety is associated with increased motivation to do well, it ironically impedes performance, attenuating estimates of examinees’ skills. Finally, the authors cover the role of prior task exposure and test-taking strategy use on scores. Given the current surfeit of available test preparation materials and services, it is important to understand which types of preparation are actually helpful for examinees. Throughout the chapter, the authors note the implications of research on these factors for current achievement testing programs. The authors also discuss the role of these factors in accounting for group differences in test scores. By the end of the chapter, readers should have a rich understanding of the contributors to achievement test scores (other than achievement itself). Chapter 4 - During the past decade in Norway —as in many other countries— accountability devices have been established in systems of education governance. The accountability systems may vary from country to country; however, this article discusses the types and uses of achievement tests in the Norwegian school system. Although Norway has a long tradition of school-leaving exams in both lower and upper secondary school, achievement tests in the tradition of e.g. PISA and TIMSS have only recently been introduced. Complementary accountability tools such as transparency of test results, school autonomy, and school inspections, are used for steering purposes. Tests and exam results play key roles in the performance management system. Together, these devices can be regarded as building blocks in sprawling external accountability systems. It would be wrong to say that the Norwegian school system at the national level has a fully-fledged accountability system. Achievement tests are, however, potential instruments for the promotion of accountability mechanisms. The Norwegian government has made it possible for governing bodies to develop local accountability systems, and achievement tests play a significant role therein. The extent to which local authorities have been able to relate to this type of delegated responsibility has varied. In this article, the authors discuss the different types of achievement tests in Norwegian compulsory education, and how they are used. Chapter 5 - The multiple demands on a mathematics teacher’s time highlights a need many have to have access to professional development opportunities through not only traditional venues (e.g., 3 hour inservice program) but also through historically nontraditional venues (e.g., web-based). Coupled with an expectation for quality and relevance along with addressing

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location and time constraints, teacher educators who engage in professional development with teachers are challenged to address these and other expectations. This paper provides a description of a graduate program that was designed with many of the expectations in mind while maintaining the rigor expected of a graduate degree. Chapter 6 - The aim of this study was to determine the impact of neuropsychological parameters on the academic aptitude test (AAT) achievement at the end of high school in 1996 when they should be graduating from high school and on job status carried out six years later during 2002. From a representative sample of 1817 Chilean school-age children (mean age 18.0 ± 0.9 y) graduating from high school in 1996 in Chile’s Metropolitan Region, 96 were selected with high (> 120 WAIS-R) and low IQ (< 100 WAIS-R) (1:1), from the high and low socio-economic strata (SES) (1:1) and of both sexes (1:1). AAT scores for university admission were obtained for 84 school-age children from University of Chile records and were divided into two groups: high AAT (≥ median (Md)= score 631) and low AAT (< Md). IQ was determined by the Wechsler Intelligence Scale for Adults (WAIS-R) and the Raven Progressive Matrices Test in the school-age children and their parents. Scholastic achievement (SA) was measured applying the standard Spanish language and mathematics tests. SES was evaluated using Graffar's modified method. Nutritional status was assessed through anthropometric measurements of weight and height to establish the body mass index (BMI) according to Garrow; head circumference (HC) was compared with Tanner, Nellhaus, Roche et al. and Ivanovic et al. tables and was expressed as Z score (Z-HC); body composition parameters such as arm circumference-for-age, triceps skinfold-for-age, arm muscle area-for-age and arm fat area-for-age were calculated using data from Frisancho. Brain morphology was determined by magnetic resonance imaging (MRI). Job status was expressed as: (1) jobless, (2) workers without further schooling, (3) students at institutes and (4) students at universities. Statistical analysis included correlation and logistic regressions using the Statistical Analysis System (SAS). Results showed that students with high AAT score presented IQ, parental IQ, SA, brain volume (BV), Z-HC, maternal schooling, house-hold head occupation and quality of housing significantly higher than their peers that achieved the lowest AAT scores of whom 19% had suffered severe undernutrition in the first year of life (Fisher p< 0.0054). However, logistic regression revealed that student IQ is the best predictor of AAT score and the odds ratio value (1.252) implies that when the IQ score increases by one point, the probability to obtain a high AAT score increases in 25.2%. Students at universities presented AAT, child and maternal

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IQ, SA, BV, birth weight and birth height, Z-HC and socio-economic conditions significantly higher than their peers from the other three job statuses; however, AAT score at the end of high school was the best predictor of the job status six years later (odds ratio value= 1.025) which indicates that when AAT score increases by one point the probability for university admission and for university graduation increases 2.5%. In a multifactorial approach, these results point out the importance of neuropsychological parameters on children’s achievement for university admission and future jobs.

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Chapter 1

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VISUAL MOTOR ASSESSMENT TESTS AND ASSISTIVE TECHNOLOGIES: DESCRIPTION AND APPLICATION IN POSTSECONDARY EDUCATIONAL ACHIEVEMENT OF ADULTS WITH LEARNING DISABILITIES Line Tremblay∗1, Elizabeth Emptage1, Cynthia Whissell1, Brahim Chebbi1 and Stephane Bouchard2 1

Laurentian University, Sudbury, Ontario, Canada 2 Université du Québec en Outaouais, Gatineau, Québec, Canada

Motor skills are defined in terms of the ability to manipulate body parts and objects physically (Branningan and Decker, 2003, Brown, Unsworth, and Lyons, 2009a), whereas visual-motor skills (or visual motor integration or coordination) reflect the degree to which visual perception and finger-hand movement are well coordinated (Beery and Beery, 2006). For almost a century, tests have been developed to measure gross and fine motor skills and visual-motor coordination. Research findings using these tests show that poor ∗

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motor performance and poor coordination were found in children with emotional, behavioral and pervasive developmental disorders (Emck, Bosscher, Beek, and Doreleijers, 2009) and in children with learning disabilities (LDs) (Getchell, McMenamin, and Whitall, 2005). LDs are a varied group of developmental disorders that interfere with learning skills; they are indicated by unexpected academic under-achievement in reading, writing, and /or mathematics in comparison to average or above average intellectual functioning (Dumas and Nilsen 2003; APA, 2000). In Canada one in ten individuals is diagnosed with a learning disability every year. Of those diagnosed, three percent are adults (HRSDC, 2006). These numbers are very likely an underestimate of the adult population with LDs, as it is not uncommon for adults to live with undetected LDs (LDAC, 2005; Essential Learning Institute, 1994). There is evidence suggesting that visualmotor challenges in children with LDs do persist and are present in adulthood (Branningan and Decker, 2003; Decker, 2007; Durand, 2005; Kirby, et al., 2008), in part because children grow up avoiding tasks involving visual motor skills. Avoidance widens the pre-existing skill-learning gap between such children and their more physically proficient peers (Wall, 2004). The past few decades have led to intense developments in technologies used to compensate for, assist, or simply maximize performance not only in tasks involving perceptual-motor skills such as elite sport training, medical training, or physical rehabilitation, (Alcaniz, Botalla, Banos, Zaragoza, and Guixeres, 2009; Gregg and Terrier, 2007) but also in language-based academic settings, to help individuals with disabilities (Raskind, 1993). This raises the question of whether these new technologies impact individuals with impaired visualmotor skills, in particular those with Learning Disorders and if so, to what extent? The present review is motivated by the limited literature available on adults with LDs in post-secondary education, the visual-motor challenges faced by post-secondary students with LDs, and the growing utilization of assistive technology in higher education. The first part of this chapter reviews the existing tests measuring visual motors skills and their use in the population of individuals with LDs. The second part is dedicated to the description and use of assistive technologies, e-tools and virtual reality tools developed to assist, remediate, or compensate for students’ disabilities. We conclude with a discussion on the effect of assistive technologies in students’ academic performance.

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Visual Motor Assessment Tests and Assistive Technologies

3

1. VISUAL-MOTOR ASSESSMENT AND DIAGNOSIS TESTS In this section we first present the theoretical background of the development of visual motor tests. In the second subsection, we provide a description of some of the tools from the A, B and C levels (types of administration) used to identify visual-motor coordination difficulties in children and adults with LDs, and we discuss their strength and limitations. The third subsection reviews the literature for evidence of the links between visual motor skills and academic achievement in adults with LDs.

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1.1. Theoretical frameworks The importance of visual-motor ability in children’s cognitive development is central to Piaget’s theory of intelligence (Miller, 1989). Piaget’s genetic epistemology can be summarized as describing the relationship between a child’s actions and thinking and his or her experience with the environment. This relationship changes across the developmental stages. For Piaget, the developing child first ‘thinks’ in acting: this marks the first stage of his development (sensorimotor). The theory’s stages describe how a child’s thinking changes and develops from a mental representation of action, to a representation of objects, to mental operations on concrete objects, to operations on thoughts, and finally to abstract thinking which is achieved through unique, universal and general process aimed at increasing the child’s abilities to categorize, reason, and manipulate mental representations (Miller, 1989). Thus, according to Piaget’s theory, intelligence is first sensorial in nature and it manifests itself in visual motors abilities which are employed as primary tools used by the developing child to learn. Piaget postulated that the child’s stages of intellectual development are universal and involve foundational visual-motor and motor challenges or skills that must be learned and mastered prior to progressing to the subsequent stage successfully (Sattler, 1990). Other theorists have made reference to the fundamental impact of visualmotor capability on cognitive development and on the ability to learn (Sattler, 1990). Vernon’s hierarchical theory of intelligence (1950) viewed general intelligence (g) as the highest level of several others subdivisions or functions grouping spatial, psychomotor, and mechanical information.

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More recent information-processing theories of intelligence assume that problem solving, learning, and memory are dependent on the individual’s perceptual skills and reflect sensory activity and nervous system integrity (e.g. ability to perceive and perform visual motor tasks effectively and efficiently; Sattler, 1990). Thus, as noted by Sternberg (1986), motor and visual motor ability are fundamental aspects in intelligence and learning. According to these theories, impaired visual motor ability has the potential to affect learning adversely in significant ways (Culbertson and Edmonds, 1996; Kozey and Siegel, 2008; Martinez -Marrero and Estrada-Hernandez, 2008; Sattler, 1990). Conversely, average and above average visual motor skills should translate into equally average/superior learning and formal achievement. This rational underlies the development and validation of visual-motor performance tests discussed in the following subsection.

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1.2. Psychological Assessment Tests and Research Tools The first tests of visual motor skills developed, such as the MacQuarrie Test for Mechanical Ability (MacQuarrie, T. W., 1927), the Stenquist Mechanical Ability Test (Stenquist, J.L, 1922), and Kohs Block Design Test (Kohs, C., 1923) measured “motor ability” and “mechanical ability” the former being defined as simple functions of isolated muscles or muscle groups, and the latter more inclusively as involving capacities and abilities necessary for certain kinds of work (i.e. work involving the manipulation of tools, the operation of machinery, and the planning and execution of pieces of work ) (Harel, 1940; Hammill, 1940, Stein, 1927). These tests were used for a variety of reasons including the making of immigration decisions, army and military recruitment, specific position recruitment (e.g. airplane mechanic), and the investigation of psychical disabilities (Harrell, 1940). Many pencil-and-paper visual-motor assessment measures have been developed since, and they are mainly used to assess and diagnose individuals with disabilities such as mental retardation, LDs, personality dynamics, and brain injuries (Decker, 2007). Table 1 lists some of the classical tools used in child and adult populations with LDs.

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Table 1. Classical Tests of Motor Skill Assessment (excludes motor tests used for stroke or physically disabled patients) Test

Measurement Category

Sample

Psychometric Properties

C-LEVEL Tests

Visual Objects and Space Perception Battery (VOSP); Warrington, E. K. and James, M. (1991)

Visual-spatial

Age range: 20.0-84.0 Normative sample: N1= 200 (given tests 1,5,6,7, and8); N2: 150 (given tests 2, 3,and4); US hospital patient sample populations; both sample groups were evenly distributed by gender, age, social class, and hemispheric lesion in brain (left or right).

Reliability Test-retest (1 month interval): 0.88 (Silhouette subtest) Internal consistency coefficient α: 0.70 (of subtests) (Bonello et al., 1997); exception: Number Location, Cube Analysis, and Silhouette subtests (0.70 – 0.89). Sources; (Bonello et al., 1997; Rapport et al., 1998, Strauss et al., 2003). Validity Content validity: factor-Analysis (sample of older individuals) determined a two-factor model of object and space perception; however, the object perception measures were stronger and more reliable indicators of their respective latent construct than were the space perception measures (Rapport et al., 1998). A comparative study of elderly Spanish population VSOP means and that of an American population study (Bonello et al., 1997) revealed no significant differences, with the exception of the Progressive Silhouettes subtest (Herrera-Guzmán et al., 2004)

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Table 1. (Continued) Test

Measurement Category

Sample

Psychometric Properties Reliability (manual) Fine Motor: content sampling (0.96), time sampling (0.93) (1 week interval), and interrater differences (0.98). Gross Motor: content sampling (0.96), interrater differences (0.97), time sampling (0.89) (1 week) Overall: Internal Consistency α =0.97 Test Retest (1 week interval): 0.89, 0.96

Peabody Developmental Motor Scales – 2nd edition (PDMS-2); Folio, R. M. and Fewell, R. R. (2000)

Fine and Gross Motor

Age range: birth – 72 months (6.0 years); Normative sample of 2003 individuals from 46 states in the United States and British Columbia, Canada (mainly US children involved); sample evenly distributed in age, gender, and race/ethnicity.

Test-Retest and inter-rater correlation coefficients varied from 0.84 to 0.99 (Van Hartingsveldt and Oostendorp, 2005). Validity Concurrent validity: PDMS-2 and the Bayley Scales of Infant Development comparison with Native American children Age-equivalent scores correlation analysis for 2year-olds for the Fine Motor scales = 0 .87 and for the Gross Motor scales = 0 .83. Correlations for standard scores were lower (0 .64 and 0 .49, respectively). (Provost, Crowe, and McClain, 2000). Content validity: item analysis and item response theory (satisfied) Criterion-prediction validity: PDMS scores correlated with the Mullen Scales of Early Learning: AGS Edition

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Test

Measurement Category

Sample

Psychometric Properties (Mullen, 1995). Gross and Fine Motor correlations = 0 .80.

Bender Gestalt Visual Motor Test, 2nd edition (BG-II); Original: Bender, L. (1938); 2nd edition: Brannigan, G.G., and Decker, S.L. (2003). Bender Motor Gestalt Test, Second Edition. Itasca, IL: Riverside Publishing

Bruininks-Oseretsky Test of Motor Proficiency, 2nd edition (BOT-2); Bruininks, R., H. and Bruininks, B., D (2005)

Visual Motor

Visual-Spatial (learning and recall)

Age range: 4.0-85.0; Conormed with the StanfordBinet Intelligence Scales, 5th edition, BG-II standarded on 4000 U.S. individuals between 4 and 90 years of age (sample stratified by race/ethnicity, education, and socioeconomic status) to match 2000 US census) (Decker, 2008) Age range: 4.0-21.0 Normative sample of 1,520 individuals from 38 states (421 years). Sample stratified by gender, race/ethnicity, Socioeconomic status, geographic region, and disability status

Convergent validity: 0 .83 with the PDMS Fine Motor Scale; 0 .80 with the PDMS Gross Motor Scale. (Van Hartingsveldt and Oostendorp, 2005). Reliability Internal Consistency (Split-half) (across age levels):0.860.95 Interrater consistency: Copy phase (0.83-0.94; mean=0 .90); Recall phase (0.94-0.97; mean = 0.96. Test-retest (2-3 week intervals; means): Copy phase 0.85; Recall Phase 0.83 Validity (N/A) Reliability Internal consistency coefficients (Split-half): 0.70 – 0.89 (subtests); 0.78 - 0.97 (composites); total motor composite ~0.95 Test-Retest (1 – 6 week intervals): .52-.95 (mean ~0.85) Inter-rater coefficients: >0.92 Wuang and Su (2009) study with children with intellectual disabilities: Test–retest reliability and internal consistency of the total scale: 0.99; α of 0.92, respectively. Criterion-related validity

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Table 1. (Continued) Test

Full Range Test of Visual Motor Integration (FRTVMI); Hammill, D.D., Pearson, N., A., Voress, J. K., Reynolds, C. R. (2006)

Measurement Category

Visual Motor and Visual Perception

Sample

Age range: 5.0-74.0 Normative sample of 3151 individuals from 22 American states. Sample stratified by gender, race/ethnicity, Socioeconomic status, geographic region, and exceptionality.

Psychometric Properties With Battery Composite and Total Motor Composite ( 0.80); Peabody Developmental Motor Scales, Second Edition, Total Motor Composite and Total Motor Quotient ( 0.73); Test of Visual Motor Skills-Revised, fine motor integration and Visual-Motor Skills Composite (0.74) Reliability Internal consistency: >.80 (Brown et al., 2009b) Content sampling using coefficient α for computing item correlations: mean of 0.85 for 5- to 10-year-olds and 0.90 for 11- to 74-year-olds (Salkind, 2006). Interrater coefficients: 0.92 (5 – 10 year olds); 0.96 (11 to 74 year olds) Test-retest (1 week interval): 0.85 for 5- to 10-year-olds and 0.85 for 11- to 74-year-olds. Validity Content validity: α> 0.80; 42% of which > 0.90. Concurrent validity: with the Beery VMI for 5 to 10 year olds (0.85) Criterion-prediction validity: with the DTVP-A for 11- to 74-year-olds (0.93 for the Copying subtest and 0.63 for the Visual-Motor Integration Index). (Hammill et al., 2006).

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Test

Movement Assessment Battery for Children-Second edition (Movement ABC-2); Henderson, S. H., Sugden, D. A (2007)

Measurement Category

Fine and Gross Motor

Sample Age range: 3- 16.11; N1= 1000 N2= 400 Normative Sample of 1172 individuals (age range of 3.0 – 16.11) from the United Kingdom. Sample stratified by age, gender, race/ethnicity, level of parent education, and geographical region. Sample closely matched the proportion for each of these variables as indicated by the 2001 census.

Psychometric Properties Reliability Test-retest (1and 2 week intervals): 0.73-0.84 (component scores; 0.80 (total score). Test-re-test: 92-.98 (Croce et al., 2001) Inter rater: .93 - .99 (Chow et al., 2006) and .95-1.00 (Smits-Engelsman, et al., 2008) Validity Correlations between subtest 0.25-0.36; correlations beween test components and total test score 0.65 – 0.73 Content validity: Item analysis Concurrent validity: - 0.53 correlation with Bruininks– Oseretsky Test of Motor Proficiency; -0.74- to-0.51 correlation between ball catching test and M-ABC test (Van Waelvelde, et al., 2004).

B-LEVEL Test Beery-Buktenica Developmental Test of Visual-Motor Integration, 5th edition (Beery VMI); Beery, K. E., Buktenica and Beery N. A. (2004)

Visual Motor

Age range: 2.0-100.0 Normative sample of 2512 individuals ages 2 to 18 years of age and 1021 adults ages 19-100 VMI includes age-specific norms, with 600 age specific norms from ages 2–6. Since the original Beery VMI (early 1960s), the test has has been re-normed five times with over 11,000 children.

Reliability Internal consistency: 0.93 Interrater: 0.94 – 0.98 Test-retest: 0.92 ( 2 week interval); 0.63 (7 months); 0.87 (3 week interval; for total Test); 0.84 (3 week interval; for visual motor integration subscale); 0.83 (3 week interval; for motor-coordination Subscale) Split half: 0.82–0.93; 0.88 (median) Odd-even split half reliability for visual motor integration scale: 0.88

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Table 1. (Continued) Test

Koppitz Developmental Scoring System for the Bender Gestalt Test- Second edition (KOPPITZ-2); Original: Koppitz, E. 2nd edition: Reynolds, C. R. (2007)

Measurement Category

Visual Motor

Sample

Psychometric Properties

Age range: 5.0-85.0 Normative sample: 3535 U.S. individuals. Sample stratified by socio-economic status, race/ethnicity, region, community size, and other variables. (Uses the same standardization sample as the Bender-Gestalt II).

Validity Construct validity and predictive validity studies with the DTVP-2, WRAVMA, and Bender-Gestalt assessment tools determined the Beery VMI to be a valid measure. Concurrent validity: 0.52 with the WRAVMA (Drawing test); 0.63 with the Comprehensive Tests of Basic Skills; 0.80 with chronological age; 0.66 with the Wechsler Intelligence Scale for Children-Revised (WISC-R). Bender-Gestalt correlated with earlier editions of the Beery VMI 0.29 to 0.93 (median 0 .56). Beery VMI scores highly correlated (0.89) with chronological age Individual and group administrations correlated at 0.65. Predictive validity: manual references a number of studies; yet, few findings are reported. Only one of the referenced studies is less than 5 years old; the rest are 10-20+ years old. Validity research of supplemental tests not reported Reliability Internal consistency α: 0.84 - 0.93. (exception for 5-7 year old children: 0.77 - 0.81); Mean across ages: 0.88. Test-retest (~2 week interval): 0.75 - 0.84 (across four age groups); mean of 0.78. Interrater: 0.91 (5-7 year old); 0.93 (8-85 year old) Convergent validity: with the WISC-III Performance Scale (0.63); the WAIS-III Performance IQ (0.38); StanfordBinet 5 Nonverbal IQ (0.40).

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Test

Measurement Category

Sample

Psychometric Properties Concurrent validity: with the Woodcock-Johnson-Third Edition (0. 07 - 0.46; median of 0.23); the Wechsler Individual Achievement Test-II (0.20 - 0.52; median of 0 .37); Beery-VMI 4th Edition (0.46). KOPPITZ-2 manual compares mean scores from seven samples of exceptional students with demographically matched samples of non-exceptional students. Mostly, individuals with disorders demonstrated slightly lower mean performance than the matched control sample.

Wide Range Assessment of Visual Motor Abilities (WRAVMA); Adams, W., Sheslow, D. (1995)

Visual Motor

Age range: 3.0 to 17.0; Normative sample of 2600 individuals from 22 American states.

Visual Motor Assessment (ViMo); Fuller, Gerald, B. (2006). Visual Motor Assessment.

Visual Motor

Age range: 6.0-99.0; Normative sample of more than 10000 U.S. children with no clinical diagnosis; 1800 with an emotional disturbance, brain damage, or schizophrenia; 400 with brain damage.

Test of Visual Motor Skills-Revised (TVMS-R); Gardner, M. F. (1995)

Visual Motor and Visual Perception

Age range: 3.0- 13.11; Normative sample of 1500 U.S. individuals.

Reliability Internal consistency: > 0.90 (all 3 subtests) Test-retest (2 week interval): 0.81- 0.91. Construct validity: Item separation of .99. Reliability Test-retest (3 month interval; sample of 717 children, aged 6 to 14): 0.53 - 0.70. Test re-test (one year interval; separate sample of 605 children): 0.49 to 0.65. Test-retest (3month interval; separate sample of 1,402 adults): 0.72 to 0.86. Validity N/A Reliability Internal consistency: 0.72 -0.84; median of 0.79 (each age level); 0.90 (entire sample);

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Table 1. (Continued) Test

Test of Visual Motor Skills: (Upper Level) (TVMS-UL); Gardner, M. F. (1995)

Measurement Category

Visual Motor

Sample

Age range: 12.0- 18.11; Normative sample of 900 U.S. individuals

Psychometric Properties Validity Content validity: item analyses Construct validity: item-test correlations Concurrent validity: 0.33 with the Test of Nonverbal Intelligence, 0.23 to 0.34 with four subscales of Stanford Achievement Test, 0.59 with TVMS, 0.25 with Otis-Lennon School Ability Test non-verbal subscale and 0.37, Test of Auditory-Perceptual Skills processing subtest; 0.50 with Bender Visual-Motor Gestalt Test, and 0.51 with VMI. Reliability Internal consistency: 0.81-0.89; 0.83 (median); 0.86 (across all age levels) Validity Content validity: Item analysis 0.25 – 0.65 (item total correlation of items) Construct validity: correlations with Bender Visual-Motor Gestalt Test 0.31-0.80 (median 0.59); VMI moderately high; in 12-year-old group, correlation between lower and upper form of TVMS are 0.69; correlation with WAIS-R and WISC-R are lower.

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Test Slosson Visual-Motor Performance Test (SVMPT); Slosson, R. and Nicholson, C. (1996)

Test of Gross Motor Development-2nd edition (TGMD-2); Ulrich, D. A. (2000)

Measurement Category

Sample

Visual Motor

Age range: 4.0 – 70.11; Normative sample of 1381 U.S. individuals; 53% (736) females, 47% (645) males; 7% African American, 89% European American, 3% other.

Gross Motor

Age range: 3.0-10.0; Normative sample of 1,208 individuals from 10 American states

Psychometric Properties Reliability Test-retest (2 week interval): 0 .870 Internal consistency (Split-half): 0.82-0.97 (age range intervals); 0.97 (total sample) Inter-rater: 0.86 Validity S-VMPT manual reports only one source of construct validity evidence involving age differentiation (Brown and Unsworth 2009) Content validity: item analyses Concurrent validity: 0.62 with VMI; 0.59 with Bender Visual-Motor Gestalt Test; and 0.64 with WISC-III Perceptual Organization Index Reliability Test-retest (2 week interval): .85 - .91 (Locomotor subtest mean 0.85; Object Control subtest mean 0.88; Gross Motor composite mean 0.91).; 0.88 (3-5 year olds); 0.94 (6-8 year olds); 0.86 (9-10 year olds) Interrater: 0.98 (subtest and Gross-motor quotient) Internal consistency mean: 0.91 Validity Content validity: with Basic Motor Generalizations subtest of the Comprehensive Scales of Student Abilities (0.63 for the Locomotor subtest; 0.41 for the Object Control subtest; 0 .63 for the Gross Motor Quotient)

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Table 1. (Continued) Test

Measurement Category

Purdue Pegboard Test; Originally produced by Tiffin, J. (1948). Latest edition: Lafayette Instruments® (2002)

Fine and Gross Motor

Sample Age Range: 5.0-89.0; Manual reports normative data for various occupational groups; however, the data are quite old; are not stratified by age; information regarding hand preference is not provided.

Psychometric Properties Reliability Test-retest (1 - 2 week intervals): 0.37-0.82 (one-trial administrations); 0.76-0.89 (three-trial administrations). Right-Left hand score difference/ratios: 0.22-0.61 Validity Content validity: factor analysis; one factor; defined, “the ability to make rapid, skillful, controlled manipulative movements of small objects, where fingers are primarily involved”. Construct validity: 0.78 with finger-tapping; 0.52-0.68 with other manual dexterity tasks; hand preference (0.70).

A-LEVEL Tests Developmental Test of Visual Perception2nd edition (DTVP2); DTVP-2 is the 1993 revision of Marianne Frostig’s Developmental Test of Visual Perception (DTVP). Hammill, D.D., Pearson, N. A., and Voress, J. K. (1993)

Visual Motor

Age range: 4.0–10.11 Normative sample of 1972 children from 12 U.S. states. Sample stratified by race/ethnicity, gender, residence, geographic area, and handedness.

Reliability Internal consistency: 0.83 - 0.95 (all subtests); General Visual Perception (composite of all subtests) 0.96 – 0.98 Test-Retest (2 week interval): 0.71 - 0.86 (subtests and 0.89 0.93 (composite scores) (across all age groups) Inter-rater: 0.93 - 0.99 (for each subtest score) and 0.95 - 0.98 (for each composite score) Validity: Content validity: DTVP-2 test content on constructs articulated by area experts; DTVP-2 structure compared to other tests of visual perception; item analysis (all test items satisfy content validity). Criterion-related validity: with the Beery VMI (0.89) and the Motor-Free Visual Perception Test (0.73). Factor analysis and item-test correlation results also support the construct validity of the DTVP-2.

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Test Developmental Test of Visual Perception-Adult and Adolescent (DTVPA); DTVP-A is an extension to Marianne Frostig’s Developmental Test of Visual Perception (DTVP). Reynolds, C. R.., Pearson, N. A., and Voress, J. K. (2002)

Test of Handwriting Skills-Revised (THSR); Milone, M. (2007)

Measurement Category

Sample

Visual Motor and Visual Perception

Age range: 11.0 – 74.11 Normative sample of 1664 individuals ages 11 through 74 years, 11 months in 19 U.S. states. Sample stratified by race/ethnicity, gender, geographic area, age, disability status, family income, educational attainment.

Visual Motor

Age range: 6.0-18.11; Normative sample of 1,608 U.S. individuals. No explicit stratification of demographic groups is reported.

Psychometric Properties Reliability Internal consistency: 0.77 to 0.89 (subscales); 0.84 total scale correlation Test-retest (1 week): 0.70 - 0.84. (subtests); 0.81 - 0.84 (composite indexes); 0.81 (total scale correlation); 1 week, > 0.8 (across all age groups) (Zoltan, 2007) Interrater: 0.95 - 0.99 (subtests); 0.94 - 0.99 (composite indexes). Validity Content-description validity, criterion-prediction validity, and construct-identification validity are supported in the manual (statistics N/A; refer to test manual). Reliability Internal Consistency: 0.70 - 0.92 (across subtests and 14 age intervals); 0.95-0.98 (subtests) Test-retest (~2 week interval): 0.82 (overall); 0.71-0.78 (subtests) Inter-rater reliability: .0.84-0.90 (total score agreement); 0.700.93 (subtest score agreement) 0.89 (overall) Validity Content validity: assumed (the use of Differential Item Functioning found no item bias) Construct validity: scores increase up to age 10, then decrease, due to less formal handwriting instruction and the increased usage of computers. Individuals diagnosed as ADD/ADHD or learning disordered obtained significantly lower scores than the general population. Factor analysis: THS-R loads into only two factors-Subtests 1 through 10 loaded onto one (Basic Handwriting Skills), whereas the ancillary scores loaded onto another (Ancillary Tasks).

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16

Visual Motor Assessment Tests and Assistive Technologies

We found that several tests measure visual perception, motor skills and visual-motors integration or coordination whereas some tests only measure fine/gross motor skills or visual-spatial perceptions. These tests can be found in all of the three categories of administration levels, that is Level A allowing for any person to administer; Level B allowing for allied health or special education professionals with relevant qualifications (involving studies in research methods, statistics and testing) to administer; Level C allows for only a registered psychologist to administer. Tests measuring visual-motor integration of coordination are mostly of the paper and pencil type, and usually require the participant to copy geometric forms with increasing levels of difficulties (with corresponding age norms or mental status norms). Other test tasks require the ability accurately relate visual stimuli to motor responses. Reviewed visual motor integration tests include; the Beery-Burtenica Visual Motor Integration (BEERY VMI, 5th edition), the Developmental Test of Visual-Motor Integration (DTVMI), the Visual-Motor Assessment (ViMo), the Koppitz Developmental Scoring System for the Bender Gestal Test-2nd edition, (KOPPITZ-2), the Developmental Test of Visual Perception 2nd edition, (DTVP-2), the Developmental Test of Visual Perception in Adult and Adolescent (DTVP-A), the Full Range Test of Visual Motor Integration (RTVMI), the Wide Range Assessment of Visual Motor Abilities (WRAVMA), the Test of Perceptual Skills-3 (TVPS3), the Test of Visual Motor Skills 3rd edition(TVMS-3), the Test of Visual Motor Skills (Upper Level) Adolescents and Adult (TVMS-UL), the Slosson Visual Motor Performance Test (SVMPT) and the Test of Visual Motor Skills-Revised (TVMS-R). Test measuring fine/gross motor skills may require the participant to manipulate objects (e.g. sorting, assembling, grasping, throwing or catching for an object), perform gross movements (hopping, jumping) or fine movements (pegboard , finger-hand movements). Fine/gross motor measurement tests reviewed are The Bruininks-Oseretsky Test of Motor Proficiency, the 2nd edition (BOT-2), the Movement Assessment Battery for Children, the 2nd edition (ABC-2), the Bender Gestalt Visual Motor Test, the 2nd edition (BG-II), the Test of Gross Motor Development 2nd edition (TGMD2), the Test of Handwriting Skills-Revised (THS-R), and the Purdue Pegboard Test. We reviewed three tests measuring visual perceptual strength means of space perception, object recognition or object aspect assessment. These tests are the Location Learning Test (LLT), the Visual Objects and Space

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Perception Battery (VOSP), and the Peabody Development Motor Scale, 2nd edition, (PDMS-2). The two most widely-used tests are the Developmental Test of Visual Motor Integration (VMI; Beery, 1997) and the Bender Visual Motor Gestalt Test (BG-II; Brannigan, and Decker, 2003). Both tests show good psychometric properties. The BG-II has a coefficient of 0.91 for internal consistency and the test-retest reliability coefficients range from 0.77 to 0.81. The BEERY VMI (5th edition) coefficients of inter-scorer reliability are 0.90, 0.91 and 0.92 for the visual-motor scale, the visual perception and motor coordination scales. Internal consistency measured by Cronbach Alpha and split-half tests ranges from 0.81 to 0.88 (Beery and Beery, 2006). Brown, Unsworth, and Lyons, (2009a) report similar findings with coefficients alpha higher than 0.80 for both tests. Correlations between age and visual motor performance are strong (0.84 to 0.89), supporting the test’s validity (2003 norming data, Beery and Beery, 2006). On the other hand, the authors report correlations with scores from intelligence tests (WISC-R; Slosson IQ, WAIS, Stanford-Binet-Suzuki) and achievement tests (Test of Basic Skills) to be only moderate (ranging from 0.37 to 0.71). The developmental version of the test (The Child and Adolescent Developmental Test of Visual Motor Integration DTVMI; Beery and Beery, 2006) also shows good psychometric properties. However, some research findings suggest that the DTVMI is not correlated to scores on achievement and intelligence tests scores and does not predict written language skills of students with LDs. (Aiello-Cloutier, 1995). Other researchers found that the DTVMI tend to overestimate visual motor skills since students who had been identified as having LDs obtained significant higher results on the DTVMI than the Bender Gestalt (DeMers, Wright, and Dappen, 1981). The correlation between the Bender Gestalt and the BEERY-VMI tests is at best moderate, as shown by previous studies on the earliest versions of these tests (DeMers, Wright, and Dappen, 1981; Skeen, Strong, and Book, 1982), which suggests that the tests measure different aspects of visual motor integration. It might be that the Beery-Vim measure, and more specifically the developmental component of visual-perceptual skills, as suggested by Brown, Unsworth, and Lyons (2009b) is at fault. These authors’ factor analyses suggest that visual motor integration is not a one-dimensional concept, but that it encompasses several traits, attributes, or skills. Some research findings showed that the BEERY-VMI is more valid and reliable than the BG-II (Breen, 1982; Siewert, and Breen, 1982; Breen, Carlson, and Lehman, 1985).

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The Developmental Test of Visual Perception-2nd edition (DTVP-2; Hammill, Pearson, and Voress, 1993) and its version for adults and adolescents (DTVP-A;Reynolds, Pearson, and Voress, 2002) show good internal and test-retest reliabilities (all coefficients exceed 0.8). However, a more recent study, Brown, Mullins, and Stagnitti, (2008) reports only low to moderate internal consistency coefficients and moderate test-retest reliability coefficients for DTVP-A. The authors do not recommend the use of this test to measure visual motor ability in LDs investigation. The Wide Range Assessment of Visual motor Skills (WRAVMA) provides assessment information in the areas of visual motor, visual spatial, and fine motor functioning and shows good internal consistency (< 0.90) and test-retest reliability (0.81 to 0.91). Glidden’s (1996) research indicated that the WRAVMA allows for the discrimination of students with LDs, as lower scores on its drawing performance subtest predict similar performance on an IQ test (WISC-III) and a standard test of academic achievement (WIAT). Among the tests measuring motor skills, The Peabody Development Motor Scales – 2nd edition (PDMS-2; Fewell, and Folio,2000) demonstrates internal consistency, test-retest reliability, and inter-rater coefficients ranging from 0.89 to 0.97. The Test of Gross Motor Development-2nd edition (TGMD-2; Ulrich, 2000) follows with internal consistency coefficients ranging from 0.85 to 0.91, and test-retest reliability coefficients ranging from 0.84 to 0.96. The Movement Assessment Battery for Children (M-ABC; Henderson and Sugden, 1992) was specifically developed for the identification and evaluation of children with mild to moderate motor impairment. Research findings show good psychometric properties (Brown and Lalor 2009; Chow, S.M.K., Hsu, Henderson, Barnett, and Lo 2006); Croce, Horvat, and McCarthy, 2001) and usefulness of this tool although the test’s criterion predictive validity varies, as demonstrated by Waelvelde, De Weerdt, DeCock, and Smits-Engelsman’s (2004) who reported that only some items will significantly predict performance on motors tasks such as ball catching and balance. Among all the tests we reviewed, three tests, the Test of Visual-Motor Skills (TVMS-R and TVMS-UL) and the Slosson Visual-Motor Performance Test (SVMPT) are less strongly recommended for the assessment of visual motor skills for the following reasons. First, although these tests show good internal consistency with alphas higher than .80, there is still limited external psychometric evaluation (Brown and Unsworth, 2009b). Second, the TVMR manual reports only one source of construct validity evidence involving itemtest correlations and no factor analytic studies. Third, comparison of TVMS with the BEERY-VMI and the Bender Gestalt suggest less predictive power in

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evaluating LD (Goldstein, Peterson, and Sheaffer (1989). Brown and Unsworth (2009a) critiqued the construct validity of the (SVMPT) which, in their opinion, should not serve as an in-depth measure of visual-motor perception. In sum, most of the tests we reviewed (excluding the TVMS and the SVMPT) represent reasonable measures of visual motor integration, and are useful tools for the identification of visual motor difficulties , in particular in children and adults with LDs . However, the results of these tests must be interpreted with caution. Visual motor skills are explained not only by biological factors but also by psychological and social factors and the interactions of all factors (Emck et al., 2009). Poor performance on these tests may be attributable to lack of motivation or to anxiety. Indeed, research suggests that children with anxiety or depression demonstrate poor gross motor skills and low self-perceived motor competence (Emck et al., 2009). Moreover, children with poor motor skills are less physically active and they participate in less active play (Cairney, Hay, Faught, Mandigon, and Flouris, 2005; Cairney, Hay, Faught, Mandigon, Wade, Corna and Flouris 2005) , suggesting the avoidance of a situation potentially associated with failure. In view of all these caveats, it is possible that low performance in testing settings can be partly explained by the participant’s lack of motivation for a disliked task, or by an increased level of anxiety which tends to decrease attention and visual motor control (Oatley, Keltner, and Jenkins, 2006).

1.3. Empirical Evidence of the Relationship between Visual Motor Abilities and Learning Evidence of the role of visual motor skills in learning comes from a large body of research on LDs. Although the biological mechanisms involved are not yet known, research suggests that LDs result from impairments in one or more processes related to perceiving, thinking, remembering, or learning, such as phonological processing, visual motor and visual spatial processing, memory, attention and executive functions such as planning and decision making(Martinez-Marrero and Estrada-Hernandez, 2008; Durand, 2005; Getchell, McMenamin, and Whitall, 2005; Dumas and Nilsen, 2003; Estil, Whiting, Sigmundsson, and Ingvaldsen, 2003; LDAC; 2001; Jing, Dequig, Longhui, 2001; Culbertson and Edmonds, 1996; Day and Edwards, 1996; Reiff, Gerber, and Ginsberg, 1993; Bergman, 1987; Skeen, Strong and Book, 1982; R.M.Sutherland, and Algozzine, 1979). The large variety of LDs

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symptoms suggests that several areas of the brain might be involved. Research findings tend to reflect the problems noted in behavioral observations and psychometric measures of LD symptomatology. According to these findings, children with LDs display slower brain wave activity than children without (Fenger, 1998; Lubar, Bianchichini, Calhoun, Lambert, Brody, and Shabsin, 1985), and these differences have been found in both right and left brain hemispheres (Dumas and Nilsen, 2003). Recently, some researchers hypothesized the predominant role of the cerebellum (Ecmck, et al., 2009) in the explanation of motor control deficits in individual with LDs. As a group, these findings suggest that there is no single specific area of the brain responsible for deficits in perceptual, attentional, and motor control skills deficits. Possible damage or dysfunction of the cerebellum in individuals with LDs is supported by the finding that children with impaired language experience problems in relation to visual discrimination tasks (Powell and Bishop, 1992) and fine and gross motor tasks such as finger movements, peg moving or fastening buttons, or balance (Hill, 1998; Owen and McKinlay, 1997; Powell and Bishop, 1992). Also, compared to their non-learning disabled peers, children with LDs show difficulties in coordinating the visual-perceptual, cognitive and motor activities required in reading and writing (Dumas and Nilsen, 2003). Their difficulties do not appear to be related to specific damage in either the visual-perceptual or the motor systems but are rather associated with both the motor-coordinative component and the integration between the visual-perceptual and the motor-coordinative components disturbances. Similar findings have been reported in numerous other studies on both child and adult students with LDs (Vinter and Chartel, 2009; Mapou, 2008; Jing, Dequig, Longhui, 2001; Mattison, McIntyre, Brwon, and Murray, 1986; Skeen at al., 1982; Silver and Hagin, 1982; DeMers, Write, and Dappen, 1981; Korhonen, 1976). Developmental research findings suggest that the early visual motor and motor coordination of children (who display poor coordination, clumsiness, and awkwardness in motor and visual-motor integration skills) can be considered broad and early signs of LDs (Brown, Unsworth, and Lyons, 2009; Getchell, et al., 2005; Hosley, et al., 1998; Jongman, Bouwien, SmitsEngelsman, and Schoemaker, 2003; Motohide, et al., 1997; Gerber, and Ginsberg, 1993; Skeen, Strong and Book, 1982; Sutherland, and Algozzine, 1979; Jing, Dequig, Longhui, 2001; Reiff; Vogel, 1999; Vogel, 2001; Woodard and Sturburg, 1997). For example, Getchell, McMenamin, and Whitall (2005) found that the majority of parents of children with LDs who

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participated in their study reported fine and gross motor difficulties of their children compared to typically developing age-matched children, and that the children with LDs did not improve in their gross motor skills after short-term practice. Jongman, Bouwien, Smits-Engelsman, and Schoemaker (2003) noted that there is higher risk for children with LDs to present with developmental coordination disorder (DCD), which, is a disorder that causes an individual’s performance in daily motor-coordinated activities to be substantially below what would be expected, in respect to the individuals’ chronological age and cognitive ability. Moreover, Jongman, et al. (2003) found that LD-DCD comorbidity increases the severity of perceptual-motor dysfunction in comparison to matched children with only a DCD diagnosis. The higher incidence of visual motor and motor coordination difficulties in individuals with LDs might explain the overrepresentation of males, who represent about 60 percent of the population (Vogel and Holt, 2003; Rosenblum, Larochette, Harrison, and Armstrong, 2009; Sutherland and Algozzine, 1979; Woodard and Surburg, 1997), in LDs diagnoses. Although in the general population females tend to exhibit higher verbal ability, while males tend to exhibit higher visual-spatial and mathematical abilities (Duran and Fisher, 1996; Woodard and Surburg, 1997; Vogel, 2001)), some research has indicated that females tend to perform better on traditional visual-motor measures than males. Specifically females with LDs have been found to display better finger dexterity and better speed and accuracy of finger tapping, coding, and copying geometric designs, writing, and typing; while males, have been found to display superiority in mathematical reasoning skills (Vogel, 1999; Vogel, 2001). Other research suggests that male children with LDs tend to display better motor proficiency and gross motor coordination than females (Hosley, et al., 1998; Sutherland and Algozzine, 1979; Woodard and Sturburg, 1997). A possible explanation of these discrepant findings is that male overrepresentation might be the result of errors in diagnosis because externalized disruptive behavior problems are more often present in boys than in girls, and these problems are very often associated with learning difficulties (Dumas and Nilsen, 2003), attention deficit and hyperactive disorders, and visual motor coordination problems (Emck et al., 2009). Compared to those focusing on children, there are very few studies on visual motor coordination in adulthood (Getchell, McMenamin, and Whitall, 2005). Comparisons between adults and children with LDs show very few differences; both children and adults with LDs demonstrate average intelligence, generally poor academic achievement and test performance, and a pattern of mild neurological deficits characterized by relatively poor motor,

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attentional, and language-related task performance in contrast to good nonverbal problem solving (McCue, Shelly, and Goldstein, 1986). Postsecondary students with LDs typically continue to experience difficulties in at least one of the following areas: reading; written language; spelling; mathematics; organization; attention; and memory. However, probably because mastery is considered necessary to the attainment of academic success (Kirby et al., 2008), written language disorders or difficulties appear to be the most commonly reported for postsecondary students, in comparison to younger students . Such difficulties are noted in 80 to 90 percent of all adult LDs (Day and Edwards, 1996; Martinez -Marrero and Estrada-Hernandez, 2008). Students’ writing difficulties involve both mechanical (spelling, punctuation, and capitalization) and contextual (lack of organization and coherence) aspects of writing (Martinez -Marrero and Estrada-Hernandez, 2008) problems. The main source of written language difficulties has been related to the physical (visual-motor) demands and conventions of writing with fluent sentence production (Martinez -Marrero and Estrada-Hernandez, 2008). The impairment on mechanical skills is likely to be a result of impaired motor ability or impaired auditory, visual, or tactile sensation (Culbertson and Edmonds, 1996; Kozey and Siegel, 2008; Martinez -Marrero and EstradaHernandez, 2008; Skeen, Strong and Book, 1982; Sutherland, and Algozzine, 1979). Difficulties in adults with LDs are also likely to reflect delays or impairment present early in the individual’s development as research shows that the development of visual-motor abilities begins in childhood and continues into early adulthood (Decker, 2007; Durand, 2005), and that visualmotor challenges in children with LDs do persist and are present in adulthood (Branningan and Decker, 2003; Decker, 2007; Durand, 2005; Kirby, et al., 2008; McCue, Shelly, and Goldstein, 1986; Silver, Hagin, 1985; Wagner Newman, Cameto, Garzaand Levine, 2005). Research findings on how these visual motor difficulties develop are less consistent. For example, Decker (2008) reported that visual-motor ability develops in childhood, continues to develop into adolescence, plateaus in adulthood, and declines rapidly into late adulthood; these findings contrast with other studies (Branningan and Decker, 2003;Koppitz,1963) suggesting that visual motor skills peak in late childhood and remain steady from there on across the lifespan. Silver and Hagin’s (1985) longitudinal research showed that visual motor skills matures throughout adolescence and then plateaus, thus when visual motor deficits are present, challenges (such as problems with spatial and temporal organization ) can persist into late adulthood.

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In general, research findings support the importance of the role of visualmotor skills in explaining difficulties and symptoms of LDs in children and adults. However, there is still little research conducted with adults to better understand how these difficulties develop and how they continue to affect postsecondary students’ academic achievement. Several reasons might explain this. First, LD diagnoses often lack of precision because there is no validated test that directly measure learning disability; a diagnosis is achieved by statistically comparing the individual’s score on IQ tests to his or her performance on standardized achievement tests (DSM-IV-R, 2000). Second, research has demonstrated that clinicians do not adhere very well to regulations regarding LD classification in younger students (Ferrari, 2007; Gottlieb, et al., 1994; MacMillan, 1998; Wagner 2005). As adults, these students do not always meet the diagnostic criteria of LDs (Ferrari 2009). Low adherence to criteria can be explained by the fact that an LD diagnosis is heavily influenced by administrative factors, as student who has been identified will received expensive specialized services (Dumas and Nilsen 2003; Dumas, 2007). As a third issue, there is the lack of LD awareness and diagnosis in the post-secondary adult population mainly because most of individuals with LDs would not consider pursuing post-secondary education (LDAC, 2005). This situation has changed since the development of tools designed to compensate sensory limitations in individuals with LDs (Day and Edwards, 1996; Raskind and Higgins, 1998; Draffan, Evans, and Blenkhorn, 2007; Martinez -Marrero and Estrada-Hernandez, 2008). Such tools and research evidence of their impact on the academic achievement of adults with LDs will be discussed in the next section.

2. ASSISTIVE TECHNOLOGIES, E-TOOLS, VIRTUAL REALITY AND ACADEMIC ACHIEVEMENT Research on intervention using assistive technologies in post-secondary institutions is a very new field, as demonstrated by the observation that no scientific papers have been published on this topic before the early 1990s (Raskind, 1993). This situation has changed since several Canadian and American laws have been passed, such as the Americans with Disabilities Act 2000; Canadian Provincial Acts such as the Ontario Education Act 1990 and the Accessibility for Ontarians with Disabilities Act 2005, which ensure that Canadians and Americans are provided with a broad range of assistive

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technologies (AT) (i.e. equipment used by individuals with disabilities to compensate for their deficits) and, more recently, Virtual Reality or VR (i.e. computer-simulated environments which offer visual, auditory or tactile experiences and that simulate real world environments. Such legislation influenced a shift in researchers’, educators’ and professionals’ viewpoints on supportive services (i.e. AT, VR). It is recognized that these tools are not useful exclusively within a rehabilitative or remediational contexts for persons with sensory or physical impairments, but rather that they show potential benefits in educational settings and are also advantageous when compared to traditional educational supports (e.g. scribes) for students of all ages with and without LDs (Ofiesh, et al., 2008). In the past, AT has typically referred to print magnification systems for individuals with visual impairments, enlarged computer keyboards for individuals with mobile or motor impairments, and speech output devices for individuals with communication or speech impairments (Raskind, 1993). The current definition provided by the Government of Canada, Industry Canada section (2010) states the following: Assistive Technology can be defined as any hardware, software or system that overcomes or reduces the barriers inherent in standard technology. For example, standard computer input devices such as keyboards and mice require a certain level of mobility and dexterity from users. The standard computer output device—a video display—requires the user to have adequate vision. Assistive Technology overcomes such barriers by providing alternate modes of input and/or output suited to the abilities of different users. (Increasingly referred to as "Enabling technologies"). (Industry Canada, 2010).

Similarly, the American Technology-Related Assistance for Individuals with Disabilities Act of 1988 defines AT as: “any item, piece of equipment, or product system, whether acquired commercially or off the shelf, modified, or customized, that is used to increase, maintain, or improve functional capabilities of individuals with disabilities (American government, 1988)”

This first part of this section of the chapter describes AT, E-tools, and VR environments available for post-secondary students with LDs. We present research evidence on the impact that these tools have on academic achievement in the second part.

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2.1. Assistive Technologies Post-secondary students with LDs typically experience difficulties in at least one of the following areas: reading; written language; spelling; mathematics; organization; attention; or memory. Also, the increasing proportion of post-secondary students with dyslexia (LD specific to reading; Draffan, Evans, and Blenkhorn, 2007) account for the common difficulties in these students with respect to accurate and/or fluent word recognition, poor spelling abilities, and reduced reading comprehension (Draffan, et al., 2007; Kirby, et al., 2008). Among these, written language difficulties is the most reported by students (Day and Edwards, 1996; Martinez -Marrero and EstradaHernandez, 2008). The main source of written language difficulties has been related to the physical (visual-motor) demands and conventions of writing with fluent sentence production (Bahr, Nelson, VanMeter, 1996; Brown, 2008; Bryant and Bryant, 2002; Christ, 2008; Day and Edwards, 1996; Draffan, Evans, and Blenkhorn, 2007; Martinez -Marrero and Estrada-Hernandez, 2008; Englert, Wu, and Zhao, 2005; Orr and Hammig, 2009; Raskind and Higgins, 1998; Wetzel, 1996). In particular, the writing ability of students with LDs show a lack of cohesiveness, poor text planning, and poor text production; limited accuracy in terms of editing, grammar, spelling and punctuation are also quite common (Bahr, Nelso, and VanMeter, 1996; Bryant and Bryant, 2002; Day and Edwards, 1996; Draffan, et al., 2007; Englert, et al., 2005; Martinez Marrero and Estrada-Hernandez, 2008; Rosenblum, Larochette, Harrison, and Armstrong, 2009). These difficulties constitute the main barriers to success for students with LDs (Bryant and Bryant, 2002; Christ, 2008; Day and Edwards, 1996; Draffan, et al., 2007; Floyd, 2009; Kirby, et al., 2008; Martinez Marrero and Estrada-Hernandez, 2008; Orr and Hammig, 2009; Raskind and Higgins, 1998; Raskind, 1993; Reiff, Gerber, and Ginsberg, 1993; Rosenblum, Larochette, Harrison, and Armstrong, 2009). A few key post-diagnostic recommendations for children and adults with LDs include extended test-taking time, modified assignment expectations, note-taking services, and AT (AT; Orr and Hammig, 2009).There are two distinct approaches with respect to AT (Garner and Campbell, 1987): 1) the compensatory approach views AT as a device which can enhance the individual’s ability by circumventing deficits; whereas 2) the remedial approach focuses on improving the specific area of difficulty or deficit of the individual. Although both approaches can overlap and each is beneficial in its respective way, the compensatory approach is viewed as more effective as it

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provides the student with strategies that can be generalized to different areas of difficulties, whereas the remedial approach requires specific AT and training for each specific deficit (Brown, 2008; Day and Edwards, 1996; Higgins, Boone, and Lovitt, 1996; Martinez-marrero and Estrada-Hernandez, 2008). Different AT devices have been developed to compensate for the different difficulties experienced by students with LDs. Examples of these are presented in Table 2. Descriptions of these tools, as well as their specific advantages and limits are described below. We have identified four main categories of AT; writing/reading AT; organizational memory AT; motor specific AT; and mathematics AT. Writingreading ATs are the most common and include word processors, spell checkers, proof-reading programs, and speech recognition software and enlarged screen hardware. Word processors (the computer software used to produce, edit, and format any printable material; MacArthur, 1998; Wilson, Majsterek, and Simmons, 1996) and speech recognition software (used in combination with word-processing programs) are among the most utilized and successful tools for post-secondary students with LDs and writing difficulties (Day and Edwards, 1996; Draffan, et al, 2007; MacArthur, 1998; Martinez Marrero and Estrada-Hernandez, 2008; Raskind, 1993; Raskind and Higgins, 1998; Stanberry and Raskind, 2010). The writing/reading ATs described in Table 2 include word processors (typewriters, pencil and paper, and computer-based systems); portable word processor software; audible (talking) word processors; spell checking, dictionary and thesaurus software; typing tutoring programs; proofreading programs; outlining or “brainstorming” software; abbreviation expanders; speech recognition; paper-based computer pen; optical character recognition and large screen magnification hardware. These tools aim to compensate for the mechanical aspects of writing by enhancing visibility of the text on the screen, by providing the user with visual or auditory word-processing functions such as editing, spell checking, dictionary, thesaurus, and by facilitating text composition via proofreading features (for example, they may alert students to probable errors in punctuation, or grammar; Day and Edwards, 1996; Raskind, 1993; Raskind and Higgins, 1998; Stanberry and Raskind, 2010).

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Table 2. Assistive Technology Available for use with LDs in Post-secondary Education Reading and Written Language Assistive Technology

Full Reference

Details Conventional devices included typewriters, pencil and paper, and personal computer computer-based systems; all of which require the student to make manual corrections much more difficult than correcting with modern day computer systems.

Portable Word Processor software

Day and Edwards, 1996; Draffan, et al, 2007; Martinez -Marrero and Estrada-Hernandez, 2008; Raskind, 1993; Raskind and Higgins, 1998; Stanberry and Raskind, 2010 (Stanberry and Raskind, 2010)

Audible (Talking) Word Processors

(Day and Edward, 1996; Stanberry and Raskind, 2010)

Word processors: typewriters, pencil and paper, and personal computer computerbased systems

Spell Checking, Dictionary, Thesaurus Software

Day and Edwards, 1996; Raskind, 1993; Raskind and Higgins, 1998

Typing Tutoring programs

Draffan et al., 2007

Programs or Devices Available

N/A

Neo™, Fusion™, Quickpad™ Has editing, spell checking, dictionary, thesaurus, and proofreading features that facilitate student’s revision of his or her written work

Permits the student to check for errors within the document, look up words, and look up synonyms and antonyms to words before a final copy is made.

Talking Word processor™ (Premier Assistive Technology, 2006) Ginger™, (spellcheck, proof read) Franklin Electronic Publishers (spelling and reference tool), Clever Keys (dictionary and thesaurus online), WorldWeb Pro (dictionary and thesaurus) Mavis Beacon™; Kaz™

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Table 2. (Continued) Reading and Written Language Assistive Technology

Full Reference

Details

Programs or Devices Available

Proofreading Programs

Day and Edwards, 1996; Raskind, 1993; Raskind and Higgins, 1998; Stanberry and Raskind, 2010

WinProof ™ (Intellect Systems, 2006); Grammer Checker (Grammer Checker, 2007)

Outlining or “Brainstorming” Software

Day and Edwards, 1996; Draffan et al., 2007; Martinez -Marrero and Estrada-Hernandez, 2008; Raskind, 1993; Raskind and Higgins, 1998; Stanberry and Raskind, 2010

Abbreviation Expanders

Raskind, 1993; Raskind and Higgins, 1998

Software packages that scan documents and alert students of probable errors in punctuation, grammar, word usage, structure, spelling, style, and capitalization Facilitates students with written language LDs by providing brainstorming maps that can easily be rearranged as new ideas emerge. After completion, the student can convert their brainstorming map into an organized and well-structured outline with the touch of a button. This sort of software utilzes colours, shapes, and spatial distribution to aid students with Dyslexia and written language difficulties in their planning of written texts. This software allows the student to create their own abbreviations for frequently used words or phrases; thus reducing the number of keystrokes, simplifying the visual-motor requirements, and ultimately reducing the amount of time for the production written documents.

Inspiration™, Kidspiration™, DraftBuilder™, MindManager™

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Reading and Written Language Assistive Technology

Full Reference

Speech Recognition

Day and Edwards, 1996; Draffan et al., 2007; Raskind, 1993; Raskind and Higgins, 1998; Martinez -Marrero and EstradaHernandez, 2008; Stanberry and Raskind, 2010; Wetzel, 1996.

Consisting of speech recognition hardware (internal board), software, headphones, and a microphone, this AT enables students to operate the computer by speaking to it. Particularly useful for postsecondary students whose oral language exceeds their verbal language

Stanberry and Raskind, 2010

This AT records and links audio to written input using a pen and a specialized paper. The student is able to take notes while simultaneously recording someone speaking. By doing this, the student can later listen to any section of his or her noted by touching the pen to her corresponding handwriting diagrams

Draffan et al., 2007; Education World, 2005; Raskind, 1993; Raskind and Higgins, 1998; Stanberry and Raskind, 2010

“Speech synthesis” refers to a synthetic or computerized voice output system usually consisting of an internal board or external hardware device. In conjunction with software that involves reading from a screen, a speech synthesizer reads back the text displayed on the screen so that the student can hear as well as see the words. For students with stronger oral language skills compared to their written language skills, this technology allows the student to recognize errors in punctuation, grammar, or spelling that may have otherwise gone unnoticed. In addition, the auditory feedback of the text may provide the student with insight into the coherency and semantic integrity of their document

Paper-Based Computer Pen

Speech Synthesis/Screen Review

Details

Programs or Devices Available DragonDictate™, Dragon Naturally Speaking™ (ScanSoft, 2006); ViaVoice™, iListenV, SpeakQ™, Microsoft Windows Vista Speech Recognition™: 64 IBM Voice Type Dictation (Dragon Systems, Inc.) Pulse Smartpen™ (Livescribe), Penfriend™, Instant Text™ (Textware Solutions)

ClassMate Reader© (Johnson, D. 2010), Read and Write Gold™, Read and Write Gold Mobile™, Write:Outloud® , Read and Write (TextHelp)

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Table 2. (Continued) Reading and Written Language Assistive Technology Optical Character Recognition (OCR) Systems/ Speech Synthesis Large Print /Screen Magnification Hardware and Software

Full Reference

Details

Education World, 2005; Raskind, 1993; Raskind and Higgins, 1998

Can be thought of as a “reading machine” as this type of AT allows the postsecondary student to scan any sort of document into the system and then hear the text read out loud to him or her through speech synthesizers.

Education World, 2005; Floyd, 2009

Software functions like a magnifying glass, automatically moving over the page; thus allowing for visually impaired students to more easily read text

Variable Speech Control (VSC) Tape Recorders

Raskind, 1993; Raskind and Higgins, 1998; Stanberry and Raskind, 2010

Portable audiocassette recorders support of auditory processing and writing difficulties. VSC tape recorders allow the student to listen to someone speaking (e.g. from a lecture or reading a textbook) on tape at variable speeds without having the intelligibility of the speech affected.

Organization and Memory Assistive Technology

Full Reference

Details

Raskind, 1993

Software programs that support students in remembering, organizing, and managing personal information. Typical features include monthly calendars, daily schedules/planners, clocks/alarms, contact/address books, telephone directories, bank/financial managers, and maps via satellite or internet access

Personal Data Managers

Programs or Devices Available

The Class Mate Reader

Programs or Devices Available

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Reading and Written Language Assistive Technology

Full Reference

Details

Free-Form Databases

Raskind, 1993; Raskind and Higgins, 1998; Stanberry and Raskind, 2010

Similar to personal data managers, free-from databases is valuable to individuals with organizational and/or memory difficulties. Free-form databases is a storage for memo, note, or reminder in the computer memory system and that can be retrieved while engaged in any other computer program , removing the risk of losing paper-written reminders.

Motor-Specific Assistive Technology

Full Reference

Details

Mouse emulators

Stanberry and Raskind, 2010

For students with specific fine or gross motor impairment; helps student operate a computer more effectively standard size computer keyboards with very large keys; customizable alternative; keyboards according to one's need (e.g. a one-handed keyboard; - expanded keyboard with a variety of keyboard layouts,) - a Light Operated Mouse and Keyboard that works with using a head based laser that is only activated while pointed at the keyboard; pen-touch keyboard with mouse functions from; 100% Touchless Keyboard - keyboard that tracks any pointing device optically using infrared sensors; smaller one handed keyboard, uses membrane surfaces rather than actual keys.

Alternative Keyboards

ATRC, 2010; Stanberry and Raskind, 2010

Mathematic Assistive Technology

Full Reference

Details

Programs or Devices Available

Programs or Devices Available BigTrack Trackball, KeyTools, Ltd. EZ Keys for Windows™ (Words+, Inc.) Gus! Access Keyboard™ (Gus Communications, Inc.) TypeIt4Me™ (Shareware)

Programs or Devices Available

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Table 2. (Contniued) Reading and Written Language Assistive Technology Talking Calculators Electronic Math Worksheets and Math programs

Full Reference Raskind, 1993; Raskind and Higgins, 1998; (Stanberry and Raskind, 2010 Stanberry and Raskind, 2010; Zunker, 2009

Details

Programs or Devices Available

Calculator with a speech synthesizer imbedded inside. When numbers, symbols, or mathematical operation keys are pressed, the calculator vocalizes them; providing the student with visual and auditory feedback.

Orion TI-36x Talking Scientific Calculator, Access Ingenuity, 2007.

Improvement in mathematics and motivational levels after utilizing the computer-based mathematics interventions

Picture Math (Hohlt, 2005), Theme Park Math (Griggs, 2006)

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Optical Character Recognition and Speech recognition AT engages the oral/verbal vocabulary skills of the student by either scanning documents that the student can hear or bypassing the writing difficulties; as the student speaks or dictates into a microphone, his spoken words appear on the computer screen as text (Day and Edwards, 1996; Draffan et al., 2007; MacArthur,1996; Raskind, 1993; Raskind and Higgins, 1998; Martinez -Marrero and EstradaHernandez, 2008; Stanberry and Raskind, 2010; Wetzel, 1996). Outlining or “Brainstorming” Software, Abbreviation Expanders, and Paper-Based Computer Pens facilitate the writing process for students with a written language LD by providing brainstorming maps that can easily be rearranged as new ideas emerge, by allowing students to create their own abbreviations for frequently used words and to record and link audio to written input using a pen and a specialized paper which allow the student to simultaneously take notes and record someone speaking (Day and Edwards, 1996; Draffan et al., 2007; Martinez -Marrero and Estrada-Hernandez, 2008; Raskind, 1993; Raskind and Higgins, 1998; Stanberry and Raskind, 2010). Research shows that writing/reading ATs benefit students in several ways. First, word processors and audible word processors basically compensate for the mechanical aspects in combining editing with the use of typing, which has the advantage of giving students the freedom to focus on the meaning and quality of their communication, rather than on its mechanical (written) aspects (Day and Edwards, 1996; Draffan, et al, 2007; MacArthur, 1996; Martinez Marrero and Estrada-Hernandez, 2008; Raskind, 1993; Raskind and Higgins, 1998; Stanberry and Raskind, 2010). For example, overall written performance is enhanced in primary school children with LDs by the use of oral prompts on text (Englert, et al., 2005; Bahr, et al., 1996; Day and Edwards, 1996; Draffan et al., 2007; Higgins, et al., 1996; Kirby et al., Martinez-Marrero and EstradaHernadez, 1998; Raskind and Higgins, 1998; 2008; Wetzel, 1996). Reading AT was found to largely benefit post-secondary student (Floyd, 2009) and proof-reading programs were found to be effective in helping students who struggle with writing (e.g. spelling, grammar, punctuation, word usage, and sentence structure; Day and Edwards, 1996; MacArthur, 1996; Raskind, 1993; MacArthur, 1996; Raskind and Higgins, 1998; Wetzel, 1996). Researchers and educators have pointed out some of the limitations of writing/reading AT. First, because of the nature of LDs, which includes visual motor skills deficits, some features of written/reading AT might be challenging for students. For example, the typing that is required in written/reading AT can also be seen as a limitation in the sense that the motor skills required for typing are similar to those required for handwriting text

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(MacArthur, 1996). Also, visual-spatial perceptual skills required by spell checkers and proof-reading programs often exceeding those of students with LDs (Day and Edwards, 1996; Raskind, 1993; MacArthur, 1996; Raskind and Higgins, 1998; Wetzel, 1996). Therefore, training is often necessary for these students to really benefit from these tools (Draffan at al., 2007). For example, a student with an LD specific to reading, has difficulties distinguishing letters (e.g. b and d). When a spelling error is detected, the software may offer a list other words as a replacement option, which might be difficult for the reader to choose from because of his visual perceptual difficulties. Second, written/reading tools cannot compensate for the benefit of individualized teaching environments, as teachers are able to prove immediate and motivating feedback. As suggested by some researchers a combination of both AT and teacher-directed instruction maximize student learning (MacArthur, 1996; Wilson, Majsterek, and Simmons, 1996). Third, to enhance student motivation, word processing software tools typically come with graphics and sounds. This may be viewed as advantageous; however the additional stimuli may result in reduced focus on task as graphics and sounds demand more time and attention and they may further distract students and redirect their attention from the written text. Research showed that although no significant differences in performance were found between the two software types (text-based versus graphics based), the benefit of each software type depends on the student’s level of organisational ability, with text based software being more appropriate in children with low levels of organisational skills (Bahr, Nelson, and VanMeter, 1996). Fourth, there are limitations associated with the efficiency of the software, regardless of the user’s abilities. For example, some spell checker programs sometimes fail to take the context of a text into account and are therefore inefficient at detecting incorrect grammar within a text (Day and Edwards, 1996; Raskind, 1993; Raskind and Higgins, 1998). Also, these programs’ immediate error alerts interrupt students, and may prevent those who have attention or memory difficulties from focusing on the task at hand (Day and Edwards, 1996; Raskind, 1993; Raskind and Higgins, 1998). In closing we note that, some proofreading programs pick up only 25% of grammatical errors. Furthermore, the post-secondary student with an LD may find these programs demoralizing or demeaning, for he or she may conceive of themselves as being critiqued and/or criticized by an electronic teacher. Such perceptions may perpetuate a low level of self-confidence in the student’s writing ability confidence (Day and Edwards, 1996; Raskind, 1993; Raskind and Higgins, 1998; Stanberry and Raskind, 2010).

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Speech recognition systems are still in an early stage of development and do not always reproduce voices accurately: they are still quite expensive and time consuming (Issenman and Jaffer, 2004;Wetzel, 1996; Borgh and Dickenson, 1992). The second category of AT tools are Organizational and Memory ATs such as Personal Data Managers and Free-Form Databases including monthly calendars, daily schedules/planners, clocks/alarms, contact/address books, telephone directories, bank/financial managers, and maps via satellite or internet access (Raskind, 1993). These tools are particularly useful to individuals with organizational and/or memory difficulties who often loose paper-written documentation and reminders. Motor-specific AT constitute the third category and include mouse emulators and alternative keyboards. Mouse emulators (such as BigTrack Trackbal, KeyTools, Ltd.) are helpful for students with specific fine or gross motor impairment in that they aid students in operating a computer more efficiently. For individuals with a motor impairment, it is often easier to operate and manipulate a trackball mouse than a standard one due to the enlarged rolling ball that is on the face of the mouse (Stanberry and Raskind, 2010). A variety of alternative keyboards also exist that benefit the user in terms of his or her fine or gross motor deficiency, as these are designed to increase typing efficiency and accuracy (ATRC, 2010; Stanberry and Raskind, 2010). The main problem of motor-specific ATs is their cost; they can be quite expensive, in particular when they need to be customized to fit the specific needs or deficits of an individual with an LD (Bahr, Nelson, and VanMeter, 1996). The last category is Mathematics ATs and includes talking calculators and electronic math worksheets, aimed to aid students with LDs who struggle with the phonological processing of visual-information, visual spatial processing, and attention ( LDAC; 2001; Raskind, 1993; Raskind and Higgins, 1998; (Stanberry and Raskind, 2010). The calculators have a speech synthesizer embedded within. When numbers, symbols, or mathematical operation keys are pressed, the calculator vocalizes them, thus providing the student with both visual and auditory information. This AT is beneficial in that it can serve to decrease the risk of error associated with hitting the wrong key (which is likely an issue with visual-motor deficits). Importantly, the speed of execution of mathematical operations may decrease for students using this AT as more time is required for the speech synthesizer to vocalize every key that is pressed. Some research findings suggest that Mathematics ATs significantly improve students’ overall motivational levels in mathematics, which, in turn, is likely to

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increase their academic success (Stanberry and Raskind, 2010; Zunker, 2009). On the other hand, Motor-specific Mathematics AT are expensive, in particular for post-secondary students who are likely to require costly scientific programmable calculators (Raskind, 1993; Raskind and Higgins, 1998; (Stanberry and Raskind, 2010).

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2.2. E-Tools and Virtual Reality VR technology integrates real-time computer graphics and sensory input and feedback (i.e. auditory, tactile and visual) to create an environment within which the user can actively or passively interact (Gregg and Terrier, 2007; Glantz, Rizzo, and Graap, 2003; Holden, 2005; Riva, 2002; Riva, 2003; Vincelli, 1999; Wiederhold and Wiederhold, 1998). Virtual and simulator technologies typically make use of hand-held devices (e.g. joy stick), advanced computer technology (e.g. “fast stereoscopic 3D graphic images”), and immersion technology (e.g. “head-mounted display”, “motion trackers”) which contribute to its users’ experience through one or more of the senses of sight, hearing, touch, smell and proprioception (Baus and Bouchard, 2010; Bouchard, Côté and Richard, 2006; Glantz, et al., 2003; Holden, 2005; Lederman and Klatzky, 2007; Rive, 2003; Riva, 2000; Wille, 2009; Vincelli, 1999). VR as a training, screening, diagnosing, and treatment tool has been and is currently being developed in an increasing number of areas such as education (e.g. training on surgical procedures), psychotherapy, physical rehabilitation, architectural design, and research (Alcaniz, Botella, Banos, Zaragoza, and Guixeres, 2009; Bouchard et al., 2006; Gaggioli, Keshner, Weiss and Riva, 2009; Gregg and Terrier, 2007; Glantz, Rizzo, and Graap, 2003; Holden, 2005;Riva, 2002; Riva, 2003; Stanney, 2002;; Wiederhold and Wiederhold, 1998). VR technology application in education includes display devices, interface devices and auditory input devices. VR display devices (Holden, 2005; Sveistrup, 2004) can involve monoscopic displays (3D images seen in 2D, like in traditional movies) or stereoscopic displays (3D images seen in what is commonly referred to as 3D, such as in recent movies like Avatar) . The stereoscopic displays enhance the illusion of depth with the perception that stimuli are floating in front and behind the display monitor. Head mounted displays (HMD) allow for stereo viewing through the use of small monitors mounted in front of each eye. In addition, most VR systems include some type of motion tracking to allow pairing the images displayed in the HMD to the user’s head movements. The main difference between using an HMD and a computer monitor is that the HMD limits the field of view to only the computer-generated images while the computer monitor allows the viewer to

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see the physical environment surrounding the monitor. At the opposite end of the immersive spectrum, fully immersive rooms are the most realistic and costly type of VR. They typically include high-resolution, stereoscopic images displayed on room-sized walls. The goggles in such systems do not block the field of view like HMD do with their small monitors. They are used only to provide the depth perception (somewhat like in recent 3D movies), hence allowing the user to see his or her own body interacting in the virtual scene. These sophisticated VR environments are often referred to by the trademark CAVE and could vary in shape (images could be displayed on four walls or more) and in peripherals (i.e., other pieces of technology could be added to provide auditory, motor or haptic cues) which provide additional feedback to the user, and ultimately allow a more realistic experience inside the virtual world (Holden, 2005; Stanney, 2002; Sveistrup, 2004). Interface devices play a fundamental role in the world of VR by permitting the user to navigate through large 3D environments and, depending on the device, they can even allow for the simulation of kinetic movement and sensory feedback (i.e. sensations of touch, pressure, or force) (Holden, 2005; Stanney, 2002; Sveistrup, 2004). Auditory input enhances the spatial orientation and localization of VR users within the virtual environment by mean of speakers mounted in a room, a head set with speakers, and ear pieces (Holden, 2005; Sveistrup, 2004). Two visual-motor specific and unique VR programs that have been used in education to date include the PITS (Wille, et al., 2009) and Superscape VRT Software (Jeapes, 2007). The PITS allows for students with motor dysfunction to practice specific movements of upper limbs and offers immediate motor performance feedback. Components of the PITS include a custom-made table on wheels with adjustable height, data gloves in different sizes, a monitor with speakers, and a personal computer. The data can measure finger flexion and extension in the student’s hand. Haptic feedback is provided to the student through a vibration motor attached to each glove (Willie et al., 2009). Compared to existing ATs, VR has several advantages. First, virtual and simulated environments enable educators, therapists, and researchers to present a wide variety of controlled stimuli and then record, measure, and monitor a broad possible range of human movements and provide the user with systematic feedback on performance being provided to the user (Cromby, Standen, Newman, and Tasker, 1996; Groner, Koga, and Tsuji, 2004; Holden, 2005; Farrell, Arnold, Pettifer, Adams, Graham, and MacManamon, 2003; Perpina, Botella, and Banos, 2003; Riva, 2003; Wiederhold and Wiederhold, 1998; Riva, 2003; Sveistrup, 2004; Wille, et al., 2009; Ziv, Wolpe, Small, Glick, 2008). Thus, VR can be used as both an assessment and a training tool.

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Secondly, whereas the use AT require a certain amount of visual motor skills that are often limited in students with an LD, VR technology can be used as a teaching tool for visual-motor skills (Cromby, et al., 1996), for neurological impairment rehabilitation, and for sensory re-training (e.g. visual impairment, distance-estimation problems). This is accomplished by presenting real-world situations within which the user can interact and learn (Wilson, Foreman, and Stanton, 1997; Waller and Richardson, 2008; Zeuner and Malloy, 2008). Thirdly, VR technology can provide naturalistic training environments permitting the user to generalize learning to real life situations. Combined with our knowledge of the nervous system research on VR, training outcomes can help us to better understand the neural basis of visual-motor abilities by comparing individuals with and without disabilities, and at the same time, it will improve our method to compensate and to remediate deficits. Fourthly, an increasing number of research findings suggest that VR environments can provide motor training and motor rehabilitation, as well as positive transfer of vocational, visual-motor, and visual spatial skills from virtual environments to real-world environments, in adults with LDs and other persons with neurological, intellectual, psychological, and physical impairments (Chebbi, Heijdens, Boisvert, Riches, Saracino, Sweeney, and Vollebek, 2009; Goncharenko, Svinin, Kanou, and Hosoe, 2006; Holdem, 2005; Klatzky, Lederman, and Langseth, 2003; Kneebone, Scott, Darzi, and Horrocks, 2004; Holden, 2005; Schmorrow; Sveistrup, 2004; Tam, Wai-Kong, Chan, Sze, Wong, 2005; Sveistrup, 2004; Unger, Nicolaidis, Berkelman, Thompson, Lederman, Klatzy, and Hollis, 2002; Waller and Richardson, 2008; Wilson, Foreman, and Stanton, 1997). For example, Wille, et al. (2009) significantly improved in hand-motor function in children with motor dysfunction (caused by cerebral palsy, traumatic brain injury, or brain tumor) with three weeks of virtual training using the PITS program. Others have found significant improvements in VR-training of visual motor skills in adults with LDs which is generalized to a real life environment (Rose, Brooks, and Attree (2000). Similar effects with respect to the generalization of learning motor skills in VR environments were found by Brooks, Rose, Attree, and Elliot-Square (2002), who reported that students with an LD trained in virtual food-preparation performed better than students receiving traditional real life modeling. Finally, the immediate and feedback provided by the safe environments of VR have been found to increase self-esteem, empowerment, and training compliance in adult post-secondary students (Holden, 2005; Wille, 2009).

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Although VR technologies are promising, they are relatively novel tools, in particular in education. To our knowledge, very few research studies exist that utilize VR with individuals who have an LD in the context of visual-motor measurement, assessment, or training. These studies (Holden, 2005; Wille, et al., 2009) used small sample size without control groups. The value of VR technologies in the assessment, measurement, diagnosis, and support of students with LDs remains to be demonstrated. Virtual Reality technologies are still quite expensive, even more so than AT, and less available to educators and students. In overview, the different ATs described in this section show the advantage of compensating for the mechanical difficulties of writing and support for visual-spatial perception deficits. As such, these tools appear to impact positively on students’ overall academic performance by giving them the freedom to focus on the meaning and quality of their communication, and by allowing them to work independently as well as improving their motivation. On the other hand, these technologies show some limitations as the motor skills required for typing are similar to those required for handwriting text (MacArthur, 1996). Also, visual-spatial perceptual skills required by spell checkers and proof-reading programs often exceed those of students with LDs (Day and Edwards, 1996; Raskind, 1993; MacArthur, 1996; Raskind and Higgins, 1998; Wetzel, 1996). AT are not substitutes for individualized teaching environments and their benefits. Although promising, Virtual Reality technologies are still in their early stage of development in education; they are costly and they are not easily available for students with LDs. As the use of ATs is implemented in our post-secondary institutions, it is important to address question of whether they have an impact on the academic achievement of post-secondary students with LDs, and if so, how? The next section will review research evidence of AT’s impact on students’ academic achievement.

2.3. The Impact of Assistive Technologies on Post-Secondary Students’ Academic Achievement According to Day and Edwards, (2008), there is a lack of formal investigation on the effectiveness of ATs for post-secondary students with LDs. To date the effectiveness of AT has relied on anecdotal reports, case studies, and measures using the AT itself by recording overall pre and post

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grade point averages. Existing literature, until now, failed to examine the effect of AT on students with LDs through formal C and B-level psychological measures used in the assessment and diagnosis of LDs, including some of the tests of visual-motor skill we reviewed in the first section of this chapter. We found four types of research evidence that can be used to evaluate the impact of AT on students’ academic achievement; data on the numbers of students with LDs enrollments in post-secondary institutions, data on number of program completions and graduations, self-report data on the efficacy of AT by its users and experimental research comparing performance with and without AT. Some epidemiological data on the growing number of students with LDs enrolled in post-secondary programs tend to support the efficacy of AT in their academic success. Recent research has suggested that students with LDs currently comprise 41 to 60 % of all students with disabilities enrolled in postsecondary education, a statistic which continues to grow in both two-year and four-year program enrolment numbers each year (Day and Edwards, 1996; Martinez-Marrero and Estrada-Hernandez, 2008; Orr and Hammig, 2009, Raskind and Higgins, 1998; Voytecki et al, 2009). Considering that many students do not disclose their LD upon admission to post-secondary institutions, it has been estimated that 1.61% of the entire undergraduate student population in the United States has an LD; research by the American Council on Education between 1991 and 1998 obtained even higher estimates (Kirby, Silvestri, Allingham, Parrila, and LaFave, 2008). Research suggests that students with an LD are more likely to abandon high school before obtaining their degree (Ferrari, 2009; Wagner Newman, Cameto, Garza and Levine, 2005; Vogel and Holt, 2003). For example, a Canadian study revealed that the average number of years of schooling for individuals with a self-reported LDs was 11.20, compared to 13.33 for individuals without an LD, and that nearly half of students with an LD had not graduated from secondary school, compared to 24.08 percent of students without LDs (Vogel and Holt, 2003). In addition, the National Longitudinal Transition Study conducted by Wagner (2005) demonstrated that barely three in four youth with an LD had completed secondary school. Data revealed an increase in the number of students with LDs in elementary and secondary schools that succeeded and graduated from their respective educational institutions. The result is that more students with LDs now have the opportunity to attend and successfully complete their post-secondary program of choice (Carle, Jaffe, Miller, 2009; Day and Edwards, 1996; Draffan; Evans, and Blenkhorn, 2007; Kirby, Silvestri, Allingham, Parrila, and LaFave, 2008;

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Martinez -Marrero and Estrada-Hernandez, 2008; Orr and Hammig, 2009; Raskind, 1993, Raskind and Higgins, 1998). Although encouraging for students with LDs, this data does not tell us the reason for the increase of enrollment. It is possible that AT play a small role, and that other factors such as better awareness of disabilities, greater openness in the post-secondary institution toward students with special needs, or diagnostic inaccuracies, might explain the increasing number of students with LDs in colleges and universities. Research employing a self-report questionnaire on the perceived benefits of AT revealed that post-secondary students express high levels of satisfaction with the AT and with the academic support services they receive at their respective institutions (Brown, 2008; Draffan, Evans, and Blenkhorn, 2007; Day and Edwards, 1996; Draffan, et al., 2007). Furthermore, students with LDs originating in childhood or adulthood report being able to learn and understand material at a deeper level , make greater use of their study aids and their time management strategies (Kirby, et al., 2008), express themselves more thoroughly and effectively through writing (Bahr, et al., 1996; Englert, et al., 2005; Wetzel, 1996); increase their independence in academia, increase their self-esteem, decrease their academic-related anxieties (Day and Edwards, 1996), support their specific challenges in personal learning such as visual motor delays (Martinez -Marrero and Estrada-Hernandez, 2008; Raskind and Higgins, 1998), and, most importantly, improve and maximize their overall academic achievements and success (Day and Edwards, 1996; Floyd, 2009; Kirby, et al., 1996; Raskind, 1993; Raskind and Higgins, 1998; Martinez Marrero and Estrada-Hernandez, 2008; Stanberry and Raskind, 2010; Wetzel, 1996; Voytecki, et al., 2009; Zunker, 2009). Two experimental studies report contradicting findings. Martinez Marrero and Estrada-Hernandez (2008) found that with AT support, students with LDs (specifically writing disabilities) do not significantly differ from students without LDs in terms of course completion rates and grades. On the other hand, Higgins and Raskind (1995) found that when post-secondary students were required to provide a written sample by 1) using a scribe; 2) without any assistance, or 3) using speech recognition software, the students using the latter received higher scores on their essays compared to the first two groups. The paucity of control research, the limits of self-report studies (social desirability bias), the difficulties in interpreting enrollment rates data, and the lack of consistency in experimental research findings show that more research is needed on the use of AT to support post-secondary students with LDs. In particular, there is a need for experimental studies with control groups as well

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as longitudinal research to verify the long-term impact of AT on students’ academic performance.

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CONCLUSION In this chapter, based on the theoretical premise that motor and visual motor ability are fundamental aspects in intelligence and learning, we discussed the role of visual-motor skill deficits in individuals identified with LDs and how these deficits impacted their academic achievement. We found several clinical tools with good psychometric properties which could be applied to the assessment of visual-motor skills in children and adults with LDs. Although these tools show their efficacy in assessing visual-motor skills in individuals with LDs, test scores might very likely be moderated by motivational factors such as anxiety and the lack of motivation, two problems often associated with learning disabilities. We found good evidence of the presence of visual motor deficit in postsecondary adults with LDs, and we note that these deficits are likely to explain academic difficulties, in particular those associated with written language. However, there is still little research conducted with adults which addresses how these difficulties develop, and how they continue to affect post-secondary students’ academic achievement. Among possible explanations for this, we discussed the issue of diagnostic precision as the identification of LDs is influenced by administrative factors, since students who have been identified will receive expensive specialized services. Research investigating the links between clinical assessment, ATs development and outcome would help answering to this question. For example, such studies could compare students with and without LDs performance using standardized tests of visual motor abilities, and using other objective measures of academic performance. Although we noted the presence of some evidence of the benefits associated with AT and VR technologies some of the findings did not support the purported benefits of AT for learning. Also, as tempting as it is, assumptions that any treatment or intervention is necessarily beneficial, or at least better than no intervention, should not be made. Any treatment or intervention can show iatrogenic effects. For example, it is possible that the use of AT prevents students from improving their skills in developing their own cognitive strategies to compensate for some part of their deficits. It is also possible that AT have only a short term effect on learning and achievement or,

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measured or perceived outcome, may simply reflect a placebo effect were the students only believe that they are doing better because of technology. Given the cost of these technologies, educators, mental health care providers and governments should at least open the debate on how the very limited resources on treatment and intervention in education are distributed. Stakeholders must keep in mind that AT developments are lucrative for the private companies that sell them. There is a need for independent research to assess and evaluate the effectiveness of such tools. Our review outlined the importance of understanding how AT impacts on students with LDs. It also emphasizes how the development of new assessment and diagnostic tools, which take into account the developmental aspect of LDs, are still required in research and across all levels of education. So far, only the classical visual-motor tests we described in this chapter have been used in conjunction with other neurological methods, to study differences in the brain’s functioning between students with and without LDs; these traditional tests are not able to determine if any physiological, perceptual, or cognitive changes have occurred as a result of AT utilization. Researchers and educators alike need to better understand how students’ with LDs perceive and interact with their learning environments and what characteristics of these learning tools (ATs) optimally enhance performance. Such research has the potential to reveal the ability, for example, to identify which proportion of AT’s effect is compensatory or remedial, and which methods are the most effective for various specific skills. The results of such studies could be used to improve early screening, diagnosis and assessment of adults with LDs. Virtual Reality (VR) tools have the potential to offer measurement, registration, training and rehabilitation. Educators and researchers need to understand the implications of VR technology on visual motor performance in students with LDs, the manner in which VR technology and training compares to traditional measures of visual motor ability, the assimilation of VR in AT devices, and the effectiveness of VR technology in formal classroom settings. In addition, the sensorimotor integration, movement production, and learning and transfer that manifests in VR technology, as well as its psychosocial benefits, are all critical issues that should be addressed in ongoing and future studies and research (Sveistrup, 2004).

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achievement in Attention Deficit Disored and Learning Disabled students. Jounral of Neurotherapy, 3(1), p. 9-18. Ferrari, M. (2009). A comparative assessment of the cognitive functioning of adults with childhood histories of learning disabilities and adults with noncognitive disorders. Journal of Developmental and Physical Disability, 21, p. 355-368. Floyd, K.K. (2009). How does the use of assistive technology influence reading comprehension performance of postsecondary students with learning disabilities? Humanities and Social Sciences, 70(4-A), p. 1232. Folio MR, Fewell RR. (2000). Peabody Developmental Motor Scales: Examiner’s Manual (2nd ed.). Texas: Pro-ED. Fuller, Gerald, B. (2006). Visual Motor Assessment (ViMo). Visual Motor Assessment. North Tonawanda, NY: Multi-Health Systems Inc. Gaggioli A, Keshner EA, Weiss PL, Riva, G. Advanced technologies in rehabilitation. In the series Studies in Health Technology and Informatic. 145, Amsterdam, IOS Press (2009). Gardner M.F. (1995a) Test of Visual Motor Skills (Upper Level). Burlingame, CA: Psychological and Educational Publications. Gardner M.F. (1995b) Test of Visual Motor Skills -Revised. Burlingame, CA: Psychological and Educational Publications. Getchell, N., McMenamin, S., Whitall, J. (2005). Dual motor task coordination in children with and without learning disabilities. Adapted Physical Activity Quarterly, 22, p.21-38. Glantz, K., Rizzo, A., and Graap, K. (2003). Virtual reality for psychotherapy: Current reality and future possibilities. Psychotherapy: Theory, Research, Practice, Training, 40 (1/2), 55-67. Glidden, R. (1999). The validity of the Wide Range Assessment of Visual Motor Abilities. Dissertation Abstracts International, Section B: The Science and Engineering, 60(2-B), p. 0828. Goldstein, Peterson, and Sheaffer (1989). Concurrent validity of the Gardner Test of Visual-Motor Skills. Perceptual Motor Skills. 69(2), p. 605. Goncharenko, I., Svinin, M., Kanou, Y., Hosoe, S. (2006). Motor training in the manipulation of flexible objects in haptic environments. CyberPsychology and Behaviour, 9(2), p. 171-174. Gottlieb, J., Alter, M., Gottlieb, B. W., and Wishner, J. (1994). Special education in urban America: It’s not justifiable for many. Journal of Special Education, 27, 453–465. Gregg, L., and Tarrier, N. (2007). Virtual reality in mental health. Social Psychiatry and Psychiatric Epidemiology , 42, 343-354.

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Chapter 2

THE USE OF TEACHER RECOMMENDATIONS AND ACHIEVEMENT TESTS AS VALID INSTRUMENTS FOR SELECTING ETHNIC MINORITY STUDENTS INTO SECONDARY SCHOOL TRACKS

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Geert Driessen∗ and Ed Smeets ITS – Institute for Applied Social Sciences, Radboud University Nijmegen, 6500 KJ Nijmegen, the Netherlands

ABSTRACT In the Netherlands, the transition from primary school to the tracked secondary school system is the most decisive moment in a student’s school career. This transition is based upon the teacher’s recommendation as to the most appropriate school track for a student and supported by the results of achievement tests. For some time now there has been a vigorous discussion as to whether the use of the teacher’s recommendations and achievement tests are valid instruments for ethnic minority students. In the past, evidence was found for ‘over∗

Contact: Tel (++31)243653545; Fax (++31)243354599; E-mail [email protected]; Web www.geertdriessen.nl

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recommending’ or ethnic minority students receiving higher educational recommendations than were warranted on the basis of their achievement test results. More recently it was suggested that the opposite or ‘underrecommending’ was becoming common practice among teachers or ethnic minority students receiving lower recommendations than justified by their school performance. Both under- and over-recommending signify a deviation from the meritocratic principle according to which such recommendations should only be given on the basis of demonstrated talent and ability. Using recent large-scale data from the Dutch national PRIMA cohort study, which includes more than 10,000 students from almost 500 primary schools, analysis of variance and multi-level analyses were performed to examine differences in the levels of recommendation provided. The aim was to better understand the influence of achievement test performance, student background characteristics (e.g., ethnicity, social milieu, sex), and school characteristics (e.g., ethnic and social class composition, degree of urbanization) on the transition from primary to secondary education in the Netherlands. The results showed the phenomena of over- or under-recommending or, in other words, groups of students receiving an educational recommendation which is not in line with their school performance, to no longer exist. Student achievement appeared to be the most important factor for the explanation of the level of recommendation, which clearly provides support for the meritocratic principle.

INTRODUCTION Most western European countries have experienced a massive influx of non-western immigrants after World War II. What many of these immigrants have in common is that they have received little or no education and that they are often illiterate. This holds for the so-called guest workers in particular, but also for many immigrants from former colonies. Over the course of the years, it has become apparent that the educational position of the children of such immigrants leaves much to be desired (OECD, 2009). Despite specific attempts on the part of policymakers to combat the educational delays of these groups, relatively little progress can be detected and one certainly cannot speak of substantial improvements in the social positions of the groups (Driessen and Dekkers, 2008; Stevens, Clycq, Timmerman and Van Houtte, 2010). And although students from the second generation are generally more successful in their educational achievement than those from the first generation, the gap between second-generation immigrant students and their native peers is still wide (Herweijer, 2009).

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From a meritocratic perspective this situation can be called problematic, as the occupation of a particular social position should only be determined by a person’s talents, capacities and efforts (i.e., his or her ‘merits’). Competencies should thus play the central role in the social positions of people and not such factors as social milieu, race/ethnicity or sex. Research nevertheless shows that in particular students coming from lower social milieus, many of them of immigrant origin, must demonstrate substantially more ‘merits’ than students coming from more privileged milieus to acquire comparable starting positions in society (Goldthorpe and Jackson, 2006; Kloosterman, Ruiter, De Graaf and Kraaykamp, 2009; Schneider, 2008). Politicians and policymakers in western countries frequently assert that one can speak of increased meritocratization, but the results of empirical research raise some major doubts about these assertions, especially with regard to ethnic minorities1 (Tolsma, Coenders and Lubbers, 2007). Research into factors which appear to influence the reduction of educational inequality is therefore called for also to gain insight into the mechanisms of intergenerational mobility. In the present chapter, the most decisive step in the school careers of Dutch children, namely the transition from primary to secondary school, was studied. The type of secondary education recommended by the primary schools is of particular interest because this recommendation determines the societal prospects of the children to a large extent (Tieben, 2009). The main reason for this is that secondary education in the Netherlands constitutes an early selection tracked system. Once a student is in a certain track it is very difficult for him or her to move up to another, higher track. Within the framework of Dutch research on the establishment of recommendations for secondary education, the so-called phenomenon of ‘over-recommending’ for particularly minority children in the Netherlands has been observed since the end of the 1980s (De Jong, 1987; Driessen, 1991). This means that given comparable achievement minority children are actually given a higher educational recommendation than non-minority children. From a theoretical perspective, however, over-recommending represents a deviation from the meritocratic principle and thus constitutes a form of positive discrimination with alternative explanations nevertheless available. In a broad interpretation of the meritocratic principle, both cognitive and non-cognitive competencies are used to determine educational recommendations. The characterization of non-cognitive competencies may be quite broad, but 1

In the Dutch context ‘immigrant’, ‘minority’ and ‘ethnic minority’ are used interchangeably.

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various aspects of motivation and effort are typically meant by the term ‘noncognitive competencies.’ In a narrow interpretation of the meritocratic principle, only cognitive competencies — that is, achievement test performance — are used to determine educational recommendations (Tesser and Iedema, 2001). According to the latter approach, the correlation between performance and educational recommendation should be 100% or perfect. According to the broad approach, this need not be the case as motivation and effort (i.e., noncognitive competencies) can also contribute either directly or indirectly to educational recommendations and thus influence the association between cognitive competencies and educational recommendations. And when the association of cognitive (and possibly non-cognitive) competencies with educational recommendations is not perfect, one can often speak of ‘overrecommending’. The opposite of over-recommending, namely ‘under-recommending’, also exists. Under-recommending pertains to the situation where lower types of education are recommended than justified by the capacities of the student. In this context, over- and under-recommending always are relative phenomena and therefore depend on which group is taken as the norm (Claassen and Mulder, 2003). From a pragmatic perspective both over- and underrecommending are seen as undesirable. Over-recommending may lead to loss of motivation and school drop-out, while under-recommending implies that chances and talents of children are not fully used (Van de Werfhorst and Van Tubergen, 2007). When compared to the results of research conducted on educational recommendations in the 1980s and 1990s, recent research shows substantial decreases in the incidence of over-recommending for minority students in the Netherlands (Luyten and Bosker, 2004). Instead of over-recommending it has recently been suggested that the opposite or under-recommending could become the norm, especially in the large cities (Gemeente Amsterdam, 2007a, 2007b; Babeliowsky and Den Boer, 2007). In order to determine the extent to which we can speak of a change of trend, recent national data on the educational advising of primary-school students were analyzed. In the present chapter, the results of these analyses are described and thereby the state-of-the-art with regard to the overrecommending of minority students in the Netherlands and the question of whether we can speak — or still speak — of such over-recommending, i.e., positive discrimination.

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THE EDUCATIONAL RECOMMENDATION The Dutch Education System Dutch primary education is for 4 to 12 year-olds and consists of 8 grades. In grades 1 and 2 play takes up a central place; in grade 3 formal instruction in reading, arithmetic/mathematics and writing starts. When the children are in the final grade of primary school they are given a recommendation with respect to the most suitable type of secondary education. In the secondary education system today, all students receive a basic secondary education during the first year or two which means a national common core curriculum with only a difference in the level of the subject matter. In actual practice, however, the students to a large extent are pre-sorted right from the very beginning of secondary school into separate tracks. In mainstream secondary education, three basic tracks are discerned:

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•

VMBO:  pre‐vocational  secondary  education  (age  12‐16;  4  years),  which comprises four learning pathways:  − basic vocational program (BBL);  − middle‐management vocational program (KBL);  − combined program (GL);  − theoretical program (TL).  • HAVO: senior general secondary education (age 12‐17; 5 years);   • VWO: pre‐university education (age 12‐18; 6 years).  In addition to this mainstream education in the VMBO track special needs support is available to ensure that as many students as possible complete one of the learning pathways and obtain a VMBO certificate:

• •

learning  support  (LWOO)  is  provided  for  students  who  need  additional help to cope with their chosen program;  practical  training  (PRO)  is  available  for  students  who  are  not  expected to obtain a VMBO certificate and which prepares them  for direct entry to the regional labor market. 

Depending on the level of secondary education attended, students can progress to a middle-level vocational or general education (MBO; 2-4 years)

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or a higher level education, namely higher professional education (HBO; 4 years) or university education (WO; 4-6 years). The structure of the mainstream Dutch education system is depicted in Figure 1 (MECS, 2007).2

Figure 1. Overview of the mainstream Dutch education system.

The Practice of Recommending On grounds of efficiency (and thus costs), the current policy is intended to discourage the stacking of educational programs (e.g., VMBO followed by HAVO, or HAVO followed by VWO). Because of the hierarchical nature of the Dutch school system, it is therefore crucial that the most appropriate — in other words, highest — educational recommendation be provided for the transition from primary to secondary school at the end of primary school (Tieben, 2009). In practice, the recommendation includes three elements, not only 1) cognitive competences (performance, achievement test results), but also 2) 2

In addition to the mainstream schools, special secondary schools for students with special needs can be discerned; they are not the topic of the present investigation, however.

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non-cognitive factors such as attitudes, motivation and interests, and 3) the teacher’s judgments with regard to the child’s home situation (i.e. socio-ethnic milieu, which, to a certain extent, forms the basis for the aspiration levels set by the teacher) (Driessen, 2005; Luyten and Bosker, 2004; Tieben, 2009; Van der Hoeven - Van Doornum, Voeten and Jungbluth, 1989). For admission to the general, non-vocational types, students must be assessed to establish their suitability. The commonest method of assessment is for students to be tested halfway through the final year of primary school, using tests developed centrally to gauge students’ level of knowledge and understanding. The test employed for this assessment by over 90 percent of all primary schools is the CITO (Central Institute of Test Development) Primary School Leavers’ Attainment Test. Under the auspices of the principal of the school, primary schools advise parents as to the type of secondary education most suited to their child. When there is some doubt about the specific track the school can give a so-called combined track recommendation, e.g. VMBO/HAVO or HAVO/VWO. Parents have the right to choose a secondary school for their child, but the school decides whether or not to admit the child. Secondary schools may demand a minimum score on the CITO test. To help schools and parents, CITO has constructed standard tables in which the relation between specific (ranges of) test scores and the recommendation for the most suitable type of secondary school is indicated. Various parties are thus involved in the formulation of an educational recommendation. The students themselves have certain desires or preferences, just as their parents. The teachers often weigh the level of performance against the socio-ethnic composition of the class during their formulation of recommendations for individual students. In addition, the admission policies of secondary schools, the degree of community urbanization (and related to this the availability of schools and the socio-ethnic composition of the neighborhood) and any agreements which have been made between school administrations with regard to the admission and distribution of students within a region can all play a role. The advising of students on the verge of entering secondary school is thus a complicated process involving various forces which can sometimes lead to undesired effects. Information on decision processes of school directorates is virtually absent, not only in the Netherlands but in other countries as well (Driessen, 2005; McGee, Ward, Gibbons and Harlow, 2003; Schnepf, 2002; West and Pennell, 1998). Over-recommending, which was the starting point of the present line of research, has been associated with the ethnic minority background of the child

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since the end of the 1980s. In particular, Turkish and Moroccan students with the same levels of performance as other students have been found to be given a higher level of educational recommendation. In contrast to students advised in keeping with their actual performance, over-recommended students start with an immediate lag and over-recommending has therefore been placed in a negative light. According to Tesser and Iedema (1993), such delays explain their lower marks and higher rates of drop out from secondary school. Mulder (1993) and Mulder and Suhre (1995) nevertheless argue that the less successful school careers of minority students may be more a consequence of the selection of an overly high type of secondary school than the receipt of an overly high educational recommendation; that is, an overly high type of secondary education may be forced upon the student by parents who may certainly have the student’s best interests in mind but do not have sufficient insight into what is needed for the student to live up to such expectations (Smit, Driessen and Doesborgh, 2005; Van der Veen, 2001). Many students who start out too high — that is, higher than the educational recommendation provided by the primary school —indeed end up repeating a year. The choice of an overly high type of secondary education can also lead to reduced motivation and inferior performance. Van der Werf and Kuyper (2004) have also observed in recent research that schools are providing increasingly higher recommendations — due in part to pressure from parents who apparently do not want their children to end up in the lowest levels of secondary education and thus block their chances of progressing to higher forms of education. Nevertheless, there are also signs that over-recommending may not always turn out negatively. In hindsight, that is, over-recommending can be seen to provide a clear challenge and stimulus for at least some children to fulfill their ambitions or even surpass these (Hustinx, 2002; Koeslag and Dronkers, 1994).

Explanations for Over-Recommending Alternative explanations for the ‘ethnic over-recommending’ observed within the Dutch situation have been put forth (Inspectie van het Onderwijs, 2007). The explanations concern the levels of the student, the class and the school, the broader context and the inter-relations between the different levels. Student level. A frequently offered explanation for over-recommending is that teachers explicitly take the negative effects of the children’s immigrant past into consideration (De Jong, 1987). Despite the poor Dutch language skills of some students, for example, the teachers trust that the intellectual

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capacities of the children are sufficient to handle a higher type of secondary education. The teachers thus allow educational potential to weigh heavier than actual performance on a language test. It is also conceivable that the teachers weigh such non-cognitive aspects as motivation and effort heavier in some cases as well. These students are thus given the benefit of the doubt (Kerkhoff, 1988). It is also possible that teachers are afraid of discriminating and therefore display what can be considered politically correct behavior. There is, in fact, an increased mention of discrimination these days but then positive discrimination. And within the framework of counteracting educational disadvantage and particularly when the positive discrimination has no negative effects for other students, it may be relatively easy to accept (Driessen, 1991). Therefore, we expect that students from ethnic minorities will receive a recommendation for secondary education that is higher than that of other groups of students with the same cognitive and non-cognitive competencies. Class/school level. An alternative explanation for ethnic overrecommending concerns the cognitive composition of the class. It is wellknown that teacher judgments of individual students can be influenced by the level of the other children in the class or a so-called frog pond effect (Driessen, 2007). Teachers thus appear to rank order their students for purposes of evaluation which means that the slightly better children in a class with a generally lower cognitive level will be more easily given a higher recommendation than children showing otherwise comparable performance but in a class with a generally higher cognitive level (Brandsma and Doolaard, 1999; Mulder and Tesser, 1992). The cognitive level of the class is associated, in turn, with the social and ethnic composition of the student population (Driessen, 2002). Minority children and children from lower social-economic milieus perform lower on average than native-Dutch children and children from higher social-economic milieus. To the extent that we can speak of a concentration of minority children and/or children from a lower socialeconomic milieu within a single class, the level of class performance will be lower and, as a direct consequence of the aforementioned frog pond effect, over-recommending may be more likely to occur. From this, we thus expect that children in a class with a lower cognitive level will receive a recommendation for secondary education that is higher than that of students with similar cognitive and non-cognitive competencies in classes with a higher cognitive level. Context level. Dronkers, Van Erp, Robijns and Roeleveld (1998) found a higher incidence of over-recommending in big cities even after various student and school characteristics were taken into consideration (cf. Van Langen and

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Suhre, 2001). According to these researchers, this can be attributed to the assertive lifestyle and competitive climate which characterize big cities and the pressure which these place on parents. Differences in advising with regard to secondary education choices can also be traced back to a combination of several factors, namely the small percentage of low educated native-Dutch parents, the large percentage of minority parents and the predominance of nondenominational or public schools in big cities. In the big cities most schools are public schools that cater for relatively many minority and relatively few native-Dutch students. Following this line of reasoning, we expect that children in big cities will receive a recommendation for secondary education that is higher than students with the same cognitive and non-cognitive competencies living in other parts of the country.

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Recent Developments While the aforementioned results suggest that ethnic over-recommending takes place in the Netherlands, the results of some recent longitudinal analyses of educational advising in the Netherlands provide a more subtle picture. In fact, Claassen and Mulder (2003) report that the over-recommending observed in 1988 and 1992 reverted to under-recommending in 2000. In 2007, a study conducted in the city of Amsterdam received a lot of media attention (Babeliowsky and Den Boer, 2007). From this study it was concluded that relatively many Turkish and Moroccan students received lower recommendations than were justified by their achievement test results.3 These results suggest a change of trend in the Netherlands. These recent findings also seem to confirm results of research into educational advising in other West European countries such as Germany and Switzerland where one can speak of a to a certain extent comparable secondary education system (see Imdorf, 2003; Kristen, 2000; Schnepf, 2002).4 Kristen (2000) found that Italian and Turkish children of guestworkers 3

For that matter, analyses of the same data performed by other researchers did not support this conclusion, however. Both over- and under-recommending occurred for ethnic minority students, but there were no systematic differences regarding these phenomena between minority and native Dutch students (Gemeente Amsterdam, 2007a, b). 4 In contrast to countries such as the Netherlands, Germany and Switzerland, where one can speak of a selective education system, countries such as the UK and the US have a more comprehensive educational system and thus less tracking. The choice of secondary education is also made relatively late in the school careers of students in the latter countries,

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in Germany were referred significantly more frequently than their classmates to the lowest form of secondary education, after the language and mathematics achievement of the children had been taken into account. In addition to this negative ethnic effect at the level of the student, she also found a negative effect at the level of the class: The more minority children in the class, the lower the educational recommendation. These results were not confirmed, however, by another German research conducted by Schnepf (2002) who used the mathematics, science and reading tests from the Programme of International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). Research by Imdorf (2003) has shown under-recommending to also occur for the children of guestworkers in Switzerland which has a tracked secondary education system as well. Given equal achievement, both boys and the children of guestworkers were generally referred to lower tracks than girls and native Swiss children, which indicates under-recommending. One possible explanation for the reversal from over- to underrecommending in the Netherlands may lie in the fact that big cities have made some procedural agreements over the past few years to better attune educational recommendations to achievement (Tesser and Iedema, 2001). Some other factors may also be at work, however. Lower levels of educational recommendation can also be expected to occur as teachers acquire greater experience with minority children and thus better insight into their capacities and prospects. The political climate has also changed dramatically in the Netherlands under the influence of the (highly controversial) politicians Pim Fortuyn and Geert Wilders during the past few years and opened up public debate on topics which were previously taboo. In the discussion of a multicultural Dutch society, for example, the reserve which was previously exercised with respect to the imposition of strict requirements and fears of being accused of discrimination have decreased markedly. Finally, it is also possible that schools are under greater pressure to perform and therefore apply more stringent admission policies as a result of the marketization of education and its accompanying developments of accountability and competition (Teelken, Driessen and Smit, 2005). In order to determine whether the observed shift from over-recommending to under-recommending in the Netherlands is still the case, recent national data which means that the phenomenon of over-recommending is less likely to present itself or may not occur at all (cf. Hanushek & Wöβmann, 2006).

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were analyzed. In our analyses the main focus is on cognitive competencies as measured by two sets of achievement tests. In doing this, we have adopted a strict interpretation of the meritocratic principle. The focus of much of the research to date predominantly has been upon the relations between student performance and educational recommendation. In our analyses, the influences of environmental factors, i.e. the class composition and the degree of urbanization, are also taken into consideration. The main research question guiding the present study thus is whether for some categories of students, specifically ethnic minorities living in the big cities, one can speak of over- or under-recommending.

METHOD

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Sample The data from the sixth measurement round of the Dutch Primary Education (PRIMA) study from 2004/5 were used. PRIMA is a large-scale national cohort study among 600 primary schools (which is 8.5 percent of all Dutch primary schools). The study includes a total of 60,000 students from the grades 2, 4, 6 and 8 with eight grade constituting the final year of primary school in the Netherlands. Information on not only the students but also parents, teachers and school administrations has been included in the study. The total PRIMA sample can be further subdivided into a nationally representative sample of 420 primary schools and a supplemental sample of 180 schools with an over-representation of students from disadvantaged situations (Driessen, Van Langen and Vierke, 2006). Because in the present study the focus is on establishing the correlations between achievement test scores and teacher recommendations, and not on presenting a representative picture, the data from the total sample of students in the final grade of primary school were used (cf. Luyten, Cremers - Van Wees and Bosker, 2001). An important advantage of this approach is that in the analyses also relatively small categories of students are sufficiently represented. The first part of the present analyses was intended to gain insight into the distributions of the variables and bivariate associations between the variables. The second part aimed at getting a better understanding of the multivariate associations between the different variables. The sample used in the analyses contained 10,457 students from 490 eight grade classes.

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Instruments The data were collected with the aid of questionnaires and tests and from databases. Background information on the students and their parents was provided by the school administrations, and this information was then used to calculate class averages. Intelligence tests and two sets of achievement tests were administered to the students. The schools reported background information of the students and the level of educational recommendation provided for the students. And information on the background characteristics of the schools was obtained from the databases of the Ministry of Education. The final set of variables can be divided into three blocks, namely school career characteristics and cognitive competencies, background characteristics of the students, and background characteristics of the classes/schools. In Table 1, the variables employed are briefly described. To arrive at an evaluation which is as reliable as possible, we employed two different sets of achievements tests in our study, viz the PRIMA tests and the CITO tests. The PRIMA tests are standard achievement tests in the domains of language, mathematics and reading. These three tests are employed in each of the PRIMA cohort study measurement rounds. For admission to the school types VMBO, HAVO and VWO, students must be assessed to establish their suitability. The commonest method of assessment is for students to be tested by a test developed centrally to gauge students’ level of knowledge and understanding. The National Institute for Educational Measurement (CITO) has developed a Primary School Leavers’ Attainment Test, a wide-ranging achievement test which gives a general indication of individual students’ level of attainment. A new version has been issued each year since 1970. The CITO test comprises three subtests in the domains of language, mathematics and study skills (which generates three subtest scores and one total test score). The test is intended as an aid for teachers in advising parents as to the right secondary school for their child. Use of the test is optional, but over 90 percent of all primary schools make us of it. And although a school may employ the CITO test, there are schools that do not have all of their students take the test for one reason or another. To check whether there is a difference between schools and students who take the CITO test and schools and students who do not take the test we will control for this in our analyses (cf. Kloosterman et al., 2009). In the past, some attempts have been made to detect possible test bias and differential item functioning of the CITO test. Such studies into culture-fair testing specifically aimed at differences between native Dutch students and

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second generation immigrant students. The results showed that secondary school success is predicted with less accuracy for immigrant students than for native Dutch students. Table 1. Overview cognitive competencies and background characteristics of students and schools Cognitive competencies Recommendation Intelligence School career PRIMA language PRIMA mathematics PRIMA reading CITO total

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CITO language CITO mathematics CITO study skills

(1) VMBO-PRO* – BBL; (2) VMBO-BBL – KBL; (3) VMBO-KBL – TL; (4) VMBO-TL – HAVO; (5) HAVO – VWO** Non-verbal intelligence test. Sum score for 34 items (range: 4 – 34) Indication of having repeated a year: (1) delayed; (2) nondelayed Proficiency score based upon 64 test items (range: 966 – 1261) Proficiency score based upon 120 test items (range: 56 – 160) Proficiency score based upon 50 test items (range: 0 – 100) Standardized test score based upon 200 items (range: 501 – 550) Sum score based upon 100 items (range: 0 – 100) Sum score based upon 60 items (range: 1 – 60) Sum score based upon 40 items (range: 1 – 40)

Background characteristics of students Sex Parental education Ethnicity

(1) boy; (2) girl (1) PO; (2) VMBO; (3) MBO; (4) HBO/WO ** (1) native-Dutch; (2) mixed native-Dutch and minority; (3) Surinamese and Antillean; (4) Turkish; (5) Moroccan; (6) other ethnic minority background

Background characteristics of schools Percentage native-Dutch disadvantaged (= low SES) students in the class Percentage ethnic minority disadvantaged students in the class Type of municipality (= degree of urbanization) *

Mean percentage; three categories used for the descriptive analyses: (1) 0%; (2) 1-24%; (3) ≥ 25% Mean percentage; three categories used for the descriptive analyses: (1) 0%; (2) 1-24%; (3) ≥ 25% (1) Amsterdam; (2) other three largest municipalities: Rotterdam, The Hague and Utrecht; (3) other large municipalities; (4) modal; (5) rural

Which includes the learning support via the LWOO pathway. For an explanation of the various types see Figure 1.

**

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The recommendation given by the primary school teacher is the most reliable indicator for school success. The question whether some test items have another meaning or weight for immigrant students than for native Dutch students proved very difficult to answer. A limited number of items seemed to work to the advantage and also a limited number of items to the disadvantage of immigrant students (Uiterwijk and Vallen, 2003; Van Schilt-Mol, 2007). In addition to the two sets of achievement tests, we also employed a test to gauge a student’s intelligence. Because of the ongoing discussion pertaining to culture-fair testing a non-verbal test was opted for in the PRIMA study. In this test the possible negative influence of language problems of immigrant students was controlled for. Also, an indicator of delays in the school career was introduced, namely whether a student had had to repeat a year or not.

Design of the Analyses Analyses of variance were primarily used for the descriptive analyses. To start with, the educational recommendation was bivariately related to the various cognitive competencies. Thereafter, educational recommendation and competencies were analyzed in relation to the characteristics of the students and the characteristics of the classes/schools. Because of the hierarchical nature of the Dutch school system and the strong linear correlation between the students’ scores on achievement tests such as the CITO test and the recommendation for the most suitable type of secondary school, researchers into Dutch school careers have always treated educational recommendation as an interval variable (e.g., Brandsma and Doolard, 1999; Dronkers et al., 1998). We will follow this convention and treat recommendation, the dependent variable, as an interval variable. Given the nested structure of the data, i.e. students within classes, multi-level regression analyses were conducted to analyze the multivariate associations between the different variables (Rasbash, Steele, Browne and Prosser, 2004). More specifically, it was attempted to predict educational recommendation on the basis of the various competencies and background characteristics. Given the large numbers of students and classes, the usual levels of significance have little practical meaning (Koeslag and Dronkers, 1995); associations may quickly reach significance but have little or no relevance. For this reason, the emphasis in the descriptive analyses was placed upon the strength of the observed associations or the so-called eta coefficient. Cohen (1988) calls an association of 0.10 ‘weak’; an association of 0.30 ‘medium’; and an association of 0.50 ‘strong.’ An eta of 0.15 (or at least than 2% explained variance) is frequently taken to be the lower limit for the relevance

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of an association (Bosker, Mulder and Glas, 2001). In the multi-level analyses we employed for each of the individual variables the traditional statistical significant criterion, and in addition we computed the amount of (extra) variance explained by the various blocks of variables.

RESULTS

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Bivariate Associations In Table 2, the results of the analysis with recommendation as the dependent variable are presented. The question that is answered here is whether students who received a low-level recommendation have other achievement test scores than students with a high-level recommendation. In the bottom part of the table we present the percentage of students at schools that did not employ the CITO test, and the percentage of students at schools that did employ the CITO test but where the student did not take the test. In addition, in the bottom part the number (n) of students per category of recommendation are presented. To give an indication of the strength of the association, in the right hand column we present the correlation coefficient eta. We speak of relevance of an association when eta amounts to at least 0.15. From Table 2 it can be concluded that students who received lower recommendations score monotonously lower with regard to all of the cognitive competency indicators. The table also shows that with the exception of the lowest recommendation (VMBO PRO-BBL5) nearly all of the students have taken the CITO test (except when the school did not employ this test of course). According to the CITO guidelines two categories of students may be exempted from taking the CITO test: immigrant students who have stayed in the Netherlands for less than four years and have not mastered the Dutch language sufficiently well, and students who will probably be referred to provisions for students with special needs (the LWOO and PRO pathways and special secondary schools). We performed extra analyses to check for the latter and indeed this proved to be the case for most of the relevant students. In the subsequent analyses, the associations between educational recommendation and the cognitive competencies — on the one hand — and the background characteristics of the students and schools — on the other hand 5

Which includes the learning support via the LWOO pathway.

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— were examined. Whether relevant differences (i.e., an eta ≥ 0.15) occurred according to sex was first examined. Table 2. Cognitive competencies according to recommendation (means) Recommendation VMBO VMBO VMBO VMBO- HAVO/ PRO- BBL- KBL- TL/HAVO VWO TL KBL BBL

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Intelligence Non-delayed (%) PRIMA language PRIMA mathematics PRIMA reading CITO total test CITO language CITO mathematics

total

eta

22 46 1079 106 37 516 46 24

24 62 1093 111 43 523 54 31

25 77 1109 115 51 531 64 38

27 86 1126 120 60 538 73 45

28 95 1150 126 72 545 82 51

26 76 1115 117 54 532 66 40

0.46 0.36 0.64 0.70 0.72 0.87 0.75 0.74

CITO study skills

18

22

27

31

35

28

0.75

School no CITO test (%) School CITO test, student no CITO test (%)

10 17

14 3

14 1

15 1

15 0

14 3

0.05 0.30

1183 11

2020 19

2361 23

2453 23

n %

2209 10457 21 100

This generally appeared to not be the case. There was only one exception: The mean mathematics score for the boys was slightly higher than that for the girls (42 v. 38; eta = 0.16). With respect to the educational background of the parents there appeared to exist numerous medium-sized associations. The higher parental education, the higher their children score on each of the cognitive indicators. In this, a clear distinction can be observed between on the one hand children of parents with only a primary education or pre-vocational education and on the other hand children of parents with at least a middle-level vocational or general education.

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This finding can be interpreted as a clear confirmation of the so-called reproduction thesis; it places these students — just as their parents before them — in a position to pursue a higher level of post-secondary education.

Table 3. Cognitive competencies according to ethnicity (means) Ethnicity Dutch

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Recommendation: . VMBO PRO-BBL (%) . VMBO BBL-KBL (%) . VMBO KBL-TL (%) . VMBO TL/HAVO (%) . HAVO - VWO (%) Mean recommendation Intelligence Non-delayed (%) PRIMA language PRIMA mathematics PRIMA reading CITO total test CITO language CITO mathematics CITO study skills School no CITO test (%) School CITO test, student no CITO test (%)

1 17 27 24 24 3.4

mixed Surinam. / Turkish Moroccan other total eta Antillean minority 12 17 25 23 23 3.3

26 25 24 16 8 2.5

21 31 27 16 6 2.6

24 27 24 18 8 2.6

15 22 27 19 16 3.0

26 81 1122 118 56 534 69 41 29

26 74 1116 117 55 533 68 40 28

24 70 1098 112 47 527 60 34 24

25 62 1086 114 45 527 57 37 24

24 62 1094 114 47 528 60 37 24

25 57 1101 115 51 530 63 38 26

16 3

14 2

5 4

5 5

3 5

9 3

11 19 26 23 20 3.2 26 76 1115 117 54 533 66 40 28

0.18 0.11 0.02 0.07 0.16 0.24 0.15 0.20 0.34 0.18 0.25 0.26 0.26 0.16 0.27

14 0.14 3 0.05

In Table 3, the results for educational recommendation and competencies according to ethnicity are presented.6 6

Ethnic minorities in the Netherlands can be divided into four categories. (1) Immigrants from former Dutch colonies (Surinam and the Netherlands Antilles). As a result of their ties with their motherland, they are often already familiar with the Dutch language and culture. (2) So-called ‘guest workers’ from the Mediterranean countries (e.g., Turkey and Morocco). They predominantly came to the Netherlands during the 1960s and were followed by subsequent waves of immigration for family formation or reunification purposes. One characteristic shared by these immigrants is their low level of education. (3) A more recent

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The native Dutch and the mixed category of ethnic background students showed the highest level of educational recommendation while the Surinamese/Antillean, Turkish and Moroccan categories clearly showed the lowest. As expected, the differences in test performance were most marked for language performance. One aspect which could play a role in recommending ethnic minority students is the teachers’ uncertainty with regard to these students’ capacities in combination with the support they may receive at home. This uncertainty could find its expression in teachers giving minority students more often socalled combined recommendations (e.g. VMBO/HAVO). Analysis of the data however clearly showed that Surinamese/Antillean, Turkish and Moroccan students less often receive such a combined recommendation than native Dutch and mixed origin students. This thus does not support the expectation that teachers are having more doubts as regards the recommendation for their minority students. We also analyzed the cognitive competencies according to the percentage of native-Dutch disadvantaged students, but found no differences. There appeared to be some differences with regard to the percentage of ethnic minority disadvantaged students, however. For all of the competencies a dichotomy emerged: students in classes with less than a quarter minority students produced much higher scores on average than students in classes with a quarter or more of such students. With respect to the type of municipality (i.e., degree of urbanization), only a few relevant differences appeared; see Table 4. In general, the students in the rural and modal municipalities score the most favorable. The students in the four biggest cities in the Netherlands — namely Amsterdam, Rotterdam, The Hague and Utrecht — clearly score much lower, but within this category Amsterdam performs better than the other three big municipalities.

influx of asylum seekers/refugees from Eastern Europe, Africa and the Middle East. These immigrants constitute a very heterogeneous group with respect to language, culture and religion. (4) Immigrants from Western countries such as Belgium and Germany. This category normally is not seen as an ethnic minority. Applying ‘country of birth of a person or his or her parents’ as the criterion, in 2009 the largest ethnic minority groups were of Turkish, Moroccan, Surinamese, and Antillean origin, with 378,000, 342,000, and 339,000 and 135,000 people, respectively, out of a total Dutch population of 16.5 million (CBS, 2009).

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Explaining Educational Recommendations A description of the various bivariate relations between the competencies and characteristics examined in this study was presented in the preceding. In this section, the results are reported of the multivariate analyses conducted using the multi-level program MLwiN (Rasbash et al., 2004). In our analyses, we modeled three alternatives with the achievement test scores as explanatory factors. First, the scores on the CITO test (total); then the scores on each of the three CITO subtests (language, mathematics, and study skills); and finally the scores on each of the three PRIMA tests (language, mathematics, and reading). Table 4. Cognitive competencies according to type of municipality (means) Type of municipality

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Amster- other 3 dam largest

other large

modal

rural

total

eta

Recommendation: . VMBO PRO-BBL (%) . VMBO BBL-KBL (%) . VMBO KBL-TL (%) . VMBO TL-HAVO (%) . HAVO/VWO-VWO (%) Mean recommendation

20 20 22 20 19 2.9

21 29 26 16 8 2.6

14 21 27 22 16 3.0

9 18 27 23 23 3.3

Intelligence

25

24

25

26

26

26

0.10

Non-delayed (%)

71

65

70

78

83

76

0.12

1105 115 52 531 64 39 26

1093 114 48 528 56 36 24

1109 116 52 531 64 38 26

1118 117 55 533 68 40 28

1123 117 57 534 70 41 29

1115 117 54 533 66 40 28

0.19 0.11 0.14 0.16 0.18 0.11 0.17

0 3

0 13

7 2

17 3

23 2

14 3

0.20 0.12

PRIMA language PRIMA mathematics PRIMA reading CITO total test CITO language CITO mathematics CITO study skills School no CITO test (%) School CITO test, student no CITO test (%)

8 18 27 25 23 3.4

11 19 26 23 20 3.2

0.12 0.06 0.03 0.04 0.09 0.14

We were not only interested in the CITO total test scores, but also in each of the three subtests because different effects may appear per domain, possibly in interaction with ethnic minority group. We performed separate analyses

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with the PRIMA tests because all of the students in the sample have taken those tests, whereas some of the students did not take the CITO test and it was not clear in all instances whether this was rightfully. We checked the latter by introducing two dummy variables in the analyses: 1) the school does not take the CITO test; 2) the school does take the CITO test, but the student does not. Also of importance is the fact that many schools practice the CITO test with their students in advance. As a result reliability and validity problems may occur and may contaminate the results. The schools cannot practice the PRIMA tests and for that reason these results may provide a more true to nature indication of the real capacities of the students than the results of the CITO tests. In Table 5, we start with a presentation of the results of the analyses with the CITO total test scores. The table shows the unstandardized regression coefficients with an indication of the level of significance. Two levels of statistical significance are presented, p < 0.001 (bold) and p < 0.01 (italics). For the dichotomous variables (e.g. sex), the reference category is also indicated. In the stepwise analyses the parameters of a given model are compared to the parameters of the preceding model, and at the bottom of the table, the percentages of variance explained at the level of the class and the level of the student are also reported along with the percentage changes when the various competencies and characteristics are added stepwise to the model. In the first step in the multi-level analyses, the so-called empty model (model 0) was estimated. This model shows 15% of the explained variance in educational recommendation to relate to differences between the classes and the remaining 85% to relate to differences between the students. Model 1 shows that compared to the reference category of modal municipalities students in the cities receive lower recommendations, especially in the category of other three biggest cities. Model 2 however shows that once the scores of the CITO test are taken into account the effects of type of municipality are not significant any longer. In other words, differences between type of municipality are a consequence of differences in CITO test scores. There is a significant effect of the CITO test on the recommendation: an increase of one point on the CITO test is associated with an increase of one tenth of a point (0.11) in recommendation. Hardly anything changes by adding a series of background characteristics at the level of the student (model 3) or the level of the class (model 4).

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Geert Driessen and Ed Smeets

Table 5. Results multi-level analysis recommendation, with CITO total test as the explanatory variable (unstandardized regression coefficients; 8702 students at 415 schools) Model 0 Regression coefficients Intercept

1 3.2

Municipality (reference = modal): . Amsterdam . other 3 largest . other large . rural

3.3

-0.29 -0.61 -0.38 n.s.

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CITO total test

2

3

4

-53.5

-50.9

-50.8

n.s. n.s. n.s. n.s.

n.s. n.s. n.s. n.s.

n.s. n.s. n.s. n.s.

0.11

Intelligence School career (reference = non-delayed) Sex (reference = boy) Parental education Ethnicity (reference = Dutch): . mixed . Surinamese/Antillean . Turkish . Moroccan . other

0.10

0.10

0.01 -0.20

0.01 -0.20

0.05 0.08

0.05 0.08

n.s. n.s. 0.08 n.s. 0.14

n.s. n.s. 0.08 n.s. 0.14

% Dutch disadvantaged % ethnic minority disadvantaged Variance explained (in %) School level Student level Total + School level + Student level + Total Variance components School level Student level Model fit Improvement Difference d.f.

n.s. n.s.

14.9 85.1

0.237 1.352 27917.9

16.0 0.0 2.4

0.199 1.352 27863.4 54.5 4

73.4 75.5 75.2 57.4 75.5 72.8

75.5 76.4 76.3 2.1 0.9 1.1

0.063 0.331 15687.8 12175.6 1

0.058 0.319 15371.8 316.0 9

n.s.: not significant, italics: p < 0.01, bold: p < 0.001.

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76.0 76.4 76.3 0.4 0.0 0.1 0.057 0.319 15370.0 1.8 2

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Positive effects occur of intelligence, sex (girls somewhat higher), and parental education and a negative effect of school career (delayed students receive lower recommendations). In other words: more intelligent students, non-delayed students, girls, and children of higher educated parents receive (somewhat) higher recommendations than warranted by their CITO test scores; one can thus speak of some ‘overrecommendation’. There are no clear effects of ethnicity, except for the (vary heterogeneous) category of ‘other minority’ (a higher recommendation also when the CITO test score and background characteristics are controlled for; some ‘over-recommending’). In addition, there is a very weak positive effect (p < 0.01) for the students with a Turkish background, which could point to a limited degree of over-recommending. It should be noted, however, that the effects mentioned may be statistically significant but at the same time are only very modest. After controlling for the cognitive competencies and student background characteristics, no effects of the percentage of Dutch and minority disadvantaged students in the class appear. In the bottom part of the table it can be seen that adding a total of 11 background characteristics adds no more than 1% of extra variance explained, in addition to the effect of CITO test score. We have also added a number of interactions to model 4, namely ethnicity with CITO test score, ethnicity with sex, and ethnicity with type of municipality. This did not lead to any significant improvement of the model (less than 1% of additionally explained variance). In other words: the effects (or the lack of effects) of achievement, sex and type of municipality do not differ for any of the ethnic groups discerned.

In Table 6, we repeat the analyses from Table 5 with the only difference that we now introduce the three CITO subtest scores instead of the CITO total test score. The results are comparable and do not lead to different conclusions. For models 0 and 1 the results are exactly the same, because up until then the CITO test results have not been introduced in the model. In models 3 and 4 we find that the (weak) effect we found for Turkish background in Table 5 has now disappeared completely. In Table 7 we present the results of the analyses with the three PRIMA tests. As part of these analyses we have also included the two variables ‘the school does not take the CITO test’ and ‘the school does take the CITO test, but the student does not’ in the model. The results of these analyses are comparable with the ones with the CITO test. No significant effects of taking the CITO test by the school were noted. There is, however, a negative effect of not taking the CITO test by the student: students who do not take the test receive lower recommendations, also when differences in language, mathematics and reading proficiency as measured by the PRIMA tests are controlled for. Students that do not take the CITO test thus appear to be under-recommended.

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Geert Driessen and Ed Smeets Table 6. Results multi-level analysis recommendation, with CITO test language, mathematics and study competencies as the explanatory variable (unstandardized regression coefficients; 8702 students at 415 schools) Model 0

Regression coefficients Intercept

1 3.2

Municipality (reference = modal): . Amsterdam . other 3 largest . other large . rural

2

4

3.3

-1.6

-1.8

-1.7

-0.32 -0.61 -0.38 n.s.

n.s. n.s. n.s. n.s.

n.s. n.s. n.s. n.s.

n.s. n.s. n.s. n.s.

0.03 0.04 0.04

0.03 0.04 0.03

0.03 0.04 0.04

0.01 -0.22

0.01 -0.22

0.06 0.09

0.07 0.09

n.s. n.s. n.s. n.s. 0.14

n.s. n.s. n.s. n.s. 0.13

CITO test language CITO test mathematics CITO test study skills

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3

Intelligence School career (reference = non-delayed) Sex (reference = boy) Parental education Ethnicity (reference = Dutch): . mixed . Surinamese/Antillean . Turkish . Moroccan . other % Dutch disadvantaged % ethnic minority disadvantaged Variance explained (in %) School level Student level Total + School level + Student level + Total Variance components School level Student level Model fit Improvement Difference d.f.

n.s. n.s.

14.9 85.1

0.237 1.352 27917.9

16.0 0.0 2.4

0.199 1.352 27863.4 54.5 4

56.1 72.7 70.2 40.1 72.7 67.8

62.0 73.7 72.0 5.9 1.0 1.8

0.104 0.369 16775.0 11088.4 3

0.090 0.355 16402.3 372.7 9

n.s.: not significant, italics: p < 0.01, bold: p < 0.001.

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62.5 73.7 72.1 0.4 0.0 0.1 0.089 0.355 16396.4 5.9 2

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Table 7. Results multi-level analysis recommendation, with PRIMA test language, mathematics and reading as the explanatory variable (unstandardized regression coefficients; 10457 students at 490 schools) Model

Regression coefficients Intercept

0

1

3.2

3.3

Municipality (reference = modal): . Amsterdam . other 3 largest . other large . rural

-0.30 -0.65 -0.32 n.s.

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PRIMA language PRIMA mathematics PRIMA reading

2

3

-13.1

-11.7

-11.6

n.s. n.s. n.s. n.s.

n.s. n.s. n.s. n.s.

n.s. n.s. n.s. n.s.

0.01 0.06 0.03

0.01 0.05 0.02

0.01 0.05 0.02

0.02 -0.28

0.02 -0.28

-0.74

-0.74

0.04 0.11

0.04 0.11

n.s. n.s. 0.09 n.s. 0.16

n.s. n.s. 0.09 n.s. 0.17

Intelligence School career (reference = non-delayed) CITO test participation of student (reference = yes) Sex (reference = boy) Parental education Ethnicity (reference = Dutch): . mixed . Surinamese/Antillean . Turkish . Moroccan . other

4

% Dutch disadvantaged % ethnic minority disadvantaged

n.s. n.s.

CITO test participation of school (reference = yes)

n.s.

Variance explained (in %) School level Student level Total

14.6 85.4

13.4 0.0 2.0

63.6 65.9 65.6

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68.2 69.0 68.9

68.6 69.0 68.9

82

Geert Driessen and Ed Smeets Table 7. (Continued)

0

Model 2

1

+ School level + Student level + Total Variance components School level Student level Model fit Improvement Difference d.f.

50.2 65.9 63.6

0.239 1.402 33909.4

0.207 1.402 33858.1 51.3 4

0.087 0.478 22686.9 11171.2 3

3

4 4.6 3.1 3.3

0.4 0.0 0.0

0.076 0.435

0.075 0.435

21681.1 1005.8 10

21674.7 6.4 3

n.s.: not significant, italics: p < 0.01, bold: p < 0.001.

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DISCUSSION In the present contribution, the recommendations for secondary education provided during the last year of primary school in the Netherlands stood central. The core question was whether or not we can speak of valid recommending which means that students receive a recommendation that is in line with their capacities as measured by achievement tests. More specifically, the question was whether there were deviations for ethnic minority students. This question was raised after the recent publication of a study which claimed that in the municipality of Amsterdam ethnic minority students were being under-recommended or received lower recommendations than justified by their test results. This would imply a new trend because in the past ethnic minority students were over-recommended. Our analyses point to differences in level of recommendation between types of municipality or degree of urbanization: in the large cities students on average receive lower recommendations. However, once differences in achievement test scores are accounted for (CITO test, PRIMA tests) these differences between municipalities disappear. With the exception of the very heterogeneous category of ‘other ethnic minority’ and students with a Turkish origin (both very weak effects), and once achievement test scores are controlled for, ethnic minority students are not given other recommendations

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than native Dutch students. And, for that matter, one to a certain extent can even speak of over-recommending in the case of these two categories of ethnic minority students. In addition, analysis of interactions showed that within each of the types of municipalities there were no differences in recommending students. The answer to our main question was that there is no underrecommending of different ethnic groups in various types of municipalities. In our analyses we found a negative effect of students not taking the CITO test though this test was employed by the school. This probably mostly concerns students of whom the school expects to achieve very low and therefore will be referred to provisions for students with special needs. The question is whether the exemption of such students taking this test by the school is a wise decision. The CITO test may function as a second opinion and help the teacher to decide as to the most appropriate type of school. As our results show withholding the student this opportunity may lead to underrecommending. In addition to the occurrence of under-recommending, the meritocratic character of the educational recommendations provided by primary schools was also examined. The present findings show the achievement test performance of students to be decisive for the educational recommendations provided. The other cognitive and social-ethnic background characteristics of the students and the schools considered in the present study added little or nothing to the predictive power of school performance. These findings confirm the results of other Dutch research on educational advising conducted by Luyten and Bosker (2004). These researchers also concluded that performance weighs more heavily than social-ethnic background in the determination of educational recommendations. This means that students can compensate for a less favorable social-ethnic background by good school performance but a favorable social-ethnic background cannot provide solace for inferior school performance.

ACKNOWLEDGMENTS The present study is based on an investigation financed by the Dutch Inspectorate of Education (Driessen and Smeets, 2007). The data used in the analyses are from the cohort study Primary Education (PRIMA). This cohort study was financially supported by the Foundation for Behavioral Sciences which is part of the Dutch Organization for Scientific Research (NWO).

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Driessen, G., and Smeets, E. (2007). De relatie tussen prestaties en advies: onder- of overadvisering bij de overgang van basis- naar voortgezet onderwijs? In Inspectie van het Onderwijs (Ed.), Onderadvisering in beeld (pp. 59-81). Utrecht: Inspectie van het Onderwijs. Driessen, G., Van Langen, A., and Vierke, H. (2006). Basisonderwijs: Veldwerkverslag, leerlinggegevens en oudervragenlijsten. Basisrapportage PRIMA-cohortonderzoek. Zesde meting 2004-2005. Nijmegen: ITS. Dronkers, J., Van Erp, M., Robijns, M., and Roeleveld, J. (1998). Krijgen leerlingen in de grote steden en met name in Amsterdam te hoge adviezen? Tijdschrift voor Onderwijsresearch, 23, 17-30. Gemeente Amsterdam (2007a). Basisschooladviezen en etniciteit. Onderzoeksverslag, 29 januari 2007. Amsterdam: Gemeente Amsterdam. Gemeente Amsterdam (2007b). Brief aan de leden van de commissie WIJ, 20 februari 2007. Amsterdam: Gemeente Amsterdam. Goldthorpe, J., and Jackson, M. (2006). Education-based meritocracy: The barriers to its realisation. Retrieved 7 March, 2010 from http://www.equalsoc.org/uploaded_files/regular/goldthorpe_jackson.pdf. Hanushek, E., and Wöβmann, l. (2006). Does educational tracking affect performance and inequality? Differences-in-differences evidence across countries. The Economic Journal, 116, 63-76. Herweijer, L. (2009). Making up the gap. Migrant education in the Netherlands. The Hague: SCP. Hustinx, P. (2002). School careers of students of ethnic minority background after the transition to secondary education: Is the ethnic factor always negative? Educational Research and Evaluation, 8, 169-195. Imdorf, C. (2003). Organisational perspective on the transition from primary to secondary School: Tracking decisions depending on gender and ethnicity in the Swiss education system. Paper UNESCO Conference on Intercultural Education, 15-18 June 2003, Jyväskylä, Finland. Inspectie van het Onderwijs (2007). Onderadvisering in beeld. Utrecht: Inspectie van het Onderwijs. Kerkhoff, A. (1988). Taalvaardigheid en schoolsucces. Amsterdam/Lisse: Swets and Zeitlinger. Koeslag, M., and Dronkers, J. (1994). Overadvisering en de schoolloopbanen van migrantenleerlingen en autochtone leerlingen in het voortgezet onderwijs. Tijdschrift voor Onderwijsresearch, 19, 240-258.

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Van der Werf, G., and Kuyper, H. (2004). Brugklasdocenten gaan niet op de hurken. Didaktief, 34, 22-24. Van Langen, A., and Suhre, C. (2001). Ontwikkelingen in de schoolloopbanen van achterstandsleerlingen. Nijmegen: ITS. Van Schilt-Mol, T. (2007). Differential item functioning en item bias in de Cito-Eindtoets Basisonderwijs. Amsterdam: Aksant Academic Publishers. West, A., and Pennell, H. (1998). School admissions: Increasing equity, accountability and transparency. British Journal of Educational Studies, 46, 188-200.

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In: Achievement Tests Editor: Lucas E. Madsen

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Chapter 3

CONSTRUCT-IRRELEVANT VARIANCE IN ACHIEVEMENT TEST SCORES: A SOCIAL COGNITIVE PERSPECTIVE David E. Ferrier and Benjamin J. Lovett∗ Elmira College, Elmira, New York, USA

Alexander H. Jordan Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

Dartmouth College, USA

ABSTRACT Standardized achievement testing is increasingly common in educational and industrial settings. K-12 students take state assessments to comply with federal education laws. Many colleges administer assessments to place incoming students in initial courses and ensure that graduates have benefited from instruction. Professions such as law and medicine give assessments for certification and licensure. Even many employers assess job applicants’ levels of literacy and mathematical skills. Although many of these tests have shown substantial evidence of reliability and validity, there are a host of factors affecting scores on the tests that do not derive from examinees’ actual achievement levels. In this ∗

Send any correspondence concerning this manuscript to: Benjamin J. Lovett, Department of Psychology, Elmira College, 1 Park Place, Elmira, NY 14901, e-mail: [email protected]

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David E. Ferrier, Benjamin J. Lovett chapter, we consider three such factors, reviewing the research on their effects and discussing implications for achievement testing in various settings. First, we examine the influence of test-taking motivation on performance. Examinees’ motivation to do well on a test varies widely, with predictable effects on resulting scores. We then move on to the impact of test anxiety, including anxiety caused by knowledge of stereotypes concerning one’s own group (stereotype threat). Although anxiety is associated with increased motivation to do well, it ironically impedes performance, attenuating estimates of examinees’ skills. Finally, we cover the role of prior task exposure and test-taking strategy use on scores. Given the current surfeit of available test preparation materials and services, it is important to understand which types of preparation are actually helpful for examinees. Throughout the chapter, we note the implications of research on these factors for current achievement testing programs. We also discuss the role of these factors in accounting for group differences in test scores. By the end of the chapter, readers should have a rich understanding of the contributors to achievement test scores (other than achievement itself).

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INTRODUCTION At all levels of education, the standardized assessment of student achievement is increasing in visibility and importance. In K-12 education, students complete teacher-made classroom tests as well as a number of district- and state-wide tests (Wright, 2008). Colleges and universities are also being asked more and more to show that students are meeting “learning outcomes” before graduating (e.g., Dwyer, Millett, and Payne, 2006). Beyond college, achievement tests are used in graduate and professional schools, and in many employment settings as well. The most important feature of a test is its validity, and in rough terms, a test is said to be valid if it measures what it claims to measure. To state this in more technical terms, when a test is valid, the construct (ability, skill, trait, or domain of knowledge) that it is designed to measure is the source of examinees’ scores on the test. One impediment to validity, then, is when factors having nothing to do with the target construct affect examinees’ scores. For instance, if the items on a biology examination were printed in very small type, examinees’ scores would vary due to variability in visual acuity, and the test would be measuring visual acuity rather than just biology knowledge. This problem, known as “construct-irrelevant variance” (Haladyna and Downing, 2004), is the subject of this chapter.

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Specifically, we examine the influence of three factors (other than actual academic skill levels) on achievement test scores. First, we consider how an examinee’s interest in and effort on the test can affect his or her scores. Second, we review the effect of test anxiety and related conditions on achievement testing. Finally, we detail how prior exposure to test items or other relevant preparation and training can affect scores. All of these factors have the potential to yield construct-irrelevant variance in achievement test scores, threatening the validity of these tests.

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TEST MOTIVATION When evaluating students’ performance scores on achievement tests, it is important to understand when someone might be trying their hardest and when they might not be putting forth the same effort. The difference between these motivational states has a long history of investigation in assessment research; if an individual’s score on a cognitive ability test depicts their best effort, this is described as a “maximum performance,” whereas if an individual puts forth only an average amount of effort, this performance is labeled “typical” (Sackett, Zedeck, and Fogli, 1988). Although this conceptual distinction is intuitive, in practice, it can be difficult to determine whether an examinee is exhibiting maximal or typical performance. Sackett (2007) argued that if three conditions were met, then one could be confident that scores were indicative of maximum performance. First, the examinee must be aware that they are being evaluated. Second, they must be aware of and accepting of instructions to maximize effort. Third, the performance should occur over a short enough duration that the examinee is able to sustain attention and effort. Of course, these are sufficient rather than necessary conditions; maximum performance may occur over long periods of time, without instructions to maximize effort, and even when examinees do not know that their performance is being evaluated. To what degree do variations in motivation affect real-world test scores, in practice? One area in which researchers have tried to answer this question is intelligence testing. Since IQ scores have been associated with various life outcomes such as academic and job success (see Jensen, 1998, for a review), it would be both theoretically interesting and practically useful to know if the IQ–life outcome relationship is actually due to differences in motivation levels present when examinees complete the IQ tests—that is, variations in the degree to which examinees show typical versus maximum performance. To

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explore the effect of motivation on intelligence test scores, Duckworth et al. (2009) conducted a meta-analysis of previous studies assessing the effects of material incentives (i.e., cash) on intelligence test performance. Across 25 studies of a total of 2,008 participants, material incentives did in fact raise IQ scores with a medium-to-large effect size, Hedges’s g = 0.64. Interestingly, material incentives raised the scores of individuals who had baseline IQs (without an incentive) below average more than the scores of individuals whose baseline IQs were above average, g = 0.94 and 0.26, respectively. This led Duckworth et al. (2009) to suggest that since material incentives affected individuals with above-average IQ scores motivation less than their belowaverage counterparts, those with IQ scores above-average were already closer to maximal performance than their peers. Of course, if motivation affects IQ scores, the correlation between IQ and life outcomes may itself be due to motivation. This issue was examined in the second part of Duckworth et al.’s (2009) paper. The investigators used data from the Pittsburgh Youth Study, in which 10-year-old boys (approximately 50% of whom were labeled as at-risk due to prior disruptive problems in the school setting) were randomly selected from public schools and followed for three years, during which their teachers gave detailed reports every three months about each boy’s performance. When the subjects were 12 years old, they completed intelligence tests. The testing session was videotaped, and later three trained experts coded the children’s observable motivation levels. In follow-up interviews conducted when the participants were in young adulthood (average age was 24), the participants were asked to report their years of education, current employment, 12-month history of unemployment, and the number of times they had been arrested. Duckworth et al. (2009) constructed three multiple regression models for each of the outcomes (performance in school, as well as the long-term life outcomes), while controlling for demographics. One model was fit for IQ scores, the second model for the test motivation values, and the third model utilized both the IQ scores and test motivation values. When looking at how the participant performed academically as an adolescent (i.e., based on the teacher reports of school performance), both IQ and test motivation were each significant predictors when the other was not used, βs = 0.71 and 0.27 respectively. When both were entered in the same regression model, both were still significant predictors, βs = 0.68 and 0.12 respectively. Together, IQ and test motivation were also significant predictors of cumulative years of education, current employment, and number of arrests. Duckworth et al. (2009) also found that test motivation was partially responsible for the

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relationships between IQ and school performance, cumulative education, and employment. That is, the relationship between IQ and school performance was attenuated when motivation was statistically controlled. Finally, in agreement with the finding from their meta-analysis showing that material incentives were more effective in increasing IQ scores in participants with below-average IQ, Duckworth et al. found that participants who had below-average IQ scores were also rated lower in test motivation than their above-average counterparts. If motivation affects consequential real-world test scores as much as Duckworth et al. (2009) suggest, then researchers and applied assessment professionals should consider its influence when interpreting scores. However, it is difficult to know how to apply this to interpreting the scores of individual examinees. One way to do so would involve measuring each examinee’s motivation with regard to the test at hand, but as of now, there are few scales developed to do this. However, the industrial psychology literature does report on the development of such a scale, and although it was used in a personnel selection context, we would argue that educational assessment scholars should model this instrument. Arvey, Strickland, Drauden, and Martin (1990) designed a scale to assess the motivational and attitudinal dispositions of test takers after they had just completed a test in an employment context. A collection of 60 items about test-taking were administered to approximately 500 people and subjected to a factor analysis, which yielded a nine-factor solution, with the nine factors labeled as follows: motivation, lack of concentration, belief in tests, comparative anxiety, test ease, external attributions, general need for achievement, future effects, and preparation. Fifteen items were removed, leaving a 45-item instrument called the Test Attitude Survey or TAS. Initial validation data showed that the TAS yielded expected score differences between examinees taking an easy vs. a difficult test, as well as between job applicants vs. current job holders. The development of the TAS shows the possibility of assessing examinees’ motivation as an aid to interpreting their test scores. Although there are some analogous measures in the educational literature (cf. Wise and DeMars, 2005), more research is needed; equally important, available measures of motivation should be used far more frequently than they currently are.

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EXAMINATION-RELATED ANXIETY The positive association between motivation and performance is an intuitively obvious, easy-to-understand phenomenon. The effect of anxiety on achievement scores is more complex, since examination-related anxiety often stems from the desire to achieve high scores, but it can ironically thwart examinees in reaching that goal. In this section, we review the literature on test anxiety and its effects on performance. In addition, we consider a related phenomenon, stereotype threat, which appears to affect performance by increasing anxiety.

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Defining and Measuring Test Anxiety Although it is relatively common for students to describe themselves as having test anxiety, the meaning of the term is not entirely clear. Putwain (2008) suggests that test anxiety is best understood in relation to general anxiety; the former occurs only in evaluative situations or contexts in which an individual fears performing poorly on tests because a poor score might hinder their academic goals or lead to ostracism from peers. Even this definition does not really clarify the nature of test anxiety, though; at least three possibilities exist. Some literature portrays test anxiety as a stable personality trait, whereas other literature conceptualizes it as an emotional state, referring to the temporary anxiety an individual feels when in an evaluative situation. Trait test anxiety may be one of the determinants of state test anxiety, but the two are distinct, and state test anxiety may occur even when someone’s trait test anxiety is low (Zeidner and Matthews, 2005). A third way that test anxiety is conceptualized is as a clinical disorder, either as a form of generalized anxiety disorder (Sapp, Durand, and Farrell, 1995), or as a form of social phobia if the test anxiety is extreme enough (McDonald, 2001; Zuriff, 1997). Consistent with the diverse conceptualizations of test anxiety, many instruments have been designed to measure it. Early measures, such as the Test Anxiety Questionnaire (TAQ) and Test Anxiety Scale (TAS), spawned newer instruments, such as the Test Anxiety Inventory (TAI), Worry Emotionality Questionnaire (WEQ), and Children’s Test Anxiety Scale (CTAS). In addition, some researchers stress that test anxiety should be evaluated by qualitative interviews as well as standardized scales (Putwain, 2007).

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Measurement and theory development have gone hand in hand, with factor analyses of test anxiety instruments suggesting various facets of test anxiety, and theories of the nature of test anxiety leading to even more recent measures.

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The Causes and Effects of Test Anxiety Early psychodynamic and behavioral theories proposed that test anxiety was caused by such factors as a desire to please one’s parents or repeated failures in different areas of life. Unfortunately, there is little research available to support or refute these ideas. Contemporary models emphasize the interaction between personal and situational factors, suggesting that test anxiety is caused by the interaction of personality traits (e.g., one’s level of general trait anxiety) and the situation’s characteristics (Endler and Parker, 1992; Lazarus, 1999). One situational factor studied in some detail is known as the big-fish-littlepond effect: Students compare their own academic attainments with the attainments of their reference groups (e.g., peers) and use this relative impression as a basis for forming their self-perceptions and coming to conclusions about academic and social status; test anxiety occurs when the students move to a larger reference group and realize that they are no longer the best at what they do (Zeidner and Schleyer, 1999). Several personal factors have also been studied as correlates of test anxiety. In a meta-analysis conducted by Ray Hembree (1988), studies correlating test anxiety with a variety of traits were reviewed. The meta-analysis found a negative correlation between test anxiety and self-esteem (r = -0.42), but a positive correlation between test anxiety and need for achievement (r = .37). In addition, test anxiety was likely to be highest for those individuals who have low ability levels and lowest for people with high ability levels. Demographic factors had small but generally consistent relationships with test anxiety in Hembree’s (1988) meta-analysis. First, males had lower test anxiety than females, on average. This result is in accordance with gender stereotypes about anxiety generally (e.g., Swim, 1994). Although Black students showed higher levels of test anxiety than White students in elementary school, these differences diminished across time, becoming virtually nonexistent by the high school grades. Finally, younger siblings generally reported higher levels of test anxiety than siblings who were the oldest in their families.

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These correlates and causes suggest that test anxiety is real and predictable, but more relevant for the present chapter is the question of test anxiety’s effects on performance. Briefly, these effects are substantial. Hembree’s (1988) meta-analysis reported that studies comparing high and low test-anxious examinees found that high test anxiety was related to GPA (r = .46), performance on standardized tasks of problem-solving and memory (rs = -.45 and -.40, respectively), and the amount of time that examinees took to finish tests (r = .30). Can the effect of anxiety on performance be diminished? Zeidner (1998, 2007) described various moderating variables influencing the effect of (trait) test anxiety on performance (probably via effects on state test anxiety). If reassurance is provided to examinees, or if the test is highly structured with clear instructions, or if examinees have higher levels of general social support, trait test anxiety has less of an impact on performance. Other ways of minimizing test anxiety’s effects include providing examinees with external memory aids containing the information covered on the exam, giving examinees additional easy questions as a way to boost confidence, and providing examinees with information about what the test will cover in advance of actual testing. On the other hand, if the evaluative aspect of the test situation is emphasized, if the test is administered under rigid timed conditions, or if examinees are given negative feedback during the course of the exam, test anxiety will have an enhanced (negative) effect on performance. Can test anxiety ever help performance? As we noted earlier, there are theoretical reasons to think that it could, since anxiety could lead to studying harder for a test, or it could at least lead to considering test items more carefully and eliminating careless errors. Some theorists refer to “facilitating test anxiety,” a construct which encompasses these phenomena (e.g., Mandler and Sarason, 1952). Hembree’s (1988) meta-analysis did find a modest positive correlation between facilitating test anxiety and performance on aptitude/achievement tests (r = .29), although more work on this construct remains to be done. Specifically, more attention needs to be paid to the measurement of facilitating test anxiety. Currently, it is measured by selfreport questionnaires that ask examinees a variety of items similar to “I do best on exams if I’m nervous.” We still do not know whether examinees’ selfperceptions regarding facilitating test anxiety are correct—that is, whether state test anxiety actually does lead to improved test performance in some examinees. A final issue to consider in examining the effects of general test anxiety on performance involves the timing of anxiety measurement. In an intriguing

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study, Zeidner (1991) measured students’ test anxiety either before or after taking the Scholastic Aptitude Test (SAT). Among those students whose anxiety was measured before the SAT, the anxiety-performance relationship was very small (r = -.11), whereas it was substantially stronger when anxiety was measured after the SAT (r = -.40). Unfortunately, there is no available review of the literature comparing studies that measured anxiety before testing and after testing. However, Zeidner’s study should prompt more work on this point.

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A Special Case of Text Anxiety: Stereotype Threat Being aware of negative cultural stereotypes regarding the performance of specific demographic subgroups can have a negative impact on the performance of an individual who is a member of the criticized group. Although this is not a typical case of test anxiety, individuals suffer from anxiety when they worry too much about trying not to fulfill group stereotypes, and ironically, the distracting effects of the anxiety may lead them to perform worse on tests, thus fulfilling the stereotype (Steele et al., 2002). This phenomenon is called stereotype threat and was first described by social psychologists Claude Steele and Joshua Aronson (1995). Literature on stereotype threat has started to accumulate, and a great deal is known about its effects on performance (see Jordan and Lovett, 2007, for a review). In the original Steele and Aronson (1995) study, two groups of students answered difficult items taken from the Graduate Record Examination (GRE) verbal section. The first group was told that the test they were about to take was indicative of students’ verbal ability, while the second group was told that performance on the test had nothing to do with one’s ability. Black students who were told that the test was indicative of ability scored significantly worse than White students in the same condition, whereas in the other (nonthreatening) condition, White and Black participants achieved comparable scores. That the Black–White performance difference was contingent upon the subjects’ belief that their ability levels were being tested was interpreted as evidence for the existence of stereotype threat; activation of examinees’ knowledge of racial stereotypes apparently impaired test-taking ability. Stereotype threat effects have been found for a variety of groups. In addition to the aforementioned effects among Black test-takers (Blascovich, Spencer, Quinn, and Steele, 2001; Steele and Aronson, 1995), researchers have documented intellectual performance decrements due to activation of

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negative cultural stereotypes attached to Latinos (Gonzales, Blanton, and Williams, 2002) and low-SES individuals (Croizet and Claire, 1998). Women, too, have been found to suffer in their performance on math tests when stereotypes concerning gender and math ability are made salient (Swim, 1994; Davies, Spencer, Quinn, and Gerhardstein, 2002). Moreover, as a means of protecting themselves against these stereotype threat effects, some individuals whose abilities run counter to cultural stereotypes may distance themselves from their demographic groups, possibly losing part of their heritage or individuality in the process (Fryer, 2006; Steele and Aronson, 1995; Zirkel 2004). For example, women who are exceptionally gifted at math may downplay their femininity to avoid thinking about the stereotype regarding women and mathematical ability (Pronin, Steele, and Ross, 2004). While the mechanics behind stereotype threat are still not entirely understood, anxiety appears to be the most likely mechanism. Initial empirical work cast doubt on this explanation, since experimental participants’ selfreports of anxiety did not statistically mediate stereotype threat effects (Aronson et al., 1999). However, in more recent studies such as Blascovich et al. (2001), participants’ blood pressure was significantly higher in situations containing stereotype threat while participants’ blood pressure was actually lower than normal in non-threatening conditions. Other studies have shown that trained experts’ ratings of anxiety (based on participants’ fidgeting) as well as participants’ decreased heart-rate variability mediate stereotype effects (Croizet et al., 2004; Bosson, Haymovitz, and Pinel, 2004). The discrepancy between self-reports and other measures of anxiety is intriguing, and suggests that participants may not always be aware of their stereotype-related anxieties, perhaps interpreting their heightened physiological arousal as a different emotion (e.g., excitement). In any case, several studies suggest that stereotype threat is another example of how anxiety can lower test performance, leading to underestimates of students’ skills.

TEST SOPHISTICATION One way to reduce the effect of anxiety on performance is to give examinees information about the test that they are going to take; information generally comforts examinees, giving them a chance to mentally prepare for the task that they are about to begin. Giving examinees information about exams has effects far beyond anxiety reduction, though; test sophistication, a term that we will use broadly to refer to any provision of information or skills

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in an effort to increase future test performance, often does increase performance. In a classic article on this topic, Anastasi (1981) distinguished three sophistication strategies leading to improved test scores: training in general cognitive skills, training in specific test-related skills, and exposure to the actual test. As Anastasi notes, training in general cognitive skills (i.e., training to increase intelligence) has been advocated by a number of psychologists since the time of Alfred Binet, whose work on “mental orthopedics” (Binet, 1911) was designed to improve the cognitive skills of intellectually deficient school children. Admittedly, the efficacy of such exercises is hotly disputed, and there is literature to support both sides of the debate (Brown, 1978; Whimbley, 1975, 1980; Bloom, 1976). Supporters claim that increases in intelligence can even occur at later ages, although most research concludes that early training is most effective (Anastasi, 1981). Exposure to a test is a very different kind of sophistication, but it has similar effects on test performance, generally increasing it. Sometimes these effects are known as practice effects, and test scores typically tend to be higher for those who are taking a test a second time (Angoff, 1971; Droege, 1966; Powers and Camara, 1999). Although this is a potential threat to the validity of tests (if scores indicated how many times examinees were exposed to the testing situation, rather than their actual academic skills), test sophistication effects can be equated across examinees to some degree through the application of brief orientation and practice sessions (Wahlstrom and Boersman, 1968). Of course, these sessions will not equate for variability in prior exposure to actual test items, but for tests such as the SAT, where students take a different version each time that they take the test, orientation and practice sessions would at least equate for exposure to the general testing experience.

Test Preparation Effects Most test sophistication, though, does not consist of exposure to the actual test items, nor does it consist of training in general cognitive skills. Instead, most test sophistication involves training in rather specific test-related skills, something often described as test preparation or coaching. This is seen most vividly in commercial programs that brag about their ability to increase scores on college admissions tests and related measures. While many test preparation providers claim that their programs will improve an individual’s score significantly, this is often an exaggeration. Claims of 100-point gains on the

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SAT, for example, might be closer to actual improvements of only approximately 30 points, a gain equal to about three correct answers on the SAT (Briggs, 2009). This seemingly small gain in test performance is still enough to encourage many students to prepare for admissions tests in ways that they might not ordinarily for other tests. Many of these students, typically in their junior or senior year of high school, feel the pressure to take the SAT or ACT (research has shown that the percentage of high school seniors taking admission tests increased from 51% in 1992 to 63% in 2004; see Briggs, 2002; Briggs and Domingue, 2009), and if these students’ performance was not satisfactory to themselves or the institutions to which they were seeking admission, then they would have two options: look elsewhere for schools or improve their test scores. Briggs (2009), on behalf of the National Association for College Admission Counseling (NACAC), distributed hundreds of surveys to NACAC-member colleges, asking about their admissions procedures. One of the findings was that a respectable number of colleges and universities (over one-third of the institutions surveyed) said that a slight 20-point gain on the SAT could “significantly improve a student’s likelihood of admission” (Briggs, 2009, p. 17). Obviously, this would depend on where a student’s score starts out; a rise from 200 to 220 on the SAT-Verbal is unlikely to impress colleges. Still, the colleges’ responses to Briggs’s survey suggest that students may be justified in seeking even small score gains. In reviewing the literature on coaching effects, Briggs (2009) found evidence for several points. First, there was a positive effect of coaching on SAT performance, although the effect was of small magnitude, as mentioned earlier. Second, coaching often led to larger improvements on math sections of the SAT than on critical reading (e.g., Powers and Rock, 1999); indeed, it was difficult to find evidence that any coaching programs substantially increase SAT verbal performance. Third, although the percentage of students taking admissions test has increased, the purchasing of test preparing books, commercial coaching services, tutoring, and other forms of coaching generally remained the same between 1988 and 2002 (Briggs, 2002; Briggs and Domingue, 2009). Overall, it should be made clear that although some forms of coaching or test preparation are likely to increase scores in college admission tests, there is no set rule as to which type of test preparation, if any, is appropriate for any one person. Moreover, an individual’s motivation to do well on high-stakes tests is likely to moderate any effects of test preparation services. In addition, there is still little research currently available on the efficacy of online test

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preparation services, despite their recent popularity. Finally, ideal research on the assessment of test preparation effects would involve the random assignment of subjects into different test preparation conditions, and as of 2009, no research has incorporated such methodology. These coaching studies all involved college admissions tests, but it is important to note that test preparation is also used for the high-stakes accountability tests so prevalent today in K–12 schools. Recent federal legislation, such as the No Child Left Behind (NCLB) Act, has resulted in more coordinated statewide use of standardized achievement tests, and decisions about school funding and other important issues are tied to school performance. Test preparation is one strategy used by states and school districts to ensure that sufficient numbers of students are performing at adequate levels (Lai and Waltman, 2008).

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Test-Wiseness: A General Type of Sophistication A final sophistication-related variable—one not mentioned by Anastasi (1981)—that may explain variability in achievement test scores is testwiseness. A classic definition of test-wiseness is “a subject’s capacity to utilize the characteristics and formats of the test and/or test-taking situation to receive a high score” (Millman, Bishop, and Ebel, 1965, p. 707). A test-wise examinee may show a number of traits outlined in a classic taxonomy developed by Millman and colleagues. Six traits were presented in this taxonomy, four of which apply to any test. First, test-wise examinees use time-management strategies (e.g. going over answers on a test with any remaining time to reconsider/ensure confidence in responses). Second, test-wise examinees use a strategy for minimizing mistakes (e.g. making sure that the directions are clearly read and understood before answering a question). Third, test-wise examinees employ a guessing strategy (e.g. if there is no penalty for an incorrect answer, then never leave a question unanswered). Finally, test-wise examinees use deductive reasoning (e.g. getting rid of answer choices that are known to be incorrect and choosing from the remaining options). The other two traits depended on the test’s constructor and/or the purpose of the test. First, test-wise test-takers pay attention to the test constructor’s advice as to what will be on the exam (e.g. pay attention to the relevance of certain details). Lastly, test-wise examinees use cues (e.g. the correct answer is typically longer or shorter than the incorrect options).

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Test-wiseness can be a threat to the validity of scores unless it is held constant across examinees (Rogers and Yang, 1996). To be held constant would require one of two things: either test-wiseness effects are present for everyone or they are absent for everyone. To ensure the former possibility, efforts could be made to raise the test-wiseness of individuals with low testwiseness. Training programs could be incorporated into the curriculum of middle schools and continued up to the high school level (Samson, 1985; Sarnacki, 1979). Alternatively, to reduce test-wiseness effects across all examinees, the responsibility lies mainly on the test constructor. Contradictory distracters, grammatical incongruence between distracters and item stems, and leaving correct answers substantially longer or shorter in length than distracters are the sorts of cues that test-wise examinees rely on to bolster their scores. These cues could be at least partially eliminated through training of the test constructors (Rogers and Yang, 1996).

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CONCLUSION Motivation, anxiety, and sophistication are all associated with variability in performance on standardized cognitive tests, across a large number of studies, measures, and populations. The degree to which examinees have an incentive to perform well, feel nervous about their performance, and are knowledgeable about the tests they are about to take can all have a significant impact on the resulting scores on achievement tests. The evidence suggests that motivation, anxiety, and sophistication are not just potential sources of construct-irrelevant variance, but actual sources. Although some scholars and assessment practitioners may find this conclusion depressing, we prefer to view it as a challenge. One way to eliminate sources of construct-irrelevant variance is to keep them from varying across examinees. For instance, we discussed above how test-wiseness effects could be diminished if all examinees were trained to be test-wise. This is a more general solution to the problems discussed in this chapter. The research on motivation should challenge test users (and researchers) to maximize motivation for all examinees, by providing effort-based incentives, designing briefer, more engaging test stimuli, and building rapport before asking examinees to put forth their best efforts. Similarly, the research on test anxiety should lead to widespread efforts to screen students for evaluative anxiety so that intervention can occur early; we have effective treatments (Zeidner, 1998) and no excuse for not using them more widely.

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We conclude on a brief theoretical note. Recent approaches to measurement validity (e.g., Kane, 2006; Messick, 1995) argue that it is not tests themselves that are valid, but uses of tests and inferences made on the basis of test scores. The research reviewed in the present chapter should be taken as suggesting that achievement tests should be used cautiously when motivation, anxiety, or sophistication are likely to vary across examinees. Moreover, achievement test scores may at times lead, in the context of other information, to valid inferences about examinees’ levels of motivation, anxiety, and sophistication, rather than their academic skills. For instance, when a student who has always done very well in school does poorly on an achievement test, the score may be evidence of the student’s anxiety, or lack of preparation, or low motivation. All of this complicates the interpretation of achievement test scores, but as we have argued, it is to be hoped that this realization will lead ultimately to more motivated, less anxious, and increasingly test-wise examinees, hardly a consequence to be derided.

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REFERENCES Aronson, J., Lustina, M. J., Good, C., Koeogh, K., Steele, C. M., and Brown, J. (1999). When White men can’t do math: Necessary and sufficient factors in stereotype threat. Journal of Experimental Social Psychology, 35, 29-46. Arvey, R. D., Strickland, W., Drauden, G., and Martin, C. (1990). Motivational components of test taking. Personnel Psychology, 43, 695716. Blascovich, J., Spencer, S. J., Quinn, D. M., and Steele, C. M. (2001). Stereotype threat and the cardiovascular reactivity of African Americans. Psychological Science, 12, 225-229. Bosson, J. K., Haymovitz, E. L., and Pinel, E. C. (2004). When saying and doing diverge: The effects of stereotype threat on self-reported versus non-verbal anxiety. Journal of Experimental Social Psychology, 40, 247255. Briggs, D. C. (2002). SAT coaching, bias and causal inference. Dissertation Abstracts International. DAI-A 64/12, p. 4433. (UMI No. 3115515). Briggs, D. C. (2009). Preparation for College Admission Exams. National Association for College Admission Counseling.

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Zeidner, M. and Matthews, G. (2005). Evaluation anxiety. In A. J. Elliot and C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 141163). London: Guilford Press. Zeidner, M. and Schleyer, E. (1999). The big-fish-little-pond effect for academic self-concept, test anxiety, and school grades in gifted children. Contemporary Educational Psychology, 24, 305-329. Zirkel, S. (2004). What will you think of me? Racial integration, peer relationships and achievement among white students and students of color. Journal of Social Issues, 60, 57-74. Zuriff, G. E. (1997). Accommodations for test anxiety under ADA? Journal of American Psychiatry and Law, 25, 197-206.

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Chapter 4

ACHIEVEMENT TESTS IN THE NORWEGIAN SCHOOL SYSTEM: TYPES AND USES Eyvind Elstad,∗ Are Turmo and Øystein Guttersrud Departement of Teacher Education and School Development, University of Oslo

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ABSTRACT During the past decade in Norway —as in many other countries— accountability devices have been established in systems of education governance. The accountability systems may vary from country to country; however, this article discusses the types and uses of achievement tests in the Norwegian school system. Although Norway has a long tradition of school-leaving exams in both lower and upper secondary school, achievement tests in the tradition of e.g. PISA and TIMSS have only recently been introduced. Complementary accountability tools such as transparency of test results, school autonomy, and school inspections, are used for steering purposes. Tests and exam results play key roles in the performance management system. Together, these devices can be regarded as building blocks in sprawling external accountability systems. It would be wrong to say that the Norwegian school system at the national level has a fully-fledged accountability system. Achievement tests are, however, potential instruments for the promotion of ∗

e-mail: [email protected]

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Eyvind Elstad, Are Turmo and Øystein Guttersrud accountability mechanisms. The Norwegian government has made it possible for governing bodies to develop local accountability systems, and achievement tests play a significant role therein. The extent to which local authorities have been able to relate to this type of delegated responsibility has varied. In this article, we discuss the different types of achievement tests in Norwegian compulsory education, and how they are used.

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INTRODUCTION The purpose of this article is to study and discuss types and uses of achievement tests in Norwegian compulsory education. Compulsory education normally comprises 6-15 year olds in Norway (so-called primary and lower secondary schooling), while upper secondary schooling (16-19 year olds) is voluntary. First, we outline the background of national education policies and the emergence of compulsory achievement tests in Norway. Second, present continuous achievement test and exam system at the national level is discussed. The responsibility for quality in Norwegian schools is delegated to counties and local authorities. Local authorities have chosen to set up assessment-based quality systems in very different ways. In some municipalities, it is hard to find evidence of quality assurance at all. However, in other municipalities, test results are used for accountability purposes. One example is Oslo municipality, the capital of Norway, where school accountability is linked to tests and the publication of test results at the school level. Third, we outline the emergence of the local achievement test system in Oslo minicipality. Finally we summarize the paper and arrive at some conclusions.

THE EMERGENCE OF ACHIEVEMENT TESTS IN NORWEGIAN COMPULSORY EDUCATION: THE BACKGROUND Many countries’ educational systems have seen the introduction of testbased accountability policies, each with different and unique features. In Norway, accountability in education has been attempted only recently. Norway has a long-standing tradition of school-leaving exams in both lower and upper secondary schools. However, other achievement tests have only

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recently been introduced. Tests are building blocks of accountability systems, and the emergence of achievement tests in Norway can be interpreted as a manifestation of accountability thinking. However, in comparison with other countries, the use of accountability devices in Norway is still limited: Norway was a latecomer in terms of applying accountability devices in education in the 1990s (Elstad, Nordtvedt and Turmo, 2009). In the following, we describe some reasons that may imply why Norway may be viewed as a latecomer. The progressive movements in education had a strong hegemony in Norwegian educational debates before the millennium (Telhaug et al., 2006), and these movements reinforced process steering (for instance, theme-based project work as legally binding regulation in teaching) as a full-fledged policy in Norway during the 1990s (Sejersted, 2005). When the Organisation for Economic Co-operation and Development (OECD) in 1988 suggested recommendations of achievement tests and assessment policies for the Norwegian educational authorities (OECD 1988a,b), the government secretaries of state replied by adjusting the balance of power in the educational sector to be more in favor of central government (White Paper 37, 199091).Schools were obliged to implement project work and imposing processdescriptive targets on the compulsory school curriculum. Progressivism manifested itself in the way learning objectives were expressed in curricula and in the evaluation criteria suggested by the civil service, such as “students are to work with numbers”, “learn to read at their own speed”, and “work with some interesting numerical relations for instance numbers with special properties, the role of numerology in certain cultures or the attraction of number riddles” (Ministry of Education, 1996). The subject’s core and distinctive characteristics are obviously given less emphasis than the students’ skills and experience of what is considered valuable by progressive pedagogues. The Norwegian educational politics of the 1990s was fairly different from the tendencies seen in many other European countries and reached its peak at the end of the 1990s; that is, at least 60% of the time in lower primary school and at least 30% in lower secondary school should be 1 spent on theme-based project work. This became a legally binding regulation (Helgeland, 2001) and was a manifestation of detailed teaching control in schools (Tønnesen 2004: 114). In July 2001, the Director General of the Ministry’s Department of Education and Training emphasized progressive activity-oriented teaching methods: 1

Ministry of Education (1996).

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The Ministry has wanted to reform the teaching methods in schools for a long time. Now this is finally happening. To a larger extent, students will work independently and in groups with topics they develop themselves. The teachers will be more like supervisors than lecturers (Kluge, 2001; 4).

The self-satisfaction with the education policy at this time was high.2 One district superintendent actually declared in November 2001 that “The Norwegian school has never been better” (Skrede, 2004). The first results from the Programme for International Student Assessment—PISA 2000—induced a shock in December 2001: “The (mediocre) results from the first PISA survey came as a bolt out of the blue for the Norwegian authorities. The authorities supposed that Norway was among the best achieving countries in OECD”, according to the CEO of the Directorate for Education and Training (Gjerdåker, 2008). Shortly before the publication of PISA 2000, Norway got a center-conservative government (2001-5), in which the conservative party had the Minister of Education and Research. The conservative minister initiated new educational policies and a far-reaching program of reforms later called “The Knowledge Promotion” (KP). Increased learning dividends were the paramount goal of KP in the initial stage, and achievement tests were vital devices of a knowledgeenhancing policy. During the first phase of the KP reform in 2004, it was decided that a national quality assessment system should be established. New tools of steering involved autonomy through devolution, performance controlled by use of new and old types of measurements (grades, national achievement tests and value added indicators3) and pay-for-performance elements. These components constituted cornerstones of an accountability systems in education. Furthermore, it was decided that school results were to be published on the Internet4 and educational objectives were to be couched in behavior terms, with the introduction of standardized national tests in core domains: reading, writing, mathematics and English in years 4, 7 and 10 of compulsory education (and year 11 of upper secondary education). Certain 2

Despite the fact that in 1995 Norway had the lowest score in Science topics in the 4th grade of all European countries in Trends in International Mathematics and Science Study (TIMSS). 3 School performance (school contribution) indicators supplement the existing information about school quality and results. The indicators control for differences in family background. They are useful in identifying schools with good practices, and they make it possible to identify school-level decisions that enhance student achievement. School performance indicators are developed for compulsory schools and upper secondary schools in Norway. 4 http://skoleporten.utdanningsdirektoratet.no/default.aspx

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building blocks of an accountability system were introduced by a conservative Minister of education (2001-2005): performance controlled by the use of national achievement tests and examinations, school contribution indicators based on tests and examinations, publication of average results by school, et cetera. During the first phase of the education reform in 2004, it was decided that a national quality assessment system should be established. Furthermore, it was decided that school results were to be published on the Internet, at the so-called “school portal” (Norwegian Directorate for Education and Training (NDET), 2006). Educational objectives were to be couched in behavior terms (“clear goals”), with the introduction of standardized national tests in core skills (reading, writing, numeracy and English reading). The systems for reporting achievement data helped the public to hold schools accountable. The media transformed the school data to local or nationwide league tables and have since then (2005) steadily published summaries of the results of national achievement tests, often in the form of league tables of schools’ average results (exams) and the percentage of student achieving at different proficiency levels (national tests). In the 2000s, a new educational discourse was established in Norway. In general, accountability systems have never been approved for use in the Norwegian nationwide education sector, but some accountability thinking nevertheless influences how some of Norway’s local quality assurance systems are designed (Elstad, Nortvedt and Turmo, 2009). In the Norwegian educational system, accountability is first and foremost hierarchical; it is the school owners—which in most cases (about 95%) are counties and municipalities—that are legally responsible for quality.. The responsibility for quality in schools has been delegated to the governing bodies, but the extent to which counties and local authorities have been able to relate to this type of delegated responsibility has varied (White Paper 31, 2007-8). The government has, however, made it possible for governing bodies to develop local accountability systems (White Paper 31, 2007-2008). The wave of reforms known as New Public Management (NPM) has broken over Norwegian education. A performance management system called Management by Objectives and Rules was implemented relatively late in Norway in comparison with other countries and admittedly in a modified manner. NPM was introduced in the 1990s, but lacked result orientation. However, the result orientation was more important after the millennium. For example, teachers were directly instructed to apply more pressure when teaching (White Paper 30, 2003-4) as part of the effort to raise the level of knowledge in Norwegian schools.

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A red-green coalition5 won the Norwegian parliamentary election in 2005, and under the current red-green government, certain accountability tools have been changed: The government does not want the publication of results by school, but despite this, journalists manage to reconstruct league tables indicating well- and poorly-performing schools. The work to develop school contribution indicators was stopped and the potential to hold schools responsible was reduced by reducing the number of national achievement testsand move the assessments from the end of middle-school level to the beginning of year 8, i.e. immediately after many students have changed school. However, governing bodies are still allowed and encouraged to develop local accountability systems (White Paper 31, 2007-2008). The red-green government has chosen to retain the main features of The KP reform, but has weakened some of the components in a potential accountability system by e.g. obscuring the opportunities for placing responsibility by moving national achievement tests to early in the autumn term of years 5 and 8 (the majority of the students change schools between years 7 and 8), by preventing agents other than the school itself and the school owner from getting information about the school’s average results, by stopping the work on school contribution indicators, etc. Information about results is claimed to be “the lifeblood of all accountability systems” (O’Day 2002: 4). It can be assumed that the limitations in information distribution from the national level contribute to a weakening of potential accountability mechanisms. While national authorities prevent information from reaching the public, some local authorities do the opposite; for instance Oslo, which 6 publishes school performance at their own local web portal. The use of achievement tests in Oslo is discussed later in this article. National achievement tests were reintroduced by the red-green government in 2007 after a temporary halt due to major psychometrical challenges and practical problems in 2004 and 2005 (NDET, 2007). The NDET, the executive agency for the Ministry, was responsible for the implementation, and received severe critique from external evaluators (Lie et al., 2004; 2005). However, there has been a marked reduction in the number of national achievement tests from 2007 onwards (NDET, 2007). Furthermore, the opportunities for using the results for accountability purposes have been 5

The current Norwegian government, which was formed on October 17 2005, consists of the Labor Party, the Socialist Left Party and the Center Party. These parties formed a coalition majority in the Storting (the Norwegian Parliament). 6 https://lextra.oslo.kommune.no/ude/kvalitetsportalen?

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diminished in that the achievement tests are taken by students at the beginning of year 8. The red-green ministers of education decided that the results of the national achievement tests should only be published at an aggregated level, i.e., municipal level, thus making information about school results less accessible and avoiding school league tables. Since the website “The School Portal” does not contain any information on academic achievements, parents are unable to discover whether their child’s prospective school has a good academic record before they start year 8. However, the media still publish summaries of the results of the national achievement tests, often in the form of league tables (Elstad, Nortvedt and Turmo, 2009).

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A CONTINUOUS ACHIEVEMENT TEST AND EXAM SYSTEM AT THE NATIONAL LEVEL The exams are part of the national continuous achievement test and exam system (see table 1), and are summative assessments to inform society at large, including future employers and higher educational institutions, about individual student’s end of school/education competencies. However, these exams have very different stakes. In compulsory education, students only have to sit exams at the end of grade 10; no exams apply at lower grade levels. These exams could be claimed to be low-stake, as all students have the right to go on to upper secondary school, and to have one choice from a list of three in consideration of what kind of education they want to enter. Furthermore, only pass grades apply at the grade 10 level. To students in upper secondary school (grades 11-13), exams are high-stake. Students compete for admission to universities and colleges. Also, failing an exam means leaving school without full certification, and school dropout is a major concern (Markussen, 2009). Students sit one written exam at the end of compulsory school (grade 10), and the exam results and the teacher-given grade in the subject are both reported on the students’ school leaving diploma. Students also have to take one oral exam at the end of grade 10. During the last years, oral exams with time to prepare beforehand have become common. The students are typically given the topics 48 hours in advance, and have the opportunity to prepare at home or at school. The students may take the exam individually or in groups. The topics are defined by the subject teacher, who also asks the questions during the exam. The grades are assigned by an external evaluator; however, the subject teacher should also take part in the evaluation. This stands in

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contrast to written exams, in which grades are anonymously assigned by external evaluators. Furthermore, the centralized written exams are unknown to teachers and students until the examination day. Table 1. A continuous test and exam system (NDET, 2008, translations from Elstad, Nortvedt and Turmo, 2009) KEY MEASURES Diagnostic tests Diagnostic material Tests to support assessment and learning Supportive material

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National tests (grades 5 and 8)

Exams (schoolleaving examinations, craft certificates, final assessments) International assessments

PURPOSE To identify needs for individual assistance and help at individual and school level. To examine student competence at domain-specific level and to use such information to support decisions about pedagogy and teaching efforts to support student learning within subject. To make visible how school owners, schools and teachers can utilize the assessment systems. To assess the extent to which students’ basic skills are in accordance with curricula goals. To inform students, teachers, parents, school leaders, school owners, regional authorities and the nation and to provide such information as a basis for improvement and development. Summative assessment to inform society at large, including future employers, higher educational institutions, about individual students’ end-of-school/ education competence. To assess Norwegian students’ competence related to other countries. To serve as a basis for indicators on education and to form policy.

Since 2002, official grade statistics have been collected by Statistics Norway , and analysis of the grade statistics is published both by Statistics Norway and in the NDET’s annual report “The Education Mirror”. However, the statistics are not published at individual school or municipality level. Available grade statistics show that in all subjects at year 10, the mean grades from oral exams are clearly higher than the means from written exams and the teacher-given grades. Furthermore, the average written exam grades are the lowest among the grade averages in all the subjects where written exams apply. These tendencies are the strongest in Mathematics; the difference between the oral and written exam grade averages in 2007 was close to one point on the six-point grade scale (www.ssb.no). These differences may be

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explained by variations in exam formats, and by the degree to which the schools and individual teachers have direct influence over the results (Elstad, Turmo and Guttersrud, 2010). National achievement tests in core domains like mother tongue reading literacy, basic mathematical competencies and reading literacy in English language are mandatory for all students at the beginning of grades 5 and 8. The intention is to assess the extent to which students’ basic skills are in accordance with curricula goals as defined after grades 4 and 7 in the curriculum, and to inform students, teachers, parents, school leaders, school owners, regional authorities and the nation, as well as to provide such information as a basis for improvement and development. The individual schools and school owners have access to the results at the school level. However, individual school results are not publicly reported (with some few exceptions, see the case of Oslo described below). In the publication “The Education Mirror”, regional variations between (named) counties in scores from the national achievement tests are reported, as well as between different categories of (anonymous) municipalities. Results from the analyses of national achievement tests in 2007 and 2008 showed, for instance, that smaller municipalities tend to have weaker achievements than large, urban municipalities. The smallest municipalities ( 120 WAISR) and low IQ (< 100 WAIS-R) (1:1), from the high and low socioeconomic strata (SES) (1:1) and of both sexes (1:1). AAT scores for university admission were obtained for 84 school-age children from University of Chile records and were divided into two groups: high AAT (≥ median (Md)= score 631) and low AAT (< Md). IQ was determined by the Wechsler Intelligence Scale for Adults (WAIS-R) and the Raven Progressive Matrices Test in the school-age children and their parents. Scholastic achievement (SA) was measured applying the standard Spanish language and mathematics tests. SES was evaluated using Graffar's modified method. Nutritional status was assessed through anthropometric measurements of weight and height to establish the body mass index (BMI) according to Garrow; head circumference (HC) was compared with Tanner, Nellhaus, Roche et al. and Ivanovic et al. tables and was expressed as Z score (Z-HC); body composition parameters such as arm circumference-for-age, triceps skinfold-for-age, arm muscle areafor-age and arm fat area-for-age were calculated using data from Frisancho. Brain morphology was determined by magnetic resonance imaging (MRI). Job status was expressed as: (1) jobless, (2) workers without further schooling, (3) students at institutes and (4) students at universities. Statistical analysis included correlation and logistic regressions using the Statistical Analysis System (SAS). Results showed that students with high AAT score presented IQ, parental IQ, SA, brain volume (BV), Z-HC, maternal schooling, house-hold head occupation and quality of housing significantly higher than their peers that achieved the lowest AAT scores of whom 19% had suffered severe undernutrition in the first year of life (Fisher p< 0.0054). However, logistic regression revealed that student IQ is the best predictor of AAT score and the odds ratio value (1.252) implies that when the IQ score increases by one point, the probability to obtain a high AAT score increases in 25.2%. Students at universities presented AAT, child and maternal IQ, SA, BV, birth weight and birth height, Z-HC and socio-economic conditions significantly higher than their peers from the other three job statuses; however, AAT score at the end of high school was the best predictor of the job status six years later (odds ratio value= 1.025) which indicates that when AAT score increases by one point the probability for university

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admission and for university graduation increases 2.5%. In a multifactorial approach, these results point out the importance of neuropsychological parameters on children’s achievement for university admission and future jobs.

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INTRODUCTION The AAT, the baccalaureate examination for university admission with national coverage in Chile, revives a permanent controversy as regards whether this instrument is the best test to select the most capable high school graduates to enroll into higher education. The question is whether AAT score is a good predictor of successful scholastic achievement at universities and later job status. As a consequence, this instrument is the object of continuous analysis in order to optimize it; however, planners have insufficient elements to consider since research carried out to assess the impact of the test on university achievement and later occupation level is scant (Schiefelbein and Farrell, 1982). As regards to this problem, research must consider that scholastic achievement is a multifactorial process determined by multiple factors dependent on characteristics of the student, his family and the educational system that affect enrollment, attendance, school performance and desertion (Ivanovic and Ivanovic, 1988; Ivanovic, Ivanovic and Middleton, 1988). Of all the neuropsychological parameters intelligence has been described as the most important independent variable that explains educational achievement (Ivanovic, Ivanovic, Truffello, and Buitrón, 1989a; Ivanovic et al., 2000a,b,d, 2002, 2004c). Our previous results confirm that both the child’s and his parent’s intellectual quotient, undernutrition in the first year of life, brain volume, birth weight and birth height significantly condition scholastic achievement; however, the child’s intellectual quotient is the most important independent variable explaining approximately 90% of scholastic achievement variance in both sexes (Ivanovic el al. 2000a, 2002). Maternal intellectual quotient, brain volume and nutritional status during the first year of life have been described as the most relevant variables that contribute to explain child intelligence independently of age, sex and socio-economic strata (Ivanovic et al., 2002). Other findings reported by us in Chilean school-age children revealed that head circumference, sex, maternal and schooling of the head of the household, height, availability of sewerage at home and the quality of housing, have been also described as the most relevant independent variables associated with intellectual ability and this was independent of socio-economic strata and

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age (Ivanovic, Forno, and Ivanovic, 2001). These results indicate that when socio-economic, cultural, family, mass media exposure, demographic and educational variables were considered as independent variables in the statistical regression model, maternal schooling was the variable with the greatest explanatory power in the child’s intellectual ability variance (Ivanovic et al., 2001). On the other hand, several authors have confirmed that variables related to family socio-economic strata and particularly maternal schooling are consistent in explaining scholastic achievement and intelligence (Ivanovic, Castro, and Ivanovic, 1995b; Özmert, et al., 2005; Sandiford, Cassel, Sanchez, and Coldham, 1997). The lack of precision concerning the relative impact of mass media exposure on scholastic achievement is evident since several authors described that for some children some television programs, has negative effects; however, for most children television is neither injurious nor beneficial and some studies even detected a positive impact of television on school performance (Gupta, Saini, Acharya, and Miglani, 1994; Hagborg, 1995; Ivanovic and Sepúlveda, 1988; Ridley-Johnson, Cooper, and Chance, 1982; Schramm, Lyle, and Parker, 1965; Strasburger, 1986). As regards to written mass media, reading ability has been described as positively and significantly associated with scholastic achievement at the onset of high school and learning disabilities are positively and significantly associated with very low birth weight (Johnson, and Breslau, 2000; Smith et al., 1996). The nutritional status of school-age children has been reported as positively and significantly associated with scholastic achievement and intelligence. In this respect, our previous reports provide substantial evidence that this neuropsychological parameter is positively and significantly correlated with indicators of past nutrition, especially head circumference, the most important anthropometric index associated with learning and intelligence (Ivanovic, 1992; Ivanovic and Marambio, 1989; Ivanovic, Olivares, Castro, and Ivanovic, 1996; Ivanovic, Zacarías, Saitúa, and Marambio, 1988; Ivanovic et al., 1989a, 1991a, 1992, 2000a,b,c, 2002, 2004c; Toro, Almagià, and Ivanovic, 1998). Head circumference is an anthropometric indicator of both nutritional background and brain development (Rumsey and Rapoport, 1983). Although a "normal" head circumference, mean ± 2 standard deviations, could be more related to statistical normality, this may not be the case for scholastic achievement or psychological function (Ivanovic et al., 2000c, 2004b). In this respect, although microcephaly and macrocephaly are reliable indicators of brain pathology, head circumference values below the mean but still within the normal range are associated with an increased incidence of lower intellectual quotient (Ivanovic et al., 2000c, 2004b; Menkes, 1995). This means that small differences

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in head size could be important in the interrelationship head circumferenceintelligence-learning. Undernutrition at an early age may have negative long-term effects on scholastic achievement; undernourished students have significantly lower birth weights, decreased head circumference, lower intellectual quotient, brain volume, school performance and maternal schooling than their peers who did not suffer from undernutrition at comparable early age (Ivanovic et al., 2000b). In Chilean school-age children graduating from high school our recent findings confirm that, independently of socio-economic condition, age and sex, high school graduates with similar intellectual quotient have similar parameters of nutritional status, brain development and scholastic achievement and that these variables are strongly and significantly interrelated (Ivanovic et al., 2002). Undernutrition in the first year of life affects growth, especially head circumference but taking into consideration that children grow until about 18 years of age, improvements in height may be obtained through adequate nutrition. However, the brain is a notable exception since the first two years of life represent its period of maximum growth and by the end of the first year of life, 70% of it adult weight has been attained (Ivanovic, 1996; Stoch, Smythe, Moodie, and Bradshaw, 1982). In fact, head circumference has been defined by several studies as the most sensitive anthropometric index of prolonged undernutrition during infancy, associated with intellectual impairment and low scholastic achievement (Ivanovic, 1996; Ivanovic et al. 2000b; Leiva et al. 2001; Stoch et al. 1982; Winick and Rosso, 1969a). Malnutrition alters brain development and intelligence by interfering with overall health as well as with the child's activity level, rates of motor development and growth; poverty exacerbates these negative effects, especially when mothers have lower schooling levels (Brown and Pollitt, 1996; Ivanovic et al. 2000b). Independently of age, children identify their mothers as the most powerful source of nutrition information (Ivanovic, Olivares, and Ivanovic, 1991b; Ivanovic, Truffello, Buitrón, and Ivanovic, 1989b). Mothers of children who suffered from undernutrition at an early age have serious problems related to affectivity and communication with their offspring and a low degree of nonverbal expressiveness (Alvarez and Wurgaft, 1981; Alvarez, Wurgaft, and Wilder, 1982). As a result, maternal schooling may have its greatest impact on the child’s health and intellectual quotient probably because mothers are the main source of intellectual stimulation and enrichment for their psycho-social environment (Smith et al., 1996). This finding could be important for the

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intellectual development of children since the intellectual quotient is positively influenced by adequate stimulation. The impact of head size on intelligence and scholastic achievement increases significantly from the onset of elementary school until the end of high school, in the same way that the impact of body weight and body height decreases significantly (Ivanovic et al., 1996, 2000c). At the onset of elementary school, 59% of children had suboptimal head circumferences, a percentage that decreased significantly to 40% in high school graduates (Ivanovic et al., 1996). As a consequence, we may infer that school dropout or school delay correlates with head circumference and not with weight or height. Among high school graduates, approximately 70% of those students with the lowest scores in the scholastic achievement test had subnormal head circumference (Ivanovic et al., 1996). Early childhood malnutrition affects head circumference, brain development and later intelligence and scholastic achievement, but this is still a matter of controversy since these variables are influenced by socio-economic and cultural factors that are co-determinants; a head circumference below -2 standard deviations of the mean may be an indicator of severe undernutrition and accurately reflects retarded brain growth during the first year of life (Winick and Rosso, 1969a). In autopsies of children who died of severe undernutrition during the first year of life, some authors have demonstrated decreased cell division rates in the brain, resulting in decreased myelination, weight, nucleic acid and protein contents, compared with normal children died of accidental causes; the decreased head circumference is proportional to the brain's weight; in fact, the magnitude of this reduction is a reliable indicator of the severity of nutritional deprivation (Winick, 1975; Winick and Rosso, 1969a,b). The long-term effects of severe undernutrition in the first year of life may result in delay of head circumference growth, of brain development and decreased intelligence and scholastic achievement, variables that are all strongly interrelated (Grantham-McGregor and Fernald, 1997; Ivanovic, 1996; Ivanovic et al., 2000b, 2002; Leiva et al., 2001; Stoch et al., 1982; Winick and Rosso, 1969a). Findings from several studies emphasize that head circumference might reflect better than body height the impact of nutritional deficiencies at an early age; this measurement is useful in the identification of the period during which malnutrition occurred (Johnston, and Lampl, 1984; Malina et al., 1975; Yarbrough, Habicht, Martorell, and Klein, 1974). Head circumference in the first year of life may predict later intelligence as it has been described by several authors (Botting, Powls, Cooke, and Marlow, 1998; Fisch, Bilek, Horrobin, and Chang, 1976; Nelson and Deutschberger,

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1970). On the other hand, the interrelationship between scholastic achievement, intelligence and nutritional background, reflected by a decreased head circumference, may be affected by birth weight and other factors (Botting et al., 1998; Grunau, Whitfield, and Fay, 2004; Ivanovic, 1996; Ivanovic et al., 1989a, 1996, 2000a,b,c, 2002, 2004a,b,c; Leiva et al., 2001; Matte, Bresnahan, Begg, and Susser, 2001; Pennington et al., 2000; Reiss, Abrams, Singer, Ross, and Denckla, 1996; Rushton, and Ankney, 1996; Sorensen et al., 1999; Stathis, O'Callaghan, Harvey, and Rogers, 1999; Stoch et al., 1982; Toro et al., 1998; Vernon, Wickett, Bazana, and Stelmack, 2000; Willerman, Schultz, Rutledge, and Bigler, 1991). However, other authors found that impaired fetal growth was not associated with poorer cognitive performance in adult life; adaptations made by the fetus in response to conditions that retard growth seem to be largely successful in maintaining brain development (Martyn, Gale, Sayer, and Fall, 1996). Recent findings support that brain growth measured indirectly by head circumference during infancy and early childhood is more important than growth during foetal life in determining cognitive function (Gale, O'Callaghan, Godfrey, Law, and Martyn, 2004). Paul Broca (1861) and Francis Galton (1888) studied the relationships between head circumference, brain development and intelligence at the XIX century concluding that variations in brain size estimated indirectly by measuring head circumference were related with intelligence; based on these findings, many investigators tried to establish the biological basis of human intelligence (Vernon et al., 2000). Several studies have demonstrated a positive and significant correlation between head circumference, brain size and intelligence concluding that differences in human brain size are relevant in explaining differences in intelligence, although genetic and environmental factors probably affect these interrelationships (Akgun, Okuyan, Baytan, and Topbas, 2003; Botting, et al., 1998; Desch, Anderson, and Snow, 1990; Dolk, 1991; Ivanovic et al., 2000a,b,c, 2002, 2004a,b; Nelson and Deutschberger,1970; Ounsted, Moar, and Scott, 1988; Reiss et al., 1996; Rushton, and Ankney, 1996; Strauss and Dietz, 1998; Vernon et al., 2000; Willerman et al., 1991). In older people, head circumference positively and significantly correlated with intelligence and it seems to be that increased head or brain sizes may protect against intellectual impairment (Schofield, Logroscino, Andrews, Albert, and Stern, 1997; Tisserand, Bosma, Van Boxtel, and Jolles, 2001). However, some studies in monozygotic twins or in sisters, did not find any association between these variables (Schoenemann, Budinger, Sarich, and Wang, 2000; Teasdale and Pakkenberg, 1988; Yeo, Turkheimer, Raz, and Bigler, 1987) although other studies also in monozygotic and

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dizygotic twins, found a positive correlation between brain size and intelligence (Anderson, 1999; Pennington et al., 2000; Posthuma et al., 2003). Therefore, genetic and environmental factors could affect brain development, intelligence, head circumference and prenatal and postnatal nutritional status and scholastic achievement (Baker, Treloar, Reynolds, Heath, and Martin, 1996; Casto, DeFries, and Fulker, 1995; Luke, Keith, and Keith, 1997; McGue and Bouchard, 1998; Strauss and Dietz, 1998; Weaver and Christian, 1980). The first year of life is the most important period during brain development and whatever happens at that time will produce indelible repercussions later in life (Huttenlocher, and Dabholkar, 1997). The association between a small head circumference and impaired visuo-motor function may be the most reliable indicator of a cognitive disadvantage after undernutrition during infancy (Ivanovic et al., 2000a,b, 2002; Stoch et al., 1982). The objective of this study was to investigate the impact of neuropsychological parameters in a multicausal context, on the AAT achievement of Chilean high school graduates in 1996 with high or low intellectual quotient and socio-economic status and on job status carried out six years later during 2002. The aim was to confirm our hypothesis that: 1) Independently of socio-economic status, AAT achievement is positively and significantly associated with child’s intellectual quotient, parent’s intellectual quotient, and brain size and with those indicators of past nutrition especially head circumference and 2) AAT achievement is the better predictor of later job status.

SUBJECTS AND METHODS Subjects and Sample This study represents an explicatively, non-experimental, cross-sectional and longitudinal research. The sample was chosen from 1817 school-age children, the total high school graduate population who attended public, private subsidized and private non-subsidized schools in the richest and poorest counties of the Santiago Metropolitan Region in Chile, according to the UNICEF classification (United Nations International Children’s Fund, 1994). The final sample consisted of 96 right-handed high school graduate students (mean age 18.0 ± 0.9 y) who had no history of alcoholism, symptoms of brain damage, epilepsy, or heart disease and in whom physical growth and intellectual development processes were consolidated. IQ (WAIS-R), socio-

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economic strata (SES) and sex were considered for sample selection. The purpose of the main study was to compare two groups of Chilean high school graduates: Group 1, High IQ (≥ 120 WAIS-R) and Group 2, Low IQ (< 100 WAIS-R). The total IQ of the school-age children from the Group 1 (125.4a ± 5.5; n=47) was significantly higher than those from Group 2 (91.4b ± 6.8; n=49) (t= 26.934 p< 0.0001) for both males and females (Ivanovic et al., 2002). The same proportion of school-age children according to SES (high and low) (1:1) and sexes (1:1) were included in each IQ group. This study represented a comparative investigation dividing the sample in two groups according to the scores obtained in the AAT: high AAT (≥ median (Md) = score 631) and low AAT (< Md). Figure 1 shows the description of the sample that took the AAT at the end of high school (84 students) according to AAT group, SES, IQ and sex. The same proportion of school-age children from high and low SES was found in each AAT group. This study was approved by the Committee on Ethics Studies in Humans of the Institute of Nutrition and Food Technology (INTA). The subject’s consent was obtained according to the norms for Human Experimentation, Code of Ethics of the World Medical Association (Declaration of Helsinki) (The World Medical Association 1964).

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TOTAL SAMPLE 84

HIGH 42

IQ

HIGH 23

SES SEX

LOW 42

HIGH 42

AAT Group

LOW 0

LOW 19

HIGH 0

HIGH 5

LOW 0

HIGH 1

LOW 37

LOW 4

HIGH 22

LOW 15

M

12

8

0

0

0

3

11

8

F

11

11

0

0

1

1

11

7

Figure 1. Description of the sample by Academic Aptitude Test (AAT) group, intellectual quotient (IQ), socio-economic strata (SES) and sex (M= males; F= females).

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AAT Results from the AAT score, both its verbal and mathematics parts, were registered for the study sample. The AAT, the baccalaureate examination for university admission with national coverage, has a maximum score of 900 for each part (verbal test with 90 items and mathematics test with 60 items) (Universidad de Chile, 1996). The data corresponding to the AAT were analyzed dividing the sample in the median (score 631) forming two groups: high AAT (≥ Md) and low AAT (< Md). Table 1 describes the AAT scores expressed as mean ± SD, range and quartiles for both groups. Means of both groups were significantly different (p< 0.0001). Table 1. Academic Aptitude Test (AAT) score of Chilean high school graduates by AAT groups AAT Groups High AAT Low AAT Student’s “t” test Total sample

Score (Mean ± SD) 733.6 ± 36.9 (42) 436.8 ± 75.9 (42) 22.804 p< 0.0001 585.2 ± 160.6 (84)

Range 637-790 319-626

Q1 711 385

Q2 737 424

Q3 760 467

319-790

424

631

737

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Note. High AAT, score ≥ median (Md, = score 631); low AAT, score < Md.

Intellectual Quotient (IQ) In this study, IQ (total, verbal and non-verbal) was assessed by means of the Wechsler Intelligence Scale for Adults-Revised (WAIS-R) adapted for Chilean population both school-age children and their parents and was carried out at the school (Wechsler, 1981; Hermosilla, 1986). WAIS-R consists of a set of six verbal and five non-verbal subtests that are individually administered requiring about 1.5 hours, and yields an age-corrected estimate of IQ. To avoid examiner bias, the WAIS-R was administered separately to each child and parent in quiet rooms by a team of educational psychologists specially trained in this type of study. Before each phase of the test, the psychologist provided a clear explanation to each child and parent, in order to clarify the problem to be solved. On the other hand, the intellectual ability (IA) was also assessed in both school-age children and their parents through the Raven Progressive Matrices Test (Raven, 1957; Ivanovic et al., 2000d, 2001) with the purpose of

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validating the reliability of intelligence measurement. Human intelligence exceeds all that is measured by an IQ test score, but most studies have defined “intelligence” operationally as performance on IQ or similar tests.

SES, Socio-Cultural and Family Related Variables

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SES was measured applying the Graffar scale adapted for Chilean urban an rural populations which considers items such as schooling, job held by the head of the household and characteristics of the house (building materials, property status, water supply, sewerage and ownership of durable goods) (Alvarez, Muzzo, and Ivanovic, 1985). This scale allows to classify populations into six socio-economic strata: 1= High; 2= Medium-High; 3= Medium; 4= Medium-Low; 5= Low and 6= Extreme Poverty. In the present study, only High (1+2) and Low (4+5+6) SES were considered. Family variables such as the number of members and of siblings, place among siblings, crowding and promiscuity were registered. The exposure to mass media (MME), radio, cinema, TV, newspapers, magazines and books (other than school books) of the children was assessed by means of a standardized questionnaire based on open and closed questions (Ivanovic and Sepúlveda, 1988).

Scholastic Achievement (SA) SA was evaluated through the standard Spanish language (LA) and mathematics (MA) tests. Content validity was based on the fact that, for each grade, the test was designed taking into consideration the objectives pursued by the curricular programs of the Ministry of Education (Chile. Ministerio de Educación Pública, 1996). The number of items tested were 51 for LA and 65 for MA. A pilot test was carried out in 160 school-age children during which reliability was determined applying the Spearman-Brown correlation, scores being 0.92 and 0.97 for LA and MA, respectively, when comparing paired and unpaired item (Guilford and Fruchter, 1984). Item-test consistency of each item was measured by Pearson correlation, scoring values above 0.30 in all of them (Guilford, and Fruchter, 1984). Results were expressed as percentage of achievement in overall results (SA) (mean LA + MA) as well as LA and MA.

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Anthropometric Measurements

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Weight (W), height (H), head circumference (HC), arm circumference (AC) and triceps skinfold (TS) were measured by the authors on both the students and their parents through standardized procedures (Gibson, 1990). Body mass index (BMI) was calculated according to Garrow, 1981. Head circumference (HC) was compared with Ivanovic, Nellhaus, Roche et al., and the tables of Tanner (Ivanovic, Olivares, Castro, and Ivanovic, 1995a; Nellhaus, 1968; Roche, Mukherjee, Guo, and Moore, 1987; Tanner, 1984) and was expressed as Z-score (Z-HC). Z-HC values are similar when applying these tables because the correlation coefficient between these patterns was 0.98 (Ivanovic et al., 1995a). HC absolute values were adjusted by body height. Percentages of adequacy to the median of arm circumference-for-age (% AC/A), triceps skinfold-for-age (% TS/A), arm muscle area-for-age (% AMA/A) and arm fat area-for-age (%AFA/A) were calculated using data from Frisancho 1981. Birth weight was used as index of prenatal nutrition, Z-HC and % AMA/A, as indicators of postnatal nutrition and BMI was used as index of current nutritional status. Nutritional diseases, especially undernutrition at an early age, were registered.

Brain Development Study Brain development was evaluated at the German Clinic of Santiago by magnetic resonance imaging (MRI). Using the lowest margin of the cerebellum in a midsagittal view to align the first axial (horizontal) MRI slice, 18 mixed-weighted images (spin-echo pulse sequence with a TR of 2000 msec and a TE of 30 msec) were obtained from a Signa MRI General Electric unit with a field strength of 1.5 Tesla. All images were 5 mm thick and separated by 2.5 mm. Each image was 256 x 256 pixels with 256 levels of gray. The MRI tape was read into a VAS computed and the image analyzed after removing identifying information. Analyses were carried out by a trained specialist without foreknowledge of IQ or sex. For each slice, a Roberts gradient traced the boundary of the scalp by outlining large-intensity differences between adjacent pixels. All gray scale intensity values of < 96 within this boundary were converted to zero. This deleted the skull, most of the meninges, and the interhemispheric fissure; other brain membranes were deleted manually with a cursor. The computer then counted all pixels with nonzero gray scale values for brain size in each slice, and their added value

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serving as the index for overall brain size. Cortex thickness data, brain volume (BV), absolute and adjusted for body size (weight and height), biparietal (BD) and anteroposterior (APD) diameters are reported; corpus callosum length (CC) absolute and adjusted for brain volume (CC/BV), the thickness of the genu (CCGT), body (CCBT) and splenium (CCST), absolute and adjusted for CC (Matano, and Nakano, 1998), the presence of neuronal migration disorders, qualitative and quantitative evaluation of white matter, cortical and basal subarachnoid space and ventricular system size. Currently, there is no meaningful basis for the comparison of brain sizes within and between racial groups and sexes; the control for body size across racial groups (and sexes) is rendered difficult because bodies do not just differ only in height and weight (Peters et al., 1998). As there were practically no significant differences between absolute and adjusted values for brain parameters, only absolute values are shown in this study (Ivanovic et al., 2004a).

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Job Status 2002 Student’s job status 2002 was determined after six years of high school graduation and was expressed as jobless, workers without further schooling, students at technical institutes, students at universities. The procedures put in place to locate students were to send a letter to student’s home, phone calls, to consult public records such as phone books and the Chilean university’s registrations.

Statistical Analysis Data were analyzed using the Statistical Analysis System (SAS) package by means of variance tests (PROC ANOVA), Scheffe’s test for comparison of means, correlation, (PROC CORR) and logistic regression (PROC LOGISTIC) with the option Stepwise was used to establish the most important independent variables that affect the AAT score, dependent variable, categorized as high AAT and low AAT groups (probability modeled was high AAT=yes) and that affect job status six years later from high school graduation (probability modeled was university studies=yes). Chi-square test (X2) and Fisher’s test (PROC FREQ) were used to determine significant

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differences between the categorical variables (Guilford and Fruchter, 1984; SAS, 1990).

RESULTS Table 2 shows the IQ of Chilean high school graduates and their parents by AAT groups. School-age children from the high AAT group exhibited total, verbal and non-verbal IQ significantly higher than their peers from the low AAT group (p< 0.0001). Total (p< 0.01), verbal (p< 0.001) and non-verbal (p< 0.01) paternal IQ were significantly higher in the high AAT group compared with the low AAT group; however, differences between both groups were more significant for maternal IQ (p< 0.0001). All school-age children from the high AAT group had high IQ and in the low AAT group, 88% had low IQ (figure 1).

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Table 2. Intellectual quotient (IQ) of Chilean high school graduates and their parents by Academic Aptitude Test (AAT) groups IQ

High AAT (42)

Low AAT (42)

Student’s “t” test

Student IQ Total Verbal Non-verbal

125.6 ± 5.5 125.9 ± 5.5 121.8 ± 9.0

96.7 ± 11.7 95.9 ± 12.2 98.2 ± 11.6

14.479 **** 14.609 **** 10.414 ****

Paternal IQ Total Verbal Non-verbal

112.8 ± 12.3 114.7 ± 11.8 108.7 ± 13.3

99.4 ± 16.7 100.5 ± 17.8 98.0 ± 14.9

3.300 ** 3.573 *** 2.782 **

Maternal IQ Total Verbal Non-verbal

112.3 ± 11.4 113.1 ± 12.0 107.5 ± 10.3

90.6 ± 14.6 92.1 ± 14.6 89.8 ± 14.4

7.290 **** 6.786 **** 6.014 ****

Note. Results are expressed as mean ± SD. The number of cases is indicated between parentheses. ** p< 0.01; *** p< 0.001; **** p< 0.0001.

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The educational variables by AAT groups are shown in table 3. Students who obtained high AAT scores registered at the end of high school total SA, LA and MA significantly higher than the low AAT group (p< 0.0001). On the other hand, the number of repeated years was significantly higher in the low AAT group than their peers of the high AAT group (p< 0.0001). The time devoted to schoolwork at home did not differ between both groups. Table 3. Educational variables of Chilean high school graduates by Academic Aptitude Test (AAT) groups Educational variables High AAT (42) Low AAT (42) Scholastic achievement (SA) (percentage of achievement) Total SA (mean LA + 78.0 ± 10.4 33.1 ± 18.0 MA) Language SA (LA) 75.6 ± 16.5 43.7 ± 17.6 Mathematics SA (MA) 79.6 ± 11.6 24.1 ± 20.6 Schoolwork at home 97.6 ± 74.8 123.4 ± 105.5 (minutes/day) Repeated years (number) 0.02 ± 0.16 0.52 ± 0.71

Student’s “t” Test 12.852 **** 7.981 **** 13.680 **** 1.288 NS 4.420 ****

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Note. Results are expressed as mean ± SD. The number of cases is indicated between parentheses. **** p< 0.0001; NS = not significantly different.

The analysis of the brain development parameters by sex and AAT groups (table 4) revealed that males with the high AAT scores exhibited BV (p< 0.01), APD (p< 0.05) and CCST (p< 0.05) values significantly higher than those from the low AAT group and for CC length a tendency was observed. Females from the high AAT group presented BV, BD, APD and CC length values higher than their peers from the low AAT group but these differences were not significant. Table 5 illustrates the relationships between nutritional status by sex and AAT groups. As regards to prenatal nutritional parameters, birth weight and birth height values were higher in school-age children from the high AAT group both males and females, although these differences were not significant. Z-HC indicator of postnatal nutritional background and brain development was significantly higher in school-age children from the high AAT group compared with their peers from the low AAT group (p< 0.01) (figure 2). Table 5 shows that Z-HC values were significantly higher in males from the high AAT group compared with those from the low AAT group (p< 0.01) and in females a tendency was observed. Current nutritional status and body

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composition parameters did no register significant differences between both groups. Figure 3 indicates the AAT score in students with and without undernutrition in the first year of life. All undernourished children and 44.7% of the non-undernourished group achieved low AAT score (Fisher= 0.00541).

Table 4. Brain development parameters of Chilean high school graduates by sex and Academic Aptitude Test (AAT) groups Brain Development Parameters

BV (cm3) BD (mm)

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APD (mm) CC CC Length (mm) CCGT (mm) CCBT (mm) CCST (mm)

Males (42)

Student’ Females (42) s “t” Test High Low High Low AAT AAT AAT AAT (20) (22) (22) (20) 1543.3 1461.3 2.827** 1421.0 ± 1392.7 96.4 ± 69.3 ± 85.2 ± 102.6 132.0 ± 133.3 0.633 129.7 ± 129.5 NS 6.3 ± 6.6 5.6 ± 7.8 167.4 ± 163.4 2.245* 163.1 ± 161.1 6.6 ± 4.3 5.2 ± 6.4 72.9 ± 4.1 11.6 ± 1.7 6.5 ± 0.8 12.0 ± 1.6

70.6 ± 4.6 11.3 ± 1.8 6.0 ± 0.8 10.9 ± 1.5

1.716 (t) 71.4 ± 5.8 0.605 10.5 ± NS 1.5 1.569 6.3 ± 0.7 NS 2.180* 11.0 ± 1.7

70.4 ± 3.7 11.3 ± 1.6 6.7 ± 0.8 11.7 ± 1.7

Student’ s “t” Test

1.100 NS 0.139 NS 1.224 NS 0.682 NS 1.669 NS 1.398 NS 1.437 NS

Note. Results are expressed as mean ± SD. The number of cases is indicated between parentheses; BV= brain volume; BD= biparietal diameter; APD= anteroposterior diameter; CC= corpus callosum; CCGT= genu thickness; CCBT= body thickness; CCST= splenium thickness. * p< 0.05; ** p< 0.01; t = tendency (p> 0.05 y < 0.10); NS= not significantly different.

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Table 5. Nutritional status of Chilean high school graduates by sex and Academic Aptitude Test (AAT) groups

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Nutritional status parameters

Males (42) High Low AAT AAT (20) (22) Prenatal Nutritional Background Birth weight 3,341.8 3,083.6 (g) ± 585.9 ± 537.7 Birth height 50.6 ± 49.2 ± (cm) 2.6 2.6 Postnatal Nutritional Background Z-HC 0.56 ± -0.38 ± 1.10 1.02 Nutritional Males (42) status High Low parameters AAT AAT (20) (22) Current Nutritional Status BMI 22.4 ± 22.5 ± 2.0 2.6 % W/H 104.6 ± 106.2 ± 9.2 12.8 Body Composition % AC/A 95.4 ± 93.6 ± 8.2 11.9 % TS/A 128.5 ± 124.8 ± 65.8 49.0 % AMA/A 87.3 ± 84.7 ± 15.0 18.0 % AFA/A 121.2 ± 119.8 ± 63.0 59.0

Student’s “t” Test

Females (42) High Low AAT AAT (22) (20)

Student’s “t” Test

1.483 NS

3,127.3 ± 625.2 48.8 ± 2.5

0.401 NS

1.771 (t)

2.885 ** Student’s “t” Test

0.183 NS 0.449 NS

0.568 NS 0.207 NS 0.516 NS 0.074 NS

3,059.0 ±475.3 48.9 ± 3.5

0.02 ± -0.48 ± 0.88 0.85 Females (42) High Low AAT AAT (22) (20)

0.050 NS

1.846 (t) Student’s “t” Test

21.6 ± 2.6 101.7 ± 12.7

22.5 ± 2.9 104.3 ± 14.4

1.008 NS

95.9 ± 9.7 97.3 ± 35.5 91.7 ± 18.5 97.5 ± 40.1

99.0 ± 9.3 99.0 ± 29.6 99.0 ± 17.8 100.1 ± 33.3

1.046 NS

0.610 NS

0.174 NS 1.301 NS 0.230 NS

Note. Results are expressed as mean ± SD. The number of cases is indicated between parentheses. Z-HC = head circumference-for-age Z score; BMI= body mass index; AC/A= arm circumference-for-age; TS/A= triceps skinfold-for-age; AMA/A= arm muscle area-for-age; AFA/A= arm fat area-for-age. ** p< 0.01; (t) = tendency (p> 0.05 y < 0.10); NS= not significantly different.

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1.5 Student’s “t ” Test= 3.310 p< 0.01

Z - HC

1 0.5 0 -0.5 -1 -1.5

High AAT

Low AAT

Figure 2. Head circumference-for-age Z score (Z-HC) by Academic Aptitude Test (AAT) groups of Chilean high school graduates in 1996.

Fisher= 0.00541

100 % of cases

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80 60 40 20 0

U

NU

High AAT Low AAT

Figure 3. Academic Aptitude Test (AAT) score of Chilean high school graduates with undernutrition (U) and without severe undernutrition (NU) in the first year of life.

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Table 6. Socio−economic conditions of Chilean high school graduates by Academic Aptitude Test (AAT) groups Variables

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Socio-economic status High Low Total House-hold head Father

Low High AAT AAT (42) (42) % of cases

Total sample (84)

Xo2(1) = 0.000 < Xt2(1) 0.05 = 3.841 54.8 45.2 100.0

54.8 45.2 100.0

54.8 45.2 100.0

95.2

85.7

90.5

Mother 2.4 Other person 2.4 Total 100.0 House-hold head schooling Illiterate 0.0

14.3 0.0 100.0

8.3 1.2 100.0

0.0

0.0

Incomplete elementary school Complete elementary school + Incomplete high school Complete high school

4.9

16.7

10.9

39.0

23.8

31.3

4.9

14.3

9.6

Variables

High AAT (42) 2.4

Low AAT (42) 11.9

Total sample (84) 7.2

48.8

33.3

41.0

100.0

100. 0

100.0

Incomplete university education Complete university education Total

X2 o Fisher

Fisher = p< 0.265. Fisher’s test was calculated considering the categories father and mother + other person.

Xo2(1) = 0.100 < Xt2(1) 0.05 = 3.841 Chi -square test was calculated joined the categories (illiterate + incomplete elementary school+ complete elementary school + incomplete high school) and (complete high school +incomplete university education + complete university education).

X2 o Fisher

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Daniza M. Ivanovic, Hernán T. Pérez, Boris P. Leiva et al. Table 6. (Continued)

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Maternal schooling Illiterate

0.0

0.0

0.0

Incomplete elementary school Complete elementary school + Incomplete high school Complete high school Incomplete university education Complete university education Total House-hold head occupation 1. Managerial positions

0.0

14.3

7.2

16.7

28.6

22.6

45.2

45.2

45.2

4.8

0.0

2.4

33.3

11.9

22.6

100.0

100.0

100.0

41.5

21.4

31.3

2. Mid-level employee 3. Specialized worker 4. Nonspecialized worker

9.8

31.0

20.5

2.4

2.4

2.4

46.3

33.3

39.8

Variables

High AAT (42)

Low AAT (42)

Total sample (84)

5. Jobless receiving state aid

0.0

7.2

3.6

Xo2(2) = 10.600 > Xt2(2) 0.01 = 9.210 Chi -square test was calculated joined the categories (illiterate + incomplete elementary school+ complete elementary school + Incomplete high school) and (incomplete university education + complete university education).

Xo2(2) = 6.710 > Xt2(2) 0.05 = 5.991 Chi -square test was calculated joined the categories (2+3) and (4+5+6).

X2 o Fisher

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Neuropsychological Parameters Affecting … 6. Jobless without state aid Total Maternal occupation 1. Managerial positions

0.0

4.7

2.4

100.0

100.0

100.0

7.1

4.8

6.0

2. Mid-level employee 3. Specialized worker 4. Nonspecialized worker 5. Jobless receiving state aid 6. Jobless without state aid 7. Housekeepers Total Quality of housing Single family unit

11.9

7.1

9.5

11.9

14.2

13.1

11.9

19.1

15.4

0.0

0.0

0.0

0.0

0.0

0.0

57.2 100.0

54.8 100.0

56.0 100.0

45.2

21.4

33.3

Solid materials Light materials Self-built Precarious housing ( “Mejora”) Total Property of housing Owner Tenant Usufructuary Total

7.2 9.5 38.1 0.0

28.6 11.9 35.7 2.4

17.9 10.7 36.9 1.2

100.0

100.0

100.0

95.2 4.8 0.0 100.0

81.0 11.9 7.1 100.0

88.1 8.3 3.6 100.0

159

Xo2(3) = 1.500 < Xt2(3) 0.05 = 7.815 Chi -square test was calculated joined the categories (1+2).

X2o (2) =9.000 > X2t (2) 0.02 = 7.824. Chi-square test was calculated joined the categories light materials + selfbuilt + precarious housing ( “Mejora”).

Fisher = p< 0.088. Fisher’s test was calculated joined the categories (tenant + usufructuary).

The socio−economic conditions by AAT groups are shown in table 6. No significant differences were found in the socio-economic composition of both groups, as explained in the description of the sample (figure 1). However, maternal schooling (p< 0.01), household head occupation (p< 0.05) and the quality of housing (p< 0.02) were significantly better in the high AAT group compared with their peers with low AAT scores; both groups had access to sewerage and drinking water at home.

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Table 7. Socio-economic, socio-cultural, family, mass media exposure (MME) and demographic of variables of Chilean high school graduates by Academic Aptitude Test (AAT) groups

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Variables Socio-economic and Socio-cultural SES (Graffar's score) Paternal Schooling (y) Maternal Schooling (y) Household head's Schooling (y) Family Number of Family Members Number of Siblings Place Between Siblings Promiscuity (number of persons per bed) Crowding (number of persons by bedroom) MME Radio (minutes per day) Cinema (times per month) Television (minutes per day) Demographic Age (y)

High AAT (42)

Low AAT (42)

Student’s “t” Test

6.7 ± 3.5 12.5 ± 3.1 12.8 ± 1.9 12.4 ± 3.1

7.5 ± 3.2 11.5 ± 3.7 10.7 ± 2.8 11.6 ± 3.7

1.187 NS 1.356 NS 3.958 ∗∗∗ 1.120 NS

5.7 ± 2.5 3.2 ± 1.5 2.2 ± 1.2 1.1 ± 0.2

5.0 ± 1.4 3.0 ± 1.1 2.0 ± 1.1 1.2 ± 0.3

1.459 NS 0.840 NS 0.794 NS 1.775(t)

1.5 ± 0.8

1.5 ± 0.5

0.294 NS

109.9 ± 158.7 1.1 ± 1.5 138.4 ± 164.9

170.1 ± 107.4 1.7 ± 2.6 160.7 ± 96.7

2.029 ∗ 1.419 NS 0.755 NS

17.1 ± 0.4

17.7 ± 1.0

3.436 ∗∗∗

Note. Results are expressed as mean ± SD. The number of cases is indicated between parentheses. SES= socio-economic strata. * p< 0.05; *** p< 0.001; t = tendency (p> 0.05 y < 0.10); NS= Not significantly different.

Table 7 illustrates the socio-economic, socio-cultural, MME and demographic variables expressed as mean ± SD by AAT groups. Maternal schooling was the only socio-economic and socio-cultural variable that was significantly higher in the high AAT group compared with the low AAT group (p< 0.001). Family variables did not differ significantly between both groups. School-age children from the low AAT group had higher levels of exposure to radio, cinema and television, but radio was the only MME that presented significant differences between both groups (p< 0.05). In the high AAT group, 83% read newspapers weekly, a percentage that decreased significantly to 43% in the low AAT group (Xo2= 23.094; 4df; p< 0.001). Books exposure were significantly different since 74% and 45% of the students belonging to the high and low AAT groups, respectively, read books (Xo2= 7.115; 1df; p< 0.01). A high percentage of students (79.8%) read magazines without significant differences by AAT groups (Xo2= 0.664; 1df; NS). As regards to age, students from the low AAT group were significantly older than their peers with high AAT score (p< 0.001).

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Table 8. Pearson correlation coefficients between Academic Aptitude Test (AAT) score and demographic, intellectual, educational, nutritional, brain development, socio-economic, socio-cultural, family and mass media exposure variables by sex Variables

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Demographic Student age Paternal age Maternal age Intellectual Student IA Student total IQ Student verbal IQ Student non verbal IQ Total paternal IQ Verbal paternal IQ Non verbal paternal IQ Total maternal IQ

Verbal maternal IQ Non verbal maternal IQ Educational Total SA (mean LA+MA) Spanish SA (LA) Mathematics SA (MA) Verbal AAT Mathematics AAT Repeated years Schoolwork at home Nutritional Birth weight Birth height Breastfeeding Z-HC BMI (P/T2) % AC/A % TSA/A % AMA/A % AFA/A Brain development Brain volume Biparietal diameter Anteroposterior diameter Corpus callosum length Genu thickness

AAT Total Sample (84)

Males (42)

Females (42)

-0.422 **** 0.023 NS 0.210 (t)

-0.401 ** 0.130 NS -0.058 NS

-0.471 ** 0.185 NS 0.309 (t)

0.885 **** 0.923 **** 0.921 **** 0.830 **** 0.478 *** 0.481 *** 0.423 ** 0.683 ****

0.848 **** 0.923 **** 0.934 **** 0.845 **** 0.594 *** 0.606 *** 0.519 ** 0.702 ****

0.922 **** 0.928 **** 0.911 **** 0.818 **** 0.248 NS 0.229 NS 0.259 NS 0.667 ****

0.677 **** 0.599 ****

0.718 **** 0.626 ****

0.647 **** 0.564 ****

0.925 **** 0.804 **** 0.910 **** 0.976 **** 0.977 **** -0.511 **** 0.120 NS

0.953 **** 0.905 **** 0.930 **** 0.979 **** 0.980 **** -0.430 ** 0.022 NS

0.894 **** 0.691 **** 0.887 **** 0.974 **** 0.975 **** -0.594 **** 0.201 NS

0.189 (t) 0.228 (t) 0.207 (t) 0.434 **** -0.060 NS -0.012 NS 0.011 NS -0.022 NS 0.004 NS

0.312 * 0.362 * 0.142 NS 0.475 ** 0.025 NS 0.143 NS 0.068 NS 0.128 NS 0.057 NS

0.060 NS 0.064 NS 0.279 (t) 0.393 ** -0.135 NS -0.177 NS -0.099 NS -0.153 NS -0.091 NS

-

0.449 **

0.237 NS

-

-0.112 NS 0.415 ** 0.252 NS 0.103 NS

-0.031 NS 0.314 * 0.198 NS -0.104 NS

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Daniza M. Ivanovic, Hernán T. Pérez, Boris P. Leiva et al. Table 8. (Continued)

Body thickness Splenium thickness Socio-economic, socio-cultural and family SES (Graffar’s score) 0.176 NS Paternal Schooling 0.193 (t) Maternal Schooling 0.435 **** Household head's Schooling 0.151 NS Promiscuity -0.289 ** Crowding -0.098 NS Number of Family Members 0.137 NS Number of Siblings 0.056 NS Place Between Siblings 0.114 NS Mass media exposure variables Radio -0.199 (t) Cinema -0.038 NS Television -0.073 NS

0.175 NS 0.228 NS

-0.084 NS -0.076 NS

0.334 * 0.156 NS 0.374 * 0.150 NS -0.293 (t) -0.348 * 0.044 NS -0.029 NS 0.128 NS

-0.021 NS 0.235 NS 0.503 *** 0.155 NS -0.287 (t) 0.022 NS 0.219 NS 0.136 NS 0.104 NS

-0.309 * 0.068 NS -0.078 NS

-0.133 NS -0.121 NS -0.106 NS

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Note. The number of cases is indicated between parentheses. IA= intellectual ability; IQ= intellectual quotient; SA= scholastic achievement; Z-HC = head circumference-for-age Z score; BMI= body mass index; AC/A= arm circumferencefor-age; TS/A= triceps skinfold-for-age; AMA/A= arm muscle area-for-age; AFA/A= arm fat area-for-age; SES= socio-economic strata. * p< 0.05; ** p< 0.01; *** p< 0.001; **** p< 0.0001; (t) = tendency (p> 0.05 y < 0.10) ; NS= not significantly different.

Table 9. Logistic regression between Academic Aptitude Test (AAT) the university graduation (dependent variable), and most relevant parameters (independent variables=SES, sex, student IQ, maternal IQ, undernutrition in the first year of life, Z-HC and BV) Parameters entered in the DF statistical model INTERCEPT 1 IQ 1

Parameter Estimate

Standard Error

Wald Chi-square

Standardized Estimate

Odds Ratio Estimates

-25.7704 0.2247

7.2157 0.0601

12.7552 *** 13.9843 ***

2.149866

1.252

Note: Probability modeled was High AAT (AAT ≥ Md) = yes. DF= degree of freedom; SES= socio-economic strata; IQ= intellectual quotient; Z-HC = head circumference-for-age Z score; BV= brain volume. n= 84; *** p< 0.001.

Pearson correlation coefficients between AAT score and demographic, intellectual, educational, nutritional, brain development, socio-economic, socio-cultural, family and MME variables in the total sample and by sex are shown in table 8. The high correlations observed in the total sample and in both sexes between AAT score with SA, student IQ, student IA, maternal IQ, number of repeated years, paternal IQ, maternal schooling, Z-HC and student

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163

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age should be noted. In the students like in their parents, a high correlation was found between IQ and IA (r= 0.911 p< 0.0001). The logistic regression analysis between the AAT score, dependent variable, categorized it as high AAT and low AAT (probability modeled was high AAT=yes) and most relevant parameters considering student IQ, maternal and paternal IQ, Z-HC, BV and undernutrition in the first year of life as independent variables and controlling by SES and sex, is summarized in table 9. Only student IQ entered in the statistical model and was the best predictor of AAT achievement. The odds ratio value (1.252) implies that when the IQ score increases by one point, the probability to obtain a high AAT score increases in 25.2%. In 2002, six years after high school graduation, 94 of these children high school graduates were located and they informed about their present job status. It found that 11.7% was jobless, 13.8% were workers without further schooling, 17% had studied at technical institutes and 57.5% had studied in universities. Table 10 describes the school-age children and parents IQ by job status 2002. Students attending universities exhibited the highest total, verbal and non-verbal IQs compared with their peers on other job categories (p< 0.0001).

Table 10. Intellectual quotient (IQ) of Chilean high school graduates in 1996 and their parents by job status 2002 (continued) Job status 2002 IQ

Jobless (11)

Workers without further schooling (13)

Students at technical institutes (16)

Students at universities (54)

F

79.1a ± 9.4 79.7a ± 10.3 81.0a ± 9.9

87.8a ± 13.2 89.6a ± 12.9 87.4a ± 13.8

108.5b ± 13.8 109.5b ± 13.9 104.0b ± 13.0

23.74**** 22.81**** 15.05****

Maternal IQ Total Verbal NonVerbal

84.9a ± 16.1 87.6a ± 14.4 84.5a ± 17.5

Note. Results are expressed as mean ± SD. The number of cases is indicated between parentheses. Means with the same letter are not significantly different at the 0.05 level based on Scheffe's test. F= ANOVA; * p< 0.05; *** p< 0.001; **** p< 0.0001.

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As regards parental IQ, fathers of students attending institutes or universities had the highest total (p< 0.001), verbal (p< 0.001) and non-verbal (p< 0.05) IQs compared with those of jobless students and with those who were workers without further schooling. However, total, verbal and non-verbal IQs of mothers of students attending universities were significantly higher than those of the other groups (p< 0.0001). The educational variables of 1996 analyzed by job status 2002 are shown in table 11. Students at universities presented AAT score, SA, LA and MA significantly higher (p< 0.0001) and their numbers of repeated years were significantly lower (p< 0.0001) compared with the other groups. Time devoted to schoolwork at home did not differ by job status 2002. Table 12 summarizes the brain development parameters in 1996 as related to job status 2002. In males, jobless students registered BV (p< 0.001), APD (p< 0.05), HC (p< 0.01) and Z-HC (p< 0.01) values significantly lower than their peers from the other groups; workers without further schooling presented CCGT significantly lower than the other groups (p< 0.05). Among females, students at universities showed HC (p< 0.05) and Z-HC (p< 0.01) values significantly higher than the other groups; workers without further schooling presented BD values (p< 0.05) significantly higher than the other job status groups. Males students attending universities had a BV 179.5 cm3 greater than the jobless group, while among the females this difference was 109 cm3, although in these, differences were not significant. Jobless’s HC was 2.1cm and 1.8 cm lower than the students at universities group in males and females, respectively.

Table 11. Educational variables of Chilean high school graduates in 1996 by job status 2002 Job status 2002 Educational variables

F Jobless (11)

AAT (score) Total AAT(mean V+M)

397.8a ± 42.3

Workers without further schooling (13)

Students at technical institutes (16)

Students at universities (54)

372.4a ± 45.0

428.0a ± 57.9

680.5b ± 108.8

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53.32****

Neuropsychological Parameters Affecting …

165

388.0a ± 358.0a ± 424.7a ± 659.1b ± 74.0 56.2 63.0 120.9 Mathematics 431.2a ± 702.0b ± 407.6a ± 386.9a ± AAT (M) 42.7 74.2 108.4 21.2 Scholastic achievement (SA) (percentage of achievement) Total SA (mean 23.6a ± 9.8 24.4a ± 5.2 24.4a ± 12.9 69.2b ± 19.8 LA+ MA) Language SA 28.5a ± 31.7a ± 8.4 40.6a ± 13.6 70.2b ± 19.6 (LA) 12.9 Mathematics SA 18.6a ± 19.5a ± 9.3 13.4a ± 14.5 69.1b ± 24.4 (MA) 10.9 Schoolwork at 91.4 ± 54.7 76.9 ± 43.7 97.2 ± 86.3 119.1 ± home 101.0 (minutes/day) Repeated 0.90a ± 0.85a ± 0.55 0.56a ± 0.72 0.11b ± 0.37 years(number) 0.83

38.38****

Verbal AAT (V)

57.30****

53.15**** 32.79**** 46.76**** 1.03 NS 11.99****

Note. Results are expressed as mean ± SD. The number of cases is indicated between parentheses. Means with the same letter are not significantly different at the 0.05 level based on Scheffe's test. F= ANOVA. AAT= Academic Aptitude Test; SA= scholastic achievement; **** p< 0.0001; NS= not significantly different.

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Table 12. Brain Development Parameters of Chilean High School Graduates in 1996 by job status 2002 Brain Parameters Males

CC CC (mm) CCGT (mm) CCBT (mm) CCST (mm) BV (cm3) BD (mm) APD (mm)

Job status 2002 Workers Jobless without (11) further schooling (13) (7) (5)

Students at technical institutes (16) (10)

Students at universities (54)

F

70.0 ± 5.3 70.4 ± 5.2 12.1a ± 1.3 9.6b ± 1.5

70.4 ± 4.7 10.9a ± 1.1

72.6 ± 4.6 11.7ª ± 1.8

0.90 NS 3.23*

(25)

6.3 ± 1.0

5.4 ± 0.9

6.2 ± 1.0

6.3 ± 0.8

1.54 NS

11.4 ± 1.6

10.2 ± 1.5

11.3 ± 0.9

11.6 ± 1.7

1.15 NS

1361.0a ± 125.0 130.1 ± 5.6 158.3a ± 5.3

1447.0ab ± 129.5 134.4 ± 8.6 163.8ab ± 6.5

1430.0ab ± 134.4 132.0 ± 7.1 163.2ab ± 7.4

1540.5b ± 88.5 132.9 ± 6.5 167.1b ± 5.6

6.31 ***

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0.47 NS 4.19 *

166

Daniza M. Ivanovic, Hernán T. Pérez, Boris P. Leiva et al. Table 12. (Continued)

HC (cm) Z-HC Females CC CC length (mm) CCGT (mm) CCBT (mm) CCST(mm) BV (cm3) BD (mm)

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APD (mm) HC (cm) Z-HC

54.3a ± 1.6 54.3ab ± 1.2 -1.14a ± -1.19a ± 0.95 1.23 (4) (8)

55.4ab ± 1.8 -0.37ab ± 1.39 (6)

56.4b ± 1.4 0.48b ± 1.06

71.5 ± 2.4

68.6 ± 5.2

69.7 ± 2.5

71.6 ± 5.4

0.86 NS

10.5 ± 1.7

11.6 ± 1.6

10.8 ± 1.8

10.9 ± 1.6

0.60 NS

7.0 ± 0.8

5.9 ± 0.8

6.7 ± 0.8

6.5 ± 0.8

2.22 (t)

11.5 ± 0.8 1374.0 ± 102.6 126.7ab ± 5.5 159.5 ± 2.7 54.4ab ± 1.2 -0.48ab ± 1.22

11.4 ± 1.9 1418.8 ± 90.7

0.20 NS 2.21 NS

128.8b ± 6.2

3.58*

163.4 ± 6.1 54.8b ± 1.2 0.01b ± 0.85

1.66 NS 4.03* 4.19**

11.0 ± 1.4 11.0 ± 1.2 1309.8 ± 1383.0 ± 44.2 62.9 128.8ab ± 135.8a ± 5.8 4.0 160.5 ± 4.4 159.6 ± 4.9 53.0a ± 0.9 53.9ab ± 1.0 -1.41a ± -0.75ab ± 0.68 0.69

5.44** 5.66**

(29)

Note. Results are expressed as mean ± SD. The number of cases is indicated between parentheses. F= ANOVA; CC= corpus callosum; CCGT= genu thickness; CCBT= body thickness; CCST= splenium thickness; BV= brain volume; BD= biparietal diameter; APD= anteroposterior diameter; HC= head circumference; Z-HC = head circumference-for-age Z score; * p< 0.05; ** p< 0.01; *** p< 0.001; NS= not significantly different; (t) = tendency (p> 0.05 y < 0.10).

Table 13. Nutritional status 1996 and 2002 by job status 2002 Job status 2002 Jobless Workers Nutritional (11) without further status schooling (13) PPrenatal Nutritional status Birth weight 2817.3a ± 2835.4a ± (g) 301.3 424.7 Birth height 46.6a ± 47.0a ± 3.2 (cm) 4.3 Current Nutritional status % W/H 1996

104.8 ± 12.4

106.1 ± 20.5

F Students at technical institutes (16)

Students at universities (54)

3084.3ab ± 586.0 49.8ab ± 1.7

3214.5ab ± 594.4 49.8b ± 2.5

104.4 ± 17.5

104.0 ± 12.0 0.08 NS

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2.80* 5.76***

Neuropsychological Parameters Affecting …

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2002

109.8 ± 19.7 1.106 NS

118.4 ± 14.0

107.7 ± 22.0

111.4 ± 14.2 0.65 NS

4.023 **

5.608****

22.6 ± 3.8 23.5 ± 4.7 4.752 ***

22.1 ± 2.5 23.8 ± 3.1 5.810 ****

0.32 NS 0.31 NS

55.4 ab ± 1.8

56.4 b ± 1.4

5.44**

55.8 ab ± 2.2

56.4 b ± 1.5

3.01*

2.792 *

1.817 NS

54.4ab ± 1.2

54.8b ± 1.2

4.03*

54.4b ± 1.4

54.9b ± 1.2

3.13*

1.000 NS

1.000 NS

96.4 ± 13.1

95.9 ± 8.8

Students at technical institutes (16)

Students at universities (54)

2.677 *

4.277 ****

107.8 ± 51.1

121.0 ± 29.2

123.3 ± 49.7

125.3 ± 49.5

109.8 ± 49.6 132.0 ± 44.6

Student “t” 0.965 NS paired test BMI 1996 22.3 ± 2.5 23.0 ± 4.4 2002 23.4 ± 3.4 24.8 ± 3.4 Student “t” 1.265 NS 1.049 NS paired test PPostnatal Nutritional status HC Males 1996 54.3a ± 54.3ab ± 1.2 1.6 2002 54.3a ± 54.6a ± 0.3 1.7 Student “t” 1.000 NS 1.000 NS paired test Females 1996 53.0a ± 53.9ab ± 1.0 0.9 2002 53.2a ± 53.9ab ± 1.2 1.0 Student “t” 1.219 NS 1.633 NS paired test Brachial anthropometry %AC/A

1996

Nutritional status Student “t” paired test %TS/A 1996 2002

93.8 ± 99.5 ± 19.7 13.4 Job status 2002 Jobless Workers (11) without further schooling (13) 0.697 0.246 NS NS 115.1 ± 65.9 110.2 ± 64.6

167

0.47 NS F

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0.26 NS 0.57 NS

168

Daniza M. Ivanovic, Hernán T. Pérez, Boris P. Leiva et al. Table 13. (Continued)

Student “t” paired test %AMA/A 1996 2002 tudent “t” paired test %AFA/A 1996 2002 Student “t” paired test

0.325 NS 0.666 NS

0.766 NS

5.641****

86.7 ± 97.5 ± 30.7 19.2 85.0 ± 93.8 ± 45.1 27.2 0.406 NS 0.382 NS

89.3 ± 21.7

91.0 ± 16.6

0.65 NS

92.0 ± 22.6

93.4 ± 13.5

0.41 NS

2.052 (t)

1.128 NS

117.3 ± 37.7

107.0 ± 49.5 132.1 ± 50.4 5.912 ****

111.1 ± 112.4 ± 71.0 75.2 102.7 ± 120.9 ± 58.1 61.5 0.442 NS 0.211 NS

123.9 ± 53.1 1.242 NS

0.16 NS 0.87 NS

100 90 80

% of cases

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Note. Results are expressed as mean ± SD. The number of cases is indicated between parentheses. Means with the same letter are not significantly different at the 0.05 level based on Scheffe's test. F= ANOVA; W/H= % weight for height; BMI= body mass index; HC= head circumference; AC/A= arm circumference-for-age; TS/A= triceps skinfold-for-age; AMA/A= arm muscle area-for-age; AFA/A= arm fat area-for-age. * p< 0.05; ** p< 0.01; *** p< 0.001; **** p 0.05 y p< 0.10); NS= not significantly different.

70

NU

60

U

50 40

54.6%

30

61.5%

20 10

12.5%

0

Jobless

Workers Students at Students at without further technical universities schooling institutes

Job status 2002 Figure 4. Undernutrition during the first year of life (U) of Chilean high school graduates in 1996 by job status 2002. NU= non-undernourished. Fisher = 0.00001.

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169

3

Z - HC

2

F = 9.35 p< 0.0001

0

b

ab

1

a

a

-1 -2 -3 Jobless

Workers without further schooling

Students at technical institutes

Students at universities

Job status 2002

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Figure 5. Head circumference-for-age Z score (Z-HC) by job status 2002 of Chilean high school graduates in 1996.

Figure 4 indicates that undernutrition in the first year of life had been significantly more prevalent among the jobless (54.6%) and in workers without further schooling students (61.5%) than among students attending institutes (12.5%) or universities (0%) (p< 0.00001). The nutritional status 1996 and 2002 by job status 2002 is shown in table 13. Figure 5 shows that university students had the highest Z-HC values than the other job statuses (p< 0.0001). When comparing the nutritional status between both periods (table 13), students attending technical institutes and especially university students significantly increased their %W/H and BMI values in 2002. The latter group also significantly increased their body composition parameters as %AC/A, %TS/A and %AFA/A (p< 0.0001). Table 14 shows details of the socio−economic conditions of high school graduates as related to job status 2002.

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Table 14. Socio−economic conditions of Chilean high school graduates by job status 2002 Socio−economic variables

Socio-economic strata High Low Total House-hold head Father Mother Other person Total House-hold head schooling Illiterate Incomplete elementary school Complete elementary school + Incomplete high school Complete high school Incomplete university education Complete university education Total

X2o or Fisher

Job status 2002 Workers without Students at Students at Jobless further schooling technical universities (11) (13) institutes (16) (54) -------------------------------- % of cases ----------------------------------

Total sample (94)

18.2 81.8 100.0

0 100.0 100.0

81.2 18.8 100.0

57.4 42.6 100.0

48.9 51.1 100.0

Xo2(3) = 24.858 > Xt2(3) 0.001 = 16.268

90.9 0.0 9.1 100.0

84.6 15.4 0.0 100.0

81.2 18.8 0.0 100.0

92.6 7.4 0.0 100.0

89.4 9.6 1.1 100.0

Fisher = 0.712. Fisher’s test was calculated joined the categories (jobless + workers without further schooling), (students at institutes + students at universities) and (mother +other person)

18.2 9.0 36.5

0.0 38.5 53.8

0.0 6.2 12.5

0.0 7.4 33.3

2.1 11.7 33.0

27.3 9.0 0.0 100.0

7.7 0.0 0.0 100.0

6.3 25.0 50.0 100.0

7.4 3.7 48.2 100.0

9.6 7.4 36.2 100.0

Xo2(3) = 17.920 >Xt2(3) 0.001 = 16.268. Chi -square test was calculated joined the categories (illiterate + incomplete elementary school+ complete elementary school + incomplete high school) and (complete high school +incomplete university education + complete university education).

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Socio−economic variables

Maternal schooling Incomplete elementary school Complete elementary school + Incomplete high school Complete high school Incomplete university education Complete university education Total

Job status 2002 Jobless (11)

X2o or Fisher Workers without further schooling (13)

Students at technical institutes (16)

Students at universities (54)

Total sample (94)

27.2 45.5

30.8 65.5

6.2 12.5

1.9 18.5

9.6 26.6

27.3 0.0 0.0 100.0

7.7 0.0 0.0 100.0

62.5 0.0 18.8 100.0

46.3 3.7 29.6 100.0

41.5 2.1 20.2 100.0

Xo2(1) = 9.270 >Xt2(1) 0.01 = 6.635. Chi -square test was calculated joined the categories (incomplete elementary school +complete elementary school + incomplete high school + complete high school), (incomplete university education+ complete university education), (jobless + workers without further schooling) and (students at institutes + universities).

House-hold head occupation Xo2(3) = 24.860 >Xt2(3) 0.001= 16.268. Chi -square test was calculated joined the categories (1+2+3) and (4+5+6). 1. Managerial positions 2. Mid-level employee 3. Specialized worker 4. Non-specialized worker 5. Jobless receiving state aid 6. Jobless without state aid Total

9.1 9.1 0.0 45.4 27.3 9.1 100.0

0.0 0.0 0.0 61.5 30.8 7.7 100.0

25.0 56.2 0.0 6.2 6.3 6.3 100.0

37.0 16.7 3.7 42.6 0.0 0.0 100.0

26.6 20.2 2.1 39.4 8.5 3.2 100.0

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Table 14. (Continued) Maternal occupation 1. Managerial positions 2. Mid-level employee 3. Specialized worker 4. Non-specialized worker 5. Jobless receiving state aid 6. Jobless without state aid 7. Housekeepers Total Quality of housing

0.0 0.0 9.0 36.4 0.0 0.0 54.6 100.0

0.0 0.0 0.0 46.2 0.0 0.0 53.8 100.0

6.2 25.0 12.5 6.3 0.0 0.0 50.0 100.0

7.4 7.4 13.0 13.0 0.0 0.0 59.2 100.0

5.3 8.5 10.6 19.2 0.0 0.0 56.4 100.0

Single family unit Solid materials Light materials Self-built Precarious housing ( “Mejora”) Total Property of housing Owner Tenant Usufructuary Total

18.2 0.0 0.0 72.7 9.1 100.0

0.0 0.0 7.7 84.6 7.7 100.0

18.8 62.5 6.2 12.5 0.0 100.0

40.7 11.1 13.0 35.2 0.0 100.0

28.7 17.0 9.6 42.6 2.1 100.0

72.7 9.1 18.2 100.0

84.6 0.0 15.4 100.0

75.0 18.8 6.3 100.0

94.4 5.6 0.0 100.0

87.2 7.4 5.3 100.0

Xo2(1) = 7.190 >Xt2(1) 0.01 = 6.635. Chi -square test was calculated joined the categories (1+2+3) and (4+5+6+7), (jobless + workers without further schooling) and (students at institutes + students at universities). Xo2(3) = 23.270 >Xt2(3) 0.001 = 16.268. Chi -square test was calculated joined the categories (Single family unit + Solid materials) and (Light materials + Self-built + Precarious housing ( “Mejora”). Fisher = 0.176. Fisher’s test was calculated joined the categories (tenant + usufructuary), (jobless + workers without further schooling), and (students at institutes + students at universities).

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173

Table 15. Socio-economic, socio-cultural, family, mass media exposure (MME) and demographic of variables of Chilean high school graduates in 1996 by job status 2002

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Variables

Job status 2002 Jobless Workers (11) without further schooling (13) Socio-economic and socio-cultural variables SES (Graffar's score) 10.8a ± 1.1 10.2a ± 2.6 Paternal Schooling (y) 7.8a ± 3.1 9.2a ± 4.1 Maternal Schooling 8.2a ± 2.2 8.5a ± (y) 2.8 Household head's 8.1a ± 3.0 8.8a ± Schooling (y) 4.8 Family variables Number of Family 5.5 ± 1.3 4.3 ± 1.1 Members Number of Siblings 3.5 ± 1.1 2.8 ± 1.5 Place Between 2.1a ± 1.0 1.7ab ± Siblings 1.3 Promiscuity (number 1.4a ± 0.4 1.2 ab± of persons per bed) 0.2 Crowding (number of 1.8 ± 0.5 1.8 ± 0.7 persons by bedroom) MME variables Radio (minutes per 163.8 ± 163.6 ± day) 79.7 82.7 Cinema (times per 1.4 ± 2.2 1.5 ± 3.0 month) Television (minutes 166.2 ± 159.1 ± per day) 143.9 92.2 Demographic variables Age (y) 18.0a ± 18.2a ± 0.8 1.1

F Students at technical institutes (16)

Students at universities (54)

6.2b ± 2.9

6.7b ± 3.3

11.01 ****

13.0b ± 2.8

12.4b ± 3.3

9.86 ****

11.9b ± 2.4

12.5b ± 2.2

18.13 ****

13.3b ± 2.8

12.4b ± 3.3

9.33 ****

5.3 ± 1.3

5.5 ± 2.3

1.26 NS

2.6 ± 0.7 1.5ab ± 0.9

3.1 ± 1.4 2.2b ± 1.1

1.34 NS 2.31 (t)

1.2ab ± 0.2

1.1b ± 0.3

5.98 ***

1.6 ± 0.5

1.4 ± 0.7

1.67 NS

217.8 ± 134.9 1.1 ± 1.2

124.0 ± 149.1 1.3± 2.1

2.15 (t)

181.9 ± 85.2

133.4 ± 146.4

0.68 NS

17.9a ± 1.1

17.2b ± 0.5

10.34****

0.13 NS

Note. Results are expressed as mean ± SD. The number of cases is indicated between parentheses. Means with the same letter are not significantly different at the 0.05 level based on Scheffe's test. F= ANOVA; SES= socio-economic strata. *** p< 0.001; **** p< 0.0001; (t) = tendency (p> 0.05 y p< 0.10); NS = not significantly different.

SES did not differ between both periods but significant differences were found by job status 2002. As a consequence, students attending technical institutes and especially university students had SES (p< 0.001), household

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head (p< 0.001) and maternal schooling (p< 0.01), household head (p< 0.001) and maternal occupation (p< 0.01) and quality of housing (p< 0.001) significantly higher than their peers who were workers without further schooling or jobless. Most of students had their father as household head, their parents owned housing and all of these had connections to sewerage and drinking water systems. Socio-economic, socio-cultural, family, MME and demographic variables expressed as mean ± SD by job status 2002 are shown in table 15. Students attending technical institutes and especially university students had Graffar scores, paternal, maternal and household head schooling scores (p< 0.0001), significantly higher than their peers from the other job statuses. University and technical institute students showed significantly lower levels of promiscuity than the other groups (p< 0.001) and the former were significantly younger than their peers from the other job statuses (p< 0.0001). The logistic regression analysis between the job status 2002, dependent variable, categorized them as university studies and non-university studies (probability modeled was university studies=yes) (table 16) and most relevant parameters considering SES, sex, student IQ, maternal IQ, AAT, SA, birth weight, birth height, undernutrition in the first year of life, Z-HC and BV as independent variables, revealed that AAT score at the end of high school was the best predictor of the job status six years later. The odds ratio value (1.025) indicates that when AAT score increases by one point the probability for university admission and for university graduation increases 2.5%. Table 16. Logistic regression between the university graduation (dependent variable), and most relevant parameters (independent variables=SES, sex, student IQ, maternal IQ, AAT, SA, birth weight, birth height, undernutrition in the first year of life, Z-HC and BV) Parameters entered in the statistical model INTERCEPT AAT

DF

Parameter Estimate

Standard Error

Wald Chi-square

1 1

-11.3055 0.0244

3.47750 0.00787

10.5690 ** 9.6399 **

Standardized Odds Estimate Ratio Estimat es 2.232627 1.025

Note: Probability modeled was university graduation=yes. DF= degree of freedom; SES= socio-economic strata; IQ= intellectual quotient; AAT= Academic Aptitude Test; SA= scholastic achievement; Z-HC = head circumference-for-age Z score; BV= brain volume; n= 74; ** p< 0.01.

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DISCUSSION The results of this study demonstrate that AAT achievement is determined by multiple factors, such as the student IQ, maternal and paternal IQ, Z-HC, BV, undernutrition in the first year of life, maternal schooling, household head occupation, quality of housing and SA. However, after controlling by SES and sex, only student IQ entered in the logistic regression model (odds ratio value = 1.252), which means that when IQ increases by one point the probability of obtaining high AAT scores increases by 25.2%. AAT score is a good predictor of later job status and when AAT score increases by one point the probability for university admission and for university graduation increases 2.5%. This finding is interesting and probably indicates that, in fact, AAT selects the most intelligent students for admission to universities. IQ is one of the most important neuropsychological determinants of educational performance and this is in agreement with our previous findings and with those of other authors who have found that neurodevelopmental and psychological factors are major determinants of educational success (Cassidy and Lynn, 1991; Ivanovic et al., 1989a, 2000a,b, 2002, 2004c; Rostain, 1997). We previously had reported that intelligence is the most important independent variable that explains SA at the onset of elementary school (Ivanovic et al., 1989a). However, one of our more recent reports pinpoint that this has been observed in all grades, from elementary to high school, and that its impact significantly increases from elementary to high school (Ivanovic et al., 2004c). Other recent findings in Chilean school-age children graduating from high school emphasize that SA is determined mainly by the child’s IQ, that explains approximately 90% of SA variance for both males and females; this was also observed in a comparative study of Chilean undernourished and wellnourished, poor high school graduates, in whom only IQ explained SA variance (Ivanovic et al., 2000a,b, 2002). Another aspect that we should take into consideration is the heritability of intelligence, estimating that between 50-60% of the child intelligence is explained by that of the other family members (Avancini, 1982; Johnson, 1991; Lynn and Hattori, 1990). However, the degree to which genetic factors influence human intelligence remains a matter of some controversy (Reiss AL, Freund LS, Baumgardner TL, Abrams MT, and Denckla, 1995; Sternberg, Grigorenko, and Kidd, 2005). In our study, school-age children with high AAT had significantly higher IQ, HC, BV and SA scores and had a significantly lower incidence of undernutrition in the first year of life, than their peers from the low AAT group, despite the fact that these were significantly older than the former. As a

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consequence, the low AAT group showed a schooling delay due to significantly more repeated years compared with their peers with high AAT scores. In addition, students from the low AAT group had significantly higher levels of exposure to radio, they attended more frequently cinemas, they watched more television and they read newspapers and books significantly less than students from the high AAT group. This could be indicative a lifestyle that does not favor learning and that might condition the quality of jobs later. We previously described that in the sample of this study, independently of SES, high school graduates with similar IQ had similar nutritional, brain development and educational parameters and this was observed in both sexes. Maternal IQ, BV and severe undernutrition during the first year of life were the independent variables with the greatest explanatory power for child IQ variance (r2 = 0.707), without interaction with age, sex or SES (Ivanovic et al. 2000a, 2002). Although paternal IQ had a significant impact on child IQ, it is important to emphasize that students from the high SES with low IQ are probably conditioned by their maternal IQ that was significantly lower from that of mothers belonging to the low SES and whose children had high IQ (Ivanovic et al., 2002). Children of the high SES with low IQ had not suffered from severe undernutrition in the first year of life but their BV, HC and SA were not different from their peers of the low SES with low IQ, of whom 64% had suffered from undernutrition in the first year of life (Ivanovic et al., 2000b, 2002). It seems that maternal IQ may be the most powerful and critical determinant of child IQ (Bacharach and Baumeister, 1998; Ivanovic et al., 2002). On the other hand, independently of SES school-age children with low IQ obtained very low SA and AAT scores (below 450) that prevented them from applying to higher education institutions (Ivanovic et al., 2002). Parental IQ, especially maternal IQ is also an important risk factor for AAT and later job status. This could be explained by the fact that maternal IQ is the main determinant of child IQ and, as in our results, this is the only independent variable that contributes to explain AAT achievement the most important parameter explaining later job status. Our previous results pinpoint that maternal IQ is also an important risk factor for child IQ, undernutrition, decreased HC, BV and SA (Ivanovic et al., 2000a,b,c, 2002). Other investigators have reported that the greater impact of maternal IQ may be related with the quality of the stimulation received by the child; in conditions of poverty this is also conditioned by both their lower maternal schooling levels and maternal IQ (Carter, Resnick, Ariet, Shieh, and Vonesh, 1992; Crandell and Hobson, 1999; Duncan, Brooks-Gunn, and Klebanov, 1994; Melhuish, Lloyd, Martin, and Mooney, 1990; Nelson and Deutschberger, 1970;

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Sandiford et al. 1997; Smith et al. 1996). As regards maternal schooling this has been underlined as the most relevant socio-economic and socio-cultural variable affecting IQ and SA like in our study (Ivanovic et al., 1995b, 2000b,c,d, 2001, 2004c). In fact, in our study AAT scores were not affected by the student’s SES but by maternal schooling, household head job and the quality of housing which were significantly better in the high AAT group; these findings are in agreement with previous results that demonstrated that these variables were significantly higher in students with high SA (Ivanovic et al., 1995b, 2000c,d, 2001, 2004c). Other socio-economic variables such as SES, the household head, maternal occupation, ownership of housing, connection to a sewerage system and adequate water supply were not found to be positively and significantly associated with AAT. Other findings have revealed that the presence of the father at home originates significant differences favoring learning of boys and girls (Sciara, 1975). In our study were fathers household head more frequently in the high AAT group, but differences were not significant. The considerable importance of mothers for child development has been especially emphasized by UNICEF which considers the education of girls as a high priority (United Nations International Children’s Fund, 2004). Mothers from the low SES and with the lowest schooling levels also have inadequate knowledge about nutrition and health (Ivanovic, Castro, and Ivanovic, 1997) while at the same time they are the most important source of nutrition information for school-age children (Ivanovic et al. 1989b, 1991b). Undernutrition in the first year of life is also another important risk factor for AAT and later job status since all undernourished students had low AAT scores; as regards, IQ, HC, BV and SA scores were significantly lower than their non-undernourished peers (Ivanovic et al., 2000b). For both undernourished and wellnourished poor Chilean high school graduates, the most relevant independent variable explaining IQ were, in decreasing order of importance, maternal schooling, brain volume and undernutrition in the first year of life (r2 = 0.714), and only IQ explained SA variance (r2 = 0.860) (Ivanovic et al., 2000b). As we previously stated, the positive and significant impact of parental schooling, especially maternal schooling, on child IQ has been described by several authors and may be related with the more intensive stimulation of the child and the same may be argued for some environmental variables such as the quality of housing in relation to SA (Agarwal et al., 1992; Carter et al., 1992; Casey, Barrett, Bradley, and Spiker, 1993; Duncan et al., 1994; Melhuish et al., 1990; Sandiford et al., 1997; Smith et al., 1996). HC was the most important anthropometric index of postnatal nutritional status associated with AAT achievement and with later job status in the sample

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of this study and our previous findings revealed that its impact increased along the school years of education; thus, in high school graduates HC is the only anthropometric parameter that explains SA and IA (Ivanovic et al., 1996, 2000c). Findings from other authors have confirmed a positive and significant correlation between head size and SA (Desch et al., 1990; Ishikawa, Furuyama, Ishikawa, Ogawa, and Wada, 1988). Moreover, as we stated previously, educational selectivity correlates with HC and not with W or H (Ivanovic et al., 1996). Considering that IQ is the most relevant independent variable explaining AAT, positive and significant correlations between intelligence, HC and SA have been reported by other authors (Botting et al., 1998; Fisch et al., 1976; Hack and Breslau, 1986; Hack et al., 1991; Ivanovic et al., 1996, 2000a,b,c, 2002, 2004a,b,c; Johnson, 1991; Nelson and Deutschberger, 1970; Ounsted et al., 1988; Rushton and Ankney, 1996; Susanne, 1979; Van Valen, 1974; Willerman et al., 1991), and in our observations. HC is a good indicator both of the quality of the nutritional background and brain development and is the most sensitive anthropometric indicator of prolonged undernutrition during infancy, which becomes associated with intellectual impairment (Stoch et al., 1982; Winick, 1975; Winick and Rosso, 1969a,b). Malnutrition and its associated conditions affect the productivity and intellectual capacity of people. This is especially significant for the quality of life in the Third World because economic development is limited by poverty of extensive social sectors that present unfavorable socio-economic, socio-cultural, intellectual and nutritional conditions; this prevents them from applying to better jobs, as seen in our study. Currently, undernutrition remains the most relevant nutritional problem in developing countries (FAO, 1996; United Nations International Children’s Fund, 2005). Several authors have shown a positive and significant association between SA and IQ, HC and BV, like in our study (Akgun et al., 2003; Botting et al.,1998; Dolk, 1991; Gale et al., 2004; Gignac, Vernon, and Wickett, 2002; Ivanovic et al., 1996, 2000a,b,c, 2002, 2004a,b,c; Nelson and Deutschberger, 1970; Ounsted et al., 1988; Reiss et al., 1996; Rushton and Ankney, 1996; Strauss and Dietz, 1998; Vernon et al., 2000; Willerman et al., 1991). In agreement with the findings of most studies, students with low AAT scores presented in our study lower IQ, HC, BV, SA and higher incidence of undernutrition in the first year of life, despite the fact that they were older than their peers with high AAT scores (Bouchard, 1998; Desch et al., 1990; Gignac et al., 2002; Ivanovic et al., 1996, 2000a,b,c, 2002, 2004a,b,c; Reynolds, Johnston, Dodge, DeKosky, and Ganguli, 1999; Vernon et al., 2000; Weinberg,

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Dietz, Penick, and McAlister, 1974); however, other authors did not find significant associations between the mean IQ and HC (Przytycki and Burgin, 1992; Sells, 1977). A positive and significant correlation between intelligence, HC and BV has confirmed by several authors, suggesting that differences in human brain size are relevant in explaining differences in IQ dependent on genetic and environmental influences (Andreasen et al., 1993; Ivanovic et al., 2000a,b,c, 2002, 2004a,b,c; Reiss et al., 1996; Willerman et al., 1991). Therefore, taking into account our results we may hypothesize once more that children with low AAT scores had suboptimal HC with some degree of alteration of brain development associated with lowered IA, lowered SA and lower job status. In consequence, neuropsychological parameters are relevant to explain AAT achievement and later job status. In summary, IQ was the best predictor of AAT achievement and this was the best predictor of later job status. This confirms that individuals with higher job status as university students have high IQ, AAT, birth weight, birth height, HC, BV, SA, better socio-economic, socio-cultural and family conditions and a lower incidence of undernutrition in the first year of life; furthermore, their parents and especially their mothers, have high IQ. This group increased significantly their %W/H, BMI, %AC/A, %TS/A and %AFA/A values from 1996 to 2002 probably because their low physical activity due to a prolonged study periods in which they remain seated. A recent review has shown that neurocognitive assessment is frequently used as a basis for making determinations regarding a person's ability to work. Using meta-analysis to quantify objectively the association between eight neurocognitive domains and employment status revealed that performance in each domain was significantly associated with employment status, and that the associations were greatest for the following domains: intellectual functioning, executive functioning, and memory. These findings support the ecological validity of neurocognitive assessment (Kalechstein, Newton, and van Gorp, 2003). Results from a 25-year follow-up study revealed that higher IQ was also predictive of better outcome represented by a better residential placement, employment, and quality of life. (Beadle-Brown, Murphy, and Wing, 2005).

CONCLUSION Our study confirm the hypothesis that: 1) Independently of socioeconomic status, AAT achievement is positively and significantly associated

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Daniza M. Ivanovic, Hernán T. Pérez, Boris P. Leiva et al.

with the child’s intellectual quotient, his/her parent’s intellectual quotient, and his/her brain size and with some indicators of past nutrition especially head circumference; and 2) AAT achievement is a good predictor of later job status. These results underline the positive impact of neuropsychological parameters on AAT achievement and later job status and may be useful for vocational guidance of school-age children in a multicausal context and in this way it may help to improve the quality of life of children through better job status.

ACKNOWLEDGMENTS The authors are very gratefully to the Ministry of Education of Chile for all the facilities given to carry out this research; to Dr. Oscar Brunser MD for helpful comments and suggestions. Supported in part by Grant 1961032 from the National Fund for Scientific and Technologic Development (FONDECYT), Grant 024/1997 from the University of Chile, Postgraduate Department and Grant SOC 01/13-2 from the Research Department (DI), University of Chile.

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INDEX

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A academic difficulties, 49 academic performance, 3, 46, 49 academic settings, 2 access, 177 accountability, xi, 77, 99, 114, 120, 123, 124, 125, 127, 128, 129, 133, 136, 137, 138, 139 accuracy, 25, 29, 41, 79, 120 achievement, xii, 158, 159, 160, 161, 162, 163, 164, 166, 170, 175, 186, 187, 189, 201, 203, 205, 207, 208, 209, 210, 212, 213, 214, 215, 218, 219, 221 acid, 164 activity level, 162 adaptations, 165 ADHD, 17, 220 adjustment, 54, 212, 221 adolescence, 209 adolescents, 21, 62, 212 Adult, 221 adulthood, 2, 25, 26, 48, 104 adults, 218, 221 advantages, 30, 44 affect, 160, 165, 173, 206, 220 Africa, 85 African Americans, 117, 120 age, xii, 158, 160, 161, 162, 164, 167, 169, 170, 171, 173, 175, 178, 179, 184, 186,

187, 191, 193, 194, 201, 202, 203, 204, 208, 211, 212, 214, 215, 216, 217, 219, 220 aging, 219 airplane, 5 alcoholism, 167 alters, 162 American Psychiatric Association, 51 American Psychological Association, 54 Americans with Disabilities Act, 27 anchoring, 135 anxiety, xi, 22, 49, 102, 106, 107, 108, 109, 110, 111, 115, 116, 117, 119, 120 aptitude, xii, 109, 120, 158 arithmetic, 55, 69 arrests, 105 aspiration, 71, 99 assessment, ix, 5, 11, 20, 21, 22, 44, 45, 46, 49, 50, 55, 57, 61, 71, 79, 102, 103, 105, 114, 116, 118, 119, 120, 124, 125, 127, 131, 135, 137, 138, 207, 210, 211 assessment procedures, 61 assessment tools, 11 assimilation, 51 assistive technology, 2, 54, 56, 60 association, 165, 206, 207, 219, 220 asylum, 85 authorities, 124, 125, 126, 129, 131, 132, 133, 136 autonomy, xi, 123, 127 autopsy, 217

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Index

availability, 160 avoidance, 22

C

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B background information, 78 balance of power, 125 barriers, 28, 29, 96 behavior, 208, 216 behavioral sciences, 95 Belgium, 85 benchmarking, 133 bias, 17, 48, 79, 98, 99, 117, 169 birth weight, xiii, 159, 160, 162, 164, 175, 193, 200, 201, 207, 209, 212, 216, 217, 220 birthweight, 209 Black students, 108, 110 blood pressure, 111 BMI, xiii, 158, 171, 177, 178, 185, 186, 191, 193, 207 body, xiii, 158, 163, 164, 171, 172, 175, 176, 178, 186, 191, 193 body composition, xiii, 158, 175, 193 body mass, xiii, 158, 178, 186, 193 body mass index, xiii, 158, 178, 186, 193 body size, 172 body weight, 163 boys, 204, 216, 222 brain, xiii, 5, 6, 13, 23, 45, 50, 55, 64, 159, 160, 161, 162, 163, 165, 166, 167, 172, 175, 176, 184, 186, 188, 191, 201, 203, 205, 207, 208, 209, 211, 212, 213, 214, 217, 218, 220, 222 brain damage, 13, 167 brain development, 161, 162, 163, 165, 175, 184, 186, 188, 203, 205, 207, 213, 214, 222 brain growth, 163, 165, 211, 212, 213 brain size, 165, 166, 172, 206, 208, 211, 218, 222 brain structure, 209 brain tumor, 45 brainstorming, 30, 33, 39 building blocks, xii, 123, 125, 127 buttons, 24

calculus, 146 case study, 144 category a, 41 causal inference, 117 CBS, 85, 95 cell division, 164 cerebellum, 23, 171 cerebral cortex, 212 cerebral palsy, 45 certificate, 70 certification, ix, xi, 101, 130, 143 child development, 204 childcare, 217 childhood, 26, 48, 55, 163, 165, 211 children, xii, 158, 160, 161, 162, 163, 164, 167, 169, 170, 173, 175, 179, 187, 202, 203, 204, 206, 208, 209, 210, 211, 212, 214, 215, 216, 217, 218, 219, 220, 221, 222 Chile, xii, 157, 158, 159, 167, 168, 170, 208, 209, 210, 212, 213, 214, 215, 219, 221 City, 156 civil service, 125 class, 72, 73, 74, 76, 77, 78, 80, 87, 88, 89, 99, 146, 149, 219, 222 classification, 167 classroom, 51, 102, 144, 145, 147, 148, 149, 150, 152 classroom events, 149 classroom settings, 51 climate, 75, 76, 146 clinical assessment, 49, 210 clinical diagnosis, 13 clinical psychology, 60 coding, 25 cognition, 59, 64, 220 cognitive abilities, 212 cognitive ability, 24, 103, 219, 221 cognitive development, 3, 4, 209 cognitive function, 55, 165, 211, 216 cognitive level, 74 cognitive performance, 165

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Index cognitive process, 144 coherence, 26, 142, 143 cohort, 217, 220 college students, 58 colleges, ix, x, 48, 101, 113, 130 color, 121 communication, 163 community, 12, 72, 145, 218 comorbidity, 58 competence, 210 competition, 77, 137 compilation, 153 complexity, 153 compliance, 45 composites, 8 composition, x, xiii, 31, 66, 72, 74, 77, 95, 99, 158, 175, 176, 193 comprehension, 218 compulsory education, xii, 124, 127, 130 computation, 146 computer, 172 computer software, 30 computer systems, 32 computer technology, 42 computing, 9 conflict, 136 consent, 167 consequence, 159, 163, 200, 202, 207 construct validity, 15, 16, 22, 52 context, 166, 208 control, 172, 212 control group, 45, 48 convention, 81 coordination, ix, 1, 3, 11, 19, 20, 24, 25, 56, 58, 59 core, 168 corpus callosum, 172, 176, 191, 216 correlation, xiii, 7, 10, 14, 16, 17, 20, 68, 81, 82, 104, 108, 159, 165, 170, 171, 173, 184, 186, 205, 206 correlation analysis, 7 correlation coefficient, 7, 82, 171, 184, 186 correlation coefficients, 184, 186 correlations, 8, 9, 10, 14, 20, 22, 78, 186, 205, 218

193

cortex, 212 cost, 41, 50 coverage, 159, 168 cues, 43, 115 cultural stereotypes, 110, 111 culture, 79, 81, 85, 118, 144, 152 curricula, 125, 131, 132, 136 curriculum, 69, 115, 125, 132, 142, 144, 146 cycles, 210

D daily living, 220 damage, 167 deductive reasoning, 115 deficiencies, 164, 211 deficiency, 41 deficit, 25, 30, 49, 55, 57 dementia, 219 democracy, 140 dependent variable, 81, 82, 173, 186, 187, 200, 201 depression, 22 deprivation, 164, 211 depth perception, 43 developing countries, 206 developmental disorder, ix, 2, 59 developmental psychology, 59 deviation, x, 66, 68 devolution, 127 diagnosis, 24, 25, 27, 45, 46, 50 diagnostic criteria, 27 dic, 157 dichotomy, 86 diet, 219 differences, 162, 165, 172, 173, 175, 176, 188, 200, 204, 206, 210, 212, 217 dimensions, 218 disabilities, 161, 216 disability, 2, 8, 17, 27, 51, 52, 63 disadvantaged students, 80, 85, 89 discipline, 220 discrimination, 21, 23, 64, 68, 69, 74, 77 discrimination tasks, 23 disease, 167, 219

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Index

disorder, 24, 57, 107 distance education, 145, 152 distracters, 115 disturbances, 24 dizygotic twins, 166 drawing, 21 drinking water, 177, 200 durable goods, 169 dyslexia, 29, 54, 58 dystonia, 64

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E ears, 205 Eastern Europe, 85 eating disorders, 59 economic development, 206 economic status, 12, 166, 180, 208, 214, 215 education, 159, 180, 181, 182, 195, 196, 203, 204, 205 Education, 170, 208, 212, 214, 215 education reform, 127 educational assessment, 105 educational attainment, 17 educational background, 83 educational objective, 127 educational programs, 71 educational psychologists, 169 educational research, 147 educational settings, 28 educational system, 76, 125, 128, 160 educationally disadvantaged, 120 elaboration, 146 elderly population, 57 election, 128 elementary school, 99, 108, 163, 180, 181, 195, 196, 202 elementary students, 64 emotion, 111, 118 emotional state, 107 emotionality, 119 employment, 102, 105, 106, 207, 216 employment status, 207, 216 empowerment, 45 England, 212

enrollment, 47, 48, 160 enrollment rates, 48 environment, 163, 209 environmental factors, 77, 165, 220 environmental influences, 206, 210, 217 epilepsy, 167 epistemology, 3 equipment, 28 equity, 99 estimating, 202 estimation problems, 44 ethnic background, 85, 94, 136 ethnic groups, 90, 93 ethnic minority, ix, x, 66, 67, 72, 75, 80, 85, 87, 89, 91, 92, 93, 96, 135 ethnicity, x, 7, 8, 9, 10, 12, 16, 17, 66, 67, 84, 85, 89, 90, 96, 98 EU, 97 evaluation, 172 exaggeration, 113 examinations, 127, 131, 136 execution, 5, 42 executive function, 23, 207 executive functioning, 207 executive functions, 23 experiences, 28, 63, 136, 151 expertise, 150 exploration, 60 exposure, xi, 52, 102, 103, 112, 113, 160, 170, 179, 183, 184, 185, 199, 202 expressiveness, 163

F factor analysis, 16, 106 factors, 160, 163, 164, 165, 201, 202, 210, 211, 215, 216, 217, 220 family, 160, 183, 184, 185, 186, 198, 199, 200, 202, 207, 219 family income, 17 family members, 202 fat, xiii, 158, 171, 178, 186, 193, 211 fears, 77, 107 feedback, 34, 37, 40, 42, 43, 44, 45 female, 216 females, 167, 168, 175, 188, 202

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Index femininity, 111 fetal growth, 164 fetus, 165 Finland, 96 first generation, 67 fish, 108, 120 follow-up, 207 food habits, 214 formal education, 98 freedom, 39, 46, 186, 201 functioning, 207, 216 funding, 114

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G gender, 218 gene, 218 general education, 70, 84 general intelligence, 4 generalized anxiety disorder, 107 genetic factors, 202 genetics, 209, 220 Germany, 76, 85, 98 Gestalt, 8, 11, 12, 14, 15, 19, 20, 22, 52, 54, 60, 61 gifted, 111, 121 girls, 204 governance, xi, 123, 132 GPA, 108 GPS, 148 grades, 48, 69, 77, 121, 127, 130, 131, 132, 134, 135, 136, 137, 202 grading, 137 graduate program, xii, 141 graduate students, 167 grouping, 4 groups, xii, 158, 167, 168, 172, 173, 174, 175, 176, 177, 178, 180, 183, 187, 188, 193, 200 growth, 162, 164, 165, 167, 210, 211, 212, 213, 220, 222 growth and development, 220 guessing, 115 guidance, 62, 208 guidelines, 82

195

H handedness, 16 handwriting, 17, 34, 40, 46, 64 health, 162, 204 heart disease, 167 hegemony, 125 height, xiii, 44, 153, 155, 158, 160, 162, 163, 164, 171, 172, 175, 177, 185, 191, 193, 200, 201, 207 heritability, 202, 216 high school, xii, 47, 108, 113, 115, 145, 158, 159, 161, 162, 163, 166, 167, 168, 172, 173, 174, 176, 177, 178, 179, 180, 181, 182, 183, 187, 189, 194, 195, 196, 199, 200, 202, 203, 205, 214, 215 higher education, x, 2, 66, 68, 130, 131, 159, 203 Hong Kong, 53 housing, xiii, 159, 160, 176, 183, 198, 200, 201, 204, 205 human behavior, 217 human brain, 165, 206, 217, 222 human cerebral cortex, 212 human intelligence, 165, 202, 221 hypothesis, 63, 166, 208

I iatrogenic, 50 ideal, 114 identification, 164 illusion, 43 image, 120, 171 images, 42, 43, 117, 171 imaging, xiii, 158, 171, 218 immersion, 42, 146 immigrants, 66, 85, 97, 98 immigration, 5, 85 impacts, 50 impairments, 23, 28, 45 in transition, 98 in utero, 216 incidence, 25, 69, 75, 161, 202, 206, 207 income, 209

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196

Index

independence, 48 independent variable, 160, 173, 186, 187, 200, 201, 202, 203, 205 indicators, 161, 166, 171, 193, 208 individual students, 72, 74, 79, 131 individuality, 111 inequality, 67, 95, 96, 97, 98 infancy, 162, 165, 166, 206, 212, 213, 220, 221 infants, 209, 210, 212, 219 inferences, 116, 119 influence, 202, 211, 217 information technology, 57 Information Technology, 63 inspections, xii, 123 institutions, 203 instructional design, 146 instructional materials, 148 integration, ix, 1, 9, 11, 19, 21, 22, 24, 51, 52, 53, 55, 121 intellectual development, 163, 167, 210, 213 intellectual disabilities, 8, 62, 209 intellectual functioning, 207 intelligence, 3, 4, 20, 25, 49, 62, 79, 80, 89, 104, 112, 118, 160, 161, 162, 163, 164, 165, 169, 202, 205, 206, 208, 209, 210, 211, 212, 214, 216, 219, 220, 221, 222 intelligence quotient, 209, 212 intelligence tests, 20, 104, 118 intensity, 172 interaction, 203, 210 interface, 43 internal consistency, 8, 20, 21, 22 Internet, 127, 137 interrelationships, 165 intervention, 27, 49, 50, 116 intravenous (IV), 212 IQ scores, 104, 105 ISC, 20 item response theory, 7

J Japan, 216 job performance, 119

jobless, xiii, 158, 172, 187, 188, 190, 193, 195, 196, 197, 198, 200 jobs, xiv, 159, 203, 206 Jordan, 101, 110, 118 journalists, 128

K knowledge, 204

L labour, 219 labour market, 97, 219 language, xiii, 158, 170, 209, 217, 220 language development, 217 language impairment, 57, 60 language proficiency, 99 language skills, 20, 34, 74 Latino women, 118 Latinos, 111 leadership, 133 learning difficulties, 25, 54, 147, 220 learning disabilities, ix, x, 2, 49, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 161, 216 learning environment, 50, 144 learning outcomes, 102, 118, 134, 135 learning skills, 2 legislation, 28, 114 les, 210 lesson plan, 149 level of education, 72, 78, 85 lifestyle, 203 light, 183 literacy, xi, 101, 132, 134, 219 local authorities, xii, 124, 128, 129 localization, 43 low birthweight, 209 LTD, 59

M machinery, 4 magazines, 170, 179 magnetic resonance, xiii, 158, 171 magnetic resonance imaging, xiii, 158, 171

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Index magnetic resonance imaging (MRI), xiii, 158, 171, 217 majority, 24, 128, 129, 135, 136 males, 167, 168, 175, 188, 202, 216 malnutrition, 163, 213, 216, 222 management, xii, 70, 115, 123, 128, 139 manipulation, 4, 56 marital status, 209 market, 219 marketing, 137 mass media, 160, 170, 183, 184, 199 mathematics, xii, xiii, 2, 25, 29, 30, 37, 42, 64, 69, 76, 78, 79, 80, 83, 84, 86, 87, 90, 91, 92, 119, 127, 141, 142, 145, 146, 147, 148, 150, 152, 156, 158, 168, 170 mathematics education, 147 mathematics tests, xiii, 158 mean, xii, 158, 161, 163, 167, 168, 170, 174, 175, 176, 178, 179, 184, 185, 188, 189, 191, 193, 199, 200, 206 measurement, 164, 169, 222 measures, 210, 221 media, 75, 127, 130, 145, 160, 170, 183, 184, 185, 199, 215, 221 median, xiii, 11, 13, 14, 158, 167, 168, 169, 171 Mediterranean, 85 Mediterranean countries, 85 membranes, 172 memory, 4, 23, 25, 29, 30, 36, 41, 108, 109, 207, 218 men, 209, 220 meninges, 172 mental ability, 118 mental development, 210 mental disorder, 51 mental health, 50, 57 mental load, 117 mental representation, 3 mental retardation, 5, 211, 218 meritocracy, 96 meta-analysis, 104, 105, 108, 109, 207 methodology, 114 mice, 28 microcephaly, 161, 211

197

Microsoft, 34, 150 Middle East, 85 migration, 172 military, 5 Ministry of Education, 78, 97, 126, 139, 140, 170, 208 minorities, 67, 74, 77, 85 minority groups, 85, 140 minority students, x, 66, 69, 73, 85, 86, 93, 118, 135, 136 modeling, 45, 134 monitoring, 135 monozygotic twins, 165, 209 morbidity, 24 Morocco, 85 morphology, xiii, 158 mother tongue, 132 mothers, 162, 187, 203, 204, 207, 209 motivation, ix, xi, 22, 46, 49, 68, 71, 73, 74, 102, 104, 105, 106, 114, 116, 118, 120, 210 motor control, 23 motor function, 166 motor skills, ix, 1, 2, 3, 4, 19, 20, 21, 22, 23, 24, 26, 39, 44, 45, 46, 49 motor system, 24 motor task, 4, 24, 56 MRI, xiii, 158, 171, 217 multifactorial, xiv, 159, 213 multiple factors, 160, 201 multiple regression, 105 muscles, 4 mutation, 218

N narratives, 149 negative feedback, 109 Nelson, 164, 165, 204, 205, 206, 217 nervous system, 4, 44 Netherlands, x, 65, 67, 68, 69, 72, 75, 76, 77, 82, 85, 86, 93, 96, 97, 98, 99 neuronal migration, 172 neuropsychology, 59, 221 New England, 212 newspapers, 170, 179, 203

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Index

No Child Left Behind, 114 non-verbal, 163, 169, 173, 187 normal children, 164 Norway, xi, 123, 124, 125, 126, 127, 128, 131, 133, 136, 137, 138, 139, 140 nucleic acid, 164 nutrition, 161, 162, 166, 171, 204, 208, 215, 216 Nutrition, 157, 167, 211, 212, 213, 214, 215, 216, 219, 222 nutritional deficiencies, 164 Nutritional deficiencies, 211 nutritional status, 160, 161, 162, 166, 171, 175, 190, 193, 205, 211, 214, 215, 222

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O observations, 205 occupational groups, 16 older adults, 218, 221 older people, 165 olfaction, 51 openness, 47 opportunities, xii, 129, 136, 141, 142, 143, 144, 145, 149, 150, 151 organization, 218 organizing, 36 outcome relationship, 104 overlap, 30 ownership, 169, 204

P pairing, 43 parallel, 153 parameter, 161, 203, 205 parents, xiii, 158, 169, 171, 173, 174, 186, 187, 200, 207 Parliament, 128 pathology, 161 pathways, 69, 70, 82 pedagogy, 131, 142, 150, 151 pediatric, 219 peer relationship, 121 peers, xiii, 159, 162, 173, 174, 175, 176, 187, 188, 193, 200, 202, 203, 205, 206

perceived outcome, 50 perceptual, 218 performance indicator, 127 perinatal, 211 permission, iv personal learning, 48 personality traits, 108 perspective, 210 Perth, 138 physical activity, 53, 207 physical environment, 43 physiological arousal, 111 pilot study, 63 PISA, xi, 76, 123, 126, 133, 137, 139 placebo, 50 politics, 126 poor, 202, 205 population, 167, 169, 212, 219 portfolio, 146, 153, 155 positive correlation, 108, 109, 166 postnatal, 166, 171, 175, 193, 205 post-secondary institutions, 27, 46, 47 poverty, 162, 204, 206, 210 Poverty, 170 power, 160, 203 predictive validity, 11, 22, 210 preparation, xi, 45, 102, 103, 106, 113, 114, 116, 117, 118, 138, 156 preschool, 62 preschool children, 62 preterm infants, 219 primary school, x, 39, 65, 67, 69, 71, 72, 73, 77, 79, 80, 93, 94, 95, 126, 140, 217 probability, xiii, 159, 173, 187, 200, 201 problem solving, 4, 25, 146, 152, 153 problem-solving, 108 productivity, 206 professional development, xii, 141, 142, 143, 144, 145, 146, 150, 152, 156 professional growth, 142, 145, 151 project, 125, 149 protein, 164 psychiatry, 220 psychologist, 19, 169 psychology, 105, 120, 220

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Index psychometric properties, 20, 21, 49 psychophysiology, 221 psychotherapy, 42, 56 public schools, 75, 104 pulse, 171

Q qualifications, 19 quality assurance, 124, 128, 133, 137 quality of life, 206, 207, 208

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R race, 7, 8, 9, 10, 12, 16, 17, 67, 98, 219, 220 racial, 172 racial groups, 172 radio, 170, 179, 202 random assignment, 114 range, 161, 168, 217 Rasch measurement, 53 Rasch measurement model, 53 Raven Progressive Matrices Test, xiii, 158, 169 reactivity, 117 reading, 2, 24, 25, 29, 30, 34, 35, 39, 40, 46, 55, 56, 61, 69, 76, 78, 80, 83, 84, 86, 87, 90, 91, 92, 113, 119, 127, 132, 134, 135, 149, 161, 221 reading comprehension, 29, 56 reading programs, 30, 39, 40, 46 real numbers, 147 reality, 3, 52, 53, 56, 57, 59, 60, 62, 63 reasoning, 25, 75 reasoning skills, 25 recall, 8 recognition, 20, 30, 34, 39, 41, 48 recommendations, ix, x, 29, 65, 67, 68, 72, 73, 75, 76, 78, 82, 85, 88, 89, 90, 93, 94, 125 reduction, 164 reforms, 126, 128 refugees, 85 regression, xiii, 81, 87, 88, 90, 91, 105, 159, 160, 173, 186, 187, 200, 201 regression analysis, 187, 200

199

regression model, 105, 160, 201 rehabilitation, 2, 42, 44, 50, 53, 56, 57, 62, 63 relationships, 165, 175 relevance, xii, 81, 82, 115, 141 reliability, xi, 8, 11, 17, 20, 21, 61, 62, 87, 101, 169, 170 religion, 85 replacement, 40 reproduction, 58, 84 reputation, 138 resolution, 43 Resonance, 157 resources, 50, 134 response, 165 responsiveness, 63 retardation, 211, 218 risk factor, 203, 204 rural population, 169

S sample, xii, 158, 167, 168, 169, 176, 180, 181, 182, 186, 195, 196, 203, 205, 218 schizophrenia, 13 scholastic achievement, 159, 160, 161, 162, 163, 164, 166, 186, 189, 201, 209, 210, 214 Scholastic achievement (SA), xiii, 158 Scholastic Aptitude Test, 109 school, xii, 158, 160, 161, 162, 163, 166, 167, 168, 169, 170, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 187, 189, 194, 195, 196, 199, 200, 202, 203, 204, 205, 208, 210, 212, 214, 215, 217, 218, 219, 220, 222 school achievement, 218 school performance, x, 66, 94, 105, 114, 129, 137, 160, 161, 162, 210, 212, 220 schooling, xiii, 47, 97, 99, 124, 159, 160, 162, 169, 172, 176, 180, 181, 186, 187, 188, 189, 190, 191, 192, 195, 196, 197, 198, 199, 200, 201, 202, 204, 205, 219 SCO, 95

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200

Index

scores, xii, 158, 163, 167, 168, 170, 174, 175, 177, 200, 201, 202, 203, 204, 206, 207, 218 SCP, 96, 98 screening, 42, 50 second generation, 67, 79, 98 secondary education, x, 2, 27, 47, 66, 67, 69, 70, 72, 73, 74, 75, 76, 84, 93, 96, 97, 98, 127, 137, 139 secondary schools, ix, 47, 70, 72, 82, 125, 127, 138 secondary students, 2, 29, 30, 42, 45, 46, 48, 49 secular, 209 selection, 167 selectivity, 205 self, 183 self-concept, 120 self-confidence, 41 self-efficacy, 53 self-esteem, 45, 48, 108 self-perceptions, 108, 109 self-report data, 46 self-reports, 111 seminars, 135, 144, 150 sensation, 26 sensations, 43 senses, 42 sensors, 37 SES, xii, 80, 111, 158, 167, 168, 169, 184, 185, 186, 187, 199, 200, 201, 203, 204 severe intellectual disabilities, 209 severity, 164 sex, x, 57, 66, 67, 83, 87, 89, 90, 160, 162, 167, 168, 172, 175, 176, 177, 184, 186, 187, 200, 201, 203, 209, 219 sex differences, 209 shape, 43 shock, 126 siblings, 108, 170 signs, 24, 73, 212 simulation, 43 situation, 221 skills, 220 social class, x, 6, 66, 117, 219, 222

social competence, 210 social desirability, 48 social environment, 163 social identity, 120 social impairment, 209 social phobia, 107 social psychology, 120 social sciences, 138 social skills, 51 social status, 108 social support, 109 socioeconomic background, 117 socioeconomic status, 8 software, 28, 30, 32, 33, 34, 40, 48, 150, 219 Southeast Asia, 211 spatial processing, 23, 41 Spearman-Brown, 170 special education, 19 speech, 28, 30, 34, 35, 37, 42, 48, 59 speed, 218 spelling, 25, 29, 32, 33, 34, 39, 40, 55 spin, 171 staff development, 143, 156 standard deviation, 161, 163 standardization, 12 standardized testing, 151 state aid, 182, 183, 197, 198 Statistical Analysis System (SAS), xiii, 159, 173 statistics, 17, 19, 131 stereotypes, xi, 102, 108, 110, 111, 120 stimulus, 73 storage, 36 strategy use, xi, 102, 114 stratification, 17 strength, 171 stroke, 6 student achievement, 54, 102, 127 student motivation, 40 students, xiii, 159, 162, 163, 167, 171, 172, 176, 179, 186, 187, 188, 190, 193, 195, 196, 197, 198, 200, 201, 202, 203, 204, 206, 207, 211, 213 subgroups, 110

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Index substitutes, 46 substitution, 153 supervisors, 126 supply, 169, 204 support services, 48, 52, 54 survey, 60, 98, 113, 126, 147 survival, 219 Switzerland, 76 symptoms, 23, 26, 167 synaptogenesis, 212 syndrome, 59 synthesis, 34, 118, 119, 147 systems, 200

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T task performance, 25 taxonomy, 115 telecommunications, 150 television, 161, 179, 203, 212, 218, 220 television commercial, 117 television viewing, 212, 218 test anxiety, ix, xi, 102, 103, 106, 107, 108, 109, 110, 116, 118, 119, 120, 121 test items, 16, 79, 80, 103, 109, 112, 113, 135, 140 test scores, xi, 49, 72, 87, 88, 89, 93, 102, 103, 104, 105, 106, 112, 113, 116 testing, 19, 22, 79, 81, 103, 104, 109, 110, 112, 118, 120, 142, 151 test-retest reliability, 20, 21 therapy, 51, 63 thesaurus, 30, 32 Third World, 206 thoughts, 3 time, 166, 174, 204, 212 time constraints, xii, 141 time management, 48 tracks, 37, 69, 76, 98 training, 2, 30, 40, 42, 44, 45, 50, 52, 53, 56, 62, 64, 70, 103, 112, 113, 115, 119, 134 training programs, 64 trait anxiety, 108 traits, 21, 108, 114, 220 transformations, 98 transparency, xi, 99, 123

201

traumatic brain injury, 45 trial, 16, 62, 134 triceps, xiii, 158, 171, 178, 186, 193 Turkey, 85, 141 tutoring, 30, 114 twins, 165, 209

U undernutrition, xiii, 159, 160, 162, 163, 166, 171, 176, 179, 186, 187, 190, 200, 201, 202, 203, 205, 206, 207, 213, 214 UNESCO, 96 unique features, 125 United Kingdom, 10, 57 United Nations, 167, 204, 221 universities, xiii, 48, 102, 113, 130, 159, 172, 187, 188, 189, 190, 191, 192, 193, 195, 196, 197, 198, 199, 201 university education, 70, 180, 181, 182, 195, 196 university students, 193, 200, 207 upper secondary schooling, 124 urbanization, x, 66, 72, 77, 80, 86, 93

V validation, 4, 106 validity, 170, 207, 210 values, 161, 170, 171, 172, 175, 188, 193, 207 variables, 160, 162, 163, 165, 170, 173, 174, 179, 183, 184, 185, 186, 187, 188, 189, 195, 196, 199, 200, 201, 203, 204, 205 variance, 160, 173, 202, 203, 205 variations, 104, 132, 165, 217 vehicles, 146 vibration, 44 video, 28, 149, 150 vision, 28 visual acuity, 103 visual stimuli, 19 visual system, 53 vocabulary, 39 vocational education, 83 vocational training, 60, 134

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202

Index

W

Y young men, 220

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Wechsler Intelligence Scale, xiii, 11, 158, 169, 210 weight gain, 216 Western countries, 85 white matter, 172 women, 209 word processing, 40 word recognition, 29

workers, xiii, 66, 85, 158, 172, 187, 188, 190, 195, 196, 197, 198, 200 working memory, 218 World Health Organization, 157 World War I, 66 worry, 110, 119 writing process, 39

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