Can genetic screening be used to personalize education for students? Genome Wide Association Studies (GWAS) screen an individual’s DNA for specific variations in their genome, and how said variations relate to specific traits. The variations can then be assigned a corresponding weight, and summed to produce polygenic scores (PGS) for given traits. Though first developed for disease risk1, PGS are now used to predict educational achievement. Using a novel simulation method, this paper examined if PGS could advance screening in schools, a goal of personalized education. Results showed limited potential benefit for using PGS to personalize education for individual students. However, further analysis showed PGS could be effectively used alongside progress monitoring measures to screen for learning disability risk. Altogether, PGS is not useful in personalizing education for every child but has potential utility when used simultaneously with additional screening tools to help determine which children may struggle academically.
This study examines the response patterns of 288 Spanish-English dual language learners on a standardized test of receptive Spanish vocabulary. Investigators analyzed responses to 54 items on the Test de Vocabulario en Imagenes (TVIP) [Dunn, L. M., D. E. Lugo, E. R. Padilla, and L. M. Dunn. 1986. Test de Vocabulario en Imganes Peaboy: Adaptacion Hispanoamericana. Circle Pines, MN: AGS] focusing on differential accuracy on items influenced by (a) cross-linguistic overlap, (b) context (home/school), and (c) word frequency in Spanish. The response patterns showed cross-linguistic overlap in phonology was a significant predictor of accuracy at the item level. After accounting for item number (expected difficulty level), context of exposure was a significant predictor of the likelihood of obtaining a correct response. Spanish word frequency was not a significant predictor of accuracy. The current findings substantiate the influence of cross-linguistic overlap in phonology and context on Spanish vocabulary recognition by Spanish-English speaking children. Children were more likely to obtain correct responses on lexical items that were associated with the home context. Researchers and practitioners should consider phonological cross-linguistic overlap in addition to context of word exposure and word frequency when designing and utilizing vocabulary assessments for children from linguistic minority backgrounds.
Part 1. Cognitive Approaches. Kaschak, Jones, Coyle, Sell, Language and Body. Rayner, Slattery, Eye Movements and Moment-to-Moment Comprehension Processes in Reading. Compton, Elleman, Olinghouse, Lawrence, Bigelow, Gilbert, Davis, The Influence of In-Text Instruction on Declarative Knowledge and Vocabulary Learning in Struggling Readers: How IQ Confounds the Story. Part 2. Developmental Approaches. Radach, Schmitten, Glover, Huestegge, How Children Read for Comprehension: Eye Movements in Developing Readers. van den Broek, White, Kendeou, Carlson, Reading between the Lines: Developmental and Individual Differences in Cognitive Processes in Reading Comprehension. Priya, Wagner, The Roles of Fluent Decoding and Vocabulary in the Development of Reading Comprehension. Part 3. Individual-Differences Approaches. Cain, Oakhill, Reading Comprehension Development from 8 to 14 Years: The Contribution of Component Skills and Processes. Nation, Reading Comprehension and Vocabulary: What's the Connection?Castillo, Torgesen, Powell-Smith, Al-Otaiba, Examining the Decision Reliability and Validity of Three Reading Fluency Measures for Predicting Outcomes on Statewide Reading Accountability Tests. Part 4. Biological-Based Approaches. Keenan, Olson, Betjemann, Assessment and Etiology of Individual Differences in Reading Comprehension. Petrill, Genes, Environments, and the Development of Early Reading Skills. Eason, Cutting, Examining Sources of Poor Comprehension in Older Poor Readers: Preliminary Findings, Issues, and Challenges. Part 5. Epilogue. Wagner, Schatschneider, Phythian-Sence, Promising Interfaces.
This project aimed to describe oral narrative retells of Spanish-English speaking dual language learners (DLLs) and examine relationships with standardized vocabulary assessments. Investigators described oral narrative retells of 145 DLLs in kindergarten and first grade by number of different words (NDW), words per minute (WPM), and macrostructural components. Hierarchical regression analyses were used to examine relationships between narrative retells and standardized vocabulary performance. Children in first grade showed significantly better narrative retells than kindergarten DLLs, characterized by greater NDW and WPM, and more macrostructural components. Regression results indicated NDW accounted for the majority of the unique variance in DLLs’ performance on standardized vocabulary assessments. Findings substantiate that narrative retells are educationally relevant tools in predicting performance on a standardized English vocabulary assessment. The study contributes to knowledge of narrative performance of typically developing DLLs and supports the utility of retells in assessment of DLLs.
This manuscript provides information on datasets pertaining to Project KIDS. Datasets include behavioral and achievement data for over 4,000 elementary-age students participating in nine randomized control trials of reading instruction and intervention between 2005-2011, and information on home environments of a subset of 442 collected via parent survey in 2013. All data is currently stored on an online data repository and freely available. Data might be of interest to researchers interested in individual differences in reading development and response to instruction and intervention, as well as to instructors of data analytic methods such as hierarchical linear modeling and psychometrics.
One of the major risk factors for reading disability is difficulty learning to read words in text in an accurate and fluent manner. This is apparent when a child at risk of dyslexia first starts to attempt to read. Dyslexic children struggle to grasp and automate the alphabetic principle (ie, they cannot “sound out” words or use phonemic decoding strategies) and therefore have difficulty deciphering unfamiliar words that they have not encountered before. Even though many of these words are part of the child's oral vocabulary, the child cannot recognize them in printed form. As a result, reading can be extremely laborious and time-consuming, fraught with errors, and altogether an unrewarding, aversive experience. To be an efficient reader, one must be able to rapidly and effortlessly recognize many words by sight, and for a child to acquire this facility requires multiple exposures to these words. The difficulty that dyslexic children have in developing reliable and efficient phonemic decoding ability makes the acquisition of a lexicon of sight words a much slower process than it is for the average reader. Several other factors can affect a child's ability to read, which are reviewed herein. However, early recognition and treatment of deficient phonologic awareness are an extremely important step in the prevention of a reading problem in the child who is at risk of dyslexia. ( J Child Neurol 2004;19:759—765).
We report findings of two validation studies of the Student Risk Screening Scale for Early Childhood (SRSS-EC). Although previous exploratory inquiry suggested a two-factor solution, results of Study 1 conducted with 274 preschool-age youth suggested mixed support for a two-factor model, with only one fit index suggesting an adequate fit (incremental index, comparative fit index [CFI] = 0.963). However, results did provide additional evidence of convergent validity between SRSS-EC and Strengths and Difficulties Questionnaire scores. In Study 2, results of convergent validity studies with 90 preschool-age children offered evidence between SRSS-EC and (a) Social Skills Improvement System–Performance Screening Guide and (b) Caregiver–Teacher Report Form (C-TRF) scores. Results suggested the following cutting scores: SRSS-EC E7 (0–5 low, 6–7 moderate, 8–21 high risk) and SRSS-EC I4 (0–2 low, 3–4 moderate, and 5–12 high risk), with C-TRF scores as the criterion. We discuss limitations and directions for future inquiry.
