Universal screening in kindergarten: Validity and classification accuracy of Istation's Indicators of Progress-Early Reading.

Universal screening in reading is a common, and often required, practice in early elementary school. Computer-adaptive screening tools, such as Istation's Indicators of Progress-Early Reading (ISIP-ER), are often chosen for this purpose in schools. In our present study, we examine the validity evidence between the ISIP-ER in kindergarten and third grade State of Texas Assessments of Academic Readiness (STAAR) reading scores, the classification accuracy of ISIP-ER to predict which students will meet STAAR reading expectations, and a cut score to maximize classification accuracy for the local context. The sample included 962 students (Mage = 6.19 years; SDage = 0.37) from 15 elementary schools in one suburban school district in Texas. As for validity, the correlation between ISIP-ER in kindergarten and the third grade STAAR was moderate (r = 0.48). Classification accuracy analyses using the vendor-recommended cut score found sensitivity (0.63) and specificity (0.70) were all below recommended levels. Using a locally determined cut score, sensitivity (0.92) was improved, but specificity (0.33) was substantially decreased. The findings suggest ISIP-ER has some limitations in the accurate identification of students at risk for poor outcomes on a state-mandated reading test and will likely need to be combined with other assessments or progress monitoring data. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exploring the Machine Learning Paradigm in Determining Risk for Reading Disability.

Purpose: Researchers have developed a constellation model of decoding-related reading disabilities (RD) to improve the RD risk determination. The model's hallmark is its inclusion of various RD indicators to determine RD risk. Classification methods such as logistic regression (LR) might be one way to determine RD risk within the constellation model framework. However, some issues may arise with applying the logistic regression method (e.g., multicollinearity). Machine learning techniques, such as random forest (RF), might assist in overcoming these limitations. They can better deal with complex data relations than traditional approaches. We examined the prediction performance of RF and compared it against LR to determine RD risk. Method: The sample comprised 12,171 students from Florida whose third-grade RD risk was operationalized using the constellation model with one, two, three, or four RD indicators in first and second grade. Results: Results revealed that LR and RF performed on par in accurately predicting RD risk. Regarding predictor importance, reading fluency was consistently the most critical predictor for RD risk. Conclusion: Findings suggest that RF does not outperform LR in RD prediction accuracy in models with multiple linearly related predictors. Findings also highlight including reading fluency in early identification batteries for later RD determination.

No Evidence of Creative Benefit Accompanying Dyslexia: A Meta-Analysis.

Research on the question of creative benefit accompanying dyslexia has produced conflicting findings. In this meta-analysis, we determined summary effects of mean and variance differences in creativity between groups with and without dyslexia. Twenty studies were included (n = 770 individuals with dyslexia, n = 1,671 controls). A random-effects robust variance estimation (RVE) analysis indicated no mean (g = -0.02, p = .84) or variance (g = -0.0004, p = .99) differences in creativity between groups. The mean summary effect was moderated by age, gender, and creativity domain. Compared with adolescents, adults with dyslexia showed an advantage over nondyslexic adults in creativity. In addition, a higher proportion of males in the dyslexia group was associated with poorer performance compared with the controls. Finally, the dyslexia group showed a significant performance disadvantage in verbal versus figural creativity. Regarding variance differences, they varied across age and creativity domains. Compared with adults, adolescents showed smaller variability in the dyslexia group. If the creativity task measured verbal versus figural or combined creativity, the dyslexia group exhibited smaller variability. Altogether, our results suggest that individuals with dyslexia as a group are no more creative or show greater variability in creativity than peers without dyslexia.

Insights into Dyslexia Genetics Research from the Last Two Decades.

Dyslexia, a specific reading disability, is a common (up to 10% of children) and highly heritable (~70%) neurodevelopmental disorder. Behavioral and molecular genetic approaches are aimed towards dissecting its significant genetic component. In the proposed review, we will summarize advances in twin and molecular genetic research from the past 20 years. First, we will briefly outline the clinical and educational presentation and epidemiology of dyslexia. Next, we will summarize results from twin studies, followed by molecular genetic research (e.g., genome-wide association studies (GWASs)). In particular, we will highlight converging key insights from genetic research. (1) Dyslexia is a highly polygenic neurodevelopmental disorder with a complex genetic architecture. (2) Dyslexia categories share a large proportion of genetics with continuously distributed measures of reading skills, with shared genetic risks also seen across development. (3) Dyslexia genetic risks are shared with those implicated in many other neurodevelopmental disorders (e.g., developmental language disorder and dyscalculia). Finally, we will discuss the implications and future directions. As the diversity of genetic studies continues to increase through international collaborate efforts, we will highlight the challenges in advances of genetics discoveries in this field.