Search

Twice-exceptionality (2e), broadly defined as the co-occurrence of a gift or talent and a disability, has been a part of the educational lexicon since the 1990s, although its empirical backing is limited. We conducted a comprehensive review of the empirical literature on 2e and created a simulation study of 2e. For the latter, we constructed population distribution functions that represent indicators of ability using a range of correlations and identification criteria guided by the reviewed literature. We observed the theoretical occurrence of 2e, even at its most relaxed definitions, to be much rarer than prevailing literature implies (probability = 0.148). As correlations and factor loadings increase, the prevalence estimates of 2e drop considerably. Our results demonstrated the need for a more informed, standardized, and quantifiable approach to the identification of individuals with 2e.

Purpose: Researchers have developed a constellation model of decodingrelated 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 thirdgrade 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.

Research into reading difficulties in Russian has been taking place for about a century, since the 1920s. Early research established a line of studies on reading acquisition difficulties in the context of highly structured practices of teaching reading. These practices were propagated in the mid-late 19th century by Konstantin Ushinskii, who designed a mass system for the directed teaching of reading in Russian based on the mastery of spoken Russian (namely its phonics, phonology, orthography, and morphology). During the Soviet period, this approach was packaged in a universal system that included programs for children and adults, and appears to have been responsible for the high literacy rates (i.e., near 100%) at the end of the last century. In the 1990s, an explosion of diverse reading programs surfaced, claiming to offer a contrast to the Ushinskii system's universal but "boring" content. Nevertheless, the Ushinskii system regained popularity in the early years of the 21st century. Reincarnated and modernized, it once again constitutes the foundation of reading instruction in Russian schools. This article investigates the distribution of various reading-related skills among Russian primary-school students (Grades 2-4) in the context of this universally strong approach to teaching reading.