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Frank Stinar; HaeJin Lee; Clara Belitz; Nidhi Nasiar; Stephen E. Fancsali; Steve Ritter; Husni Almoubayyed; Ryan S. Baker; Jaclyn Ocumpaugh; Nigel Bosch – International Educational Data Mining Society, 2025
Students' reading ability affects their outcomes in learning software even outside of reading education, such as in math education, which can result in unexpected and inequitable outcomes. We analyze an adaptive learning software using Bayesian Knowledge Tracing (BKT) to understand how the fairness of the software is impacted when reading ability…
Descriptors: Mathematics Education, Bayesian Statistics, Reading Ability, Information Management
Petscher, Yaacov; Kershaw, Sarah; Koon, Sharon; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
Districts and schools use progress monitoring to assess student progress, to identify students who fail to respond to intervention, and to further adapt instruction to student needs. Researchers and practitioners often use progress monitoring data to estimate student achievement growth (slope) and evaluate changes in performance over time for…
Descriptors: Reading Comprehension, Reading Achievement, Elementary School Students, Secondary School Students
Petscher, Yaacov; Kershaw, Sarah; Koon, Sharon; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
Districts and schools use progress monitoring to assess student progress, to identify students who fail to respond to intervention, and to further adapt instruction to student needs. Researchers and practitioners often use progress monitoring data to estimate student achievement growth (slope) and evaluate changes in performance over time for…
Descriptors: Response to Intervention, Achievement Gains, High Stakes Tests, Prediction
Bayes and Empirical Bayes Shrinkage Estimation of Regression Coefficients: A Cross-Validation Study.
Peer reviewedNebebe, Fassil; Stroud, T. W. F. – Journal of Educational Statistics, 1988
Bayesian and empirical Bayes approaches to shrinkage estimation of regression coefficients and uses of these in prediction (i.e., analyzing intelligence test data of children with learning problems) are investigated. The two methods are consistently better at predicting response variables than are either least squares or least absolute deviations.…
Descriptors: Bayesian Statistics, Equations (Mathematics), Intelligence Tests, Learning Problems


