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Cluster randomized trials are frequently used in educational research for methodological reasons. This study aims to improve the efficiency of cluster randomized trials on computer/information literacy and computational thinking. The study employs a two-level hierarchical linear model to estimate (i) intraclass correlation coefficients, (ii) the amount of explained variances given selected predictors, and (iii) minimum detectable effect sizes given the set of plausible scenarios. Two data cycles from the International Computer and Information Study were used. The covariates at the student level are gender, interest in ICT, parents’ highest education level, ICT self-efficacy, and experience with computers. The covariates at school/teacher level are teacher’s ICT use, ratio of school size to the number of computers for student use, availability of ICT resources at school, approximate teacher age, and ICT self-efficacy. Findings showed that the most precise effect could be measured when student and teacher/school covariates are both adopted. Lastly, it was revealed that increasing the number of schools is effective to get the most precise effect.

Studies of academic recovery courses (ARCs) focus almost exclusively on academic outcomes, such as postcourse grade point average (GPA) and academic standing. This study explores the role of noncognitive factors—specifically attribution perspective, shame resilience, and academic identity—for students engaged in ARCs. Pre- and postcourse data from ARC students revealed significant correlations between noncognitive factors and academic standing and statistically significant relationships among non-cognitive factors, as well as a significant difference in mean shame scores by gender. Practice implications are presented to guide educators in their retention efforts with probationary students and to encourage future research.