Although previous meta-analyses have documented the efficacy of computerassisted statistics instruction, the current study examined a range of specific features that presumably influence its effectiveness, such as the level of learner engagement, learner control, and the nature of feedback. In 45 experimental studies with a control condition, computer-assisted statistics instruction provided a meaningful average performance advantage (d = 0.33). Because of great methodological heterogeneity among the studies, the authors employed a conservative but appropriate mixed effects model to examine potential moderator effects. The authors' analyses revealed three statistically significant findings. Larger effects were reported in studies in which treatment groups received more instructional time than control groups, in studies that recruited graduate students as participants, and in studies employing an embedded assessment. A newly developed second order standardized mean effect size, d diff , reveals that additional study characteristics may serve as meaningful moderators. Tight experimental control is needed to assess the importance of specific instructional features in computer-assisted statistics instruction.

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Learning in educational settings most often emphasizes declarative and procedural knowledge. Studies of expertise, however, point to other, equally important components of learning, especially improvements produced by experience in the extraction of information:Perceptual learning. Here we describe research that combines principles of perceptual learning with computer technology to address persistent difficulties in mathematics learning. We report three experiments in which we developed and testedperceptual learning modules(PLMs) to address issues of structure extraction and fluency in relation to algebra and fractions. PLMs focus students’ learning on recognizing and discriminating, or mapping key structures across different representations or transformations. Results showed significant and persisting learning gains for students using PLMs. PLM technology offers promise for addressing neglected components of learning: Pattern recognition, structural intuition, and fluency. Using PLMs as a complement to other modes of instruction may allow students to overcome chronic problems in learning.