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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Abstract:This paper reports a theory-driven experimental study for designing and evaluating two different forms of attention-guidance functionalities integrated into an anchored-discussion system. Using social constructivism as a motivating theory, we constructed a theoretical framework that emphasizes the importance of students' attention allocation in online learning conversations and its influence on message quality and interaction patterns. The development of the functionalities, named faded instructor-led and peer-oriented attention guidance, aimed to direct students' attention toward instructional materials' central domain principles while offering them an open learning environment in which they could choose their own topics and express their own ideas. We evaluated the functionalities with heat map analysis, repeated measures general linear model analysis, and sequence analysis to assess the utility of the developed functionalities. Results show that attention guidance helped students more properly allocate their attention in online learning conversations. Furthermore, we found that the improved attention allocation led to better quality of students' online learning conversations. We discuss implications for researchers and practitioners who wish to promote more fruitful online discussions.

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