Enhancing Academic Performance Through Self-Explanation in Digital Learning Environments (DLEs): A Three-Level Meta-Analysis

Abstract

Self-explanation serves as a constructive learning scaffold in education, actively engaging learners in the identification of knowledge gaps and the rectification of erroneous mental models. This study aimed to examine the effects of self-explanation on students’ academic performance in digital learning environments and to test the possible moderating factors in this association. We focused on two issues: (a) the effectiveness of self-explanation on academic performance; (b) moderating factors (learners’ characteristics, learning environment characteristics, inducement characteristics, and learning material characteristics) associated with different studies that may have resulted in the inconsistent findings. Based on 204 effect sizes extracted from 56 studies, we found that, compared with no self-explanation conditions, self-explanation had at least a medium effect (total: k = 204, g = 0.46; retention: k = 56, g = 0.31; transfer: k = 77, g = 0.33; mixed: k = 71, g = 0.60; immediate: k = 158, g = 0.45; delayed: k = 46, g = 0.35) in enhancing academic performance. Furthermore, moderator analysis found that studies conducted in learner-centered pacing learning environments showed larger effect sizes of self-explanation on academic performance than those conducted in system-centered pacing learning environments. Self-explanation was also more effective in concept knowledge and mixed knowledge compared to procedural knowledge. In general, this meta-analysis provided confidence in utilizing self-explanation and offered evidence-based recommendations for providing self-explanation in digital learning environments. We concluded with issues for future research, such as the necessity for additional studies on the quality of self-explanation and the establishment of standardization criteria for evaluating its quality.