Interleaved Design for E-Learning: Theory, Design, and Empirical Findings

The rapid development of e-learning has drawn increasing attention to the issue of how learners’ learning activities can be better structured using technologies. This study focuses on how to improve e-learning performance by optimizing the structuring of learning sessions from the perspective of interleaving (i.e., mixing different topics in a learning session). Following the design science paradigm, this study chooses cognitive load theory as the kernel theory and proposes a new interleaving design—related-interleaving —that populates an interleaved session with related topics as a way of reducing cognitive load during an interleaved session. Drawing on the theoretical predictions, we design and instantiate a personalized learning system with the related-interleaving strategy by fusing educational strategies and machine learning techniques. The results from a two-month field experiment confirm that related-interleaving outperforms non-interleaving and unrelated-interleaving. Our findings also reveal that compared with unrelated-interleaving, related-interleaving benefits weak learners more and thus helps reduce learning performance disparities. This study demonstrates how personalized e-learning systems can be further improved from the perspective of interleaving.