INTERLEAVED DESIGN FOR E-LEARNING: THEORY, DESIGN, AND EMPIRICAL FINDINGS

• 成果类型: Article
• 署名作者: Li, Andy Tao; Liu, De; Xu, Sean Xin; Yi, Cheng
• 署名单位: Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Minnesota System; University of Minnesota Twin Cities; Tsinghua University; Tsinghua University
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2023/17206
• 发表日期: 2024
• 页码: 1363-1394
• 关键词: knowledge maps; Cognitive load; Text analysis; SYSTEM; FRAMEWORK; environments; education; category; students; BENEFIT

摘要

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.