An Analytics-Based Approach to Managing Cognitive Load by Using Log Data of Learning Management Systems and Footprints of Social Media

Abstract

Traces of learning behaviors generally provide insights into learners and the learning processes that they employ. In this article, a learning-analytics-based approach is proposed for managing cognitive load by adjusting the instructional strategies used in online courses. The technology-based learning environment examined in this study involved a video conferencing system and learning management system (LMS) for hosting course content and discussion forums. The social networking software Line was used to enhance the social presence of learners. Students (N = 869) enrolled in a summer course participated in a 9-week experiment. Their LMS log data and social media footprints were recorded, and content experts assessed the intrinsic cognitive load (ICL) of each content module through a consensus process. A learning analytics method was applied to identify candidate parameters relating learning behaviors to cognitive load. The instructor assessed the learners’ cognitive processes and adjusted the instructional strategies according to the results of statistical, discourse, and qualitative analyses. Practical guidelines related to various cognitive load effects were designed to assist the students with managing their cognitive load by using learning behaviors and analytics data as signals for making a change in learning processes. Teachers of online courses can use the proposed approach as a support tool to identify learning problems and assist learners with maintaining a cognitive load that is conducive to learning.

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Cite this paper

@article{Yen2015AnAA, title={An Analytics-Based Approach to Managing Cognitive Load by Using Log Data of Learning Management Systems and Footprints of Social Media}, author={Cheng-Huang Yen and I-Chuan Chen and Su-Chun Lai and Yea-Ru Chuang}, journal={Educational Technology & Society}, year={2015}, volume={18}, pages={141-158} }