Author ORCID Identifier
Timo Grüneke: 0000-0001-7138-0395
Abstract
Massive Open Online Courses (MOOCs) have democratized access to higher education, yet consistently struggle with high dropout rates and poor learning experiences. As MOOCs are a form of Technology-Mediated Learning, these issues are rooted in technical challenges, as well as inadequate course structures and learning methods, which can impose cognitive load on learners. Against this background, this study investigates how instructional design strategies grounded in Cognitive Load Theory can be used to address learners’ cognitive overload and improve MOOC quality. We apply text mining and topic modeling to analyze a dataset of 142,976 learner-generated reviews from 25 Coursera courses. To contextualize these learner experiences, we also conduct a descriptive analysis of the courses’ structures. Our findings uncover eight topics of learner reviews that signal course frictions leading to extraneous and intrinsic cognitive load, and five distinct course-structure patterns. We analyze both topics and patterns through a cognitive load lens and map them to theory-grounded instructional design strategies. Theoretically, we use our findings to propose an extended Technology-Mediated Learning model that integrates cognitive load as both a diagnostic and mediating construct. Practically, our research helps instructors, platform providers, and higher education institutions build pedagogically grounded online learning environments at scale.
Recommended Citation
Grüneke, T., & Kreuzer, T. (In press). Managing Cognitive Load in Massive Open Online Courses – Evidence from Coursera. Communications of the Association for Information Systems, 58, pp-pp. Retrieved from https://aisel.aisnet.org/cais/vol58/iss1/119
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