Temporal dynamics of MOOC learning trajectories

Rizvi, Saman; Rienties, Bart and Rogaten, Jekaterina (2018). Temporal dynamics of MOOC learning trajectories. In: International Conference on Data Science, E-learning and Information Systems, 01-02 Oct 2018, Madrid, Spain.

DOI: https://doi.org/10.1145/3279996.3280035

Abstract

Massive Open Online Courses (MOOCs) are a relatively new online learning phenomenon, whereby in 2017 more than 81 million learners have followed around 9,400 courses offered by more than 800 universities. Learners' retention has been one of the most vital issues associated with MOOC learning. A large body of literature can be found addressing various aspects of retention. However, few studies have examined the temporal aspects of learning processes, and why some learners complete only a few learning activities before dropping out, while others persist over time. Little is known about the nature and level of participation, or learners' progression in ordered learning activities in MOOCs, i.e., learners' learning pathways. This study aims to fill this gap in knowledge by analyzing an Open University MOOC offered via FutureLearn platform. Using exploratory methods associated with Educational Process Mining (EPM) on system logs, the study explored self-allocated time that 2,086 learners assigned to a variety of learning activities. Learners' activities were mapped to identify common and distinct learning pathways. Analyses were performed on two distinct groups of learners: Completers and Non-Completers. Using the measure of relative frequencies, the study compared participatory behaviors of both groups with expected learning behavior for all types of learning activities. Also, we explored typical weekly performance, identified and mapped most significant temporal learning pathways of subgroup of learners. The results indicated that at least one main and dominating pathway existed, but paths of dominant subgroups of Completers and Non-Completers remained noticeably distinct. We concluded the paper with practical implications and limitations of using process mining methods for temporal behavioral modeling in educational domains. Future research directions and potential benefits of such temporal modeling are also discussed.

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