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Investigation of temporal dynamics in MOOC learning trajectories: A geocultural perspective
Rizvi, Saman Zehra · Rienties, Bart · Rogaten, Jekaterina

PublishedJune 2018
ConferenceArtificial Intelligence in Education
SeriesLecture Notes in Computer Science
Volume 10948, Pages 526-530
PublisherSpringer International Publishing
EditorsPenstein Rosé, Carolyn · Martínez-Maldonado, Roberto · Hoppe, H. Ulrich · Luckin, Rose · Mavrikis, Manolis · Porayska-Pomsta, Kaska · McLaren, Bruce · du Boulay, Benedict
CountryUnited Kingdom, Europe

Openness, scalability, and reachability are intrinsic features of MOOCs. However, research studies in MOOCs indicated low participation from some cultural clusters, mostly from less privileged strata of the world's population. The impeding factors are not only related to individual student characteristics, but also are related to structure and curriculum design. This proposed PhD thesis will address this stratification, performance and achievement gap. In a cross-module analysis on Open University MOOCs at FutureLearn, temporal predictive modelling was used to explore learners' background, regional belonging and behavioral patterns that contribute towards engagement, and overall performance. Later on, clustering and temporal process mining will be employed to observe end-to-end processes of learning. Behavioral traces and learning trajectories for different clusters of learners will be explored in a variety of MOOC Learning Designs (LD). The research findings aim to provide useful actionable insights on how adaptations in LD can make MOOCs more inclusive and diverse.

Keywords cultural clusters · educational data mining · educational process mining · learning design · massive open online courses

Published atCham
Rights© Springer International Publishing AG, part of Springer Nature 2018
Export optionsBibTex · EndNote · Tagged XML · Google Scholar

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