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A surge in the number of registrants and verified learners in the MITx MicroMasters® Program in Supply Chain Management attests to the increasing appeal of online education during the Covid-19 pandemic. The numbers also suggest that supply chain management — a subject popularized by the pandemic — is attracting a growing number of virtual learners.
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The project aims at increasing learners’ engagement in massive, open, and online education in Supply Chain Management by applying learning analytics.
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This research aims to develop a data-driven framework to evaluate design changes in MOOCs. We explore a change from multiple angles-process, proficiency, and perception- and apply various analytical methods-temporal, causal and predictive to map out the outcome of instruction along multiple dimensions of learning.
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This research aims to identify the likelihood of dropping out from a MOOC-based program. Program dropout happens both, within and between courses. We identify the key dropout factors and develop a machine learning model to predict future student dropout.
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We aim to better understand learner engagement in massive open online education in Supply Chain Management by applying learning analytics. MOOC learners can be categorized into three distinct groups: Learners, Voyeurs, and Zombies.