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.
The project aims at increasing learners’ engagement in massive, open, and online education in Supply Chain Management by applying learning analytics.
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.
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.
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.