The project aims at increasing learners’ engagement in massive, open, and online education in Supply Chain Management by applying learning analytics.
In this project we investigate the fundamental trade-off involved with providing an educational platform for both learning and assessment, and propose strategies to ensure academic integrity in a MOOC-based program
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.