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.