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The project aims to support online learners’ self-regulated learning, performance, and retention by designing a learning analytics dashboard (LAD). The research questions: LAD design process: To address the theoretical gap and context-specific needs, we focused on four design dimensions as below: Meta-LAD: Key Findings: We currently completed the LAD design and usability testing. From usability […]
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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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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
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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.