Adaptive Remediation with Multi-modal Content

Remediation is an integral part of adaptive instructional systems that provide a supplement to lectures in case the delivered content proves too difficult for a user to fully grasp in a single class session. To extend the delivery of current remediation methods from single type of sources to combinations of different material types, we propose an adaptive remediation system with multi-modal remediation content. The system operates in four main phases: ingesting a library of multi-modal content files into bite-sized chunks, linking them based on topical and contextual relevance, then modeling users' real-time knowledge state when they interact with the delivered course through the system and determining whether remediation is needed, and finally identifying a set of remediation segments addressing the current knowledge weakness with the relevance links. We conducted two studies to test our developed adaptive remediation system in an advanced engineering course taught at an undergraduate institution in the US and evaluated our system on productivity. Both studies show that our system is effective in increasing the productivity by at least 50%.