Multi-view Knowledge Graph for Explainable Course Content Recommendation in Course Discussion Posts

Recommendations form an integral part of instructional process and can be instrumental to promote or maintain student engagement in various course-offering platforms. Automated evaluation of a student’s discussion forum post and proactively generating a personalized recommendation to address the student’s learning requirement are of huge interest, specifically in an in-person classroom setting, which is still considered to be a dominant mode of mainstream learning. However, the task is growingly challenging due to the ever-expanding enrollment trend, where students from the wider socio-economic backgrounds become more of the norm. The traditional support structures, such as daytime-only office hours for student advisement, are typically inadequate. Toward this, we propose a multi-modal attentive learning framework that keeps track of the temporally evolving student learning patterns and their conversation dynamics in the course discussion board to automatically estimate relevant expression (e.g. ‘confusion’, ‘question’, ‘urgency’) reflected in forum posts. Based on the classifier evaluation, the consequential content recommendation module employs information propagation on a multi-view course specific knowledge graph to obtain a more context-aware entity embedding for recommendation. The system derives a personalized ranked list or relevant documents/video clippings augmented with the explainability score that enables the system to reveal the recommendation justifications for an improved student acceptance. The experimental results, which leverage our in-house course-specific multi-modal activity details from three large in-person Undergraduate and Postgraduate level STEM courses, demonstrate the effectiveness of our approach.