This paper presents a probabilistic model to enhance social learning in online education, particularly in massive open online courses (MOOCs) through discussion forums. The model integrates topic modeling, timescale modeling for post excitation decay, and learner interest modeling into a cohesive framework. It accounts for thread structure and notification settings to improve thread recommendations. Validated on three real-world MOOC datasets, the model significantly outperformed existing methods, effectively directing learners to relevant threads and providing valuable insights into topic engagement over time.
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Lan, Andrew S., et al. "Personalized thread recommendation for MOOC discussion forums." Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part II 18. Springer International Publishing, 2019.
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