MOOC-Rec: Instructional Video Clip Recommendation for MOOC Forum Questions
Peide Zhu, Jie Yang, and Claudia Hauff
In Proceedings of the 15th International Conference on Educational Data Mining, Jul 2022
In this work, we address the information overload issue that learners in Massive Open Online Courses (MOOCs) face when attempting to close their knowledge gaps via the use of MOOC discussion forums. To this end, we investigate the recommendation of one-minute-resolution video clips given the textual similarity between captions and MOOC discussions. We first create a large-scale dataset from Khan Academy video transcripts and their forum discussions. We then investigate the effectiveness of applying pre-trained transformers-based neural retrieval models to make a ranked list of video clips for a forum discussion. The retrieval models are trained with supervised learning and distant supervision to effectively leverage the unlabeled data(over 80% of all data). Experimental results demonstrate that the proposed method is effective for this task, by outperforming baseline by 0.208 in terms of precision. To the best of our knowledge, this is the first systematic research applying pre-trained transformer-based ranking models on MOOC clip recommendation.