Print Email Facebook Twitter MOOC-Rec: Instructional Video Clip Recommendation for MOOC Forum Questions Title MOOC-Rec: Instructional Video Clip Recommendation for MOOC Forum Questions Author Zhu, P. (TU Delft Web Information Systems) Yang, J. (TU Delft Web Information Systems) Hauff, C. (TU Delft Web Information Systems) Date 2022 Abstract 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 the clips’ transcripts and MOOC discussion forum entries. 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 rank video clips in response to a forum discussion. The retrieval models are trained with supervised learning and distant supervision to effectively leverage the unlabeled data—which accounts for more than 80% of all available data. Our experimental results demonstrate that the proposed method is effective for this task, by outperforming a standard baseline by 0.208 on the absolute change in terms of precision. Subject MOOCDiscussion ForumVideo Clip TranscriptsClip Recommendation To reference this document use: http://resolver.tudelft.nl/uuid:a523e6b6-2bd8-44fc-a819-b78d28da2acb DOI https://doi.org/10.5281/zenodo.6853055 Page numbers 705-709 Event 15th International Conference on Educational Data Mining, 2022-07-24 → 2022-07-27, Durham University,, Durham, United Kingdom Part of collection Institutional Repository Document type poster Rights © 2022 P. Zhu, J. Yang, C. Hauff Files PDF 2022.EDM_posters.86.pdf 563.29 KB Close viewer /islandora/object/uuid:a523e6b6-2bd8-44fc-a819-b78d28da2acb/datastream/OBJ/view