This study investigates the rich textual dialogue within Massive Open Online Course (MOOC) forums, an area that has been largely unexplored compared to student persistence and interaction with course materials. The research applies unsupervised student understanding models, originally developed for synchronous tutorial dialogue, to MOOC forums. Using a clustering approach, the study groups similar posts and compares these clusters with manual annotations by MOOC researchers. Further qualitative investigation of the clusters is conducted. This approach represents a step towards using unsupervised models for asynchronous communication, potentially enabling large-scale automated discourse analysis and mining to better support student learning.
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Ezen-Can, Aysu, et al. "Unsupervised modeling for understanding MOOC discussion forums: a learning analytics approach." Proceedings of the fifth international conference on learning analytics and knowledge. 2015.
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