This paper addresses the challenge of managing extensive message exchanges in MOOC discussion forums by proposing a model to identify urgent posts requiring immediate instructor attention. By analyzing different feature sets and data mining techniques, the study finds that a limited number of linguistic features combined with select metadata can reliably classify urgent posts. This model can help instructors efficiently manage forum interactions, potentially reducing dropout rates and supporting better learning outcomes.
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Almatrafi, Omaima, Aditya Johri, and Huzefa Rangwala. "Needle in a haystack: Identifying learner posts that require urgent response in MOOC discussion forums." Computers & Education 118 (2018): 1-9.
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