This paper explores the use of BERT embeddings to classify urgent posts in MOOCs, helping teachers prioritize and respond to critical student queries more effectively. By fine-tuning BERT in combination with a multi-layer bi-directional Gated Recurrent Unit (GRU) model, the study achieves high classification accuracy with weighted F-scores of 91.9%, 91.0%, and 90.0% on the Stanford MOOC Posts dataset. This approach aids in managing large-scale student interactions, potentially reducing dropout rates and improving completion rates.
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Khodeir, Nabila A. "Bi-GRU urgent classification for MOOC discussion forums based on BERT." IEEE Access 9 (2021): 58243-58255.
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