ADAPT-MS achieves 0.66 RMSE unsupervised and 0.60 with 1000 labels on cross-platform MOOC satisfaction prediction by aligning representations and correcting platform biases.
By the numbers: Moocs in 2020,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CE 2years
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UNVERDICTED 2representative citing papers
TET-LLM predicts MOOC satisfaction early via temporal event transformers on behavior, LLM embeddings on text, and topic distributions, beating baselines at RMSE 0.82 and AUC 0.77 for 7-day forecasts.
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Cross-Platform Domain Adaptation for Multi-Modal MOOC Learner Satisfaction Prediction
ADAPT-MS achieves 0.66 RMSE unsupervised and 0.60 with 1000 labels on cross-platform MOOC satisfaction prediction by aligning representations and correcting platform biases.
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Early-Warning Learner Satisfaction Forecasting in MOOCs via Temporal Event Transformers and LLM Text Embeddings
TET-LLM predicts MOOC satisfaction early via temporal event transformers on behavior, LLM embeddings on text, and topic distributions, beating baselines at RMSE 0.82 and AUC 0.77 for 7-day forecasts.