MLLM embeddings predict population-level video interaction peaks as cognitive load proxies, generalize across academic fields, and link to interpretable instructional design concepts via theory-coded features.
In: EMNLP Workshop on Analysis of Large Scale Social In- teraction in MOOCs (2014)
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Scalable and Explainable Learner-Video Interaction Prediction using Multimodal Large Language Models
MLLM embeddings predict population-level video interaction peaks as cognitive load proxies, generalize across academic fields, and link to interpretable instructional design concepts via theory-coded features.