Scalable and Explainable Learner-Video Interaction Prediction using Multimodal Large Language Models

1. 1Collect video content and learner interaction data
2. 2Preprocess data using multimodal large language models
3. 3Train a predictive model to forecast watching, pausing, skipping, and rewinding behavior
4. 4Evaluate model performance and interpret results to inform instructional design decisions

Data scientists and AI engineers on a team can benefit from this research as it provides a novel approach to predicting learner behavior, while instructional designers can use the insights to improve educational video content

💡 Multimodal large language models can be used to predict learner-video interaction and provide insights into cognitive load and instructional design quality

📹 Predict learner-video interactions with multimodal LLMs! 💡