Learning Analytics: Classroom Analytics

This lecture covers the transition from learning analytics to classroom analytics, emphasizing the importance of adaptive, personalized, and individualized instruction based on students' behaviors and knowledge states. It explores methods such as cognitive diagnosis, learner modeling, and Bayesian knowledge tracing to infer and predict students' knowledge states. The lecture also delves into the analysis of handwriting difficulties and the use of machine learning models to detect dysgraphia.