Advanced Student Modeling

This lecture covers advanced student modeling techniques such as Bayesian Knowledge Tracing (BKT), Additive Factors Model (AFM), and Performance Factors Analysis (PFA). It explores the disadvantages of BKT, the integration of BKT and Item Response Theory (IRT), and the benchmarking of different student modeling techniques. The lecture also delves into neural networks, fully-connected neural networks, and recurrent neural networks, emphasizing their applications in classification, regression, and sequential data analysis. Additionally, it discusses Long-Short Term Memory (LSTM) networks and Deep Knowledge Tracing, providing insights into predicting future performance and modeling student knowledge over time.