SAICL: Student Modelling with Interaction-level Auxiliary Contrastive Tasks for Knowledge Tracing and Dropout Prediction. (arXiv:2210.09012v2 [cs.CY] UPDATED)

Knowledge tracing and dropout prediction are crucial for online education to
estimate students' knowledge states or to prevent dropout rates. While
traditional systems interacting with students suffered from data sparsity and
overfitting, recent sample-level contrastive learning helps to alleviate this
issue. One major limitation of sample-level approaches is that they regard
students' behavior interaction sequences as a bundle, so they often fail to
encode temporal contexts and track their dynamic changes, making it hard to
find optimal representations for knowledge …

arxiv dropout knowledge modelling prediction tracing