  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 tracing and dropout prediction. To
apply temporal context within the sequence, this study introduces a novel
student modeling framework, SAICL: \textbf{s}tudent modeling with
\textbf{a}uxiliary \textbf{i}nteraction-level \textbf{c}ontrastive
\textbf{l}earning. In detail, SAICL can utilize both proposed
self-supervised/supervised interaction-level contrastive objectives: MilCPC
(\textbf{M}ulti-\textbf{I}nteraction-\textbf{L}evel \textbf{C}ontrastive
\textbf{P}redictive \textbf{C}oding) and SupCPC (\textbf{Sup}ervised
\textbf{C}ontrastive \textbf{P}redictive \textbf{C}oding). While previous
sample-level contrastive methods for student modeling are highly dependent on
data augmentation methods, the SAICL is free of data augmentation while showing
better performance in both self-supervised and supervised settings. By
combining cross-entropy with contrastive objectives, the proposed SAICL
achieved comparable knowledge tracing and dropout prediction performance with
other state-of-art models without compromising inference costs.
