Automated Search Improves Logistic Knowledge Tracing, Surpassing Deep Learning in Accuracy and Explainability

This paper describes how to discover simple logistic regression models that outperform more complex approaches such as Deep Knowledge Tracing (DKT) and Self-Attentive Knowledge Tracing (SAKT). Creating student models is done either by expert selection of the appropriate terms, beginning with models as simple as Item Response Theory (IRT) or Additive Factors Model (AFM) or with more "black box" approaches like DKT, in which the model discovers student features. We demonstrate how feature search approaches (i.e., stepwise selection or Least Absolute Shrinkage and Selection Operator (LASSO)) can discover superior models that are explainable. Such automatic methods of model creation offer the possibility of better student models with reduced complexity and better fit, in addition to relieving experts from the burden of searching for better models by hand with possible human error. Our new functions are part of the preexisting R package Logistic Knowledge Tracing (LKT). We demonstrate our search methods with three datasets in which research-supported features (e.g., counts of success, elapsed time, recent performance) are computed at multiple levels (student, knowledge component (KC), item) and input to stepwise regression and LASSO methods to discover the best-fitting regression models. The approach was intended to balance accuracy and explainability. However, somewhat surprisingly, both stepwise regression and LASSO found regression models that were both simpler and more accurate than DKT, SAKT, and Interpretable Knowledge Tracing (IKT) in all datasets, typically requiring multiple orders of magnitude fewer parameters than alternatives.