Evolutionary Features for Mitigating Cold Starts in Logistic Knowledge Tracing

In educational systems, predictive models face significant challenges during initial deployment and when new students begin to use them or when new exercises are added to the system due to a lack of data for making initial inferences, often called the cold start problem. This paper tests logitdec and logitdecevol, "evolutionary" features within the Logistic Knowledge Tracing (LKT) framework. These features appear ideal to mitigate cold starts when there is very little data to train the model as well as when there is no prior data for the students and items. Logitdec, which is applied here to individual students, is a log-transformed running ratio that employs exponential decay to prioritize recent student performance. At the same time, logitdecevol is the same log-transformed ratio of successes and failures that uses prior observations for the knowledge component (KC) across all students. Evaluated on three datasets using temporal cross-validation, our results show that models composed with logitdec and logitdecevol outperform traditional methods (e.g., Additive Factors Model (AFM), Bayesian Knowledge Tracing (BKT), and Elo) in early prediction accuracy. The simplified 7-parameter LKT model outperformed alternatives with very little training data (e.g., hundreds of observations and fit better than alternatives even if they were trained on 10x more data. The proposed LKT model's simplicity (4 regression coefficients, 3 nonlinear parameters to compute input features) ensures interpretability, computational efficiency, and generalizability, making it ideal for deployment in systems that use knowledge tracing to guide pedagogy.