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Title: Automated Search for Logistic Knowledge Tracing Models
This paper presents a tool for creating student models in logistic regression. Creating student models has typically been done by expert selection of the appropriate terms, beginning with models as simple as IRT or AFM but more recently with highly complex models like BestLR. While alternative methods exist to select the appropriate predictors for the regression-based models (e.g., step-wise selection or LASSO), we are unaware of their application to student modeling. Such automatic methods of model creation offer the possibility of better student models with either reduced complexity or better fit, in addition to relieving experts from the burden of searching for better models by hand with possible human error. Our tool builds on top of the preexisting R package LKT. We explain our search methods with two datasets demonstrating the advantages of using the tool with stepwise regression and regularization (LASSO) methods to aid in feature selection. For the stepwise method using BIC, the models are simpler (due to the BIC penalty for parameters) than alternatives like BestLR with little lack of fit. For the LASSO method, the models can be made simpler due to the fitting procedure involving a regularization parameter that penalizes large absolute coefficient values. However, LASSO also offers the possibility of highly complex models with exceptional fit.  more » « less
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Proceedings of the 16th International Conference on Educational Data Mining, 17–27
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Sponsoring Org:
National Science Foundation
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