Different parameters - same prediction: An analysis of learning curves


Using data from student use of educational technologies to evaluate and improve cognitive models of learners is now a common approach in EDM. Such naturally occurring data poses modeling challenges when non-random factors drive what data is collected. Prior work began to explore the potential parameter estimate biases that may result from data from tutoring systems that employ a mastery learning mechanism whereby poorer students get assigned tasks that better students do not. We extend that work both by exploring a wider set of modeling techniques and by using a data set with additional observations of longer-term retention that provide a check on whether judged mastery is maintained. The data set at hand contains math learning data from children with and without developmental dyscalculia. We test variations on logistic regression, including the Additive Factors Model and others explicitly designed to adjust for mastery-based data, as well as Bayesian Knowledge Tracing (BKT). We find these models produce similar prediction accuracy (though BKT is worse), but have different parameter estimation patterns. We discuss implications for use and interpretation of these different models.

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@inproceedings{Kser2014DifferentP, title={Different parameters - same prediction: An analysis of learning curves}, author={Tanja K{\"a}ser and Kenneth R. Koedinger and Markus H. Gross}, booktitle={EDM}, year={2014} }