Exploration of the Robustness and Generalizability of the Additive Factors Model

March 23, 2020

Abstract

Additive Factors Model is a widely used student model, which is primarily used for refining knowledge component models (Q-matrices). We explore the robustness and generalizability of the model. We explicitly formulate simplifying assumptions that the model makes and we discuss methods for visualizing learning curves based on the model. We also report on an application of the model to data from a learning system for introductory programming; these experiments illustrate possibly misleading interpretation of model results due to differences in item difficulty. Overall, our results show that greater care has to be taken in the application of the model and in the interpretation of results obtained with the model.

Citation

EFFENBERGER, Tomáš, Radek PELÁNEK a Jaroslav ČECHÁK. Exploration of the Robustness and Generalizability of the Additive Factors Model. In Proceedings of the 10th International Conference on Learning Analytics and Knowledge. New York, NY, USA: Association for Computing Machinery, 2020. s. 472-479, 8 s. ISBN 978-1-4503-7712-6. doi:10.1145/3375462.3375491.

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