Abstract
Bayesian diagnosis tracing model (BDT) replaces the generic “wrong” response in the classical Bayesian knowledge tracing model (BKT) with a vector of procedure misconceptions. Using a novel dataset with actual student responses, this paper shows the BDT model has better interpretability of the latent factor and minor improvement in out-sample predictability in some specification than the BKT model.
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Feng, J., Zhang, B., Li, Y., Xu, Q. (2019). Bayesian Diagnosis Tracing: Application of Procedural Misconceptions in Knowledge Tracing. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_16
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