Towards Interpretability of Attention-Based Knowledge Tracing Models

Resumo


Knowledge Tracing (KT) models based on attention mechanisms have demonstrated in literature the capability to predict student performance more accurately than previous models in some datasets. However, they fail to directly infer student knowledge. In this paper, we apply a proposed extension already seen in KT literature in order to infer latent knowledge to these models. We apply the extension to four different attention-based KT models, to investigate whether these models can better infer the knowledge outside the learning system than previous models. We find that attention-based models can generate better knowledge estimate correlations with student’s scores than the previous models.
Palavras-chave: Deep knowledge tracing, Attention-based knowledge tracing, Adaptive learning

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Publicado
16/11/2022
RODRIGUES, Thales B. S. F.; DE SOUZA, Jairo F.; BERNARDINO, Heder S.; BAKER, Ryan S.. Towards Interpretability of Attention-Based Knowledge Tracing Models. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 33. , 2022, Manaus. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 810-821. DOI: https://doi.org/10.5753/sbie.2022.224685.