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Student assessment is one of the most fundamental tasks in the field of AIEducation (AIEd). One of the most common approach to student assessment isKnowledge Tracing (KT), which evaluates a student's knowledge state bypredicting whether the student will answer a given question correctly or not.However, in the context of multiple choice (polytomous) questions, conventionalKT approaches are limited in that they only consider the binary (dichotomous)correctness label (i.e., correct or incorrect), and disregard the specificoption chosen by the student. Meanwhile, Option Tracing (OT) attempts to modela student by predicting which option they will choose for a given question, butoverlooks the correctness information. In this paper, we proposeDichotomous-Polytomous Multi-Task Learning (DP-MTL), a multi-task learningframework that combines KT and OT for more precise student assessment. Inparticular, we show that the KT objective acts as a regularization term for OTin the DP-MTL framework, and propose an appropriate architecture for applyingour method on top of existing deep learning-based KT models. We experimentallyconfirm that DP-MTL significantly improves both KT and OT performances, andalso benefits downstream tasks such as Score Prediction (SP).

No Task Left Behind: Multi-Task Learning of Knowledge Tracing and Option Tracing for Better Student Assessment