  In computer-aided education and intelligent tutoring systems, knowledge
tracing (KT) raises attention due to the development of data-driven learning
methods, which aims to predict students' future performance given their past
question response sequences to trace their knowledge states. However, current
deep learning approaches only focus on enhancing prediction accuracy, but
neglecting the discrimination imbalance of responses. That is, a considerable
proportion of question responses are weak to discriminate students' knowledge
states, but equally considered compared to other discriminative responses, thus
hurting the ability of tracing students' personalized knowledge states. To
tackle this issue, we propose DR4KT for Knowledge Tracing, which reweights the
contribution of different responses according to their discrimination in
training. For retaining high prediction accuracy on low discriminative
responses after reweighting, DR4KT also introduces a discrimination-aware score
fusion technique to make a proper combination between student knowledge mastery
and the questions themselves. Comprehensive experimental results show that our
DR4KT applied on four mainstream KT methods significantly improves their
performance on three widely-used datasets.
