Deep-IRT with a temporal convolutional network for performance prediction

Abstract

Knowledge Tracing (KT) has been studied actively to facilitate effective student learning with optimal support based on student learning data. Important tasks of KT are tracing the evolving abilities of students and predicting their performance accurately. Recently, Deep item response theory (Deep-IRT) methods combining deep learning and item response theory have been proposed to provide educational parameter interpretability and to achieve accurate performance prediction. However, earlier Deep-IRTs estimate a student’s ability value using only a most recent latent ability parameter. Because current ability estimates cannot reflect past ability history data adequately, the parameter interpretability and the performance prediction accuracy might be impaired or biased. To overcome this difficulty, we propose a new DeepIRT with a temporal convolutional network that convolves past multidimensional ability states. The proposed method stores the student’s latent multi-dimensional abilities at each time point and produces a result that comprehensively reflects the long-term ability history data during performance prediction.