Knowledge tracing: A brief overview of the advances done since 2021 using deep learning techniques

Abstract

This paper aims to describe briefly the direction followed by the models based on deep learning techniques attempting to solve the Knowledge Tracing (KT) task since 2021. The goal of the KT task is to model a student's knowledge level based on their responses to a series of activities (known as interactions) provided by a learning platform. A summary of the advances made during the last year within the research area will be presented in this manuscript, with a unique focus on those models that use neural networks to provide a solution. The interest in that segment of models stems from the fact that they have achieved the highest performance over solutions based on probabilistic or logistic models. This theoretical analysis facilitates the understanding of the current state of the art for new researchers of this area. Furthermore, a brief analysis of the classification will be made accompanied with a description of relevant models. The contribution includes the timeline chart followed by the KT models, a short comparison between recently proposed taxonomies for the models within the research area, a brief description of relevant models published since 2021 falling outside any category included in the taxonomies. Lastly a short discussion of findings, possible new applications of the research area and conceivable future directions are discussed.