Investigation of recent advances in exercise-enhanced knowledge tracing models

Abstract

Exercising plays a significant role in Knowledge Tracing (KT), however, the majority of KT models struggle to effectively extract the abundant information embedded within students' exercise histories, leading to a prevalent issue of information erosion. Hence, it is vital for exploring methods to mine the data from the exercises to better forecast students' future performance. This paper is a review that aims to discuss recent improvements in KT models on mining information existing in exercises. This paper will briefly discuss DKT, a popular approach to knowledge tracing, and its limitation on mining information from exercises. And then, this review will introduce four exercise-enhanced knowledge tracing models including EERNN, EKT, HGKT and Concept-Aware Deep Knowledge Tracing. EERNN and EKT can track students knowledge acquisition by using vectors to represent knowledge concepts. HGKT goes a step further by examining the interconnectedness among different exercises based on EERNN and EKT. Concept-Aware DKT is an improvement of DKVMN, the other approach of KT, by considering the influence of knowledge concepts and corresponding exercises in more detail. Finally, the applications of exercise-enhanced KT models will be covered.