CeKT: Knowledge Tracing for Predicting Collective Performance on Exercise Sequence

The integration of artificial intelligence has become a hot topic in the field of education. However, current studies primarily focus on personalization for learners, with the aim of accurately modeling learners’ knowledge level based on their learning history and providing better personalized services, while overlooking the needs of educators. In contrast to the focus on personalization of learners, educators place greater emphasis on accurately assessing collective performance and relative differences within a group, which serves as a qualitative measure of the teaching quality. In this study, we investigate collective knowledge tracing (CeKT), a method designed to estimate the average knowledge level of all students in a course based on a sequence of exercises. To achieve this objective, we propose a graph-based solution capable of estimating the average knowledge level solely from the exercise sequence as well as capturing the intrinsic structure of the exercise sequence (i.e., the sequential order of exercises and the repetition of exercises). Through experimental validation, we affirm that our approach enables a precise estimation of the average knowledge mastery of all students given an exercise sequence and also holds distinct value in three applications within the education domain.