ACKT : Self-Attentive Convolutional Model for Knowledge Tracing
Mahsa Abazari Kia, Alexandros Koliousis · Procedia Computer Science · 2024
The task of knowledge tracing involves establishing a model that characterizes the mastery level of individual students in relation to knowledge concepts (KCs), as they engage with a sequence of learning activities. Each student’s knowledge is modelled by estimating the student’s performance on the learning activities. This domain of research holds significant importance in the development of personalized learning platforms for students. However, within the current landscape, the majority of existing Knowledge Tracing (KT) methods exhibit a gap in their approach, as they often fail to incorporate two pivotal factors simultaneously: the in-dividualization of student characteristics (i.e., the prior knowledge and learning rates) and the interconnectedness between KCs. In essence, these existing approaches tend to disregard the intrinsic variations in students’ prior knowledge and learning paces, which invariably differ from one student to another. In this study, our focus lies in predicting student performance through modelling his/her knowledge state, while also extracting dependencies between KCs and his/her prior learning interactions. To this end, we propose a novel self Attentive Convolutional Knowledge Tracing (ACKT) method for analyzing continuous learning interactions of students. Extensive experiments on several real-world benchmark datasets show that ACKT could obtain better knowledge tracing results and it outperforms existing KT methods for predicting future learner responses.