EAKT: Embedding Cognitive Framework with Attention for Interpretable Knowledge Tracing
Yanjun Pu, Wenjun Wu, Tianhao Peng, Fang Liu, Yu Liang, Xin Yu, Ruibo Chen, Pu Feng · Research Square · 2022
Abstract Recently, deep neural network-based cognitive models such as deep knowledge tracing have been introduced into the field of learning analytics and educational data mining. Despite an accurate predictive performance of such models, it is challenging to interpret their behaviors and obtain an intuitive insight into latent student learning status. To address these challenges, this paper proposes a new learner modeling framework named the EAKT, which embeds a structured cognitive model into a transformer. In this way, the EKAT not only can achieve an excellent prediction result of learning outcome but also can depict students’ knowledge state on a multi-dimensional knowledge component(KC) level. By performing the fine-grained analysis of the student learning process, the proposed framework provides better explanatory learner models for designing and implementing intelligent tutoring systems. The proposed EAKT is verified by experiments. The experimental results show that the EAKT can accurately trace changes in the students’ knowledge state and predict the future performance of student learning.