Embedding cognitive framework with self-attention for interpretable knowledge tracing.

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 EAKT 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 prov

SUBMITTER: Pu Y 

PROVIDER: S-EPMC9584970 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature