Papers › Accuracy-aware Deep Knowledge Tracing with Knowledge State Vector Loss
Accuracy-aware Deep Knowledge Tracing with Knowledge State Vector Loss
Qiushi Pan, Taro Tezuka
In major e-learning platforms such as intelligent tutoring systems (ITSs) and massive open online courses (MOOCs), the students are often recommended what course materials to take based on their past interactions. Knowledge Tracing (KT) is the task of modeling students' academic abilities. Given a sequence of student's learning history, it predicts how well they will perform in the next interaction. Deep Knowledge Tracing (DKT) uses a recurrent neural network (RNN) to capture the underlying structure of the student's understanding. In this paper, we point out the accuracy rate problem that the model won't reproduce the accuracy ratio. This is a limitation of the existing loss function in DKT that it only learns the probability of correctly answering a problem in the next interaction. We introduced the Knowledge State Vector loss, which captures the accuracy rate of all knowledge concepts, to measure and train the model.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections