Problem Solving-Oriented Programming Knowledge Tracing from Behavior to Thought
Tao Huang, Linxia Tang, Huali Yang, Xinjia Ou, Shengze Hu, Jing Geng, Junjie Hu · 2025
Programming knowledge tracing (programming KT) aims to analyze the dynamic programming states in solving problems based on historical behaviors and predict future performance. In programming, a student’s thought process can lead to multiple solutions for the same problem. However, current programming KT models attribute learners’ responses only to knowledge mastery and ability, overlooking thought factors, which creates a contradiction between data fitting and the rationalization of the model inference process. To address this, we propose a problem solving-oriented programming KT (SPKT) method that incorporates programming knowledge, computational thinking, and solving ability to improve attribution accuracy. Specifically, we designed a dual-channel attention network based on the principle of initiative gain to retrieve knowledge. Additionally, we utilized the edit tree distance algorithm to capture fine-grained trajectory representations and employed a redress mechanism with gating to update abilities based on code information. Experiments demonstrate SPKT’s superiority.