Remembering is Not Applying: Interpretable Knowledge Tracing for Problem-solving Processes

Tao Huang, Xinjia Ou, Yanghuali, Shengze Hu, Jing Geng, Junjie Hu, Zhuoran Xu

Abstract: Knowledge Tracing (KT) is a critical service in distance education, predicting students' future performance based on their responses to learning resources. The reasonable assessment of the knowledge state, along with accurate response prediction, is crucial for KT. However, existing KT methods prioritize fitting results and overlook attention to the problem-solving process. They equate the knowledge students memorize before problem-solving with the knowledge that can be acquired or applied during problem-solving, leading to dramatic fluctuations in knowledge states between mastery and non-mastery, with low interpretability. This paper explores knowledge transformation in problem-solving and proposes an interpretable model, Problem-Solving Knowledge Tracing (PSKT). Specifically, we first present a knowledge-centered problem representation that enhances its expression by adjusting problem variability. Then, we meticulously designed a Sequential Neural Network (SNN) with three stages: (1) Before problem-solving, we model students' personalized problem space and simulate their acquisition of problem-related knowledge through a gating mechanism. (2) During problem-solving, we evaluate knowledge application and calculate response with a four-parameter IRT. (3) After problem-solving, we quantify student knowledge internalization and forgetting using an incremental indicator. The SNN, inspired by problem-solving and constructivist learning theories, is an interpretable model that attributes learner performance to subjective problems (difficulty, discrimination), objective knowledge (knowledge acquisition and application), and behavior (guessing and slipping). Finally, extensive experimental results demonstrate that PSKT has certain advantages in predicting accuracy, assessing knowledge states reasonably, and explaining the learning process.

Primary Subject Area: [Experience] Multimedia Applications

Secondary Subject Area: [Engagement] Multimedia Search and Recommendation

Relevance To Conference: In distance education, personalized learning and effective content delivery are crucial. Knowledge tracing is an important research area in distance education, aiming to enhance the efficiency and effectiveness of learning through digital multimedia technologies. We propose a knowledge tracing method based on problem-solving processes, which accurately assesses students' knowledge state by modeling their multiple behavioral data and learning performance in online learning systems. Our work can help customize teaching methods and multimedia content for both teachers and students in distance education, aiming to improve engagement and learning outcomes in online learning systems. We hope that our submission will make a positive contribution to multimedia research at the conference.

Submission Number: 2319