Revisiting Knowledge Tracing: A Simple and Powerful Model

Xiaoxuan Shen, Fenghua Yu, yaqi Liu, Ruxia Liang, Qian Wan, Kai Yang, Jianwen Sun

Abstract: Advances in multimedia technology and its widespread application in education have made multimedia learning increasingly important. Knowledge Tracing (KT) is the key technology for achieving adaptive multimedia learning, aiming to monitor the degree of knowledge acquisition and predict students' performance during the learning process. Current KT research is dedicated to enhancing the performance of KT problems by integrating the most advanced deep learning techniques. However, this has led to increasingly complex models, which reduce model usability and divert researchers' attention away from exploring the core issues of KT. This paper aims to tackle the fundamental challenges of KT tasks, including the knowledge state representation and the core architecture design, and investigate a novel KT model that is both simple and powerful. We have revisited the KT task and propose the ReKT model. First, taking inspiration from the decision-making process of human teachers, we model the knowledge state of students from three distinct perspectives: questions, concepts, and domains. Second, building upon human cognitive development models, such as constructivism, we have designed a Forget-Response-Update (FRU) framework to serve as the core architecture for the KT task. The FRU is composed of just two linear regression units, making it an extremely lightweight framework. Extensive comparisons were conducted with 22 state-of-the-art KT models on 7 publicly available datasets. The experimental results demonstrate that ReKT outperforms all the comparative methods in question-based KT tasks, and consistently achieves the best (in most cases) or near-best performance in concept-based KT tasks. Furthermore, in comparison to other KT core architectures like Transformers or LSTMs, the FRU achieves superior prediction performance with approximately only 38\% computing resources. Through an exploration of the ReKT model that is both simple and powerful, is able to offer new insights to future KT research. The code is in the supplementary materials.

Primary Subject Area: [Experience] Multimedia Applications

Secondary Subject Area: [Engagement] Multimedia Search and Recommendation, [Engagement] Emotional and Social Signals, [Engagement] Summarization, Analytics, and Storytelling

Relevance To Conference: This work contributes to multimedia/multimodal processing by highlighting the pivotal role of Knowledge Tracing (KT) technology in achieving adaptive multimedia learning. By integrating KT into educational experiences, this research enables real-time monitoring of students' knowledge acquisition and performance prediction during the learning process. This not only enhances the customization of educational experiences but also provides valuable insights for educators to tailor teaching strategies and offer personalized guidance. Furthermore, the emphasis on adaptive multimedia learning underscores the importance of leveraging multimedia technology to tailor educational experiences to individual learning needs. Ultimately, by advancing the understanding and implementation of KT technology in educational settings, this work enhances the efficacy and efficiency of multimedia/multimodal processing, fostering more personalized and effective learning experiences for students.

Submission Number: 3009