# Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing
## 5 Conclusions  
In this paper, GPPKT is proposed to address two significant challenges in programming knowledge tracing: the inaccurate representation of exercises and the neglect of student answer sequence information. A programming knowledge graph was constructed based on authoritative learning resources, and the RotatE-TA knowledge graph embedding method was introduced to vectorize exercise representations. The VAE was employed to model students' answer sequences on the same exercise, effectively capturing students' learning behavior. Additionally, students' learning ability differences were obtained through the IRT to enhance the information provided by students' answer sequences. LSTM and an attention mechanism were used to adaptively aggregate hidden states, while a gating mechanism was introduced to balance the student's historical and current knowledge state for performance prediction. Extensive experiments were conducted on two real-world programming datasets to evaluate GPPKT. The results demonstrate that GPPKT outperforms state-of-the-art methods in the field of programming knowledge tracing, with an average improvement of 9.03% in AUC and 2.02% in ACC across both datasets. The GPPKT model is expected to stimulate the development of KT research within the programming field. Key contributions of this work include the integration of knowledge graphs to enhance exercise representation, the application of VAE for modeling student behavior, and the utilization of IRT to differentiate student learning abilities. These innovations have been shown to significantly improve the accuracy and effectiveness of programming knowledge tracing. In future work, we will integrate recommendation algorithms to more accurately suggest exercises and learning materials to students, aiming to achieve intelligent and adaptive programming learning. This has important implications for personalized learning and could enhance the effectiveness of programming education. Additionally, we plan to incorporate features such as code submissions and other relevant data into our model to further refine the understanding of student learning processes and outcomes.These advancements in programming knowledge tracing have the potential to transform educational practices by providing educators with deeper insights into student learning trajectories and enabling more targeted interventions. Our work not only contributes to the academic field but also offers practical applications for improving the quality of programming education. Figure 8: (**a**) Partial answer sequence of a student, (**b**) Answer sequence after VAE processing. **Author Contributions:** Conceptualization, J.P. and X.C. ; methodology, Z.D. and X.C. ; software, Z.D. ; validation, J.P. and Z.D. ; formal analysis, L.Y. and X.C. ; investigation, L.Y. ; resources, J.P. and L.Y. ; data curation, L.Y. ; writing--original draft preparation, Z.D. ; writing--review and editing, Z.D. and X.C. ; visualization, Z.D. and X.C. ; supervision, J.P.; project administration, J.P.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant 61802258, in part by the Natural Science Foundation of Shanghai under Grant 202R1455600, and the National Key Research and Development Program of China under Grant 2022 YFB4501704. **Instructional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. **Acknowledgments:** The authors would like to express sincere appreciation for the support provided by the Shanghai Engineering Research Center of Intelligent Education and Bigdata as well as the Research Base of Online Education for Shanghai Middle and Primary Schools.