Dynamics-Aware Gated Graph Attention Neural Network for Student Program Classification and Knowledge Tracing

DOI

10.2991/978-94-6463-172-2_182How to use a DOI?

Keywords

Programming Knowledge Tracing; Dynamic Program Analysis; Online Judge; Artificial Intelligence in Education

Abstract

Faced with a large number of questions on the programming OJ (Online Judge) system, students are usually mindless when choosing questions, which is not conducive to helping students quickly improve their programming ability. Programming Knowledge Tracing (PKT) is a technology that dynamically traces students’ programming knowledge states using their historical learning data including submitted programs. Relying on PKT, OJ can find students’ unmastered knowledge points, and recommend questions examining these knowledge points to students, so as to help students overcome their weakness. However, existing program analysis modules in PKT models ignore dynamic information of program. Therefore, this paper proposes Dynamics-Aware Gated Graph Attention Neural Network (DGGANN), which inputs test cases of questions into program, obtains call frequency coefficients of every node in Abstract Syntax Tree (AST) through code coverage statistical tool, and introduces such call frequency information into process of program analysis. This paper applies DGGANN to two tasks in our experiments: classifying programs by functionalities and PKT. Experimental results show that our approach can achieve higher performance than the state-of-the-art models in both tasks on datasets of two well-known OJ systems named CodeForces and Libre.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tiancheng Jin
AU  - Liang Dou
AU  - Guang Yang
AU  - Aimin Zhou
AU  - Xiaoming Zhu
AU  - Chengwei Huang
PY  - 2023
DA  - 2023/06/30
TI  - Dynamics-Aware Gated Graph Attention Neural Network for Student Program Classification and Knowledge Tracing
BT  - Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023)
PB  - Atlantis Press
SP  - 1640
EP  - 1652
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-172-2_182
DO  - 10.2991/978-94-6463-172-2_182
ID  - Jin2023
ER  -