Cognitive Diagnosis for Programming Domains

DOI

10.2991/978-94-6463-242-2_19How to use a DOI?

Keywords

cognitive diagnosis; programming; knowledge mastery

Abstract

Cognitive diagnosis plays an important role in intelligent educational scenarios as a method that can reveal students’ knowledge mastery. Existing cognitive diagnostic methods are mainly applicable to objective questions in core subject areas (e.g., mathematics); however, in the field of programming, where the questions are subjective, no research has been conducted to apply cognitive diagnostics to analyze and assess the impact of students’ subjective answers on students’ knowledge acquisition. Therefore, we explored how to design a cognitive diagnostic approach that can be applied in the programming domain. In this paper, we design a neural network-based cognitive diagnostic model, PECDM, where our approach not only exploits the interactions between the student factor and the exercise factor, but also includes the student answer source code and the difficulty of the exercise among the diagnostic factors, further considering that the student’s knowledge mastery can be captured from the student’s subjective answers (source code), and finally uses multiple fully connected layers to interactions are modeled, resulting in more accurate and interpretable diagnostics. We conduct relevant experiments on the codeforces dataset, and the experimental results show that the accuracy of our designed PECDM model is about 95%, which is better in terms of accuracy, rationality, and interpretability when compared with other cognitive diagnostic models.

Copyright

© 2024 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.

Download article (PDF)

TY  - CONF
AU  - Yongfeng Huang
AU  - Kaiyuan Wang
PY  - 2023
DA  - 2023/09/22
TI  - Cognitive Diagnosis for Programming Domains
BT  - Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
PB  - Atlantis Press
SP  - 153
EP  - 163
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-242-2_19
DO  - 10.2991/978-94-6463-242-2_19
ID  - Huang2023
ER  -