Graph semantic similarity-based automatic assessment for programming exercises

This paper proposes an algorithm for the automatic assessment of programming exercises. The algorithm assigns assessment scores based on the program dependency graph structure and the program semantic similarity, but does not actually need to run the student’s program. By calculating the node simila...

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Veröffentlicht in:Scientific reports Jg. 14; H. 1; S. 10530 - 14
Hauptverfasser: Xiang, Chengguan, Wang, Ying, Zhou, Qiyun, Yu, Zhen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 08.05.2024
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Schlagworte:
639/705/117
639/705/794
Algorithms
Artificial intelligence
Automatic assessment
Automation
Big Data
Efficiency
Humanities and Social Sciences
Internet
Learning
Methods
multidisciplinary
Program dependency graph
Program semantics
Programming exercises
Programming languages
Python
Science
Science (multidisciplinary)
Semantic analysis
Semantics
Similarity
Software
Students
Teachers
Teaching
Program dependency graph
Similarity
Programming exercises
Automatic assessment
Program semantics
ISSN:2045-2322, 2045-2322
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Zusammenfassung:This paper proposes an algorithm for the automatic assessment of programming exercises. The algorithm assigns assessment scores based on the program dependency graph structure and the program semantic similarity, but does not actually need to run the student’s program. By calculating the node similarity between the student’s program and the teacher’s reference programs in terms of structure and program semantics, a similarity matrix is generated and the optimal similarity node path of this matrix is identified. The proposed algorithm achieves improved computational efficiency, with a time complexity of O ( n 2 ) for a graph with n nodes. The experimental results show that the assessment algorithm proposed in this paper is more reliable and accurate than several comparison algorithms, and can be used for scoring programming exercises in C/C++, Java, Python, and other languages.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-61219-8