Work-in-Progress: Syntactic Code Similarity Detection in Strongly Directed Assessments
Autor: | Oscar Karnalim, Simon, Gisela Kurniawati, Rossevine Artha Nathasya, Maresha Caroline Wijanto, Mewati Ayub |
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Rok vydání: | 2021 |
Předmět: |
business.industry
Computer science 05 social sciences 050301 education 02 engineering and technology Work in process Data structure computer.software_genre Semantics Raising (linguistics) Consistency (database systems) Semantic similarity 020204 information systems Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Task analysis Artificial intelligence business 0503 education computer Natural language processing |
Zdroj: | EDUCON |
DOI: | 10.1109/educon46332.2021.9454152 |
Popis: | When checking student programs for plagiarism and collusion, many similarity detectors aim to capture semantic similarity. However, they are not particularly effective for strongly directed assessments, in which the student programs are expected to be semantically similar. A detector focusing on syntactic similarity might be useful, and this paper reports its effectiveness on programming assessment tasks collected from algorithms and data structures courses in one academic semester. Our study shows that syntactic similarity detection is more effective than its semantic counterpart in strongly directed assessments, with some irregular similarity patterns being useful for raising suspicion. We also tested whether take-home assessments have higher similarity than in-class assessments, and confirmed that hypothesis. Consistency of the findings will be further validated on other courses with strongly directed assessments, and a syntactic similarity detector specifically tailored for strongly directed assessments will be proposed. |
Databáze: | OpenAIRE |
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