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I Explain, You Collaborate, He Cheats: An Empirical Study with Social Network Analysis of Study Groups in a Computer Programming Subject
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In: Applied Sciences ; Volume 11 ; Issue 19 (2021)
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Abstract:
Students interact with each other in order to solve computer science programming assignments. Group work is encouraged because it has been proven to be beneficial to the learning process. However, sometimes, collaboration might be confused with dishonest behaviours. This article aimed to quantitatively discern between both cases. We collected code similarity measures from students over four academic years and analysed them using statistical and social network analyses. Three studies were carried out: an analysis of the knowledge flow to identify dishonest behaviour, an analysis of the structure of the social organisation of study groups and an assessment of the relationship between successful students and social behaviour. Continuous dishonest behaviour in students is not as alarming as many studies suggest, probably due to the strict control, automatic plagiarism detection and high penalties for unethical behaviour. The boundary between both is given by the amount of similar content and regularity along the course. Three types of study groups were identified. We also found that the best performing groups were not made up of the best individual students but of students with different levels of knowledge and stronger relationships. The best students were usually the central nodes of those groups.
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Keyword:
dishonest behaviour; groups collaborative learning; higher education; knowledge sharing; learning communities; social network analysis; source-code similarity
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URL: https://doi.org/10.3390/app11199328
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Scientific knowledge percolation process and social impact: A case study on the biotechnology and microbiology perceptions on Twitter
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