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.
Highlights
Studying in university involves class attendance, individual work and group work.Students interact in and out of the classroom because of previous friendships, affinities or the need for social interaction to study and to be part of the university community.group work is usually inherent in the study process, both in the study of theory and in the implementation of practical exercises.Students communicate, help each other and exchange knowledge
Once we built our network with all the duly organised and labelled relationships of the students over the four academic years, we proceeded to the analysis of the results for each of the studies proposed in this research
Automatic plagiarism detection (MOSS), collected andand an an automatic plagiarism detection tooltool (MOSS), we we collected datadata onoono the the rerelationships among the solutions given by the students during each course to a whole set lationships among the solutions given by the students during each course to a whole set of programming assignments
Summary
Studying in university involves class attendance, individual work and group work. Help each other and exchange knowledge (and it has always been so, by other means). Teachers can encourage and promote group work, and they try to create situations in which collaboration is necessary, i.e., go beyond group work and foster communication, reflection and the construction of knowledge among students. All these technologies favour knowledge sharing and improve individual learning. Technology helps to maintain active learning communities
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