{"title":"Simulating Similarities to Maintain Academic Integrity in Programming","authors":"Oscar Karnalim","doi":"10.15388/infedu.2024.21","DOIUrl":null,"url":null,"abstract":"Programming students need to be informed about plagiarism and collusion. Hence, we developed an assessment submission system to remind students about the matter. Each submission will be compared to others and any similarities that do not seem a result of coincidence will be reported along with their possible reasons. The system also employs gamification to promote early and unique submissions. Nevertheless, the system might put unnecessary pressure as coincidental similarities can still be reported. Further, it does not specifically cover self-plagiarism. We revisit the system and shift our focus to report simulated similarities from student own submission instead of reporting actual similarities across submissions. According to our evaluation with 390 students and five quasi-experiments, students with simulated similarities are slightly more aware of plagiarism and collusion, self-plagiarism in particular. Their awareness of the matter is somewhat acceptable (around 75%) and they see the benefits of our assessment submission system.","PeriodicalId":45270,"journal":{"name":"Informatics in Education","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Informatics in Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15388/infedu.2024.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
Abstract
Programming students need to be informed about plagiarism and collusion. Hence, we developed an assessment submission system to remind students about the matter. Each submission will be compared to others and any similarities that do not seem a result of coincidence will be reported along with their possible reasons. The system also employs gamification to promote early and unique submissions. Nevertheless, the system might put unnecessary pressure as coincidental similarities can still be reported. Further, it does not specifically cover self-plagiarism. We revisit the system and shift our focus to report simulated similarities from student own submission instead of reporting actual similarities across submissions. According to our evaluation with 390 students and five quasi-experiments, students with simulated similarities are slightly more aware of plagiarism and collusion, self-plagiarism in particular. Their awareness of the matter is somewhat acceptable (around 75%) and they see the benefits of our assessment submission system.
期刊介绍:
INFORMATICS IN EDUCATION publishes original articles about theoretical, experimental and methodological studies in the fields of informatics (computer science) education and educational applications of information technology, ranging from primary to tertiary education. Multidisciplinary research studies that enhance our understanding of how theoretical and technological innovations translate into educational practice are most welcome. We are particularly interested in work at boundaries, both the boundaries of informatics and of education. The topics covered by INFORMATICS IN EDUCATION will range across diverse aspects of informatics (computer science) education research including: empirical studies, including composing different approaches to teach various subjects, studying availability of various concepts at a given age, measuring knowledge transfer and skills developed, addressing gender issues, etc. statistical research on big data related to informatics (computer science) activities including e.g. research on assessment, online teaching, competitions, etc. educational engineering focusing mainly on developing high quality original teaching sequences of different informatics (computer science) topics that offer new, successful ways for knowledge transfer and development of computational thinking machine learning of student''s behavior including the use of information technology to observe students in the learning process and discovering clusters of their working design and evaluation of educational tools that apply information technology in novel ways.