Compared to traditional education, online learning environments present some problems along with advantages in data collection, independence from time and space, etc. One of the problems is plagiarism in examinations and assignments. Plagiarism is a serious problem that arises in many aspects of education. If plagiarism is not handled adequately, plagiarists will benefit more with less effort.
Various methods have been proposed to prevent plagiarism. For online task execution, technical solutions such as plagiarism detection tools or various task types (e.g., face-to-face queries, task personalization) have been proposed. In some educational institutions, for example, the assignments sent by students need to be analyzed by means of plagiarism detection software. However, students tend to be more versatile and subtle in their plagiarism.
Kim Hyok, a researcher at the Faculty of Distance Education, has worked to accurately detect plagiarized drawings and reduce learners’ plagiarism behavior in online assignments by analyzing the plagiarism characteristics of machine drawings.
First, he compared the drawing images sent by students in the past three years to find plagiarism characteristics and provided feedback on the plagiarized ones to ensure that the plagiarism features were fully studied. Then, he provided feedback on the drawings presented in the last one year to confirm whether plagiarized drawings were fully detected and whether it affects the reduction of plagiarism performance of students.
He found that after feedback was provided, the detection ratio of plagiarized drawings was 89.2% and plagiarism behavior was reduced to 37.03%.
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