ERIC Number: ED630857
Record Type: Non-Journal
Publication Date: 2023
Pages: 11
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: N/A
A Data Mining Approach for Detecting Collusion in Unproctored Online Exams
Langerbein, Janine; Massing, Till; Klenke, Jens; Striewe, Michael; Goedicke, Michael; Hanck, Christoph
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023)
Due to the precautionary measures during the COVID-19 pandemic many universities offered unproctored take-home exams. We propose methods to detect potential collusion between students and apply our approach on event log data from take-home exams during the pandemic. We find groups of students with suspiciously similar exams. In addition, we compare our findings to a proctored comparison group. By this, we establish a rule of thumb for evaluating which cases are "outstandingly similar", i.e., suspicious cases. [For the complete proceedings, see ED630829.]
Descriptors: Information Retrieval, Pattern Recognition, Data Analysis, Information Technology, Computer Assisted Testing, Supervision, COVID-19, Pandemics, Electronic Learning, Foreign Countries, College Students
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Germany
Grant or Contract Numbers: N/A