Literaturnachweis - Detailanzeige
Autor/inn/en | Langerbein, Janine; Massing, Till; Klenke, Jens; Striewe, Michael; Goedicke, Michael; Hanck, Christoph |
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Titel | A Data Mining Approach for Detecting Collusion in Unproctored Online Exams [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023). |
Quelle | (2023), (11 Seiten)
PDF als Volltext |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Monographie |
Schlagwörter | Information Retrieval; Pattern Recognition; Data Analysis; Information Technology; Computer Assisted Testing; Supervision; COVID-19; Pandemics; Electronic Learning; Foreign Countries; College Students; Germany |
Abstract | 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.] (As Provided). |
Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2024/1/01 |