Details
| Original language | English |
|---|---|
| Article number | 57-135 |
| Pages (from-to) | 84-94 |
| Number of pages | 11 |
| Journal | INFORMS Transactions on Education |
| Volume | 23 |
| Issue number | 2 |
| Publication status | Published - 1 Oct 2021 |
Abstract
Keywords
- learning analytics, event mining, text mining, online assessment
ASJC Scopus subject areas
- Social Sciences(all)
- Education
- Business, Management and Accounting(all)
- Management Information Systems
- Decision Sciences(all)
- Management Science and Operations Research
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In: INFORMS Transactions on Education, Vol. 23, No. 2, 57-135, 01.10.2021, p. 84-94.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Who’s Cheating?
T2 - Mining Patterns of Collusion from Text and Events in Online Exams
AU - Cleophas, Catherine
AU - Hönnige, Christoph
AU - Meisel, Frank
AU - Meyer, Philipp
PY - 2021/10/1
Y1 - 2021/10/1
N2 - As the COVID-19 pandemic motivated a shift to virtual teaching, exams have increasingly moved online too. Detecting cheating through collusion is not easy when tech-savvy students take online exams at home and on their own devices. Such online at-home exams may tempt students to collude and share materials and answers. However, online exams’ digital output also enables computer-aided detection of collusion patterns. This paper presents two simple data-driven techniques to analyze exam event logs and essay-form answers. Based on examples from exams in social sciences, we show that such analyses can reveal patterns of student collusion. We suggest using these patterns to quantify the degree of collusion. Finally, we summarize a set of lessons learned about designing and analyzing online exams.
AB - As the COVID-19 pandemic motivated a shift to virtual teaching, exams have increasingly moved online too. Detecting cheating through collusion is not easy when tech-savvy students take online exams at home and on their own devices. Such online at-home exams may tempt students to collude and share materials and answers. However, online exams’ digital output also enables computer-aided detection of collusion patterns. This paper presents two simple data-driven techniques to analyze exam event logs and essay-form answers. Based on examples from exams in social sciences, we show that such analyses can reveal patterns of student collusion. We suggest using these patterns to quantify the degree of collusion. Finally, we summarize a set of lessons learned about designing and analyzing online exams.
KW - learning analytics
KW - event mining
KW - text mining
KW - online assessment
UR - http://www.scopus.com/inward/record.url?scp=85154534946&partnerID=8YFLogxK
U2 - 10.1287/ited.2021.0260
DO - 10.1287/ited.2021.0260
M3 - Article
VL - 23
SP - 84
EP - 94
JO - INFORMS Transactions on Education
JF - INFORMS Transactions on Education
SN - 1532-0545
IS - 2
M1 - 57-135
ER -