Name
Title | ||
|---|---|---|
Analysis of Students' Online Interactions in the Covid Era from the Perspective of Anomaly Detection |
Abstract | ||
|---|---|---|
During the pandemic, most of the teaching has been done online. The lack of face-to-face interaction has many undesirable effects, including students being less focused, not receiving feedback on how they are approaching the current topic/task, and an increased risk of cheating. It is expected that those students with similarly graded assignments/exams would have similar interactions during online teaching sessions. The opposite is an anomaly, for better or worse. It is possible to find out if assignments/exams are legitimate by using anti-plagiarism tools or by carefully examining submissions, but it is time-consuming and only protects against one type of fraud. In this paper, we propose to apply anomaly detection techniques to the students' interactions to reduce the number of assignments/exams that need to be checked against fraud. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87872-6_30 | 14TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN SECURITY FOR INFORMATION SYSTEMS AND 12TH INTERNATIONAL CONFERENCE ON EUROPEAN TRANSNATIONAL EDUCATIONAL (CISIS 2021 AND ICEUTE 2021) |
Keywords | DocType | Volume |
Fraud detection, Anomaly detection, Class participation, Online teaching | Conference | 1400 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Otero | 1 | 552 | 24.66 |
| Luciano Sánchez | 2 | 42 | 6.85 |
| Luís A. Junco | 3 | 0 | 0.34 |
| Inés Couso | 4 | 850 | 69.91 |