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A Method for Cheating Indication in Unproctored On-Line Exams.
Dan Komosny1, Saeed Ur Rehman2
1Department of Telecommunications, Brno University of Technology, 616 00 Brno, Czech Republic.
Sensors (Basel, Switzerland)
|January 22, 2022
Summary
This study introduces an automated method to detect cheating in online exams by analyzing student IP geolocation and online activity. The system identifies potential academic dishonesty in unproctored assessments, aiding educators in maintaining exam integrity.
Area of Science:
- Educational Technology
- Computer Science
- Criminology
Background:
- The COVID-19 pandemic necessitated a rapid shift to online education, including assessments.
- Manual detection of student dishonesty in unproctored online exams presents a significant workload for academics.
- Existing methods for ensuring academic integrity in remote learning environments are often insufficient.
Purpose of the Study:
- To develop and validate an automated method for detecting cheating in unproctored online examinations.
- To assess the feasibility of using student online traces, including IP geolocation, for identifying imposters.
- To quantify and compare cheating risk scores across different courses and academic terms.
Main Methods:
- Analysis of student online traces logged by distance education systems.
- Utilization of customized IP geolocation data to derive a cheating risk score.
- Application of the method to approximately 3600 students across 22 courses with unproctored online exams.
Main Results:
- The study successfully applied the developed method to identify cheating risk scores among a large student cohort.
- Examples of indicated cheating instances were presented, demonstrating the method's practical application.
- Cheating risk scores were compared across four academic terms, including periods of university closure due to COVID-19.
Conclusions:
- The proposed automated method effectively indicates potential cheating in unproctored online exams.
- The system provides valuable data for selecting students for knowledge re-validation and comparing cheating trends.
- The findings offer insights into academic integrity challenges exacerbated by the shift to remote learning during the COVID-19 pandemic.
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