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PageFocus: Using paradata to detect and prevent cheating on online achievement tests
Birk Diedenhofen1, Jochen Musch2
1Department of Experimental Psychology, University of Düsseldorf, Universitaetsstrasse 1, Building 23.03, 40225, Düsseldorf, Germany. birk.diedenhofen@uni-duesseldorf.de.
Behavior Research Methods
|August 31, 2016
Summary
PageFocus, a JavaScript tool, effectively detects and reduces cheating on online achievement tests by monitoring page abandonment. It proved highly accurate in identifying test-takers who looked up answers, safeguarding academic integrity.
Area of Science:
- Computer Science
- Educational Technology
- Psychology
Background:
- Unproctored online achievement tests face significant validity threats due to cheating.
- Developing reliable methods to detect and prevent academic dishonesty in digital learning environments is crucial.
Purpose of the Study:
- To develop and evaluate PageFocus, a JavaScript tool designed to detect and deter cheating in online tests.
- To assess the effectiveness of PageFocus in identifying participants who abandon test pages to seek external information.
Main Methods:
- Two studies were conducted involving participants completing online knowledge tests.
- PageFocus, a JavaScript script, was implemented to track browser tab/window switching.
- Different incentive structures (cheating allowed, performance-related rewards, lottery, no incentive) were employed across studies.
Main Results:
- PageFocus demonstrated high sensitivity and specificity in detecting page abandonment.
- The tool successfully reduced cheating incidents by alerting participants.
- Performance-related incentives increased cheating, but only on easily searchable items.
Conclusions:
- PageFocus is a reliable tool for detecting and mitigating cheating in online achievement tests.
- The implementation of PageFocus is recommended for maintaining the integrity of online assessments.
- Understanding the impact of incentives on cheating behavior is vital for test design.
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