A systematic review of academic dishonesty in online learning environments

Background During the COVID‐19 pandemic, online learning has played an increasingly crucial role in the educational system. Academic dishonesty (AD) in online learning is a challenging problem that represents a complex psychological and social phenomenon for learners. However, there is a lack of com...

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Bibliographic Details
Published in:Journal of computer assisted learning 2022-08, Vol.38 (4), p.907-928
Main Authors: Chiang, Feng‐Kuang, Zhu, Dan, Yu, Wenhao
Format: Article
Language:English
Subjects:
academic dishonesty
Academic misconduct
Applied Behavior Analysis
behaviour engineering model
Cheating
Classification
Complexity
Content Analysis
COVID-19
Dishonesty
Distance Education
Distance learning
Electronic Learning
Guidance
Intervention
Literature reviews
Online Courses
online learning
Pandemics
Research Reports
Systematic review
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Summary:Background During the COVID‐19 pandemic, online learning has played an increasingly crucial role in the educational system. Academic dishonesty (AD) in online learning is a challenging problem that represents a complex psychological and social phenomenon for learners. However, there is a lack of comprehensive and systematic reviews of AD in online learning environments. Objectives This study presents a systematic study of AD in online learning environments to delineate its trends and uncover potential areas for further research. Methods We conducted this review based on various sources of evidence‐based research and followed the guidelines of the PRISMA statement and procedure for selection. After the exclusion criteria were employed, 59 eligible articles were selected and then analysed in a descriptive overview. Two frameworks were identified in the structured content analysis to analyse these articles. One was the framework of Gilbert's Behaviour Engineering Model (BEM), and the other was the types of interventions for online AD, where 36 articles were analysed. Results and Conclusions The descriptive results showed that most studies used quantitative methods and focused on students. The analysis results of influencing factors under the BEM framework showed that the category of environment support and tools accounts for the largest proportion. And the types of interventions for online AD we classified include individual AD & high technological complexity, individual AD & low technological complexity, collective AD & high technological complexity, and collective AD & low technological complexity. These findings provide a comprehensive understanding and guidance of AD in the online environment for relevant managers, designers and developers. Lay Description 1. Systematic review of academic dishonesty in online learning environments. 2. To review followed the PRISMA procedure for selecting 59 eligible articles. 3. Factors influencing AD behaviour in online learning under the BEM framework. 4. To explore the types of interventions for AD in online environments.
ISSN:0266-4909
1365-2729
DOI:10.1111/jcal.12656