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.

Teaching by subjects and contents where students passively receive knowledge is increasingly obsolete. Universities are opting for teaching strategies supporting skills development to face the labor, social, environmental, and economic conditions afflicting us. Employers demand increasingly complex skills; universities have identified experiential learning as giving access to real situations and learning by doing. One of the most advanced strategies is Challenge-Based Learning (CBL). Through real problem situations, faculty and students collaborate to solve an established challenge, with or without external stakeholders. This educational advancement has been global and is developing graduates with international skills, which ensures a world-class standard. Here we report a global study carried out in universities from three different continents, and we analyze the implementations of CBL in educational programs through cases in Mexico, The Netherlands, Ireland, and China. Developing skills and competencies is evident, and CBL is a viable way to ensure the success of Higher Education graduates. Obstacles in the transformation of faculty towards CBL are a similar fence in all cases. For CBL, the path needs to be explored, as it is on the frontline of educational developments that can be most helpful for developing a new paradigm in education.

Machine learning-based modeling technology has recently become a powerful technique and tool for developing models for explaining, predicting, and describing system/human behaviors. In developing intelligent education systems or technologies, some research has focused on applying unique machine learning algorithms to build the ad-hoc student models for specific educational systems. However, systematically developing the data-driven student models from the educational data collected over prior educational experiences remains a challenge. We proposed a systematic and comprehensive machine learning-based modeling methodology to develop high-performance predictive student models from the historical educational data to address this issue. This methodology addresses the fundamental modeling issues, from data processing, to modeling, to model deployment. The said methodology can help developing student models for intelligent educational systems. After a detailed description of the proposed machine learning-based methodology, we introduce its application to an intelligent navigation tutoring system. Using the historical data collected in intelligent navigation tutoring systems, we conduct large-scale experiments to build the student models for training systems. The preliminary results proved that the proposed methodology is useful and feasible in developing the high-performance models for various intelligent education systems.