Investigating the relationships among peer moderation, cognitive engagement, and learning achievement in online discussion forums

ABSTRACT

Online discussion forums are an important component of communication and interactions between teachers and students. The success of online discussion forums depends largely on the e-moderating behavior of the students who are assigned the role of peer moderators. Their ability to facilitate meaningful discussions, encourage diverse perspectives, and keep the conversation on track is paramount to achieving group cognitive engagement and superior performance. This study analyzed the discourse generated by learners in online discussion forums, with a particular focus on the still under-explored effort of peer moderation on groups’ cognitive engagement and learning achievement in online discussion forums. The result of multiple regression analyzes demonstrates how certain levels of cognitive engagement and peer moderation relate to learning achievement. Furthermore, this study examined the moderating role of peer moderation in the association between high-order cognitive engagement and learning achievement. The findings indicate that while peer moderation can have a positive moderating effect, this effect is weaker for a higher number of Development moderations, suggesting that excessive Development may lead to endless arguments and decrease discussion efficiency.

KEYWORDS:

• Peer learning
• discussion forum
• peer moderation
• cognitive engagement
• learning achievements

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data are not publicly available due to them containing information that could compromise student privacy. Details of the data and how to request access are available at National Engineering Research Center for E-Learning ([email protected]).

Additional information

Funding

This work was supported by the National Science and Technology Major Project (grant number 2022ZD0117101), the National Natural Science Foundation of China (grant numbers 62377016, 62077017, 62293554, 61937001, 62293550, 62307015) and Hubei Provincial Natural Science Foundation of China (2023AFA020).

Notes on contributors

Mingrui Hao

Mingrui Hao is a Ph.D. student in National Engineering Research Center for E-learning, Central China Normal University, China. His research interests include educational data mining and learning analytics. Email: [email protected].

Zhi Liu

Zhi Liu is an associate professor in National Engineering Laboratory for Educational Big Data (NELEBD), National Engineering Research Center for E-learning (NERCEL), Central China Normal University (CCNU), China, since 2015. He is dedicated to conduct the research on learning behavior analysis and educational data mining. He was a guest researcher in the Department of Computer Science at Humboldt University of Berlin from 2017 to 2018. As a PI, he is leading the research projects of the NSFC “Research on Methods of Recognition and Adaptive Intervention of Cognitive Behavioral Patterns in SPOC Interactive Discourses”. Email: [email protected].

Yanshen Liu

Yanshen Liu is the professor and director of The Research Center For Educational Information, Hubei Province. He is the visiting scholar of Columbia University and Kent State University. His research has been funded by National Science and Technology Support Plan. His research interests are multimedia technology and distance education. Email: [email protected].

Qian Wan

Qian Wan received his Ph.D. degree in Software Engineering from the School of Computer Science, National University of Defense Technology, Changsha, China, in 2022. He is currently an Assistant Researcher with the Faculty of Artificial Intelligence in Education and National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China. His research interests include information extraction, multi-modal learning and generative artificial intelligence. Email: [email protected].

Jia Chen

Jia Chen is an associate professor in School of Educational Information Technology, Central China Normal University, he received a doctor's degree in engineering in Harbin Industrial University, his main research direction includes deep learning, visual computing and education information technologies. He gained the funds from National Natural Science Foundation of China and Hubei Province Natural Science Foundation of China. Email: [email protected].

Jiangbo Shu

Jiangbo Shu is an associate professor in National Engineering Laboratory for Educational Big Data (NELEBD), National Engineering Research Center for E-learning (NERCEL), Central China Normal University (CCNU), China, since 2014. His research interests include computer application technology, cloud computing, and Educational Big Data. Email: [email protected].

Yi Liu

Yi Liu received her bachelor’s degree and master’s degree in Computer Science from Central China Normal University in 1996 and 2001, respectively. She received her Doctor of Technical Science in Management Science and Engineering in Huazhong University of Science & Technology in 2007. She is currently a senior engineer of the Central China Normal University (CCNU). Her research interests are in modern educational theory and experimental techniques. Email: [email protected].

Linghan Mei

Linghan Mei is a Ph.D. student in Dongguk University, Seoul, Korea. His research interests include remote education and artificial intelligence in K12 education. Email: [email protected].