Using AI-empowered assessments and personalized recommendations to promote online collaborative learning performance

Abstract

As an effective form of pedagogy, online collaborative learning has received increasing application in the field of education. However, learners often feel frustrated with regard to knowledge building, cognitive engagement, and socially shared regulation. To solve these problems, the current study proposed an approach featuring artificial intelligence (AI)-empowered assessments and personalized recommendations. The purpose of this quasi-experimental study was to examine the effect of the proposed approach on collaborative learning performance. In total, 135 college students, who were divided into three conditions, participated in the current study. The results of both quantitative and qualitative analysis indicated that the proposed approach could substantially enhance collaborative knowledge building, cognitive engagement, socially shared regulated behaviors, and group performance. The findings are discussed thoroughly alongside their pedagogical and technological implications.

Keywords:

• Online collaborative learning
• knowledge graph
• personalized recommendation
• knowledge building
• socially shared regulated behavior
• group performance

Disclosure statement

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

Ethical approval

This study complied with all the ethical guidelines for surveys with human participants. All participants were informed of the purpose and procedures beforehand, and asked to submit written informed consent. They were free to quit at any time.

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Additional information

Funding

This study is funded by the International Joint Research Project of Huiyan International College, Faculty of Education, Beijing Normal University [ICER202101].

Notes on contributors

Lanqin Zheng

Lanqin Zheng currently works as an associate professor at the Faculty of Education in Beijing Normal University. Her research interests include computer supported collaborative learning, learning analytics, and AIED.

Yunchao Fan

Yunchao Fan is a master student at the Faculty of Education in Beijing Normal University. His research interests focus on computer supported collaborative learning.

Lei Gao

Lei Gao is a master student at the Faculty of Education in Beijing Normal University. His research interests focus on computer supported collaborative learning.

Zichen Huang

Zichen Huang is a master student at the Faculty of Education in Beijing Normal University. His research interests focus on computer supported collaborative learning.

Bodong Chen

Bodong Chen works as an associate professor at the Graduate School of Education in University of Pennsylvania. His research interests include knowledge building, computer supported collaborative learning, learning analytics, and online Learning.

Miaolang Long

Miaolang Long is a master student at the Faculty of Education in Beijing Normal University. His research interests focus on computer supported collaborative learning.