Article 2021

MOCA: A Motivational Online Conversational Agent for Improving Student Engagement in Collaborative Learning

IEEE Transactions on Learning Technologies
Journal · Vol. 14 · Issue 5 · pp. 653-664
Abstract

The use of conversational agents in computer-supported collaborative learning (CSCL) has been identified as a useful tactic for motivational intervention. The purpose of the current study was to design and implement a conversational agent called a motivational online conversational agent (MOCA) that incorporated motivational interviewing (MI) and was based on an intelligent dialog engine to enhance learner engagement in CSCL. Additionally, the study empirically examined the effects of MOCA on promoting positive changes in collaborative learning engagement through multiturn conversation interventions. A prototype system was developed by combining MOCA and an immersive virtual world, and an effectiveness study was conducted with 40 volunteers. A series of multilevel growth models based on the framework of the hierarchical linear model was established through multiwave longitudinal data. The results indicated that the use of MOCA significantly improved student engagement scores (p < 0.001) and that female students performed better on collaborative tasks than male students (p < 0.05, t = 2.97). Additionally, time was an important predictor and significantly interacted with the MOCA-use condition. The study has implications for the design and assessment of conversational agents embodied in virtual reality. © 2008-2011 IEEE.

Keywords

Author Keywords

collaborative learning Learning engagement Conversational agent Motivational interviewing (MI)

Index Keywords

E-learning Education computing Students student engagement Collaborative learning Virtual reality cognition Job analysis Task analysis Motivational interviewing Collaboration Computer Supported Collaborative Learning Collaborative Work Conversational agents Learning engagement
Author Affiliations
Faculty of Education, Southwest University, Chongqing, China
College of Computer and Information Science, Southwest University, Chongqing, China, Chongqing No. 29 Middle School, Chongqing, China
Funding & Acknowledgements
Grant: 2018BS100
This work was supported in part by the National Science Foundation of China under Grant 61807027 and in part by the Social Science Planning Foundation of Chongqing under Grant 2018BS100.
National Natural Science Foundation of China, NSFC
Grant: 61807027
This work was supported in part by the National Science Foundation of China under Grant 61807027 and in part by the Social Science Planning Foundation of Chongqing under Grant 2018BS100.
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