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

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.