Enriching synchronous collaboration in online courses with configurable conversational agents

Abstract

This work presents a novel approach for employing conversational agent technology in the context of Massive Open Online Courses (MOOCs), aiming to support learners that work in groups to sustain productive forms of peer dialogue. An exploratory study is presented featuring 56 undergraduate computer science students, who interacted with a conversational agent in the context of an online course. The study investigates the practicability of using configurable conversational agents to provide collaborative learning support and serves as an opportunity to compare two intervention strategies: (a) converging agent interventions, presented in the form of tips relating closely to the topic of the activity and (b) diverging agent interventions, which do not relate directly to the activity topic and pose new domain-relevant questions. The study findings suggest that a convergent agent is often perceived as more helpful by the students. A discourse analysis also reveals a series of interesting interaction patterns, facilitating improvements in the design of future conversational agent systems.