Investigating Virtual Teams in Massive Open Online Courses: Deliberation-based Virtual Team Formation, Discussion Mining and Support
Creators: name: Wen, Miaomiao
Publisher: name: Carnegie Mellon University
Year: 2025
Subjects: classificationCode: 46
subject: Information and computing sciences

description: To help learners foster the competencies of collaboration and communication in practice, there has been interest in incorporating a collaborative team-based learning component in Massive Open Online Courses (MOOCs) ever since the beginning. Most researchers agree that simply placing students in small groups does not guarantee that learning will occur. In previous work, team formation in MOOCs occurs through personal messaging early in a course and is typically based on scant learner profiles, e.g. demographics and prior knowledge. Since MOOC students have di verse background and motivation, there has been limited success in the self-selected or randomly assigned MOOC teams. Being part of an ineffective or dysfunctional team may well be inferior to independent study in promoting learning and can lead to frustration. This dissertation studies how to coordinate team based learning in MOOCs with a learning science concept, namely Transactivity. A transactive discussion is one where participants elaborate, build upon, question or argue against previously presented ideas [20]. It has long been established that transactive discussion is an important process that reflects good social dynamics in a group, correlates with students’ increased learning, and results in collaborative knowledge integration. Building on this foundation, we design a deliberation-based team formation where students hold a course community deliberation before small group collaboration.The center piece of this dissertation is a process for introducing online students into teams for effective group work. The key idea is that students should have the opportunity to interact meaningfully with the community before assignment into teams. That discussion not only provides evidence of which students would work well together, but it also provides students with a wealth of insight into alternative task-relevant perspectives to take with them into the collaboration.The team formation process begins with individual work. The students post their individual work to a discussion forum for a community-wide deliberation over the work produced by each individual. The resulting data trace informs automated guidance for team formation. The automated team assignment process groups students who display successful team processes, i.e., where transactive reasoning has been exchanged during the deliberation. Our experimental results indicate that teams that are formed based on students’ transactive discussion after the community delibera tion have better collaboration product than randomly formed teams. Beyond team formation, this dissertation explores how to support teams in their teamwork after the teams have been formed. At this stage, in order to further increase a team’s transac tive communication during team work, we use an automated conversational agent to support team members’ collaboration discussion through an extension of previously published facilitation techniques. As a grand finale to the dissertation, the paradigm for team formation validated in Amazon’s Mechanical Turk is tested for external validity within two real MOOCs with different team-based learning setting. The results demonstrated the effectiveness of our team formation process.This thesis provides a theoretical foundation, a hypothesis driven investigation both in the form of corpus studies and controlled experiments, and finally a demonstration of external validation. It’s contribution to MOOC practitioners includes both practical design advice as well as coordinating tools for team based MOOCs.