A learning analytics‐based collaborative conversational agent to foster productive dialogue in inquiry learning. - de Araujo, Adelson; Papadopoulos, Pantelis M.; McKenney, Susan; de Jong, Ton (Journal of Computer Assisted Learning, 2024)
Background: Sustaining productive student–student dialogue in online collaborative inquiry learning is challenging, and teacher support is limited when needed in multiple groups simultaneously. Collaborative conversational agents (CCAs) have been used in the past to support student dialogue. Yet, research is needed to reveal the characteristics and effectiveness of such agents. Objectives: To investigate the extent to which our analytics‐based Collaborative Learning Agent for Interactive Reasoning (Clair) can improve the productivity of student dialogue, we assessed both the levels at which students shared thoughts, listened to each other, deepened reasoning, and engaged with peer's reasoning, as well as their perceived productivity in terms of their learning community, accurate knowledge, and rigorous thinking. Method: In two separate studies, 19 and 27 dyads of secondary school students from Brazil and the Netherlands, respectively, participated in digital inquiry‐based science lessons. The dyads were assigned to two conditions: with Clair present (treatment) or absent (control) in the chat. Sequential pattern mining of chat logs and the student's responses to a questionnaire were used to evaluate Clair's impact. Results: Analysis revealed that in both studies, Clair's presence resulted in dyads sharing their thoughts at a higher frequency compared to dyads that did not have Clair. Additionally, in the Netherlands' study, Clair's presence led to a higher frequency of students engaging with each other's reasoning. No differences were observed in students' perceived productivity. Conclusion: This work deepens our understanding of how CCAs impact student dialogue and illustrates the importance of a multidimensional perspective in analysing the role of CCAs in guiding student dialogue. Lay Description: What is currently known about this topic?: Collaborative inquiry learning involves students exploring scientific concepts through dialogue, ideally with active turn‐taking and contributions from all participants, but students often need guidance to engage in productive dialogue and teachers face difficulties when several groups need support simultaneously.The Academically Productive Talk (APT) framework promotes teacher guidance on student–student dialogue, by pushing students to reason together and on their own, and also provides guidelines for evaluating productivity.Collaborative Conversational Agents (CCAs) are emerging tools designed to facilitate and guide student dialogue in group settings, utilizing instructional strategies like APT. What does this paper add?: The paper describes Clair, a novel CCA that uses learning analytics to identify sensitive moments for triggering APT interventions in student dialogue.The proposed learning analytics methodology supports both CCA's triggering and evaluation, by using fuzzy logic and sequential pattern mining, respectively.The paper underscores the importance of multidimensional evaluation of dialogue productivity in order to comprehensively understand the impact of CCAs. Implications for practice and/or policy: The results shed light on which productivity goals Clair has influenced and which have not.The paper provides actionable insights for improving future CCA designs, emphasizing the need to address key goals of productive dialogue.