collaborative-learning
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- PaperReCALL7 Jul 2026
WeChat-based collaborative self-regulated learning program: Exploring self-regulated learning strategy use, writing performance, and learning behaviors
Fan Su, Ying Zhao, Di Zou, Biyun Huang
This study compared individual and collaborative self-regulated learning (SRLL) contexts using WeChat for university EFL learners' writing. The collaborative group showed higher overall SRLL strategy use (except motivational), better writing performance, and distinctive behaviors like time monitoring and feedback awareness. Findings suggest integrating individual and collaborative modes to support self-, co-, and socially shared regulation.
Original abstract
In technology-enhanced language learning (TELL), self-regulated language learning (SRLL) strategies are essential for supporting English as a foreign language (EFL) learners’ writing development. As collaborative learning becomes increasingly prominent in TELL, SRLL has expanded from individual regulation to collaborative contexts. However, limited research has compared how individual and collaborative SRLL contexts influence learners’ strategy use, writing performance, and learning behaviors. To address this gap, this study used WeChat as a mobile learning platform to compare university-level EFL learners’ SRLL strategy use, writing performance, and behavioral patterns in individual and collaborative self-regulated writing programs. Two intact classes were assigned to either a WeChat-based individual group (WIG) or a WeChat-based collaborative group (WCG). The collected data included SRLL strategy use questionnaire, writing scores, and WeChat learning logs. Results showed that the collaborative context promoted learners’ overall, cognitive, metacognitive, and behavioral SRLL strategy use, although no significant difference was found in motivational strategy use. The WCG also achieved higher writing performance and showed distinctive regulatory behaviors related to time monitoring and feedback awareness. These findings suggest that SRLL is a dynamic and cyclical process shaped by task demands, technological affordances, and social interaction. They also highlight the value of integrating individual and collaborative learning modes to support learners’ movement between self-regulation, co-regulation, and socially shared regulation.
- PaperComputers & Education2 Jul 2026
Analysing student engagement in breakout rooms in an online language course
Katja Peltola, Marjaana Veermans, Sara Routarinne
In a 14-week synchronous online language course, recordings of breakout room interactions from two small groups were analyzed using qualitative content and discourse analysis. The study found that affective, behavioral, and cognitive engagement often co-occur and can support or hinder each other, with three collaborative engagement practices identified: participation in group discussions, making and negotiating proposals, and collaborative writing.
Original abstract
As the popularity of online courses in higher education keeps increasing, the importance of student engagement gets highlighted. In an online learning environment, peer interaction gets emphasised when students work in small groups in breakout rooms. In this case study, qualitative content analysis and discourse analysis were used to analyse breakout room recordings (458 minutes in total) of two small groups in a 14-week synchronous online language course. The study focused on how engagement at small group level seems to emerge from the group’s interaction, which is a primordial site for language learning, and what kind of collaborative engagement practices seem to support it. The findings suggest that affective, behavioural, and cognitive engagement often co-occur and that they may either support or hinder each other. Also, the group members may be engaged in diverse ways at the same time. Three collaborative engagement practices were identified: participation in group discussions, making and negotiating proposals, and collaborative writing. Participation in group discussions highlighted the importance of both verbal and nonverbal communication and the importance of equity in participation. When making proposals, the role of tentativeness was emphasised as that seemed to open space for interaction in the small group. Finally, collaborative writing was found to engage students specifically from the language learning perspective. The findings suggest that small group interaction and collaboration play a key role in group level engagement. It is suggested that small group level should be taken into consideration when operationalising the concept of engagement in online learning environments.
- PaperarXiv — AI in Education (cs.CY)29 Jun 2026
Less Deliberate in Teams: Student LLM Use Across Individual and Collaborative Work
Sehrish Basir Nizamani, Zannah Ziew, Saad Nizamani, Khyati Goyal
A semester-long study tracked 96 undergraduate computing students across individual and team assignments, finding a 42.7 percentage point drop in LLM usage during team work. Students used fewer prompts, simpler strategies, and verified AI output less carefully when collaborating. Collaborative context appears to reduce deliberate LLM engagement beyond task type alone.
Original abstract
As large language models (LLMs) become common in computing courses, we need to understand how the social setting shapes how students use them. This paper reports findings from a semester-long study of 96 undergraduate students who completed six assignments, alternating between individual homework and team project milestones. We tracked LLM usage, prompting habits, and how students verified AI-generated output across all six assignments. LLM usage dropped by 42.7 percentage points when students moved from individual work to their first team milestone, then partly recovered in later team tasks. Students also wrote fewer and simpler prompts, used fewer intentional prompting strategies, and checked LLM output less carefully. The share of students who ran tests on AI-generated code fell by 19.4 percentage points during team assignments and never fully rebounded. A within-student analysis found that 18.9% of students who consistently used LLMs on their own stopped using them entirely in teams, while only 3.2% went the other direction. These results suggest that collaborative context is associated with reduced deliberate LLM engagement beyond what task type alone can explain. The moment students form teams appears to be a critical and currently unsupported turning point in computing course design.
- PaperComputers and Education: Artificial Intelligence22 Jun 2026
ASTRA: A synthetic benchmark for trace-based evaluation of socially intelligent multi-agent tutoring and participation-balanced collaboration in introductory programming
Solomon Sunday Oyelere
Introduces ASTRA, a multi-agent tutoring prototype and synthetic benchmark for studying collaborative programming with socially differentiated agents in dyads. Releases an open synthetic dataset of turn-level dialogue traces and task-level artefacts across 20 Python tasks to support log-operational research on interaction dynamics and participation balance. Emphasizes that findings are simulated evidence for benchmarking and pipeline development, not causal learning effects.
Original abstract
Generative AI is rapidly entering introductory programming, yet evidence about how learners coordinate with AI, especially in dyads, remains limited, and open datasets that support reproducible, trace-based evaluation are scarce. I present ASTRA (Adaptive Socially-intelligent Team Reasoning Agents), a multi-agent tutoring prototype and benchmark framework for studying collaborative programming with socially differentiated agents. ASTRA supports three configurations: alone_tutor (one learner with a Tutor agent), pair_tutor (two learners with a Tutor agent), and pair_multiagent (two learners with Tutor and Facilitator agents, where the Facilitator prompts coordination and balanced participation). As access to research participants is not yet available, I release an open synthetic benchmark dataset that mirrors ASTRA’s logging schema and a prespecified between-subjects design ( participants; 360 sessions; 1440 task episodes) across a bank of 20 short Python programming tasks. The dataset includes turn-level dialogue traces and task-level artefacts designed to support log-operational research questions about interaction dynamics, participation balance and reciprocal engagement in dyads, and performance and verification behaviours. Descriptive summaries and illustrative models indicate that the benchmark yields measurable condition-differentiated patterns consistent with the simulation assumptions. I emphasise that these findings are simulated evidence intended for benchmarking, measurement feasibility, and reproducible pipeline development, not causal estimates of learning effects, while providing a transparent analysis blueprint for future ethics-approved validation studies.
- PaperBritish Journal of Educational Technology5 Apr 2026
When machines join the moral circle: The persona effect of generative AI agents in collaborative reasoning
Yueqiao Jin, Roberto Martinez‐Maldonado, Wanruo Shi, Songjie Huang et al.
University students discussed an autonomous-vehicle dilemma in triads with either a supportive or contrarian AI teammate. Supportive AIs increased grounded claims and integrative reasoning around care/fairness, while contrarian AIs broadened moral framing and sustained value pluralism. AI teammates altered the process of moral reasoning more than the final decision, reducing thematic drift and improving argument quality.
Original abstract
Generative AI is increasingly positioned as a peer in collaborative learning, yet its effects on ethical deliberation remain unclear. We report a between‐subjects experiment with university students ( N = 217) who discussed an autonomous‐vehicle dilemma in triads under three conditions: human‐only control, supportive AI teammate or contrarian AI teammate. Using moral foundations lexicons, argumentative coding from the augmentative knowledge construction framework, semantic‐trajectory modelling with BERTopic and dynamic time warping, and epistemic network analysis, we traced how AI personas reshape moral discourse. Supportive AIs increased grounded/qualified claims relative to control, consolidating integrative reasoning around care/fairness, while contrarian AIs modestly broadened moral framing and sustained value pluralism. Both AI conditions reduced thematic drift compared with human‐only groups, indicating more stable topical focus. Post‐discussion justification complexity was only weakly predicted by moral framing and reasoning quality, and shifts in final moral decisions were driven primarily by participants' initial stance rather than condition. Overall, AI teammates altered the process, the distribution and connection of moral frames and argument quality, more than the outcome of moral choice, highlighting the potential of generative AI agents as teammates for eliciting reflective, pluralistic moral reasoning in collaborative learning. Practitioner notes What is currently known about this topic AI tools can support discussion in collaborative learning, but evidence on ethical reasoning processes is mixed. Moral foundations theory and argumentation frameworks offer useful lenses for analysing value‐laden dialogue. Conversation analytics (eg, ENA and topic models) can reveal changes in discourse structure beyond outcome scores. What this paper adds Supportive AI teammates increase grounded/qualified claims compared with human‐only groups, improving the quality of moral reasoning. Contrarian AI teammates sustain value pluralism by connecting grounded claims to a wider moral repertoire, with only modest shifts in specific frames. Both AI personas reduce thematic drift, stabilising discussion focus; however, final moral decisions rarely change and justification complexity gains are small. Implications for practitioners Treat AI as persona‐configured teammates: use supportive styles to scaffold integrative reasoning and contrarian styles to elicit critical contrast. Design for process gains: instrument chats, monitor framing/argument quality and avoid over‐weighting post hoc decision change as the sole outcome. Govern participation: cap consecutive AI turns, keep timing natural and align persona goals with learning goals to prevent dominance while sustaining reflective dialogue.
- PaperReCALL2 Feb 2026
Intelligent chatbot–supported collaborative learning: Impact on student engagement and English speaking skills
Ting-Ting Wu, Intan Permata Hapsari, Yueh-Min Huang
A study with 75 EFL undergraduates found that chatbot-supported collaborative learning positively impacted student engagement and English speaking skills compared to conventional collaborative learning. Engagement mediated the improvement in speaking skills, suggesting chatbots are effective tools for promoting active participation in speaking classes.
Original abstract
Efforts to integrate intelligent chatbots into academic courses, particularly for language learning, have been gaining popularity. However, the impact of chatbot-supported collaborative learning (CL) on student engagement and English speaking skills is under-researched. This study explored the impact of utilizing intelligent chatbot–supported CL on student engagement and speaking skills of English as a foreign language (EFL) learners. It investigated how chatbot-supported CL influences student engagement and speaking skills. The experimental group was taught using chatbot-supported CL, while the control group followed conventional CL. A total of 75 first-year undergraduate students participated, with 39 students in the experimental group and 36 in the control group. Data were collected through a 14-item engagement questionnaire, a speaking test based on the IELTS speaking evaluation rubric for both groups, and a 5-item CL questionnaire administered solely to the experimental group. The data were analyzed using repeated measures analysis of variance (RM-ANOVA) and linear regression analysis. The RM-ANOVA results showed that chatbot-supported CL positively affected student engagement and speaking skills. The linear regression analysis further indicated that CL supported by intelligent chatbots influenced student engagement, which in turn significantly impacted speaking skills. The findings suggested that active engagement in CL speaking classes is crucial for improving EFL speaking skills and that intelligent chatbots can be valuable and effective tools for promoting such engagement.