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.
- 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.