metacognition
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- PaperLanguage Learning & Technology29 Jun 2026
Generative AI, visual scaffolds, and metacognition in EFL writing
Mei-Rong Alice Chen
A study proposes a Generative AI-assisted Visually Guided Progressive (GAI-VGP) writing approach integrating step-by-step visual scaffolds with interactive AI feedback. In a quasi-experiment with 58 EFL university students, the GAI-VGP group outperformed the control in writing performance, planning and evaluation strategies, and reflective awareness, though critical reflection remained limited and monitoring showed no gains due to learners outsourcing regulation to AI.
Original abstract
Fostering metacognitive awareness and reflective thinking remains a challenge in English as a Foreign Language (EFL) writing instruction. These cognitive processes are essential for enabling learners to monitor, evaluate, and engage in meaningful self-reflection throughout the writing cycle. Using Flower and Hayes’s cognitive process model and Jasper’s Experience-Reflection-Action framework, this study proposes a Generative AI-assisted Visually Guided Progressive (GAI-VGP) writing approach. This pedagogical design integrates step-by-step visual scaffolds with interactive AI feedback to foreground learner agency and promote reflective engagement. To evaluate this approach, a semester-long quasi-experimental study was conducted with 58 EFL university students. Data sources included pre- and post-writing tests, metacognitive awareness questionnaires, reflective thinking scales, AI chat logs, and reflection notes. The results showed that the GAI-VGP group significantly outperformed the conventional VGP group in writing performance, planning and evaluation strategies, and reflective awareness. Qualitative analyses indicated increased engagement in understanding and reflectively evaluating writing strategies, although critical reflection remained limited across both groups. Although AI significantly enhanced planning and evaluation, monitoring showed no significant gains, with prompt-log evidence revealing a displacement pattern in which learners outsourced real-time regulation to AI validation.
- PaperJournal of Learning Analytics14 Mar 2026
Profiling Pre-service Teachers’ Computational Thinking
Tanya Chichekian, Maria Cutumisu, Annie Savard, Yi-Mei Zhang
This study used multimodal data to profile computational thinking (CT) skills among 128 pre-service teachers, identifying three profiles (Novice, Developing, Proficient) via latent profile analysis. Metacognitive strategies and prior coding experience significantly predicted profile membership, and proficient learners demonstrated greater task efficiency and perceived fewer challenges. The findings inform the design of learning analytics dashboards for adaptive teacher training to enhance CT integration into K–12 education.
Original abstract
Computational thinking (CT) is a vital skill set for pre-service teachers who will need to foster computational literacy in K–12 classrooms, yet the factors influencing their CT skills remain less understood than those for K–12 students or in-service teachers. This study leverages multimodal data to investigate how pre-service teachers (n=128) differ in CT skills, the predictive role of metacognitive strategies and prior coding experience, and variations in online behaviours. Using latent profile analysis, we identified three profiles based on digital literacy, problem-solving, and coding comfort (Novice, Developing, and Proficient), revealing heterogeneity in CT, and supporting non-linear skill acquisition. Linear discriminant analysis revealed that metacognitive strategies and prior coding experience significantly predict profile membership, validating the interplay of technical and cognitive factors in the development of CT skills. Behavioural data from an interactive problem-solving task showed that, compared to Novices and Developing learners, Proficient learners were more task efficient and perceived fewer challenges during task completion. Implications for designing a learning analytics dashboard to visualize profiles and behavioural metrics to support adaptive, equitable, and personalized teacher training are discussed, thereby enhancing pre-service teachers’ readiness to integrate CT into K–12 education.