ETR&D

Effective teaching plays a vital role in fostering cognitive and psychological development in humans. Virtual reality simulations, along with the embedded, artificial intelligence (AI)-powered virtual student agents, offer opportunities for preservice teachers to practice teaching iteratively in a dynamic, context-rich manner. However, preservice teachers face challenges when practicing in these AI-enhanced VR simulations. In this study, we designed and investigated model-based supports aimed at scaffolding preservice teachers during simulation-based pretraining, in-simulation, and post-simulation stages. This experimental study with 57 preservice teachers indicated significant positive impacts of model-based support on teaching knowledge and skills development. Furthermore, significant improvements in knowledge of teaching were observed in the experimental groups using model-based support in the AI-enhanced VR simulation, from pre- to post-test. However, the difference between the experimental and control groups in teaching self-efficacy was nonsignificant. The implications of these findings and potential future directions for designing learning support in VR simulation-based teacher education are discussed.

Students often struggle to stay engaged and effectively regulate their learning in online environments, which can negatively impact their learning experiences. Despite the established importance of self-regulation of learning skills (SRLs) in maintaining engagement, many students face significant challenges in developing and implementing these skills due to a lack of adequate feedback. This is primarily due to tutors' high workloads and the difficulties inherent in engaging students in online settings. This study examines the impact of Learning Analytics (LA)-driven interventions to improve students' SRLs in online learning environments. Specifically, it compares the impact of feedback from Generative Artificial Intelligence (GenAI) and human tutors in a nine-week statistics course delivered via MOODLE at a higher education institution, employing LA and clustering techniques to model SRLs based on Ye & Pennisi’s (2022) framework. In a quasi-experimental design, participants with varying SRLs were assigned to either a tutor-feedback or GenAI-feedback group. Feedback readability and reliability evaluations indicate that LA-driven GenAI-produced feedback was significantly more readable than human tutor feedback (p < 0.01) and demonstrated higher reliability than tutor-generated feedback. Results show that students in the low SRLs cluster receiving GenAI feedback exhibited statistically significant improvements in goal-setting skills (p < 0.05) and overall SRLs levels (p < 0.05) compared to the tutor-feedback group. In contrast, no significant differences were observed among high SRLs cluster students. This study underscores the potential of LA-driven GenAI feedback to be able to develop tailored, scalable feedback, improving SRLs performance of low SRLs students in online higher education contexts. Future research should explore these effects across diverse student groups and investigate the collaborative potential of semi-automated feedback systems that include tutors.