ETR&D

This study compares the effects of metaverse and face-to-face (F2F) classroom settings on the speaking performance of Korean middle school English as a Foreign Language (EFL) learners. Specifically, it examines how affective enjoyment and anxiety influence speaking outcomes through participation and social interaction in each context. Two hundred seventy-two students were assigned to either a F2F or metaverse environment (136 students per group). Mixed design ANOVA revealed that, although speaking proficiency improved in both settings, the metaverse group showed higher posttest scores, reported more enjoyment, and had more social interactions. The F2F group, however, had descriptively higher participation, although the difference was not statistically significant. A moderated mediation analysis revealed different learning pathways for each environment. In the F2F setting, participation was the strongest predictor of learning outcomes, with enjoyment indirectly supporting gains primarily through participation. Conversely, in the metaverse, social interaction was the main driver of performance, especially for learners with medium-to-high anxiety, and mediated the impact of enjoyment. These findings suggest that the two settings facilitate second language speaking through different mechanisms of engagement, although direct causal attributions require caution. The study highlights the potential for instructional approaches tailored to the unique features of each setting when teaching second-language speaking to young beginner-level EFL students.

Understanding teachers’ engagement in professional learning for digital resources and tools integration is important in the context of ongoing educational digitalization. Existing research has primarily focused on technological attributes and individual cognitive factors, with comparatively limited attention to task-related contextual factors across institutional settings. Drawing on TALIS 2024 data, this study integrates the SAMR framework with task-technology fit and extended technology acceptance perspectives to conduct a cross-cultural comparative analysis of teachers’ reported digital teaching activities and factors associated with their engagement in professional learning for digital resource integration in Shanghai (China) and Poland. Descriptive findings indicate a non-linear distribution of selected TALIS indicators across SAMR-informed categories, with relatively frequent endorsement of modification-related activities and fewer reports of redefinition-related practices. Cross-cultural comparisons show that Polish teachers reported higher engagement in enhancement-oriented digital uses, particularly for instructional management and communication, whereas Shanghai teachers reported higher engagement in several transformation-oriented digital practices, especially collaborative support and instructional redesign. Binary logistic regression analyses indicate that professional learning participation is closely associated with teachers’ perceived pedagogical digital-integration needs and broader task-adaptation indicators. However, the relative strength of predictors varies across contexts: task-related indicators show stronger associations in the Shanghai sample, whereas perceived usefulness shows stronger associations in the Polish sample. Overall, the findings suggest that teachers’ perceptions of the alignment between instructional tasks and digital tools are meaningfully related to their participation in professional learning for using digital resources and tools in teaching. This study provides cross-cultural empirical evidence on the antecedents of teachers’ professional learning engagement for digital resource integration and offers implications for developing pedagogically oriented digital strategies.

Grounded in social cognitive theory, this study had two purposes. First, it developed and validated the Sources of Educational Technology Self-Efficacy Scale (SETSES) to measure preservice teachers’ perceptions of the four sources of self-efficacy—mastery experiences, vicarious information, verbal persuasion, and emotional arousal—within educational technology coursework. Second, it integrated sources of self-efficacy with established social cognitive variables, including self-efficacy, outcome expectations, and interests, to propose and test a new social cognitive model explaining preservice teachers’ intentions for technology integration. Data were collected from 144 preservice teachers enrolled in an introductory educational technology course. Psychometric analyses demonstrated that the SETSES exhibited good validity and reliability, indicating strong psychometric properties. Path analysis and hierarchical regression revealed that the proposed Social Cognitive Model of Technology Integration had high explanatory power for intentions for technology integration and uncovered complex relationships among the social cognitive variables, with mastery experiences playing a central role as a source of self-efficacy. Together, the validated scale and the proposed model extend social cognitive theory and offer important implications for the design of educational technology courses aimed at strengthening preservice teachers’ self-efficacy, outcome expectations, interests, and intentions for technology integration.

The use of artificial intelligence (AI) at every stage of the education process is increasing. The integration of AI into instructional design (ID) is transforming the field from a linear, human-driven process into a dynamic, collaborative ecosystem that merges computational power with human creativity and pedagogical expertise. This study aims to systematically review research that investigates the use of AI within the instructional design process. A total of 28 articles published between 2020 and 2025 were analysed to examine the relationship between instructional design and AI. The majority of studies focused on how AI was perceived by instructional designers and teachers, while others examined its effects and proposed AI-based systems and models specific to instructional design, indicating that the field was still in an emerging phase. In these studies, AI assists designers in tasks such as generating content, providing templates, and personalizing materials, which are among the most intensive instructional design processes. The most significant benefit in the instructional design process is provided by assisting with administrative and repetitive tasks, such as monitoring student progress, grading, and providing feedback. Prominent challenges, such as the need for pedagogical alignment and the readiness of instructional design practitioners were highlighted, which pointed to fundamental limitations of AI, particularly concerns about content reliability and tool-specific constraints. Overall, AI was not only regarded as a tool but was also conceptualised as a co-worker, collaborator, and partner within the instructional design process.

Although recommender systems have the potential to advance self-regulated learning (SRL) in online environments, implementation challenges impede their development. To systematically investigate the barriers to the effective use of recommender systems to enhance SRL, this study developed a three-tiered analytical framework that considers challenges in three areas: data and experiments, user experience/interface design, and technical factors. A systematic search of five bibliographic databases identified 33 empirical studies. The findings revealed the following key challenges in the three analytical levels: (a) data and experiments: scarcity, validity, and arrangement; (b) user experience and engagement: poor matching and inadequate scaffolding for SRL (e.g., weak feedback mechanisms) and suboptimal interface and function designs; and (c) technical factors: lack of stability and security of recommender systems and limitations in modeling complex learning behaviors and balancing personalization with pedagogical goals. These challenges interact dynamically to form a self-reinforcing recursive constraint, where poor user experiences lead to disengagement, resulting in sparse, low-quality data that further constrains algorithmic capabilities. Current algorithmic approaches typically address isolated problems, failing to break this cycle by overlooking these systemic interdependencies. This underscores the critical need to move beyond isolated fixes and develop comprehensive, pedagogically-aware algorithmic architectures. To address this, this study proposes a four-phase development roadmap for creating an SRL-Scaffolding Architecture. This roadmap integrates multi-modal data validation, hybrid algorithmic intelligence combining emotional and cognitive modeling, adaptive scaffolding interfaces, and a continuous human-in-the-loop feedback cycle. By mapping these interconnected challenges and proposing a structured architectural framework, this study proposes roadmap to address these barriers and develop recommender systems that effectively empower SRL in online environments.

Manual therapy mobilizations are common therapeutic practices for musculoskeletal conditions, but their acquisition presents significant challenges as it requires the precise, coordinated, and safe execution of specific movements. Traditional teaching approaches often rely on subjective feedback and offer limited real-time guidance, which may hinder skill acquisition. In recent years, immersive Virtual Reality (iVR) has emerged as a promising educational tool, showing potential to enhance learning experiences and increase student motivation. This study aimed to develop and validate a novel teaching approach for manual therapy mobilizations by integrating iVR as a complementary tool in undergraduate education, allowing students to practice techniques with greater precision and efficiency. A mixed-methods cluster randomized controlled trial was conducted with undergraduate students who had no prior experience with the technique. An iVR application for head-mounted displays was developed to teach a knee mobilization technique. Students in the experimental group used the iVR application alongside a traditional instructional session, while the control group received traditional teaching only. Quantitative and qualitative data related to usability, iVR experience, and learning experience were collected after the session. Results showed that students in the experimental group reported higher engagement and motivation when learning manual therapy techniques, although some perceived the traditional method as simpler. Learning experience outcomes indicated improved coordination of translational and rotational movements among iVR users, which is essential for effective manual therapy mobilization. Overall, these findings suggest that iVR can serve as an effective supplementary tool to conventional teaching methods, contributing to enhanced learning and performance outcomes.

Extended reality technologies are increasingly used in teacher education. For instance, innovations like 360 videos and virtual reality have been shown to be effective in improving future educators’ noticing, self-efficacy, and sense of presence, particularly in simulations of real classrooms. However, little is known about how presence is experienced in immersive recordings of educational gaming environments (i.e., Minecraft or Roblox). This study addresses that gap by examining how pre-service teachers experience presence when viewing a 360 video of a third-grade math lesson set within Minecraft.edu. Using Rasch analysis, we validated the eXtended Reality Presence Scale with a sample of 91 pre-service teachers. We also examined the correlation between presence scores and participants’ field of view (a proxy for visual attention) and compared presence ratings between immersive recordings of a real classroom and a Minecraft.edu environment. Results support the scale as a valid tool for measuring presence in game-based immersive environments. Presence was significantly correlated with participants’ field of view, suggesting that attentional engagement is linked to the experience of presence. However, participants reported lower presence in the Minecraft.edu lesson compared to the real classroom. These findings extend theoretical understanding of presence by demonstrating its variability across extended reality contexts and validating a tool for measuring it in educational gaming environments—an area previously underexplored.