Information technology has been increasingly leveraged to promote educational equity in less-developed regions. In China, the blended synchronous classroom (BSC) enables high-quality teachers to simultaneously instruct students in their own classrooms and those in remote weak schools, thereby addressing the shortage of specialized teachers in rural areas. This study examines an optimized variant—the special blended synchronous classroom (SBSC) environment—in which a single specialized teacher delivers Music and Art instruction to students in two or three rural primary schools simultaneously, with each remote classroom supported by an auxiliary teacher. Employing a quasi-experimental design, this study investigates the factors influencing students’ learning satisfaction in both the SBSC and traditional face-to-face (TFTF) environments, and compares students’ learning gains in Music and Art courses between the two settings. Data were collected from 824 Grade 2 and Grade 3 students via questionnaires and analyzed using SPSS 22.0 and SmartPLS 3.0. The results reveal that, in both environments, learning motivation and learning strategies significantly predicted learning satisfaction, whereas learning attitude did not. Regarding learning gains, SBSC students achieved significantly higher learning gains in singing, music theory, painting, sketching and composition, and handicraft compared with their TFTF peers, whereas no significant difference was found in musical instrument performance. This study contributes to the design of technology-enhanced classroom environments that expand access to high-quality Music and Art education in rural primary schools, offering empirical evidence for educational equity initiatives.
Micro-credentials (MCs) are gaining traction in higher education as essential tools for advancing sustainable development (SD). These short, focused courses provide learners with specific skills and knowledge related to sustainability, enabling them to address pressing global challenges. Current trends indicate that institutions are increasingly integrating MCs into their curricula, collaborating with industry partners to ensure relevance and applicability. By offering stackable credentials, universities empower students to demonstrate expertise in sustainability-oriented practices, enhancing their employability. Furthermore, MCs promote lifelong learning and versatility, equipping individuals with the abilities needed to adapt to an evolving job market aligned with sustainable development goals. This study aims to explore the role of MCs in advancing SD. It examines the trends and characteristics of MCs at higher education institutions (HEIs) and investigates the provisions and the effectiveness of MCs for SD at HEIs. A bibliometric analysis was employed to characterize MCs. Both an analysis of published case studies and a survey on MCs for SD were conducted to identify the provision of MCs for SD. The findings identified that while some institutions have been offering MCs for several years, many do not or have only recently started offering them. Advancing MCs for SD will require stronger institutional coordination, collaboration with external stakeholders, clearer policy alignment, and sustained attention to educational quality and equity. Successfully implemented cases and critical challenges surrounding the implementation of MCs were discussed, and recommendations were provided with strategic measures to address some of the key barriers hindering successful implementation of MCs for SD.
Instructional videos are widely used, yet their limited interactivity often leads to passive viewing. Embedding Virtual Reality (VR) as a form of generative practice, in which learners actively create content such as drawings or visual representations, offers a promising solution to foster deeper cognitive processing. This study examines the impact of integrating generative VR practice into instructional videos through an experimental design involving 100 university students. Learners engaged with vocabulary tasks under four spatial layouts that varied in the position (left vs. right) and size (small vs. large) of the interactive area, compared with a video-only control. Outcomes included retention, transfer, attention, cognitive load, and perceived enjoyment. Transfer benefits over video-only learning were observed only in the two large-area VR conditions. Among the VR conditions, right-side positioning increased attention allocation and reduced cognitive load, whereas larger interaction areas improved retention, transfer, and usability while reducing cognitive load. Eye-tracking further showed that VR-based generative practice redirected attention toward the interactive area. No significant interaction between position and size was found. These findings extend generative learning theory into VR-supported video contexts, propose the Attentional Alignment Principle and Workspace Expansion Principle, and provide design implications for interactive instructional videos.
Developing students’ technical competencies and self-regulated learning has become a priority in robotics and computing education, yet little is known about the mechanisms through which technical competence contributes to goal-setting behaviours in authentic learning environments. Drawing on Social Cognitive Theory and Goal-Setting Theory, we examined whether science-related attitudes mediate the relationship between technical competencies and robotics goal-setting skills and whether prior knowledge moderates this process. A total of 530 undergraduate students from Nigerian higher education institutions participated in a 15-week Arduino-supported Project-Based Learning intervention. Prior knowledge was assessed before the intervention, whereas technical competencies, science-related attitudes, and robotics goal-setting skills were measured concurrently at post-intervention. The hypothesised first stage moderated mediation model was analysed using Partial Least Squares Structural Equation Modelling with the SmartPLS PROCESS module and examined in a complementary robustness analysis using Hayes’ PROCESS macro in SPSS, with 10,000 bootstrap resamples. Results showed that technical competencies significantly and positively predicted robotics goal-setting skills and science-related attitudes. However, science-related attitudes did not significantly predict goal-setting skills or mediate the relationship between competence and goal setting. Prior knowledge did not significantly moderate the relationship between technical competencies and science-related attitudes, and the conditional indirect effect was unsupported. These results refine the joint application of Social Cognitive Theory and Goal-Setting Theory by suggesting that technical competence is associated with goal setting primarily through a direct competence-based pathway rather than the proposed attitudinal mechanism. This study underscores the importance of combining hands-on, feedback-rich robotics experiences with explicit goal-setting and self-regulatory strategies in higher education.
Abstract The rapid advancement of artificial intelligence has made AI-based instruction, including intelligent tutoring systems, learning analytics dashboards, adaptive learning platforms, and generative AI, increasingly prevalent in K-12 education. However, AI-generated information does not automatically lead to pedagogical action; it gains instructional meaning only when teachers interpret, judge, and translate it into situated classroom support. Following PRISMA guidelines, this systematic review analyzed 29 peer-reviewed English-language studies published between 2016 and 2025 across six databases to examine how teacher intervention is constituted in K-12 AI-based instruction and what conditions shape its reported effects. The synthesis was organized around three dimensions: process, strategy, and effect. Findings indicate that teacher intervention operates as a cyclical process of monitoring, judgment, intervention, and orchestration, through which AI-generated information is transformed into pedagogical action and subsequent classroom adjustment. Three broad intervention strategies were identified: pedagogical translation of AI outputs, design of learning support, and reconstruction of interaction structures. Reported benefits for student learning and teacher orchestration were generally promising but conditional, varying according to the interpretability of AI information, intervention timing, target level, teachers’ implementation feasibility, and students’ autonomy. By presenting an integrated conditional framework, this review argues that the teacher’s role in AI-based instruction is being reconfigured rather than diminished, offering implications for teacher professional development and the design of teacher-facing AI systems.
