ETR&D
1 day 1 hour ago
With the deep integration of AI technologies across all aspects of basic education, teacher AI competency (TAIC) has become a core driver of digital transformation in education. However, existing assessment instruments lack specialized measures tailored to K-12 educational scenarios. Drawing on frameworks and standards such as UNESCO's “AI Competency Framework for Teachers,” this study constructs a three-dimensional integrated framework of “AI foundational competency–AI application competency–AI ethical norms” by analyzing the primary work scenarios of K-12 teachers and typical applications of AI technologies. Within this framework, Application Competency is anchored in three core scenarios: Teaching Practice, Educational Research, and Classroom Management. Through four stages—literature review, expert interviews, expert validation, and pilot testing—exploratory and confirmatory factor analyses based on 18,295 valid responses demonstrate that the final 22-item scale exhibits strong reliability and validity. Overall, AI competency for K-12 teachers (AIC-K12) is at a moderately high level, with significant differences observed across age, teaching experience, school stage, subject area, school location, and partially across gender. Based on these findings, several research implications are proposed. This study provides a robust instrument and empirical evidence for assessing AIC-K12 and supporting professional development, offering important insights for advancing the development of the teaching workforce and improving educational quality in the AI era.
1 day 1 hour ago
Data science is a field that merges advancements in computer science, statistics, and machine learning. To date, much data science instruction has been through graduate programs or self-directed course offerings. There are therefore fewer opportunities for formal data science education, especially for K-12 educators and learners who have limited exposure to these fields. While some efforts have emerged to introduce data science in pre-college settings, there is little research regarding learning design strategies that support data science for inservice teachers. As such, this project employs a design-based research (DBR) approach to develop accessible and engaging open-access learning resources as a way to reduce barriers to data science instruction across the educational spectrum. Given the gap regarding data science in K-12, our DBR study identifies the following design elements: (a) revised data science content scope, (b) design standardization for interactive learning, (c) reframing data science for case-based learning, (d) artificial intelligence (AI) towards data personalization, and (e) data science coding through goal-based learning. The project also introduces aspects of the curriculum via a teacher professional development, which provides important insights and challenges regarding data science at the middle and high school levels. Implications for learning design, open-educational resources, data science standards, and others are discussed.
2 days 1 hour ago
This study develops and validates an interpretable transformer-based model for detecting cognitive presence (CP) in MOOC discussions to serve as a diagnostic foundation for instructional design and facilitation. Specifically, we built this model by fine-tuning the RoBERTa architecture with theory-derived structural cues from the Community of Inquiry framework to capture causal, synthesizing, and action-oriented language. Using data from six Social Work MOOCs (4,260 learners; 227,863 sentences), the model classified discourse into four CP phases and a fifth non-CP category. The cue-augmented RoBERTa achieved strong performance (macro F1 = 0.714; κ = 0.666), and SHAP analyses confirmed the model’s internal logic aligns with theory-consistent linguistic markers, ensuring the pedagogical transparency of the results. Learner-level hierarchical regressions revealed that while discussion quantity predicted behavioral persistence, Integration predicted motivation and satisfaction while Resolution predicted perceived knowledge transfer, functioning as diagnostic proxies that participation volume alone does not capture. These findings shift the role of CP analytics from descriptive observation to prescriptive action through a proposed Pedagogical Diagnostic & Engineering (PDE) framework. By mapping automated structural signals to specific instructional failure points, this framework offers concrete tactics for real-time scaffolding, such as Socratic prompts and practitioner spotlights, to move learners beyond exploratory monologues toward deeper knowledge construction and professional application.
3 days 1 hour ago
Learners often rely on visual cues, such as color highlights, to recall important information in instructional texts. Color can serve as an attention-guidance cue but also a memory cue to promote learning. However, research has not yet thoroughly explored how colors can serve both purposes: signaling relevant information within the learning material and serving as memory cues to foster retrieval during testing. Therefore, this study investigated whether different signaling colors in digital learning environments can serve as memory cues to enhance retrieval and improve learning outcomes for secondary students. In a 2 (signaling color: monochromatic vs. polychromatic) × 2 (memory cue presence: during learning vs. during learning and retrieval) between-subjects experiment, 168 participants learned about auroras in a digital learning environment, presented across five texts and seven instructional pictures. The study assessed learning outcomes, cognitive load during learning, and mental load and mental effort during testing, as well as prior knowledge. Results revealed no effects of memory cue presence or signaling color, nor any interactions. Additional Bayes factors largely supported the null hypothesis across all measures. Results are discussed in terms of both color encoding and superficial processing. Color-based signaling does not automatically function as a memory cue, highlighting the need for instructional approaches that promote meaningful cue encoding and consistent reuse across learning and assessment contexts.
3 days 1 hour ago
With the rapid development of AI, children have become both frequent users and potential co-creators of intelligent technologies. However, research on AI education in primary schools remains limited, with a lack of effective, developmentally appropriate tools for systematically teaching AI concepts, applications, and ethics. To address this gap, this study develops AIgnite, an AI literacy toolkit for upper primary school students in Grades 4–6. The toolkit combines AI knowledge cards with two stages supported by these cards: (i) the Board Game stage and (ii) the AI Storydrawing stage. The two stages create a progressive learning pathway that supports students in building AI knowledge, collaboratively expressing ideas, and reflecting on ethical issues engagingly and interactively. Developed using a child-centered approach, the toolkit underwent multiple rounds of participatory co-creation and iterative prototyping with 30 participants. A user study with another 42 participants demonstrated the toolkit’s effectiveness in conveying AI concepts and applications and in fostering ethical reflection. Additionally, this research concludes by summarizing design insights for developing AI educational tools for children, providing practical guidance and inspiration for future tool development and media innovation in AI education.
6 days 1 hour ago
Generative artificial intelligence is increasingly framed as a catalyst for deeper learning rather than simple automation. This design-based study investigated how personalised GPT agents were used as cognitive partners by pre-service teachers in an undergraduate Initial Teacher Education course in Digital Technologies within a Bachelor of Education (Secondary) programme. Drawing on distributed cognition, constructivism, cognitive load theory, and critical AI literacy, the intervention embedded personalised GPT agents within a university Canvas Learning Management System. Fourteen students configured and used agents aligned to curriculum goals, teaching contexts, pedagogical preferences, and weekly Prompt–Reflect–Refine tasks. Data included approximately 7,000 AI interactions, reflective journals, assessed lesson and assessment artefacts, and tutorial-group discussions. A hybrid inductive–deductive thematic analysis identified four recurrent functions in students’ use of the agents: conceptual tutor, co-designer, reflective mirror, and cognitive challenger. Across data sources, students reported clearer conceptual support, iterative planning, and reflective prompting, alongside risks of over-reliance, factual drift, and culturally narrow exemplars. The study contributes a context-specific model for LMS-integrated generative AI that foregrounds partnership, personalisation, judgement, and ethical reflexivity. Findings should be interpreted cautiously because the study was small-scale, single-site, and partly reliant on self-reported reflection.
1 week 2 days ago
Emotional engagement plays a central role in learning, yet it is commonly assessed through retrospective self-report measures that may not fully capture learners’ emotional experiences as they emerge during classroom interaction. This study examined observable behavioral manifestations associated with emotional engagement in face-to-face classrooms and their associations with lesson-specific learning outcomes within three classroom contexts. Participants were 88 students from one primary school classroom, one middle school classroom, and one university classroom. Multiple verbal and non-verbal indicators, including vocal characteristics, facial expressions, gestures, postures, and head movements, were systematically coded and integrated into an observational engagement-related behavior index. Results showed differences in the observational index among students in the three classroom contexts examined, with lower scores observed in the university classroom than in the primary and middle school classrooms. Within each of the three classroom contexts, the observational index was positively associated with lesson-specific learning outcomes, but no statistically significant differences were detected among the three within-classroom correlation coefficients. These findings illustrate the potential of multimodal classroom observations for characterizing behavioral manifestations associated with emotional engagement in authentic learning environments while highlighting the importance of interpreting such behaviors in relation to the classroom contexts in which they occur.
1 week 2 days ago
This study examines post-intervention self-reported outcomes associated with a short intervention designed to introduce coding, educational robotics, and introductory concepts of machine learning into initial teacher education for Primary Education. The proposal is grounded in Papert’s principles and the Creative and Computational Problem Solving model, combining active learning methodologies with accessible technologies. A quasi-experimental design with a non-equivalent control group was employed. A total of 144 first-year students from three Spanish universities participated (105 in the experimental group and 39 in the control group). The intervention consisted of a two-hour workshop with hands-on stations and technological challenges, designed as a feasible microintervention within regular teacher education contexts. Assessment was carried out through an ad hoc questionnaire focused on three dimensions: knowledge of programming and machine learning, block-based programming and game engines, and active learning, all assessed through self-reported perceptions. The results showed statistically significant post-intervention differences in favor of the experimental group, with a small effect size for knowledge of programming and machine learning and large effect sizes for block-based programming and game engines and active learning. Item-level analyses revealed that favorable overall perceptions coexisted with lower self-reported familiarity in specific technical areas, particularly machine learning, prior experience with block-based programming, and programming in game engines. Three exploratory response profiles were also identified: reluctant, intermediate, and enthusiastic. The findings suggest that well-structured microinterventions may support higher self-reported knowledge of programming and machine learning, greater familiarity with block-based programming and game engines, and more positive perceptions of active learning in teacher education.
1 week 4 days ago
This study examines digital education exchange between South Korea and Cambodia through teachers’ experiences in educational ODA programs. Using Engeström’s third-generation Activity Theory, we analyzed interactions within the Busan-based LEAD Teacher Group (B-LTG) and Cambodian Teacher Group (C-TG). Data from reflective journals, focus groups, and program documents were analyzed through reflexive thematic analysis with coding based on activity system components. Contradictions among educational tools, goals, and cultural contexts drove transformative learning. B-LTG teachers refined instructional strategies for local needs, and C-TG participants developed proactive ICT integration approaches, which they then extended to colleagues through self-organized workshops. On this evidence, ODA programs should prioritize context-specific, interactive training models over one-way technology transfer. The study contributes a multi-level account of how contradictions between an advanced donor system and a resource-constrained partner system generate a shared learning object.
1 week 4 days ago
The educational practicum is a critical yet challenging threshold for pre-service teachers, often triggering “survival anxiety” due to limited immediate feedback. This 16-week longitudinal cluster-randomized trial (N = 356) investigated the heterogeneous effects of artificial intelligence (AI) feedback (AF), human expert feedback (EF), and an active self-guided reflection condition (SR) on pre-service physical education teachers’ instructional efficacy. Linear mixed models (LMM) revealed an efficiency–depth trade-off: AI feedback significantly outperformed expert feedback in maximizing students’ moderate-to-vigorous physical activity (MVPA) through low-latency spatial monitoring, effectively alleviating novices’ survival anxiety. Conversely, expert feedback showed a distinct and sustained advantage in cultivating deep specialized content knowledge (SCK) and metacognitive reflection. Cross-lagged panel network (CLPN) analysis further revealed distinct temporal patterns: in the AI condition, informing behaviors predicted subsequent task extension, consistent with an external “scaffolding” of procedural execution, whereas in the expert condition task refinement predicted greater subsequent skill application and fewer management episodes—a pattern consistent with internal “pedagogical control.” These findings challenge the paradigm of viewing AI as a universal substitute for mentorship. We propose the TOL-PE framework, advocating a layered synergistic ecosystem in which AI stabilizes baseline physical order while human experts elevate higher-order pedagogical reasoning, offering insights for teacher preparation in contexts facing mentor scarcity, pending cross-cultural validation.
1 week 6 days ago
A growing body of research on board games demonstrates that these games can function as learning environments and provide opportunities for learning across diverse contexts. Although learning is often conceptualized contextually within this literature, studies increasingly demonstrate that meaningful learning can occur through participation in board game environments. However, the relationship between board games and learning has been examined predominantly through the study of modern board games, with comparatively limited attention given to classic African board games. This literature review explores learning in classic African board games. This study serves two purposes as it highlights the state of research on classic African board games and the relationship of these games with learning. The fi ndings show that classic African board games are spaces where learning occurs and players engage in a constellation of literacies. The studies analyzed indicate that playing these games can facilitate mathematical learning, engage players in computational thinking and songoputation, and foster the development of skills related to science, engineering, and technology (i.e. STEM). These board games also allow for the exploration of artificial intelligence (AI), which results in the learning of new AI techniques. Classic African board games have the potential to simplify the learning of various subjects and the acquisition of skills relevant to the 21st century. As such, more empirical studies should be conducted to further investigate the connection between playing thesegames, their game mechanics and learning; and particularly the link between their gameplay and the learning of specific subjects such as mathematics, science, and engineering.
2 weeks ago
Open Educational Resources (OER) are widely promoted as a digital means of expanding access and reducing student costs, yet institutions often lack evidence on whether large-scale adoption is financially sustainable. This study examines the institutional costs and returns associated with OER degree pathways at five U.S. community colleges, conceptualizing institutional return on investment (ROI) as a mechanism shaping the adoption and sustainability of program-level educational technology innovations. Institutional costs were estimated using Levin’s ingredients method based on financial records and faculty and staff time-use logs. To assess potential institutional returns, we analyzed the association between OER course enrollment and students’ credit hours attempted using propensity score matching with student transcript data. Estimated changes in credit attempts were then translated into tuition and mandatory-fee revenue and compared with institutional costs to derive site-level and cross-site ROI estimates. Average institutional investments exceeded $500,000, and several colleges recouped these costs within three years through increased tuition revenues associated with higher levels of credit-taking. However, returns varied substantially across sites, indicating that financial viability is contingent on implementation strategies and institutional context. Overall, the findings suggest that OER degree pathways can generate modest institutional returns and that jointly tracking implementation resources and enrollment behavior provides institutions with a pragmatic basis for assessing sustainability and informing decisions about scaling.
2 weeks 1 day ago
Drawing on 11 evidence-based design principles, we developed a digital tool for training attention during academic activities. Two naturalistic, single-group pre-post studies—including a pre-registered direct replication—evaluated the impact of this tool on the attentional focus of adolescents across multiple public schools (Study 1: N = 322; Study 2: N = 695). Students used the tool during independent work, and focus was assessed through real-time check-ins during tool use as well as pre- and post-intervention surveys. Students’ real-time focus improved within individual sessions and cumulatively across multiple sessions, with sensitivity analyses revealing that improvements emerged early and continued over time. Across-session gains extended to students with attention-deficit/hyperactivity disorder, though within-session gains did not. Students also reported improved overall focus during class and independent assignments, with the largest gains among students who completed the most sessions. These findings demonstrate the viability of a scalable, curriculum-agnostic approach to training attention in schools and underscore the value of theory-driven intervention design.
2 weeks 1 day ago
Despite growing attention to academic writing instruction, few studies have explored how to authentically support doctoral students—particularly non-native English speakers—in navigating the complex and high-stakes process of publishing in international journals. This study addresses this critical gap by designing, implementing, and refining an online Writing for Publication Training (WFPT) tailored to the needs of doctoral students in English Language Teaching. Grounded in design-based research (DBR) and informed by authentic learning principles, the WFPT simulates the real-world scholarly publishing process through tasks such as journal selection, article drafting, peer review, and responding to editorial feedback. Over two iterative cycles, qualitative data from interviews, reflections, online meetings and field notes were thematically analyzed to inform design improvements and derive instructional principles. The results indicate several reported enhancements on participants’ writing for publication knowledge and skills. The study also proposes two novel authentic learning principles—design for confidence-building and support for self-regulated time management—as essential to effective writing-for-publication instruction. All in all, the study offers both a replicable training model and actionable guidelines for fostering scholarly publishing readiness in doctoral education.
2 weeks 2 days ago
The acquisition of science concepts is necessary for fostering analytical and problem-solving skills. The complexity of heart anatomy, obscured within the human body, presents a challenge that high-immersion virtual reality (VR) holds promise in addressing. This study explored the impact of VR versus screencast video instruction in learning anatomy, which is a core life science topic within the broader science, technology, engineering, and mathematics (STEM) domain. College students (N = 91) were randomly distributed into two groups. One group learned heart anatomy through an immersive VR tutorial, while the other group watched the identical tutorial presented via screencast video. Both groups were assessed on (1) learning content, (2) enjoyment, (3) sense of presence, and (4) cognitive load. The multivariate analysis of covariance (MANCOVA) indicated that the VR group, despite reporting significantly higher enjoyment (p ≤ .0001) and sense of presence (p ≤ .0001), scored significantly lower on a heart anatomy knowledge test (p = .003) compared with the video group. Both groups reported comparable levels of cognitive load (p = .65). This study adds evidence confirming the potential benefits and challenges of integrating VR into higher educational settings, suggesting that, in a tightly controlled, single, and short lesson on anatomy, VR enhances enjoyment and presence but does not lead to superior immediate learning outcomes compared with video instruction.
2 weeks 2 days ago
This paper presents a conceptual framework for deliberate instructional design grounded in three interrelated theoretical constructs: dynamic decision-making, self-regulated design, and systems thinking. Dynamic decision-making emphasizes the iterative nature of design reasoning and judgment. Self-regulated design extends this by framing the designer as an active agent who monitors, evaluates, and adjusts both cognitive strategies and affective states throughout the process. Systems thinking integrates these constructs, emphasizing the interconnectedness of design decisions. The framework offers implications for research, professional practice, and instructional design education. By integrating these constructs, the framework advances a theory of instructional design as a dynamic, self-regulating, and systemic process aimed at creating learning experiences that are both intentional and sustainable.
2 weeks 2 days ago
This study examined how a sequenced virtual reality (VR)–supported learning activity influenced learning and motivation in undergraduate engineering education. Drawing on cognitive load theory, generative learning theory, and the cognitive theory of multimedia learning, the study evaluated a desktop VR instructional sequence that included exploration of a simulated mechanical system, scaffolded interaction tasks, and a reflective self-explanation activity. A small sample of 38 undergraduate students was randomly assigned to either a VR-supported instructional condition or a traditional instruction control group. Students in the VR condition interacted with a desktop simulation of an excavator, completed scaffolded measurement and analysis tasks, and later responded to a structured self-explanation prompt before solving a novel statics problem. Compared with the control group, students in the VR-supported condition demonstrated higher procedural knowledge, stronger problem-solving performance, and greater self-efficacy. They also reported lower extraneous and intrinsic cognitive load. Declarative knowledge scores were higher following the introduction of scaffolded guidance, although the between-group difference did not reach statistical significance, and no significant differences were observed for germane load or maintained situational interest. In summary, the results suggest that outcomes varied across phases of the VR-supported activity relative to traditional instruction. While the study does not isolate the independent effects of individual instructional components, its small sample and use of a single VR application limit the generalizability of the findings. Nevertheless, the findings provide evidence that structuring VR learning activities with scaffolded interaction and reflective explanation may support learning efficiency and learner confidence in engineering contexts. These results offer design-relevant insights for structuring instructional supports within simulation-based learning environments.
2 weeks 2 days ago
Generative Artificial Intelligence (GenAI) is rapidly entering educational contexts, yet little is known about how primary-aged students perceive, understand, and critically reflect on its applications and limitations. This exploratory study investigates the development of AI literacy among 92 upper elementary students (5th graders) through a structured educational intervention that introduces AI concepts and ethical considerations. A mixed-methods approach was adopted, combining pre- and post-intervention questionnaires with focus group discussions to capture participants’ quantitative trends and qualitative insights into GenAI outcomes, specifically unfair, biased, or misleading outcomes. Findings indicate that students already had some prior exposure to AI and could identify examples of its use, while, after the intervention, their responses became more accurate and nuanced, encompassing a broader range of real-world applications. Importantly, students demonstrated early signs of critical AI literacy, evidenced by statistically significant changes: they recognized that AI can make mistakes, reproduce stereotypes, and reflect human biases, while expressing caution about over-reliance on AI systems. The results suggest that primary-aged learners can meaningfully and ethically engage with AI when provided with structured, age-appropriate guidance. Lastly, these findings underscore the significance of introducing AI and data ethics education early in school curricula.
2 weeks 3 days ago
This study examined the impact of learning analytics-based personalized feedback on student engagement behavior, learning outcomes, and perceptions of the feedback in a large introductory STEM course. Students were randomly assigned to either an experimental group, which received four personalized feedback messages between Weeks 6 and 14, or a control group, which received generic messages consistent with the instructor’s previous teaching practices. The feedback was personalized based on students’ engagement with homework and practice problems and was designed to promote specific behaviors in these two areas. Results showed that the experimental group exhibited greater engagement in the targeted behaviors compared to the control group. However, no significant differences were found between the groups in perceived helpfulness of the feedback or learning outcomes. This study addresses the limitations of the current implementation of personalized feedback and highlights the value of integrating behavioral data with learning outcomes and self-report responses to more accurately evaluate its impact.
2 weeks 3 days ago
Reading comprehension remains challenging in secondary language education, particularly in digital environments that provide limited adaptivity, interactivity, and multimodal support. This study developed and evaluated a multimodal e-learning platform integrating artificial intelligence (AI), game-based learning (GBL), and augmented reality (AR). The AI module provided adaptive feedback and vocabulary scaffolding, the GBL component offered interactive comprehension tasks, and the AR feature visualized textual content. A quasi-experimental mixed-methods design involved 214 tenth-grade language learners allocated to an experimental group (n = 107) or a control group (n = 107). During the six-week intervention, the experimental group used the platform for reading instruction, whereas the control group received conventional teaching. Data were collected through a pilot-tested 25-item reading comprehension test, a student engagement and motivation scale, and semi-structured interviews. The experimental group’s mean comprehension score increased from 78.6 to 90.2, compared with an increase from 78.3 to 82.1 in the control group. Analysis of covariance indicated a significant group effect, F(1, 211) = 66.08, p < .001, partial η2 = .24. Hierarchical regression showed that platform use was significantly associated with comprehension gain (final adjusted R2 = .45), and exploratory component-level associations were strongest for AI-use frequency (β = 0.42, p < .001). Interview findings indicated greater motivation, confidence, and cognitive engagement, alongside reduced reading anxiety. These findings suggest that coordinated AI, GBL, and AR features can support cognitive and motivational dimensions of secondary reading instruction.