ETR&D
1 day 17 hours ago
Foreign language learning has become one of the most important educational goals around the globe. Among the four skills of listening, speaking, reading, and writing, listening training is one of the more challenging tasks for foreign language learners owing to the lack of practice contexts. Programming-based applications provide learners with a ubiquitous learning, listening training, and personalized feedback environment. Most programming-based applications are currently designed for English language learners. This study proposes a method of learning Japanese language through programming-based tasks to motivate students to listen to lessons and provide meaningful learning. To motivate students in listening class and to provide meaningful Japanese-assisted learning, this study proposed a learning-by-doing approach by participating in programming-based tasks. To evaluate the effectiveness of this approach, a quasi-experiment design was implemented in a JFL (Japanese as foreign language) listening class at a university in northern Taiwan. The participants were two classes of first-year university students. One class was the experimental group (N = 57) learning with the programming-based listening training approach, while the other class was the control group (N = 59) using the conventional listening training approach. The pretest and posttest were subjected to quantitative analysis, while the students’ responses to the questionnaire were analyzed both quantitatively and qualitatively. The findings provide preliminary evidence that a programming task-facilitated listening training approach may support JFL learners’ listening performance and positive learning perceptions. Given the quasi-experimental design, the use of intact classes, and the absence of a delayed post-test, the results should be interpreted cautiously as an initial evaluation of an integrated instructional approach rather than as conclusive evidence of the isolated effect of programming tasks.
4 days 17 hours ago
The rapid integration of AI into K–12 education is transforming classroom interaction from a teacher–student dyad into a teacher–AI–student triad. This shift presents teachers with new challenges in collaborating effectively with AI systems while mitigating potential risks. However, existing research primarily focuses on general AI literacy but falls short of capturing the fine-grained professional demands teachers encounter when engaging in process-oriented, pedagogically grounded co-teaching with AI systems. To address this gap, this study develops and validates a dedicated framework and measurement instrument for K–12 Teacher–AI Collaborative Teaching Competence (TAICT-C). A two-phase sequential mixed-methods design was adopted. In Phase 1, a three-round Delphi study involving 24 educational technology experts was conducted to develop and refine the TAICT-C framework. The framework comprised four dimensions: collaborative mindset, precision design, joint teaching, and continuous development. In Phase 2, a corresponding scale (TAICT-CS) was developed and validated using standardized procedures. An initial pool of 65 items was generated from the framework, reviewed for content validity, and subsequently administered to 755 K–12 teachers across 23 provinces in China. Item analysis, EFA, and CFA supported a 32-item, four-factor structure aligned with the proposed framework. The scale demonstrated strong internal consistency (overall Cronbach’s α = .973; subscales α = .845–.955) as well as satisfactory convergent and discriminant validity. These findings provide an initial theoretically grounded and psychometrically supported foundation for advancing AI-integrated educational research and for examining potential applications in teacher professional development.
4 days 17 hours ago
Integrating playful and maker-oriented approaches into mathematics teacher education remains challenging, particularly in relation to curricular alignment and teacher agency. This design-based research examines a workshop centred on the Hidden Blocks game, structured in three stages: play, digital modelling, and fabrication through 3D printing. The intervention was designed to support teachers’ mathematical reasoning, their articulation of links between the activity and the curriculum, and their professional agency as designers of resources and learning experiences. Across four cycles in Austria, Brazil, and Uruguay involving mathematics teachers, we analysed interviews, workshop recordings, facilitators’ notes, and lesson plans. Through iterative refinement, we developed a set of design principles organised into three interrelated dimensions: (a) mathematics, focusing on eliciting and formalising mathematical reasoning through play, modelling, and fabrication; (b) curriculum, focusing on explicit connections between mathematical ideas and curricular aims; and (c) professional agency, focusing on teachers’ ownership, authorship, and adaptation of resources for classroom use. These principles show how a technology-enhanced intervention that combines gameplay, modelling, and fabrication can support mathematisation, curriculum articulation, and teacher agency in mathematics teacher education.
5 days 17 hours ago
Digital learning environments require instructional designs that account for learners’ cognitive processes, individual differences, and learning contexts. This editorial introduces 14 articles in the special issue “Cognitive Dimensions in Digital Learning: Implications for Instructional Design”. The contributions address five related areas. These are cognitive resources and instructional support; cognitive diversity, accessibility, and inclusion; generative artificial intelligence as a cognitive partner; learning analytics for examining cognitive processes; and computational thinking and design deliberation. Across these areas, the articles show that instructional effectiveness depends not only on technological features but also on how support aligns with learners’ prior knowledge, cognitive capacities, and task demands. They also highlight the importance of designing for diverse learners, promoting active reasoning when using artificial intelligence, and interpreting learning analytics through pedagogical theory. Taken together, the contributions support a shift toward cognitively informed instructional design that integrates empirical evidence, learner characteristics, and contextual considerations across different digital learning environments.
5 days 17 hours ago
In English as a Foreign Language (EFL) learning, students often struggle to develop speaking skills due to limited opportunities for authentic conversation, which frequently leads to insufficient task engagement. Although the flipped classroom model is widely adopted to address these challenges, it still faces issues such as delayed feedback during the pre-class phase, disparities in peer proficiency during in-class activities, and increased teacher workload—factors that hinder sustained learner engagement. The rapid rise of Generative AI (GenAI) chatbots presents a promising opportunity to enhance engagement; however, their effective integration into existing flipped classroom pedagogies remains relatively underexplored. Grounded in social constructivist theory, this study incorporated a GenAI chatbot into the flipped classroom framework to investigate its impact on EFL learners’ task engagement. Using a sequential mixed-methods design, a 12-week quasi-experimental study was conducted with 32 students in a traditional flipped classroom (control group) and 31 students in a GenAI chatbot-enhanced flipped classroom (experimental group). Quantitative data were analyzed via ANCOVA, and thematic analysis of semi-structured interviews with 16 students and one instructor provided qualitative insights. Results indicate that the GenAI chatbot-supported flipped classroom—where the chatbot’s adaptive scaffolding was implemented through researcher-designed, rule-based pedagogical logic—significantly improves learners’ task engagement across behavioral, emotional, cognitive, social, and agentic dimensions compared to conventional flipped instruction, with varying effect sizes. Students described the chatbot as “a confidence booster,” “a digital polyglot,” and “a tireless tutor,” while the instructor referred to it as “a 24/7 teaching assistant,” “a personalized tutor,” and “an engagement catalyst.” Nonetheless, both students and the instructor acknowledged limitations, characterizing the chatbot as “the technical tango” and “a double-edged sword.” This study offers valuable implications for language educators, researchers, and policymakers seeking to integrate GenAI chatbots into flipped classroom pedagogy to promote greater task engagement and improve the overall learning experience.
1 week ago
Prior knowledge is a central cognitive factor shaping how learners benefit from instructional support in digital learning environments. Pretraining and worked-out examples are known as single support strategies for activating or building prior knowledge and facilitating the integration of new knowledge. The interaction between prior knowledge and single support strategies has been widely studied. Yet the use of single support strategies does not reflect actual teaching practice, in which multiple forms of instructional support are tailored to learners’ characteristics so that all students can benefit effectively from instruction. This study examines the potential interaction between prior knowledge and the instructional support strategies of pretraining and worked-out examples, which can be applied sequentially when teaching adaptively. A total of 139 primary school students were randomly assigned to one of the four conditions in a 2 (pretraining vs. no pretraining) × 2 (partially vs. fully worked-out example) between-subjects factorial design, with prior knowledge as a moderator. Cognitive load and learning outcomes are examined. Results show that prior knowledge moderates learning outcomes, with lower prior knowledge learners profiting from both pretraining and worked-out examples. The higher the learners’ prior knowledge, the greater the effect shifted from positive to nonsignificant to negative. A significant three-way interaction emerged, indicating that the design of digital learning environments depends not only on learners’ prior knowledge but also on the combined influence of multiple support strategies. Practical implications point to the design of adaptive, technology-enhanced learning environments that dynamically tailor pretraining and worked-out examples to learners’ evolving knowledge states.
1 week 1 day ago
Techno-centric and compliance-oriented approaches to accessibility in instructional design and educational technology literature and models have resulted in anemic theoretical engagement with accessible learning design. In this paper, I argue that accessibility is a learning problem masqueraded as a technical problem, and that better strategies for developing accessible learning require reframing accessibility through the lens of learning and instructional theories. Specifically, I explore implications for accessibility as reframed through cognitive message design and cognitive theories of cognitive load, generative learning, and socio-cognitive frameworks that emphasize learner interaction with content, other learners, instructors, and the learning interface. By reframing accessibility in this way, I propose the construct of accessible learning strategies and a theory of accessible design that yields testable axioms and hypotheses that facilitate empirical study to better inform evidence-based accessible learning strategies for practice.
1 week 2 days ago
Large language models can be used to design instructional tasks to align to students’ needs. These generative AI models can create tasks that correspond to students’ out-of-school interests in areas like sports or music, which may enhance students’ interest in learning. However, little is known about teachers’ processes for creating or modifying tasks with generative AI, despite commercial AI companies’ claims that they will save teachers time. In the present study, 7 middle school mathematics teachers used ChatGPT to create personalized versions of problems in their curriculum to correspond to their students’ interests. The prompting moves teachers made and their students’ open-ended feedback on personalized problems were examined through thematic analysis. Teachers’ efficiency when creating problems and the ratings of their 7th grade students who received personalized assignments were examined. Results from this sample suggest that having a teacher-in-the-loop may result in generative AI-enhanced personalization being enacted at a broad grain size, whereas students often expressed preference for a smaller grain-size where they receive specific popular culture references that interest them. Teachers used prompting to adjust popular culture references and address issues with the depth, ethics, or realism of the problems generated, in some cases showing epistemic agency. The article closes with an exploratory, design-focused look at scalability trade-offs with teacher-facing personalization, patterns of teacher prompting practices, and opportunities for teachers to exercise epistemic and ethical monitoring of personalization outputs.
2 weeks ago
Fused deposition modeling (FDM) 3D printing has become increasingly important in STEM and design education, yet introductory training in shared laboratories often exposes learners to high cost-of-failure conditions, including material waste, equipment downtime, and intensive instructor support. These constraints make procedural error recovery effortful, increase time pressure, and can destabilize early procedural learning. To address this challenge, this study proposes recoverability as an instructional design mechanism and develops a resilience-oriented virtual reality (VR) safe-sandbox that functions as pre-laboratory scaffolding. The environment constrains novice decision spaces, externalizes canonical failure states through explicit diagnostic feedback, and enables reversible, low-cost recovery actions to reduce recovery burden before authentic hands-on practice. A randomized between-subjects experiment involving 150 learners compared the VR safe-sandbox with a conventional physical-interface workflow under matched instructional content. Learners completed standardized introductory FDM training and were evaluated using usability, multidimensional workload, technology acceptance, threshold-based analyses, and Facilitator Intervention Load (FIL). Compared with the physical-interface workflow, the VR condition demonstrated higher usability, lower subjective workload, and more favorable technology acceptance. The largest improvement was observed in Temporal Demand (Hedges' g = −0.99), together with substantial reductions in Effort and Frustration, whereas self-rated Performance and Behavioral Intention remained comparable across conditions. Threshold-based analyses further demonstrated fewer high-strain learner states and reduced facilitator intervention requirements. These findings support a mechanism-oriented interpretation in which recoverability reduces perceived recovery burden, stabilizes learner progression, and improves early procedural learning without replacing authentic laboratory practice. Beyond evaluating a specific VR application, this study introduces a resilience-oriented instructional framework and evidence-based design principles that position recoverability as a transferable instructional design objective for technology-enhanced procedural learning in authentic resource-constrained educational environments.
2 weeks ago
With advances in artificial intelligence (AI), textual sentiment analysis has become increasingly popular for extracting word relationships and uncovering hidden emotions. This study applied sentiment analysis to explore students’ emotions during learning because emotions significantly impact motivation and outcomes. Effective teacher-student communication fosters understanding and enhances learning but requires strong empathetic skills to recognize and address students’ emotions. To support this, we developed an AI-assisted learning system integrating KeyMoji, ChatGPT, and Unity that analyzes emotions in real time and provides empathetic responses. The results showed that learning emotions were associated with teacher-student interactions and self-reported academic performance. Students who reported receiving empathetic responses also reported higher engagement and perceived learning outcomes, although these relationships cannot be interpreted as causal given the study design. This study provides preliminary evidence that AI sentiment analysis and empathetic communication are statistically associated with students’ learning emotions, motivation, engagement, and perceived learning outcomes. These findings suggest that AI-generated empathetic feedback supports effective motivational learning processes. The post-test findings indicate that AI-generated empathetic feedback is statistically associated with perceived learning outcomes indirectly through learning emotions and motivation, although these relationships should be interpreted as associational rather than causal, given the post-test single-group research design. Consequently, this study provides a transferable blueprint that advances both affective computing theory and practical digital pedagogy.
2 weeks 2 days ago
Despite the proliferation of artificial intelligence (AI) literacy frameworks across educational contexts, substantial inconsistencies remain in how key constructs are defined and operationalized, leading to conceptual fragmentation. To address this issue, this study introduces Human-AI Collaborative Meta-Synthesis (HACS), a systematic literature synthesis procedure in which human-in-the-loop governance leverages large language models to execute grounded theory-inspired open, axial, and selective coding. Applying HACS to a corpus of 71 peer-reviewed studies, the analysis reconciles divergent conceptualizations of AI literacy and develops an integrated design reference framework for higher education. The resulting framework offers a theoretically grounded five-dimensional structure comprising Knowledge and Understanding, Usage and Skill, Evaluation and Modification, Ethics and Responsibility, and Continuous Learning and Development, operationalized through thirteen sub-dimensions and thirty-two indicators suitable for instructional design and program evaluation. Rather than treating these dimensions as parallel or independent competency lists, the framework reconceptualizes AI literacy as a sociocognitive ecosystem in which the five dimensions are dynamically and recursively intertwined, positioning adaptability, reflexivity, and ethical situatedness as central developmental targets. This study further demonstrates how HACS extends traditional qualitative synthesis by combining computational scalability with sustained human interpretive control, and how its iterative design enables principled framework renewal as AI technologies and educational contexts evolve.
3 weeks 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.
3 weeks 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.
3 weeks 1 day 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 weeks 2 days 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 weeks 2 days 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.
3 weeks 5 days 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.
4 weeks 1 day 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.
4 weeks 1 day 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 month 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.