Search Results (57)

Multimedia learning requires optimising information presentation in line with learners’ cognitive processes. Active processing depends on selecting relevant information, organising it into coherent mental representations, and integrating it with prior knowledge. When grounded in instructional design principles (cognitive load management, active learning, and alignment with learning objectives), video animations can be highly effective. In teaching complex content, visual strategies such as examples, analogies, images, infographics, and videos help make abstract or invisible concepts accessible. This review examines research on animations in formal education, noting its rapid growth (partly driven by AI), STEM predominance, and mixed findings on learning gains and persistent biases. Full article

Second language (L2) writing, particularly in demanding areas like argumentative discourse, requires learners to effectively manage interpersonal resources such as stance and modality. Despite the centrality of stance in academic literacy, its development in L2 Hebrew writing remains largely underexplored. This study addresses this gap by examining how Arabic-speaking learners of Hebrew realize epistemic, deontic, and evaluative stance in their interlanguage writing. Using a qualitative, concept-oriented, function-to-form analytical approach, the analysis examined 92 authentic argumentative essays (11,572 words) produced by L1 Arabic speakers under standardized examination conditions and systematically classified each modal expression into one of three empirically derived interlanguage developmental levels. The findings reveal a clear and consistent developmental progression across all three modal domains. Developmental patterns are inferred from interlanguage variation across proficiency levels rather than tracked longitudinally. Basic-level expressions relied primarily on high-frequency, spoken-like vocabulary and explicit personal opinion markers. Intermediate-level expressions displayed greater lexical variety and a shift toward a more abstract stance but remained marked by morphosyntactic instability and L1 influence, often producing hybrid or non-target-like constructions. Advanced writers effectively deployed idiomatic, low-frequency, and structurally more complex modal constructions aligned with conventions of Hebrew academic writing. Full article

Blockchain technology, with special features of decentralization, immutability, consensus mechanisms, and smart contracts, has been integrated into different areas of digital applications recently. However, its abstract concepts present a steep learning curve for beginners, especially in the absence of online resources that offer dynamic, hands-on learning experiences. In response to this problem, we developed a digital interactive teaching tool using the OwlSpace platform to explain what blockchain truly is in its four core foundational concepts. Interactive operations, guided workflows, and visual simulations are applied in the system to assist the learner in interpreting decentralized architectures, immutability of data interactively, the consensus formation process, and the mechanics behind smart contract operation. The system has also put a focus on conceptual understanding and gamified experiences rather than competitive ones, providing a practical and engineering-focused tool for introductory information engineering students. Full article

Simulation-based training systems are increasingly deployed to prepare learners for complex, safety-critical, and dynamic work environments. While advances in computing have enabled immersive and data-rich simulations, many systems remain optimized for procedural accuracy and surface-level task performance rather than the macrocognitive processes that underpin adaptive expertise. Macrocognition encompasses higher-order cognitive processes that are essential for performance transfer beyond controlled training conditions. When these processes are insufficiently supported, training systems risk fostering brittle strategies and negative training effects. This paper introduces a macrocognitive design taxonomy for simulation-based training systems derived from a large-scale meta-analysis examining the transfer of macrocognitive skills from immersive simulations to real-world training environments. Drawing on evidence synthesized from 111 studies spanning healthcare, industrial safety, skilled trades, and defense contexts, the taxonomy links macrocognitive theory to human–computer interaction (HCI) design affordances, computational data traces, and feedback and adaptation mechanisms shown to support transfer. Grounded in joint cognitive systems theory and learning engineering practice, the taxonomy treats macrocognition as a designable and computable system concern informed by empirical transfer effects rather than as an abstract explanatory construct. Full article

This systematic integrative review examines methodological frameworks used to evaluate educational technologies in biomedical higher education. We synthesize five complementary approaches frequently reported in the literature: the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), the System Usability Scale (SUS), Technology Readiness Levels (TRL), and the ARCS motivational model. Each framework addresses distinct but interrelated dimensions of evaluation, including technology acceptance and intention to use, perceived usability and user experience, technological maturity and implementation risk, and learner motivation. Drawing on representative studies in e-learning platforms, virtual and extended reality environments, and clinical simulation, we discuss the strengths, limitations, and common pitfalls of applying these models in isolation. Based on this synthesis, we propose a pragmatic, multi-phase evaluation workflow that aligns usability, acceptance, motivation, and technological maturity across different stages of educational technology development and adoption. Finally, we outline exploratory future perspectives on how existing evaluation models might need to evolve to address emerging AI-driven, immersive, and haptic technologies in biomedical education. This abstract was prepared in accordance with PRISMA 2020 for Abstracts, ensuring structured reporting and transparency. Full article

This study examines how different programming paradigms are associated with learning experiences and cognitive challenges as encountered by non-computer science novice learners. Using a case-study approach situated within specific instructional contexts, we integrate survey data from undergraduate students with large-scale public question-and-answer data from Stack Overflow to explore paradigm-related difficulty patterns. Four instructional contexts—C, Java, Python, and Prolog—were examined as pedagogical instantiations of imperative, object-oriented, functional-style, and logic-based paradigms using text clustering, word embedding models, and interaction-informed complexity metrics. The analysis identifies distinct patterns of learning challenges across paradigmatic contexts, including difficulties related to low-level memory management in C-based instruction, abstraction and design reasoning in object-oriented contexts, inference-driven reasoning in Prolog-based instruction, and recursion-related challenges in functional-style programming tasks. Survey responses exhibit tendencies that are broadly consistent with patterns observed in public Q&A data, supporting the use of large-scale community-generated content as a complementary source for learner-centered educational analysis. Based on these findings, the study discusses paradigm-aware instructional implications for programming education tailored to non-major learners within comparable educational settings. The results provide empirical support for differentiated instructional approaches and offer evidence-informed insights relevant to curriculum-oriented teaching and future research on adaptive learning systems. Full article

This article presents and validates the Metacognitive Knowledge Intervention for Thinking (MKIT)—an educational framework designed to assess and develop domain-general metacognitive knowledge (MK) in children aged 5 to 9. Moving beyond traditional approaches that examine metacognition within isolated subject areas, this research reconceptualizes MK as a transferable learning resource across content domains and developmental stages. Moreover, by employing a stepped-wedge design—a rigorous but rarely used approach in education—the study introduces a methodological advancement. Simultaneously, MK is operationalized through an ecologically valid and developmentally appropriate format, using visually engaging stories, illustrated scenarios, and interactive tasks integrated within classroom routines. These adaptations enabled young learners to engage meaningfully with abstract metacognitive concepts. Therefore, across three interconnected studies (N = 458), the MKIT provided strong psychometric evidence supporting valid inferences about metacognitive knowledge, age-invariant effects, and substantial gains among children with initially low MK levels. In addition, qualitative data indicated MK transfer across contexts. Thus, these findings position MKIT as a scalable tool, supported by multiple strands of validity evidence, that makes metacognitive knowledge teachable across domains—offering a practical approach to strengthening learning, reducing early achievement gaps, and supporting the development of core components of intelligence. Full article

An experiment was conducted to examine whether knowledge of word meanings enables learners to infer the meanings of related words, and whether such transfer is based on memory for related exemplars or for abstract knowledge. Participants completed a word root learning task in which they learned definitions of several English words derived from a shared root (e.g., ambler, noctambulant). At an immediate test, they were assessed on definitions of studied words, new unstudied derivatives (e.g., ambulate), and word roots (e.g., ambul). A multiple regression analysis showed that accuracy on word roots, but not on studied words, predicted performance on new derivatives. These results suggest that transfer of learning was based primarily on more abstract knowledge of word root meanings rather than on memory for specific words. These findings provide novel evidence that learners can apply root-based knowledge to new word forms, and are consistent with theories proposing that transfer is supported by abstract representations. Full article

This article introduces the ATHENA competency model, a systemic framework designed to conceptualize and support the development of creativity and complex skills in professional and educational contexts. Creativity, increasingly seen as essential across sectors, requires the coordination of cognitive, motivational, emotional, social, and sensorimotor resources. ATHENA conceptualizes competencies as emergent, agentic behaviors, not static possessions, arising from the coordination of five dimensions: cognition, conation, knowledge, emotion, and sensorimotion. These are subdivided into 60 facets, each described across four progressive mastery levels, enabling fine-grained diagnosis and developmental roadmaps. To operationalize this framework, ATHENA includes three modules: Skills, which models the requirements of professional tasks; Profile, which analyzes learner populations and contextual constraints; and LEARN, a repertory of pedagogical activities linked to ATHENA facets. The article illustrates the system through two case studies of creative job activities—graphic design and workshop facilitation—demonstrating how ATHENA aligns abstract competencies with practical training interventions. The model bridges theoretical research in psychology, creativity, and education with instructional design. Future work aims to refine its applicability, scalability, and cross-cultural relevance. Full article

This paper presents a novel visual approach to teaching multiplication to elementary school pupils using stick intersections. Within the South African context, where students consistently demonstrate low mathematics achievement, particularly in foundational arithmetic operations, this research explores an alternative pedagogical strategy that transforms abstract multiplication concepts into visual, concrete, countable representations. Building on theories of embodied cognition and visual mathematics, this study implemented and evaluated the stick intersection method with 45 Grade 4 students in Polokwane, Limpopo Province. Using a mixed-methods approach combining quantitative assessments with qualitative observations, the results revealed statistically significant improvements in multiplication performance across all complexity levels, with particularly substantial gains among previously low-performing students (61.3% improvement, d = 1.87). Qualitative findings demonstrated enhanced student engagement, deeper conceptual understanding of place value, and overwhelmingly positive learner perceptions of the method. The visual approach proved especially valuable in the multilingual South African classroom context, where it transcended language barriers by providing direct visual access to mathematical concepts. High retention rates (94.9%) one-month post-intervention suggest the method facilitated lasting conceptual understanding rather than temporary procedural knowledge. This research contributes to mathematics education by demonstrating how visually oriented, culturally responsive pedagogical approaches can address persistent challenges in developing mathematics proficiency, particularly in resource-constrained educational environments. Full article

This study aims to address the challenges junior high school students often encounter when learning abstract spatial vector concepts. By developing and implementing a virtual robotic system, this research intends to improve students’ spatial reasoning, deepen their conceptual understanding, and increase engagement through an interactive, visual, and experiential learning environment that remedies the shortcomings of traditional teaching methods. The system was developed with the Unity Game Engine to deliver 3D visualization, interactive manipulation, and real-time feedback, thereby enhancing conceptual learning. In addition, the instructional design employed the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) to enhance students’ understanding of spatial vector concepts. A quasi-experimental design was conducted involving 60 eighth-grade students divided evenly into experimental and control groups. Pre- and post-tests—including achievement assessments, learning attitude questionnaires, and cognitive load scales—were administered to evaluate learning outcomes. The main findings are as follows: (1) The experimental group demonstrated significantly higher learning achievement compared to the control group. (2) Both groups showed improvements in mathematics learning attitudes, with the experimental group exhibiting greater gains in practicality and confidence. (3) Although the experimental group experienced a slightly higher cognitive load, this difference was not statistically significant. (4) The experimental group reported high satisfaction with the system, especially in perceived usefulness. This study demonstrates that integrating virtual reality with the ADDIE model can substantially enhance learners’ conceptual understanding and motivation. Full article

With globalization and technology advancement, traditional teaching models are facing challenges due to the diverse needs of modern learners. It is necessary to enhance learner engagement and motivation, and incorporating Internet of Things (IoT)-assisted teaching tools has become a major concern for educators. However, the time it takes to develop new teaching tools and integrate IoT technology must be shortened by combining educational content with game mechanics seamlessly. Therefore, we developed a gamified teaching model by incorporating IoT technology. We used the “System, Indicators, Criteria” framework to develop a three-tiered board game evaluation and development model. Based on this framework, a teaching tool was designed to provide personalized learning experiences with IoT technology. The tool provides abstract knowledge, fosters interaction and collaboration among learners, and thus enhances engagement. To ensure a rigorous design and evaluation process, we employed quality function deployment (QFD), analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE). The developed model facilitates the integration of IoT technology with innovative design concepts and enhances the application value of teaching tools in education. The model also enhances intelligence, interactivity, and creativity for traditional education to revitalize learning experiences. Full article

In cognitive science and education research, learning has been described to occur at surface and deep levels. Learners are thought to orient more toward one of these approaches to learning versus the other. In cognitive science, this has been assessed with a concept-building framework using objective function learning tasks to classify students as exemplar (surface) or abstraction (deep) learners. In education, the student approach to learning (SAL) framework has used self-report survey measures to classify learners as relying on surface approaches or deep approaches to learning. In two studies, we directly compared these two frameworks using self-report data from the Modified Approaches and Study Skills Inventory (M-ASSIST) and the Revised Study Process Questionnaire (R-SPQ-2F) along with objectively determined concept-building classifications from a computer-based function learning task. Potential links between exemplar learning and surface approaches and between abstraction learning and deep approaches were not found. We discuss possible explanations for the absence of empirical links, including inaccuracies in students’ metacognitions regarding their learning, the measures, and possible differences between learning-content-dependencies of the survey responses versus content neutrality of the concept-building task. We conclude by suggesting directions for future work in assessing and comparing surface and deep learning across frameworks. Full article

Critical data literacy (CDL) has emerged as a crucial component in data science education, transcending traditional disciplinary boundaries. Promoting CDL requires collaborative approaches to enhance learners’ skills in data science, going beyond mere quantitative reasoning to encompass a comprehensive understanding of data workflows and tools. Despite the growing literature on CDL, there is still a need to explore how students use data science practices for supporting the learning of CDL throughout a summer-long data science program. Drawing on situative perspectives of learning, we utilize a descriptive case study to address our research question: How do data science practices taught in a classroom setting differ from those enacted in real-world social justice projects? Key findings reveal that while the course focused on abstract principles and basic technical skills, the Food Justice Project provided students with a more applied understanding of data tools, ethics, and exploration. Through the project, students demonstrated a deeper engagement with CDL, addressing real-world issues through detailed data analysis and ethical considerations. This manuscript adds to the literature within data science education and has the potential to bridge the gap between theoretical knowledge and practical application, preparing students to address real-world data science challenges through their coursework. Full article

This study integrates the 2017 United Nations ESD framework and UNESCO’s ESD priorities with the Sustainable Development Goal (SDG) of “quality education” and the CDIO (Conceive, Design, Implement, Operate) framework to propose an innovative programming teaching model. A central component is an automatic architecture diagram generation system that visualizes program code structures in real-time, reducing cognitive load and enhancing comprehension of abstract programming concepts such as recursion and data structures. Students complete a project-based assignment—developing a Scheme interpreter—to simulate real-world software development. This model emphasizes system thinking, modular design, and problem solving, aligning with CDIO’s structured learning progression. The experimental results show that students using the system significantly outperformed the control group in their final project scores, demonstrating improved practical programming ability. While cognitive load remained stable, learning motivation decreased slightly, indicating the need for additional affective design support. The findings confirm that the integration of visual learning tools and project-based pedagogy under the CDIO framework supports the development of critical competencies for sustainable development. This approach offers a transformative step forward in programming education, cultivating learners who are capable, innovative, and ready to meaningfully contribute to global sustainability. Full article