Search Results (2,560)

Background/Objectives: The transformation of nursing and midwifery education through digital technologies has gained momentum worldwide, with algorithm-based video instruction and virtual reality (VR) emerging as promising tools for improving clinical learning. This quasi-experimental study explores the impact of an enhanced flipped classroom model on Bulgarian nursing and midwifery students’ self-perceived competence. Methods: A total of 228 participants were divided into a control group receiving traditional instruction (lectures and simulations with manikins) and an experimental group engaged in a digitally enhanced preparatory phase. The latter included pre-class video algorithms, VR, and clinical problem-solving tasks for learning and improving nursing skills. A 25-item self-report questionnaire was administered before and after the intervention to measure perceived competence in injection techniques, hygiene care, midwifery skills, and digital readiness. Results: Statistical analysis using Welch’s t-test revealed significant improvements in the experimental group in all domains (p < 0.001). Qualitative data from focus group interviews further confirmed increased student engagement, motivation, and receptiveness to digital learning tools. Conclusions: The findings highlight the pedagogical value of integrating structured video learning, VR components, and case-based learning within flipped classrooms. The study advocates for the wider adoption of blended learning models to foster clinical confidence and digital competence in healthcare education. The results of the study may be useful for curriculum developers aiming to improve clinical readiness through technology-enhanced learning. Full article

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills. Full article

Learning is occurring increasingly online, often asynchronously, and sometimes that presents a barrier to instructional delivery on metacognitive behaviors that might easily be modeled in traditional classrooms. However, such metacognitive behaviors are essential to engaging deeply with academic texts. The research team involved in this paper is part of ongoing design-based research exploring the use of social annotation to support students as metacognitive readers of digital, academic texts in online asynchronous contexts. In the most recent iteration of this research, the authors designed asynchronous instruction on metacognitive reading using the gradual release of responsibility (GRR) framework. This paper provides rich descriptions of instructors’ instructional moves to scaffold and support students as metacognitive readers of digital, academic texts in asynchronous online classes. Future research should explore the efficacy of GRR as a pedagogical approach used online. Full article

The integration of Inquiry-Based Learning (IBL) and mobile technologies can transform science education, offering experimentation opportunities to students from budget-constrained schools. This study investigates the efficacy of smartphone-assisted experimentation (SAE) within IBL to enhance pre-service science teachers’ understanding of human physiology and presents a newly developed and validated rubric for assessing their scientific skills. Students (N = 286) from a Science and Mathematics Teacher Education Program participated in a summative IBL activity (“Investigating the Human Physiology”—iHPhys) where they designed experimental projects using smartphone applications to collect body sign data. The scoring rubric, assessing seven criteria including hypothesis formulation, methodological design, data presentation, and conclusion writing, was validated as substantial to almost perfect inter-rater reliability. Results reveal that students exhibited strong skills in hypothesis clarity, theoretical grounding, and experimental design, with a high degree of methodological innovation observed. However, challenges persisted in predictive reasoning and evidence-based conclusion writing. The students were strongly interested in inquiring about the cardiovascular and nervous systems. Correlational analyses suggest a positive relationship between project originality and overall academic performance. Thus, integrating SAE and IBL fosters critical scientific competencies, creativity, and epistemic cognition while democratizing access to scientific experimentation and engaging students in tech-savvy pedagogical practices. Full article

The integration of artificial intelligence (AI) in education offers novel opportunities to enhance critical thinking while also posing challenges to independent cognitive development. In particular, Human-Centered Artificial Intelligence (HCAI) in education aims to enhance human experience by providing a supportive and collaborative learning environment. Rather than replacing the educator, HCAI serves as a tool that empowers both students and teachers, fostering critical thinking and autonomy in learning. This study investigates the potential for AI to become a collaborative partner that assists learning and enriches academic engagement. The research was conducted during the 2024–2025 winter semester within the Pedagogical and Teaching Sufficiency Program offered by the Audio and Visual Arts Department, Ionian University, Corfu, Greece. The research employs a hybrid ethnographic methodology that blends digital interactions—where students use AI tools to create artistic representations—with physical classroom engagement. Data was collected through student projects, reflective journals, and questionnaires, revealing that structured dialog with AI not only facilitates deeper critical inquiry and analytical reasoning but also induces a state of flow, characterized by intense focus and heightened creativity. The findings highlight a dialectic between individual agency and collaborative co-agency, demonstrating that while automated AI responses may diminish active cognitive engagement, meaningful interactions can transform AI into an intellectual partner that enriches the learning experience. These insights suggest promising directions for future pedagogical strategies that balance digital innovation with traditional teaching methods, ultimately enhancing the overall quality of education. Furthermore, the study underscores the importance of integrating reflective practices and adaptive frameworks to support evolving student needs, ensuring a sustainable model. Full article

The sustainable development of teaching expertise in research-intensive universities remains a critical global challenge. This study investigates the distinctive characteristics of expert teachers—exemplary faculty in research universities—addressing their developmental trajectories and motivational mechanisms within prevailing incentive systems that prioritize research productivity over pedagogical excellence. Employing grounded theory methodology, we conducted iterative coding of 20,000-word interview transcripts from 13 teaching-awarded professors at Chinese “Double First-Class” universities. Key findings reveal the following: (1) Compared to the original K-12 expert teacher model, university-level teaching experts exhibit distinctive disciplinary mastery—characterized by systematic knowledge structuring and cross-disciplinary integration capabilities. (2) Their developmental trajectory transcends linear expertise acquisition, instead manifesting as a problem-solving continuum across four nonlinear phases: career initiation, dilemma adaptation, theoretical consciousness, and leadership expansion. (3) Sustainable teaching excellence relies fundamentally on teachers’ professional passion, sustained through a virtuous cycle of high-quality instructional engagement and external validation (including positive student feedback, institutional recognition, and peer collaboration). Universities must establish comprehensive support systems—including (a) fostering a supportive and flexible learning atmosphere, (b) reforming evaluation mechanisms, and (c) facilitating interdisciplinary collaboration through teaching development communities—to institutionalize this developmental ecosystem. Full article

This systematic review examines how machine learning (ML) and generative AI (GenAI) have been integrated into learning analytics (LA) in higher education (2018–2025). Following PRISMA 2020, we screened 9590 records and included 101 English-language, peer-reviewed empirical studies that applied ML or GenAI within LA contexts. Records came from 12 databases (last search 15 March 2025), and the results were synthesized via thematic clustering. ML approaches dominate LA tasks, such as engagement prediction, dropout-risk modelling, and academic-performance forecasting, whereas GenAI—mainly transformer models like GPT-4 and BERT—is emerging in real-time feedback, adaptive learning, and sentiment analysis. Studies spanned world regions. Most ML papers (n = 75) examined engagement or dropout, while GenAI papers (n = 26) focused on adaptive feedback and sentiment analysis. No formal risk-of-bias assessment was conducted due to heterogeneity. While ML methods are well-established, GenAI applications remain experimental and face challenges related to transparency, pedagogical grounding, and implementation feasibility. This review offers a comparative synthesis of paradigms and outlines future directions for responsible, inclusive, theory-informed AI use in education. Full article

Digital competence refers to the integration of digital technology in teaching and learning, as outlined in the national curriculum of Estonia for upper secondary schools. This study presents original research findings on Estonian science teachers’ use of digital tools and materials, their digital competence, and the main benefits and challenges they face. The findings emphasize the need for continued professional development, accessible digital resources, and equitable digital infrastructure to maintain Estonia’s leadership in digital science education. A survey of 58 secondary school science teachers revealed that computers (desktops, laptops, and tablets) are the primary digital tools used. The article explores digital literacy advancements in secondary science education, research methodologies used to assess digital tool usage, and key findings from recent studies. However, challenges such as digital equity, technological barriers, and digital fatigue persist. Additionally, discrepancies were found in teachers’ responses regarding digital tool effectiveness, implementation strategies, and perceived barriers. While some teachers reported a successful integration and improved student outcomes, others highlighted difficulties in aligning digital resources with curriculum requirements and pedagogical approaches. Full article

This study examines the moral agency of French secondary school headteachers through the lens of the theory of conventions. Using qualitative data from interviews with fifteen headteachers involved in professional development, this study explores how these leaders justify their practices within a centralized, bureaucratic, and hierarchical education system. It identifies a variety of conventions—civic, domestic, industrial, project, market, inspired, and fame—that headteachers draw on to navigate institutional constraints, manage professional relationships, and foster pedagogical and organizational change. Particular attention is given to how civic and domestic conventions shape leadership discourse and practices, especially regarding trust building, decision making, and reform implementation. We also compare the French context with international examples from the International Successful School Principalship Project (ISSPP), focusing on Nordic countries, where leadership emphasizes democratic participation, professional trust, and shared responsibility. This study underscores the uniqueness of the French leadership model, which resists managerial and market logics while remaining rooted in republican and egalitarian ideals. It concludes by advocating for a more context-aware, ethically grounded, and dialogical approach to school leadership. Full article

Teachers often raise their vocal volume to improve intelligibility or capture students’ attention. While this practice is common in second language (L2) teaching, its effects on tone perception remain understudied. To fill this gap, this study explores the effects of loud speech on Mandarin tone perception for L2 learners. Twenty-two native Mandarin speakers and twenty-two Thai L2 learners were tested on their perceptual accuracy and reaction time in identifying Mandarin tones in loud and normal modes. Results revealed a significant between-group difference: native speakers consistently demonstrated a ceiling effect across all tones, while L2 learners exhibited lower accuracy, particularly for Tone 3, the falling-rising tone. The loud speech had different impacts on the two groups. For native speakers, tone perception accuracy remained stable across different speech modes. In contrast, for L2 learners, loud speech significantly reduced the accuracy of Tone 3 identification and increased confusion between Tones 2 and 3. Reaction times in milliseconds were prolonged for all tones in loud speech for both groups. When subtracting the length of the tones, the delay of RT was evident only for Tones 3 and 4. Therefore, raising the speaking volume negatively affects the Mandarin tone perception of L2 learners, especially in distinguishing Tone 2 and Tone 3. Our findings have implications for both theories of L2 tone perception and pedagogical practices. Full article

The application of extended reality (XR) in design education has grown substantially; however, empirical evidence on its educational benefits remains limited. This two-year study examines the impact of incorporating a virtual reality (VR) learning module into undergraduate landscape architecture (LA) construction courses, focusing on brick masonry instruction. A conventional learning sequence—lecture, sketching, CAD, and 3D modeling—was supplemented with an immersive VR experience developed using Unreal Engine 5 and deployed on Meta Quest devices. In Year 1, we piloted a preliminary version of the module with landscape architecture students (n = 15), and data on implementation feasibility and student perception were collected. In Year 2, we refined the learning module and implemented it with a new cohort (n = 16) using standardized VR evaluation metrics, knowledge retention tests, and self-efficacy surveys. The findings suggest that when sequenced after a theoretical introduction, VR serves as a pedagogical bridge between abstract construction principles and physical implementation. Moreover, the VR module enhanced student engagement and self-efficacy by offering experiential learning with immediate feedback. The findings highlight the need for intentional design, institutional support, and the continued development of tactile, collaborative simulations. Full article

Advances in neuroscience have improved the understanding of cognitive, emotional, and social processes involved in learning. Simultaneously, technologies such as artificial intelligence, augmented reality, and gamification are transforming educational practices. However, their integration into formal education remains limited and often misapplied. This study aims to evaluate the impact of technology-supported neuroeducational models on student learning and well-being. A systematic review was conducted using PubMed, the Web of Science, ScienceDirect, and LILACS, including open-access studies published between 2020 and 2025. Selection and methodological assessment followed PRISMA 2020 guidelines. Out of 386 identified articles, 22 met the inclusion criteria. Most studies showed that neuroeducational interventions incorporating interactive and adaptive technologies enhanced academic performance, intrinsic motivation, emotional self-regulation, and psychological well-being in various educational contexts. Technology-supported neuroeducational models are effective in fostering both cognitive and emotional development. The findings support integrating neuroscience and educational technology into teaching practices and teacher training, promoting personalized, inclusive, and evidence-based education. Full article

This study investigates the evolving pedagogical strategies and professional identity development of two novice college English teachers in China through a semester-long classroom-based inquiry. Drawing on Norris’s Multimodal (Inter)action Analysis (MIA), it analyzes 270 min of video-recorded lessons across three instructional stages, supported by visual transcripts and pitch-intensity spectrograms. The analysis reveals each teacher’s transformation from textbook-reliant instruction to student-centered pedagogy, facilitated by multimodal strategies such as gaze, vocal pitch, gesture, and head movement. These shifts unfold across the following three evolving identity configurations: compliance, experimentation, and dialogic enactment. Rather than following a linear path, identity development is shown as a negotiated process shaped by institutional demands and classroom interactional realities. By foregrounding the multimodal enactment of self in a non-Western educational context, this study offers insights into how novice EFL teachers navigate tensions between traditional discourse norms and reform-driven pedagogical expectations, contributing to broader understandings of identity formation in global higher education. Full article

As an example of pedagogical approaches that blend online and face-to-face instruction, the flipped classroom model has seen exponential growth in business schools. To explore its effectiveness, expectancy-value theory and cognitive load theory were employed to develop a framework linking students’ perceived usefulness of the online and in-person content to their pre-class preparation, class participation, perceived learning, and skills development. A preliminary test of this framework was conducted using a flipped Organizational Behavior course within a business diploma program at a publicly funded Canadian college. The perceived usefulness of the online component was positively associated with students’ pre-class preparation, which, in turn, was positively related to both their perceived learning and skills development. Implications for practice and directions for future research are discussed. Full article

In distance learning environments, learner engagement directly impacts attention, motivation, and academic performance. Signs of fatigue, negative affect, or critical remarks can warn of growing disengagement and potential dropout. However, most existing approaches rely on a single modality, visual or text-based, without providing a general view of learners’ cognitive and affective states. We propose a multimodal system that integrates three complementary analyzes: (1) a CNN-LSTM model augmented with warning signs such as PERCLOS and yawning frequency for fatigue detection, (2) facial emotion recognition by EmoNet and an LSTM to handle temporal dynamics, and (3) sentiment analysis of feedback by a fine-tuned BERT model. It was evaluated on three public benchmarks: DAiSEE for fatigue, AffectNet for emotion, and MOOC Review (Coursera) for sentiment analysis. The results show a precision of 88.5% for fatigue detection, 70% for emotion detection, and 91.5% for sentiment analysis. Aggregating these cues enables an accurate identification of disengagement periods and triggers individualized pedagogical interventions. These results, although based on independently sourced datasets, demonstrate the feasibility of an integrated approach to detecting disengagement and open the door to emotionally intelligent learning systems with potential for future work in real-time content personalization and adaptive learning assistance. Full article