Search Results (1,245)

E-Learning is an emerging dominant phenomenon in education, making the development of robust models that can accurately represent the dynamic behavior of learners in MOOCs even more critical. In this article, we propose the Temporal Sparse Attention-Gated Recurrent Unit (TSA-GRU), a novel deep learning framework that combines TSA with a sequential encoder based on the GRU. This hybrid model effectively reconstructs student response times and learning trajectories with high fidelity by leveraging tthe emporal embeddings of instructional and feedback activities. By dynamically filtering noise from student interactions, TSA-GRU generates context-aware representations that seamlessly integrate both short-term fluctuations and long-term learning patterns. Empirical evaluation on the 2009–2010 ASSISTments dataset demonstrates that TSA-GRU achieved a test accuracy of 95.60% and a test loss of 0.0209, outperforming Modular Sparse Attention-Gated Recurrent Unit (MSA-GRU), Bayesian Knowledge Tracing (BKT), Performance Factors Analysis (PFA), and TSA in the same experimental design. TSA-GRU converged in five training epochs; thus, while TSA-GRU is demonstrated to have strong predictive performance for knowledge tracing tasks, these findings are specific to the conducted dataset and should not be implicitly regarded as conclusive for all data. More statistical validation through five-fold cross-validation, confidence intervals, and paired t-tests have confirmed the robustness, consistency, and statistically significant superiority of TSA-GRU over the baseline model MSA-GRU. TSA-GRU’s scalability and capacity to incorporate a temporal dimension of knowledge can make it acceptably well-positioned to analyze complex learner behaviors and plan interventions for adaptive learning in computerized learning systems. 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

Road traffic accidents are a leading cause of injury and death among adolescents, making road safety education crucial. This study assesses the performance of and users’ opinions on the Route 2 School (R2S) traffic safety education program, designed for secondary school students (13–17 years) in Belgium. The program incorporates gamified e-learning modules containing, among others, podcasts, interactive 360° visuals, and virtual reality (VR), to enhance traffic knowledge, situation awareness, risk detection, and risk management. This study was conducted across several cities and municipalities within Belgium. More than 600 students from school years 3 to 6 completed the platform and of these more than 200 students filled in a comprehensive questionnaire providing detailed feedback on platform usability, preferences, and behavioral risk assessments. The results revealed shortcomings in traffic knowledge and skills, particularly among older students. Gender-based analysis indicated no significant performance differences overall, though females performed better in risk management and males in risk detection. Furthermore, students from cities outperformed those from municipalities. Feedback on the R2S platform indicated high usability and engagement, with VR-based simulations receiving the most positive reception. In addition, it was highlighted that secondary school students are high-risk groups for distraction and red-light violations as cyclists and pedestrians. This study demonstrates the importance of gamified, technology-enhanced road safety education while underscoring the need for module-specific improvements and regional customization. The findings support the broader application of e-learning methodologies for sustainable, behavior-oriented traffic safety education targeting adolescents. Full article

Aims/Background: Polypharmacy, or the concurrent intake of five or more medications, is a significant issue in clinical practice, particularly in multimorbid elderly individuals. Despite its importance for patient safety, medical education often lacks systematic training in recognising and managing polypharmacy within the framework of patient-centred care. We investigated the impact of a structured learning intervention introducing polypharmacy as a chronic condition, assessing whether it enhances medical students’ diagnostic competence, confidence, and interprofessional collaboration. Methods: A prospective cohort study was conducted with 50 final-year medical students who received a three-phase educational intervention. Phase 1 was interactive workshops on the principles of polypharmacy, its dangers, and diagnostic tools. Phase 2 involved simulated patient consultations and medication review exercises with pharmacists. Phase 3 involved reflection through debriefing sessions, reflective diaries, and standardised patient feedback. Student knowledge, confidence, and attitudes towards polypharmacy management were assessed using pre- and post-intervention questionnaires. Quantitative data were analysed through paired t-tests, and qualitative data were analysed thematically from reflective diaries. Results: Students demonstrated considerable improvement after the intervention in identifying symptoms of polypharmacy, suggesting deprescribing strategies, and working in multidisciplinary teams. Confidence in prioritising polypharmacy as a primary diagnostic problem increased from 32% to 86% (p < 0.01), and knowledge of diagnostic tools increased from 3.1 ± 0.6 to 4.7 ± 0.3 (p < 0.01). Standardised patients felt communication and patient-centredness had improved, with satisfaction scores increasing from 3.5 ± 0.8 to 4.8 ± 0.4 (p < 0.01). Reflective diaries indicated a shift towards more holistic thinking regarding medication burden. The small sample size limits the generalisability of the results. Conclusions: Teaching polypharmacy as a chronic condition in medical school enhances diagnostic competence, interprofessional teamwork, and patient safety. Education is a structured way of integrating the management of polypharmacy into routine clinical practice. This model provides valuable insights for designing medical curricula. Future research must assess the impact of such training on patient outcomes and clinical decision-making in the long term. Full article

Domain-Specific Languages with JSON grammar (JSON-DSLs) are specialized programming languages tailored to specific problem domains, offering higher abstraction levels and simplifying software implementation through the JSON standard. RhoArchitecture is an approach for designing and executing JSON-DSLs, incorporating a modular programming model, a JSON-based evaluation engine, and an integrated web development environment. This paper presents RestRho, a RESTful NodeJS server developed using two JSON-DSLs designed with RhoArchitecture: SQLRho and DBRestRho. These languages enable declarative specification of database operations and HTTP requests, respectively, supporting modularity, reuse, and template-based transformations. We validate the RestRho implementation through a dual approach. First, we apply software metrics to assess code quality, maintainability, and complexity. Second, we conduct an empirical study involving 39 final-year computer engineering students, who completed 18 structured tasks and provided feedback via questionnaires. The results demonstrate the tool’s usability, development efficiency, and potential for adoption in web application development. Full article

This study proposes a non-semantic multimodal approach to predict segment access frequency (SAF) in lecture archives. Such archives, widely used as supplementary resources in modern education, often consist of long, unedited recordings that are difficult to navigate and review efficiently. The predicted SAF, an indicator of student viewing behavior, serves as a practical proxy for student engagement. The increasing volume of recorded material renders manual editing and annotation impractical, making the automatic identification of high-SAF segments crucial for improving accessibility and supporting targeted content review. The approach focuses on lecture archives from a real-world blended learning context, characterized by resource constraints such as no specialized hardware and limited student numbers. The model integrates multimodal features from instructor’s actions (via OpenPose and optical flow), audio spectrograms, and slide page progression—a selection of features that makes the approach applicable regardless of lecture language. The model was evaluated on 665 labeled one-minute segments from one such course. Experiments show that the best-performing model achieves a Pearson correlation of 0.5143 in 7-fold cross-validation and 61.05% average accuracy in a downstream three-class classification task. These results demonstrate the system’s capacity to enhance lecture archives by automatically identifying key segments, which aids students in efficient, targeted review and provides instructors with valuable data for pedagogical feedback. Full article

Artificial intelligence (AI) holds considerable promise to transform education, from personalizing learning to enhancing teaching efficiency, yet it simultaneously introduces significant concerns regarding ethical implications and responsible implementation. This exploratory survey investigated the perceptions of 376 teachers, university students, and future educators from the University of Salerno area concerning AI integration in education. Data were collected via a comprehensive digital questionnaire, divided into sections on personal data, AI’s perceived impact, its usefulness, and specific applications in education. Descriptive and inferential statistical analyses, including mean, mode, standard deviation, and 95% confidence intervals, were applied to the Likert scale responses. Results indicated a general openness to AI as a supportive tool for personalized learning and efficiency. However, significant reservations emerged regarding AI’s capacity to replace the human role. For instance, 69% of participants disagreed that AI tutors could match human feedback efficiency, and strong opposition was found against AI replacing textbooks (81% disagreement) or face-to-face lessons (87% disagreement). Conversely, there was an overwhelming consensus on the necessity of careful and conscious AI use (98% agreement). Participants also exhibited skepticism regarding AI’s utility for younger learners (e.g., 80% disagreement for ages 0–6), while largely agreeing on its benefit for adult learning. Strong support was observed for AI’s role in providing simulations and virtual labs (89% agreement) and developing interactive educational content (94% agreement). This study underscores a positive inclination towards AI as an enhancement tool, balanced by a strong insistence on preserving human interaction in education, highlighting the need for thoughtful integration and training. Full article

This paper shows that artificial intelligence is fundamentally transforming college curricula by enabling data-driven personalization, which enhances student outcomes and better aligns educational programs with evolving workforce demands. Specifically, predictive analytics, machine learning algorithms, and natural language processing were applied here, grounded in constructivist learning theory and Human–Computer Interaction principles, to evaluate student performance and identify at-risk students to propose personalized learning pathways. Results indicated that the AI-based curriculum achieved much higher course completion rates (89.72%) as well as retention (91.44%) and dropout rates (4.98%) compared to the traditional model. Sentiment analysis of learner feedback showed a more positive learning experience, while regression and ANOVA analyses proved the impact of AI on enhancing academic performance to be real. Therefore, the learning content delivery for each student was continuously improved based on individual learner characteristics and industry trends by AI-enabled recommender systems and adaptive learning models. Its advantages notwithstanding, the study emphasizes the need to address ethical concerns, ensure data privacy safeguards, and mitigate algorithmic bias before an equitable outcome can be claimed. These findings can inform institutions aspiring to adopt AI-driven models for curriculum innovation to build a more dynamic, responsive, and learner-centered educational ecosystem. Full article

The present study explores the use of Instagram as an innovative strategy in the teaching–learning process in the context of animal reproduction topics. In the current era, with digital technology and social media transforming how information is accessed and consumed, it is essential for teachers to adapt and harness the potential of these tools for educational purposes. This article delves into the need for teachers to stay updated with current trends and the importance of promoting digital competences among teachers. This research aims to provide insights into the benefits of integrating social media into the educational landscape. Students of Veterinary Science degrees, Master’s degrees in Equine Sport Medicine as well as vocational education and training (VET) were involved in this study. An Instagram account named “UCOREPRO” was created for educational use, and it was openly available to all users. Instagram usage metrics were consistently tracked. A voluntary survey comprising 35 questions was conducted to collect feedback regarding the educational use of smartphone technology, social media habits and the UCOREPRO Instagram account. The integration of Instagram as an educational tool was positively received by veterinary students. Survey data revealed that 92.3% of respondents found the content engaging, with 79.5% reporting improved understanding of the subject and 71.8% acquiring new knowledge. Students suggested improvements such as more frequent posting and inclusion of academic incentives. Concerns about privacy and digital distraction were present but did not outweigh the perceived benefits. The use of short videos and microlearning strategies proved particularly effective in capturing students’ attention. Overall, Instagram was found to be a promising platform to enhance motivation, engagement, and informal learning in veterinary education, provided that thoughtful integration and clear educational objectives are maintained. In general, students expressed positive opinions about the initiative, and suggested some ways in which it could be improved as an educational tool. Full article

With continuous advancements in semiconductor technology, mastering efficient designs of high-quality and advanced chips has become an important part of science and technology education. Chip performances will determine the futures of various aspects of societies. However, novice students often encounter difficulties in learning digital chip designs using Verilog programming, a common hardware design language. An efficient self-study system for supporting them that can offer various exercise problems, such that any answer is marked automatically, is in strong demand. In this paper, we design and implement a web-based Verilog programming learning assistant system (VPLAS), based on our previous works on software programming. Using a heuristic and guided learning method, VPLAS leads students to learn the basic circuit syntax step by step, until they acquire high-quality digital integrated circuit design abilities through self-study. For evaluation, we assign the proposal to 50 undergraduate students at the National Taipei University of Technology, Taiwan, who are taking the introductory chip-design course, and confirm that their learning outcomes using VPLAS together are far better than those obtained when following a traditional method. In our final statistics, students achieved an average initial accuracy rate of over 70% on their first attempts at answering questions after learning through our website’s tutorials. With the help of the system’s instant automated grading and rapid feedback, their average accuracy rate eventually exceeded 99%. This clearly demonstrates that our system effectively enables students to independently master Verilog circuit knowledge through self-directed learning. Full article

This paper presents the development, implementation, and outcomes of the ELEVATE-US-UP (Engaging Learners through Exploration of Visionary Academic Thought and Empowerment in UnderServed and UnderPrivileged communities) teaching methodology, an equity-centered, culturally responsive pedagogical framework designed to enhance student engagement, academic performance, and science identity among underrepresented learners. This framework was piloted at Northern New Mexico College (NNMC), a Hispanic- and minority-serving rural institution. ELEVATE-US-UP reimagines science education as a dynamic, inquiry-driven, and contextually grounded process that embeds visionary scientific themes, community relevance, trauma-informed mentoring, and authentic assessment into everyday instruction. Drawing from culturally sustaining pedagogy, experiential learning, and action teaching, the methodology positions students not as passive recipients of content but as knowledge-holders and civic actors. Implemented across upper-level environmental science courses, the method produced measurable gains: class attendance rose from 67% to 93%, average final grades improved significantly, and over two-thirds of students reported a stronger science identity and a newfound confidence in their academic potential. Qualitative feedback highlighted increased perceptions of classroom inclusivity, community relevance, and instructor support. By centering on cultural context, student voice, and place-based application, the ELEVATE-US-UP framework offers a replicable and scalable model for educational transformation in underserved regions. Full article

Network security courses rely on hands-on labs where students configure virtual Linux networks to practice attack and defense. Automated feedback is scarce because no standard exists for exchanging detailed configurations—interfaces, bridging, routing tables, iptables policies—between exercise software and large language models (LLMs) that could serve as tutors. We address this interoperability gap with an exercise-oriented YANG profile that augments the Internet Engineering Task Force (IETF) ietf-network module with a new network-devices module. The profile expresses Linux interface settings, routing, and firewall rules, and tags each node with roles such as linux-server or linux-firewall. Integrated into our LiNeS Cloud platform, it enables LLMs to both parse and generate machine-readable network states. We evaluated the profile on four topologies—from a simple client–server pair to multi-subnet scenarios with dedicated security devices—using ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash. Across 1050 evaluation tasks covering profile understanding (n = 180), instance analysis (n = 750), and instance generation (n = 120), the three LLMs answered correctly in 1028 cases, yielding an overall accuracy of 97.9%. Even with only minimal follow-up cues (≦3 turns) —rather than handcrafted prompt chains— analysis tasks reached 98.1% accuracy and generation tasks 93.3%. To our knowledge, this is the first exercise-focused YANG profile that simultaneously captures Linux/iptables semantics and is empirically validated across three proprietary LLMs, attaining 97.9% overall task accuracy. These results lay a practical foundation for artificial intelligence (AI)-assisted security labs where real-time feedback and scenario generation must scale beyond human instructor capacity. Full article

The evaluation of open-ended questions is typically performed by human instructors using predefined criteria to uphold academic standards. However, manual grading presents challenges, including high costs, rater fatigue, and potential bias, prompting interest in automated essay scoring systems. While automated essay scoring tools can assess content, coherence, and grammar, discrepancies between human and automated scoring have raised concerns about their reliability as standalone evaluators. Large language models like ChatGPT offer new possibilities, but their consistency and fairness in feedback remain underexplored. This study investigates whether ChatGPT can provide stable and fair essay scoring—specifically, whether identical student responses receive consistent evaluations across multiple AI interactions using the same criteria. The study was conducted in two marketing courses at an engineering school in Chile, involving 40 students. Results showed that ChatGPT, when unprompted or using minimal guidance, produced volatile grades and shifting criteria. Incorporating the instructor’s rubric reduced this variability but did not eliminate it. Only after providing an example-rich rubric, a standardized output format, low temperature settings, and a normalization process based on decision tables did ChatGPT-4o demonstrate consistent and fair grading. Based on these findings, we developed a scalable algorithm that automatically generates effective grading rubrics and decision tables with minimal human input. The added value of this work lies in the development of a scalable algorithm capable of automatically generating normalized rubrics and decision tables for new questions, thereby extending the accessibility and reliability of automated assessment. Full article

At the Michael G. DeGroote School of Medicine, a significant component of the MD curriculum involves written narrative reflections on topics related to professional identity in medicine, with written feedback provided by their in-person longitudinal facilitators (LFs). However, it remains to be understood how generative artificial intelligence chatbots, such as ChatGPT (GPT-4), augment the feedback process and how MD students perceive feedback provided by ChatGPT versus the feedback provided by their LFs. In this study, 15 MD students provided their written narrative reflections along with the feedback they received from their LFs. Their reflections were input into ChatGPT (GPT-4) to generate instantaneous personalized feedback. MD students rated both modalities of feedback using a Likert-scale survey, in addition to providing open-ended textual responses. Quantitative analysis involved mean comparisons and t-tests, while qualitative responses were coded for themes and representational quotations. The results showed that while the LF-provided feedback was rated slightly higher in six out of eight survey items, these differences were not statistically significant. In contrast, ChatGPT scored significantly higher in helping to identify strengths and areas for improvement, as well as in providing actionable steps for improvement. Criticisms of ChatGPT included a discernible “AI tone” and paraphrasing or misuse of quotations from the reflections. In addition, MD students valued LF feedback for being more personal and reflective of the real, in-person relationships formed with LFs. Overall, findings suggest that although skepticism regarding ChatGPT’s feedback exists amongst MD students, it represents a viable avenue for deepening reflective practice and easing some of the burden on LFs. Full article

In Korea, the Public Health Center Mobile Healthcare Project was implemented in 2016. This project utilizes Information and Communication Technology (ICT) and big data to establish a health-related service foundation and a healthcare service operation system. Equipment and methods: This study recruited 1261 adolescents (660 males (13.40 ± 1.14 years, 156.12 ± 10.59 cm) and 601 females (13.51 ± 1.23 years, 154.45 ± 7.48 cm)) from 22 public health centers nationwide. Smart bands were provided, and the ‘Future Health’ application (APP) was installed on personal smartphones to assess body composition, physical fitness, and physical activity. Results: A paired sample t-test revealed height, 20 m shuttle run, grip strength, and long jump scores significantly differed after 24 weeks in males. Females exhibited significant height, 20 m shuttle run, grip strength, sit-ups, and long jump differences. Moderate physical activity (MPA, p < 0.001), vigorous physical activity (VPA, p < 0.001), and moderate-to-vigorous physical activity (MVPA, p < 0.001) were significantly different after 24 weeks in adolescents. These results establish that an ICT-based health promotion service can provide adolescent students with individual information from a centralized organization to monitor health behaviors and receive feedback regardless of location in South Korea. Full article