Search Results (4,282)

Achieving adequate thermal comfort in classrooms in hot cities in southern Mexico is challenging. A heterogeneous distribution of air conditioning flow leads to thermal discomfort, affecting occupants’ academic performance and increasing energy consumption. This study evaluates the thermal comfort of occupants in an air conditioned classroom using computational fluid dynamics. We determined the effects of variations in air conditioning operating parameters (supply angle, velocity, and temperature) on PMV and modified PMV indices. An operating configuration of 60°, 3 m/s, and 22 °C ensures that thermal comfort remains within regulations while optimizing energy consumption, in contrast to the original PMV model. Using the modified PMV model, the values are 0.38 for students and 0.31 for the teacher, with percentages of dissatisfied individuals of 10% and 7.7%, respectively. This study demonstrates the importance of analyzing air conditioning operating parameters to enhance thermal comfort while reducing energy consumption. Full article

Objectives: We aimed to develop expert-informed recommendations for colleges and universities to support students with celiac disease (CeD) managing a gluten-free (GF) diet. Methods: A multidisciplinary panel of 40 stakeholders, including physicians, dietitians, a disability rights attorney, university staff, and students, was convened by the Celiac Disease Foundation to create expert-based and experience-informed recommendations. Over a 6-month period, the group conducted literature reviews, stakeholder interviews, and expert consensus discussions to identify common barriers and accommodations aligned with federal disability law. The expert panel collaboratively developed and revised an initial set of recommendations. Two rounds of structured voting were held during which panelists provided feedback to refine content and ensure clarity. All final recommendations were adopted with at least 90% of panelists voting in support. Results: The panel identified 24 accommodations across four domains: academics, housing, dining, and campus life. Academic recommendations include flexibility for illness-related absences, support for remote learning, and classroom modifications. Housing recommendations emphasize access to priority placement, appropriate appliances, and proximity to safe dining. Dining accommodations address GF food availability, ingredient transparency, staff training, and meal plan flexibility. Campus life recommendations ensure full participation in athletics, study abroad, social events, and internships, with supports for psychosocial well-being. Conclusions: This manuscript presents the first expert-informed recommendations focused specifically on the needs of college students with CeD. These recommendations are intended to support institutions as they develop strategies to enhance access to GF food, quality of life, educational supports, and student experience for those living with this chronic autoimmune condition. Full article

Tutoring has played a significant role in pandemic-related learning recovery, supporting student learning and engagement. This paper follows up on a recent randomized controlled trial (RCT) estimating that one-on-one virtual early literacy tutoring was nearly twice as effective as two-on-one tutoring for improving student learning. To better understand this gap, we analyze transcripts from 16,629 tutoring sessions from this RCT—which included over 3.7 million tutor utterances—using natural language processing and machine learning techniques. We explore how tutors allocate attention across content instruction, relationship building, and classroom management between one-on-one and two-on-one formats. While tutors dedicate similar time to content instruction and relationship building across both formats, students receiving one-on-one tutoring receive more attention and personalized support. To improve the effectiveness of two-on-one tutoring, it may be beneficial to equip tutors with strategies that engage multiple students simultaneously, thereby reducing downtime and minimizing the potential for disengagement. Full article

Pragmatic language is a core component of school-based social participation, yet children with Autism Spectrum Disorder (ASD) and Social Communication Disorder (SCD) frequently experience persistent difficulties in using language appropriately across everyday learning contexts. This study investigated the effectiveness of a culturally adapted, school-based immersive Virtual Reality (VR) learning program designed to enhance pragmatic language and social communication skills among Thai primary school children. Eleven participants aged 7–12 years completed a three-week, ten-session VR program that simulated authentic classroom, playground, and canteen interactions aligned with Thai sociocultural norms. Outcomes were measured using the Social Communication Questionnaire (SCQ) and the Pragmatic Behavior Observation Checklist (PBOC). While SCQ scores showed a small, non-significant reduction (p = 0.092), PBOC results demonstrated significant improvements in three foundational pragmatic domains: Initiation and Responsiveness (p = 0.032), Turn-Taking and Conversational Flow (p = 0.037), and Politeness and Register (p = 0.010). Other domains showed no significant changes. These findings suggest that immersive, culturally relevant VR environments can support early gains in core pragmatic language behaviors within educational settings, although broader social communication outcomes may require longer or more intensive learning experiences. Full article

The Immersive Decentralized Digital space (IDDs), derived from blockchain technology and Massively Multiplayer Online Games (MMOGs), enables real-time multisensory interactions that support social connection under metaverse concepts. Although recognized as a technology with significant potential for educational innovation, IDDs remain underutilized in health professions education. Health profession students are often unaware of how IDDs’ features can be applied to their learning through in- or after-classroom activities. This study employs a quantitative research design to evaluate students’ perceptions of next-generation digital learning without any prior exposure to IDDs. An electronic survey was developed to examine four dimensions of learning facilitation: “Remote Learning” for capturing past experiences with digital competence during the COVID-19 era; “Digital Evolution,” reflecting preferences in utilizing digital spaces; “Interactive Communication” and “Knowledge Application” for applicability of IDDs in the health professions education. Statistical analyses revealed no significant differences in perceptions based on gender or major on all factors. Nevertheless, significant differences emerged based on nationality in “Digital Evolution”, “Interactive Communication”, and “Knowledge Application”, highlighting the influence of cultural and educational backgrounds on receptiveness to virtual learning environments. By recognizing the discrepancies and addressing barriers to digital inclusion, IDDs hold strong potential to enhance health professional learning experiences and educational outcomes. Full article

The integration of emojis into Early Childhood Education (ECE) presents a promising avenue for enhancing student engagement, emotional expression, and comprehension. While prior studies suggest the benefit of visual aids in learning, systematic frameworks for pedagogically aligned emoji recommendation remain underdeveloped. This paper presents EduEmoji-ECE, a pedagogically annotated dataset of early-childhood learning text segments. Specifically, the proposed model incorporates Bidirectional Encoder Representations from Transformers (BERTs) for contextual embedding extraction, Gated Recurrent Units (GRUs) for sequential pattern recognition, Deep Neural Networks (DNNs) for classification and emoji recommendation, and DECOC for improving emoji class prediction robustness. This hybrid BERT-GRU-DNN-DECOC architecture effectively captures textual semantics, emotional tone, and pedagogical intent, ensuring the alignment of emoji class recommendation with learning objectives. The experimental results show that the system is effective, with an accuracy of 95.3%, a precision of 93%, a recall of 91.8%, and an F1-score of 92.3%, outperforming baseline models in terms of contextual understanding and overall accuracy. This work helps fill a gap in AI-based education by combining learning with visual support for young children. The results suggest an association between emoji-enhanced materials and improved engagement/comprehension indicators in our exploratory classroom setting; however, causal attribution to the AI placement mechanism is not supported by the current study design. Full article

While the international influence of the Programme for International Student Assessment (PISA) on education policy debates is well recognized, the degree to which PISA findings drive actual policy reforms and classroom practices remain debated. Using PISA as a case, this article examines how educational research is translated into policy responses and practices in German federalism, focusing specifically on immigrant students—a key group within German education reform discourse. It analyzes the reflection of PISA findings from the 2000, 2018, and 2022 assessments on immigrant student performance in the resolutions of the Standing Conference of Ministers of Education and Cultural Affairs, the process of implementation by the federal states (Länder), and the effect on school-level practice. Framed by research knowledge mobilization theory, the article investigates the relationships among research production, mediation, and usage, clarifying the interplay between educational research, policy, and practice in Germany’s federal system. Historical analysis exposes consistent gaps between research-derived recommendations and binding, actionable change at both policy and practice levels, often due to challenges in developing evidence-based and consistently applied policy measures across the Länder. The article concludes with practical recommendations for improving the impact of interdisciplinary, policy-oriented research on policy and practice, considering the complexities of Germany’s federal governance. Full article

University dropout is a problem that affects all universities around the world. It is multidimensional and multicausal in nature. The consequences of a student dropping out affect them not only financially but also in terms of their self-perception. In this article, an analysis of in-depth interviews was carried out to determine what motivations students have when leaving classrooms to provide solutions to the new cohorts that enter. The sample consisted of 21 students, including 14 men and 7 women, from different Andalusian universities. A logical minimization analysis was carried out, showing the necessary implications for each of the subjects who participated. Afterwards, frequency and percentage analyses were carried out for each of the dimensions that made up the interview, with success in primary education having the lowest percentage (4.7%) and academic orientation having the highest percentage (90.47%). Full article

This study explored dynamic assessment (DA) of writing in a linguistically and culturally diverse context. Drawing on conceptualizations of DA and ecological languaging competencies (ELC), an ELC-based Loop Pedagogy was designed and adapted in a primary English language teaching (ELT) classroom aiming to foster ongoing development of a dynamic, dialogic, and differentiated assessment approach. A mixed methods research design was adopted with data sources including questionnaires, lesson observations, interviews, and documents/artifacts of student works. Research findings indicated that with optimized choices of learning, timely scaffolding, personalized written feedback, as well as a caring and supportive environment, students with diverse learning needs improved their writing abilities, enhanced their language awareness, and increased their positive affect toward writing activities. Full article

Academic Development (AD) represents a fundamental strategy for improving the quality of university teaching in the digital era. This entry proposes a critical analysis of technologies supporting AD, examining theoretical models, emerging practices, and contemporary challenges through a systematic review of academic literature. The TPACK (Technological Pedagogical Content Knowledge) framework emerges as a crucial model for the effective integration of educational technologies, while innovative approaches such as blended learning, flipped classroom, and communities of practice demonstrate significant potential in promoting teaching innovation. However, the analysis highlights structural criticalities: resistance to change, lack of institutional recognition, technological pedagogical gaps, and identity tensions related to the teaching role. The concept of “Age of Evidence” orients future perspectives toward evidence-based, personalized, and collaborative programs. The entry concludes with operational recommendations for policymakers and institutions, emphasizing the need for systemic investments that valorize teaching as a core scholarly activity. The original contribution lies in the critical integration of established theoretical frameworks with analysis of post-pandemic transformations and in identifying strategic directions to make universities “transformative” in addressing global challenges of sustainability, technological innovation, and critical thinking education. Full article

Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were improved, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations. Full article

Large Language Models (LLMs) are increasingly used in higher education, but their pedagogical role in fields like cybersecurity remains under-investigated. This research explores integrating LLMs into a university cybersecurity course using a designed pedagogical approach based on active learning, problem-based learning (PBL), and computational thinking (CT). Instead of viewing LLMs as definitive sources of knowledge, the framework sees them as cognitive tools that support reasoning, clarify ideas, and assist technical problem-solving while maintaining human judgment and verification. The study uses a qualitative, practice-based case study over three semesters. It features four activities focusing on understanding concepts, installing and configuring tools, automating procedures, and clarifying terminology, all incorporating LLM use in individual and group work. Data collection involved classroom observations, team reflections, and iterative improvements guided by action research. Results show that LLMs can provide valuable, customized support when students actively engage in refining, validating, and solving problems through iteration. LLMs are especially helpful for clarifying concepts and explaining procedures during moments of doubt or failure. Still, common issues like incomplete instructions, mismatched context, and occasional errors highlight the importance of verifying LLM outputs with trusted sources. Interestingly, these limitations often act as teaching opportunities, encouraging critical thinking crucial in cybersecurity. Ultimately, this study offers empirical evidence of human–AI collaboration in education, demonstrating how LLMs can enrich active learning. Full article

Growing interest in food sustainability education aims to increase awareness of food distribution systems, environmental degradation, and the connectivity of sustainable and ethical food practices. However, recent scholarship has questioned whether such pedagogical efforts are meaningfully internalized by students or lead to sustained behavioral change. Prior studies document persistent gaps in students’ understanding of sustainability impacts and the limited effectiveness of existing instructional approaches in promoting transformative engagement. To address these concerns, the Food Ethics, Sustainability and Alternatives (FESA) course was implemented with 21 undergraduate and graduate students at Montclair State University (Montclair, NJ, USA). Course outcomes were evaluated using a mixed-methods design integrating qualitative analysis with quantitative measures informed by the Theory of Planned Behavior, to identify influences on students’ attitudes, and a Transtheoretical Model (TTM) panel survey to address progression from awareness to action, administered pre- and post-semester. Qualitative findings revealed five central themes: increased self-awareness of food system contexts, heightened attention to animal ethics, the importance of structured classroom dialogue, greater recognition of food waste, and increased openness to alternative food sources. TTM results indicated significant reductions in contemplation and preparation stages, suggesting greater readiness for change, though no significant gains were observed in action or maintenance scores. Overall, the findings suggest that while food sustainability education can positively shape student attitudes, the conversion of attitudinal shifts into sustained behavioral change remains limited by external constraints, including time pressures, economic factors, culturally embedded dietary practices, structural tensions within contemporary food systems, and perceptions of limited individual efficacy. Full article

Higher education continues to encounter the challenge of redesigning writing pedagogy beyond the rapid adoption of emerging technologies. This challenge is particularly evident in English writing courses, which play a role in developing students’ writing and research skills in universities across the United Arab Emirates (UAE). While generative artificial intelligence (GenAI) tools offer practical affordances for writing instruction, their growing use has also raised concerns about academic integrity, authenticity, and critical engagement. Although early discourse has focused on the risks and potential of GenAI, there remains a clear dearth of frameworks to guide instructors in designing meaningful and engaging writing assignments. This paper introduces Ai-RACE, an adaptable pedagogical framework for designing purposeful and innovative writing tasks. Grounded in classroom-based insights, principles of writing pedagogy, constructivist and multimodal learning theories, Ai-RACE conceptualises assignment design around five interconnected components: AI integration, Relevance, Authenticity, the 4Cs, and Engagement. Employing a design-focused qualitative approach, the study uses instructional practices and student reflections to examine the implementation of Ai-RACE in writing contexts. Although situated within a specific institutional context, the study offers transferable guidelines for designing writing assignments across international higher education settings. By positioning Ai-RACE as a design heuristic, the study demonstrates its potential in supporting engagement, critical thinking, writing skills and ethical use of AI, and highlights the importance of rethinking writing pedagogy and the professional development in AI- influenced contexts. Full article

With the advancement of artificial intelligence and educational informatization, there is a growing demand for intelligent teaching assistance systems in universities. Focusing on the university “Algorithms” course in the computer science department, this study develops a multi-terminal collaborative knowledge service system, Course-Oriented Knowledge Service–Based AI Teaching Assistant System (CKS-AITAS), which consists of a PC terminal and a mobile terminal, where the PC terminal integrates functions including knowledge graph, semantic retrieval, intelligent question-answering, and knowledge recommendation. While the mobile terminal enables classroom check-in and teaching interaction, thus forming a closed-loop platform for teaching organization, resource acquisition, and knowledge inquiry. For the document retrieval module, paragraph-level semantic modeling of textbook content is conducted using Word2Vec, combined with approximate nearest neighbor indexing, and this module achieves an MRR@10 of 0.641 and an average query time of 0.128 s, balancing accuracy and efficiency; the intelligent question-answering module, based on a self-built course FAQ dataset, is trained via the BERT model to enable question matching and answer retrieval, achieving an accuracy rate of 86.3% and an average response time of 0.31 s. Overall, CKS-AITAS meets the core teaching needs of the course, provides an AI-empowered solution for university teaching, and boasts promising application prospects in facilitating education sustainability. Full article