Search Results (54)

School textbooks are central to the teaching, studying, and learning processes because they mediate the interaction between the prescribed curriculum and the educational experience in the classroom. Evaluating their didactic structure critically allows us to determine the degree to which they align with current curriculum guidelines and promote meaningful learning. This study aimed to analyze the extent to which Ecuadorian natural science textbooks reflect constructivist learning principles and promote the development of key competencies established in the National Priority Curriculum. This curriculum guides the achievement of essential results and strengthens fundamental competencies for students’ comprehensive development. Content analysis was adopted as the methodological approach given its relevance in examining the didactic and curricular dimensions of educational materials. The analysis covered twelve eighth-grade General Basic Education textbooks and their supplementary materials. The analysis was based on two instruments: specialized summary analysis sheets (RAE) and a purpose-built checklist. The ATLAS.ti 25 and IRaMuTeQ programs supported the systematization and visualization of the data. The results showed limited integration of constructivist strategies, such as teaching for comprehension, inquiry-based learning, and problem solving, in most of the analyzed texts. These findings underscore the need to expand and strengthen the incorporation of contextualized, critical, and meaningful learning experiences to improve the didactic design of school textbooks. Such improvements would promote coherent articulation between objectives, content, methods, resources, and assessment in line with constructivist principles of the Ecuadorian curriculum. Furthermore, given these approaches’ affinity with curricular frameworks in other regional countries, the results could offer relevant guidance and starting points for reflection on developing and using textbooks in Latin American contexts with comparable educational characteristics. Full article

Integrating information and communication technology (ICT) into STEM education enhances instructional quality and cultivates students’ interdisciplinary problem-solving. STEM teachers’ attitudes—driven by perceived ease of use (PEOU) and perceived usefulness (PU)—are pivotal in ICT adoption, and pre-service training offers a vital opportunity to shape these attitudes. Yet, empirical studies investigating how specific training strategies influence ICT attitudes via PEOU and PU remain scarce. Using a mixed-methods approach combining questionnaires and interviews, the results indicate that pre-service training significantly improved STEM teachers’ attitudes toward ICT-enhanced teaching. Socially interactive strategies (role models and collaboration) enhanced attitudes via PEOU by boosting confidence and reducing technology-related anxiety, cognitive design strategies (reflection and instructional design) operated through PU by emphasizing ICT’s pedagogical value, and experiential feedback strategies (authentic experience and feedback) influenced attitudes through both PEOU and PU, fostering integrated technical and pedagogical development. These findings support an integrated SQD–TAM framework and provide practical guidance for designing pre-service STEM teacher programs to promote sustained ICT adoption in China, and meanwhile highlights the importance of strategically sequencing training to cultivate both technological competence and pedagogical insight among future STEM educators. Full article

Undergraduate design education faces a structural contradiction characterized by high cognitive load (CL) and relatively low innovation output. Meanwhile, existing generative AI tools predominantly emphasize the generation of visual outcomes, often overlooking the logical guidance mechanisms inherent in design thinking. This study proposes a Dual-Path teaching model integrating critical reconstruction behaviors to examine how AI enhances design thinking. It adopts structured interactions with the DeepSeek large language model, CL theory, and Structural Equation Modeling for analysis. Quantitative results indicate that AI-assisted paths significantly enhance design quality (72.43 vs. 65.60 in traditional paths). This improvement is attributed to a “direct effect + multiple mediators” model: specifically, AI reduced the mediating role of Extraneous Cognitive Load from 0.907 to 0.017, while simultaneously enhancing its investment in Germane Cognitive Load to support deep, innovative thinking. Theoretically, this study is among the first to integrate AI-driven critical reconstruction behaviors (e.g., iteration count, cross-domain terms) into CL theory, validating the “logical chain externalization → load optimization” mechanism in design education contexts. Practically, it provides actionable strategies for the digital transformation of design education, fostering interdisciplinary thinking and advancing a teaching paradigm where low-order cognition is outsourced to reinforce high-order creative thinking. Full article

(1) Deficits in adaptive behavior significantly hinder individuals with intellectual disabilities from performing essential daily tasks and participating in community life. Although virtual reality shows promise for supporting adaptive behavior in this population, systematic reviews on this topic remain scarce. (2) Methods: Twenty-five experimental studies from the databases Web of Science, PubMed, Scopus, and ERIC, published between 2005 and 2024, were analyzed in the context of a systematic review. (3) Results: The studies revealed a significant surge in research on VR interventions for adaptive behavior in individuals with intellectual disabilities, particularly after 2021. The most frequently applied domain was practical skills, while social and conceptual skills received relatively less attention. Most studies employed high-immersion head-mounted displays as the primary technology type and adopted controller-based unimodal interaction as the dominant interaction mode. Pedagogical strategies such as ABA, structured teaching, and contextual learning are favored in interventions. (4) Conclusions: VR interventions have been increasingly applied to support adaptive behavior development in this population. However, further exploration is needed to tailor VR designs to better accommodate the individual differences and specific needs. This review synthesizes current evidence, identifies key trends and limitations, and offers guidance for future research. Full article

Background/Objectives: The integration of AI chatbots in nursing education, particularly in simulation-based learning, is advancing rapidly. However, there is a lack of structured evaluation models, especially to assess AI-generated simulations. This article introduces the AI-Integrated Method for Simulation (AIMS) evaluation framework, a dual-phase evaluation framework adapted from the FAITA model, designed to evaluate both prompt design and chatbot performance in the context of nursing education. Methods: This simulation-based study explored the application of an AI chatbot in an emergency planning course. The AIMS framework was developed and applied, consisting of six prompt-level domains (Phase 1) and eight performance criteria (Phase 2). These domains were selected based on current best practices in instructional design, simulation fidelity, and emerging AI evaluation literature. To assess the chatbots educational utility, the study employed a scoring rubric for each phase and incorporated a structured feedback loop to refine both prompt design and chatbox interaction. To demonstrate the framework’s practical application, the researchers configured an AI tool referred to in this study as “Eval-Bot v1”, built using OpenAI’s GPT-4.0, to apply Phase 1 scoring criteria to a real simulation prompt. Insights from this analysis were then used to anticipate Phase 2 performance and identify areas for improvement. Participants (three individuals)—all experienced healthcare educators and advanced practice nurses with expertise in clinical decision-making and simulation-based teaching—reviewed the prompt and Eval-Bot’s score to triangulate findings. Results: Simulated evaluations revealed clear strengths in the prompt alignment with course objectives and its capacity to foster interactive learning. Participants noted that the AI chatbot supported engagement and maintained appropriate pacing, particularly in scenarios involving emergency planning decision-making. However, challenges emerged in areas related to personalization and inclusivity. While the chatbot responded consistently to general queries, it struggled to adapt tone, complexity and content to reflect diverse learner needs or cultural nuances. To support replication and refinement, a sample scoring rubric and simulation prompt template are provided. When evaluated using the Eval-Bot tool, moderate concerns were flagged regarding safety prompts and inclusive language, particularly in how the chatbot navigated sensitive decision points. These gaps were linked to predicted performance issues in Phase 2 domains such as dialog control, equity, and user reassurance. Based on these findings, revised prompt strategies were developed to improve contextual sensitivity, promote inclusivity, and strengthen ethical guidance within chatbot-led simulations. Conclusions: The AIMS evaluation framework provides a practical and replicable approach for evaluating the use of AI chatbots in simulation-based education. By offering structured criteria for both prompt design and chatbot performance, the model supports instructional designers, simulation specialists, and developers in identifying areas of strength and improvement. The findings underscore the importance of intentional design, safety monitoring, and inclusive language when integrating AI into nursing and health education. As AI tools become more embedded in learning environments, this framework offers a thoughtful starting point for ensuring they are applied ethically, effectively, and with learner diversity in mind. Full article

The latest Relationships Education, Relationships and Sex Education, and Health Education (RSHE) Draft Guidance seeks to reduce the inclusion of LGBT themes in English schools. Additionally, the Gender Questioning Draft Guidance for Schools and Colleges and the Cass Review overlook the rights of trans and non-binary young people, further intensifying the heated debates surrounding their lives. In response, the author draws upon research conducted in a primary school in Greater London in 2021, when statutory RSHE, including LGBT content, was first introduced. The research aimed to understand how teachers felt about teaching RSHE and to collaborate with them to enhance pupil learning within and beyond the RSHE curriculum. This paper critiques lesson observations and teachers’ reflections on their lessons using a Framework for Sexuality Education and Queer Theory. The researcher’s call to rethink how RSHE is taught should not be taken to mean it should not be taught. To the contrary, the findings suggest a need for the school to broaden its curriculum, teaching methods, and strategies to become a truly ‘LGBT-inclusive’ environment. However, the paper also illuminates the apprehensions these primary school teachers experienced, which in turn influenced pedagogical decisions. The article concludes by recommending specific whole-school approaches and effective pedagogical practices for RSHE in the school, which could be beneficial to other primary school settings. Effective teaching of LGBT themes requires clear support for educators, especially within the complexity of a primary school setting and given the changing political and social climate. Full article

In 2019, the TIMSS study offered a closer look at how Moroccan eighth-grade students were doing in mathematics. The data came from a sample of 8390 students; 37% performed well, while the remaining 63% struggled. The goal was to better understand which contextual factors truly influence academic success. The dataset was dense, with over 700 variables drawn from students, teachers, and school questionnaires. To make sense of it, advanced machine learning techniques were applied, including an autoencoder to reduce dimensionality. This process helped narrow things down to 20 key variables that strongly correlated with student performance. These factors covered a range of influences, from teaching strategies and student engagement to teacher training and school-level resources. The insights from the study offer practical guidance for educators and policymakers looking to design targeted, effective interventions. At its core, the study underscores a familiar truth: success in math does not hinge on a single element but on a web of interconnected conditions. Improving outcomes requires a holistic approach, one that supports both learners and the people guiding them. Full article

Blended learning has emerged as a popular trend in education; however, as students from higher vocational colleges who are often in the lower 50% of national standardized entrance exam achievers, their teachers face unique challenges in implementing blended learning. This study summarized effective blended learning strategies in higher vocational education and training (VET) classrooms by comparing 53 award-winning and 40 daily course videos in China. Firstly, video analysis and Lag sequential analysis were employed to identify effective strategies for implementing blended learning in diverse VET course types. A set of general and specific blended learning strategies was developed to help VET teachers adapt instructional approaches accordingly. Secondly, a questionnaire survey among 215 VET teachers revealed positive perceptions of the strategies in terms of usability, ease of use, perceived behavioral control, and intention to use them. The present research provides valuable guidance for VET teachers to effectively implement blended learning strategies in diverse course types, contributing to the understanding of effective blended learning strategies in VET and addressing the gap in research for this unique teaching stage. Full article

This study aims to investigate the views of mentor teachers on sustainable teacher education in Northern Cyprus. The Teaching Practice course (internship) was taken as the basis, and the views of mentor teachers on the meaning, difficulties, responsibilities, and feedback process of teaching practices were examined. Data were collected through open-ended interviews with 50 classroom teachers using the qualitative research approach phenomenological model. When the findings are examined, it is revealed that mentor teachers see internship as very important in terms of applying theoretical knowledge, gaining practical experience, and developing professional identity. Difficulties include classroom management difficulties, problems with students, inadequate preparation, and communication problems. Mentor teachers emphasize that their roles of providing guidance, feedback, and support are important. It was stated that preparation, time management, and effective communication are among the responsibilities of candidate teachers. The study emphasized the importance of mentoring in teaching practices in the context of effective communication and providing constructive feedback. Recommendations include improved internship program design focusing on classroom management, communication skills, and material preparation. Additionally, fostering a supportive learning environment and providing professional development opportunities for both mentors and student teachers are essential. The study contributes to understanding the complexities of teacher education and the critical role of effective mentoring in shaping future educators. Evaluating the findings of this study in the context of sustainable teacher education highlights how mentors’ role model practices, ongoing support for pre-service teachers, and reinforcement of effective teaching strategies can contribute to the sustainability of pre-service teacher education. Full article

This study investigates the use of generative AI tools to create culturally inclusive case studies in postgraduate project management education, addressing a critical gap in existing research. While prior literature highlights the benefits of culturally responsive teaching (CRT) practices, there is notable lack of exploration into how generative AI can be leveraged to develop culturally relevant learning materials. Using an interpretivist philosophy and action research methodology, the study engaged eight international students to evaluate the effectiveness of AI-generated case studies tailored to diverse cultural contexts. The major contribution of this study is the development of a structured framework of strategies and considerations that guides educators in designing culturally inclusive materials using generative AI tools. The inclusion of clearly defined strategies provides educators with practical guidance, while the accompanying considerations act as essential safeguards, encouraging critical reflection on potential risks such as bias, stereotyping, and ethical misuse. The findings hold significant implications for educational practice, emphasising the ethical use of AI, targeted professional development for educators, and the potential for scalable, inclusive teaching strategies that enhance student engagement, equity, and learning outcomes in multicultural classrooms. Full article

Professional experiences are vital in teacher education programmes, especially in early childhood teacher education programmes, as this allows pre-service teachers (PSTs) to apply their theoretical knowledge in real-world settings. Hence, PSTs’ preparation for their placement experience is crucial. Recently, the innovative use of virtual reality (VR) in teacher training has begun to support the professional development of PSTs, although this approach is studied less extensively compared to fields like medicine and aviation. This paper draws upon the cultural–historical concept of social situation of development (SSD) to explore how VR experiences enhance PSTs’ preparation before they enter their first professional experiences. This study involved 66 diverse post-graduate PSTs within an Australian post-graduate Initial Teacher Education programme in early childhood (EC) education. The VR experiences allowed them to engage with avatar children aged 3–5 within a realistic environment, fostering valuable insights regarding their practice in future professional experiences. By studying the PSTs’ reflections, this study identified the dramatic events that arose from PSTs’ interactions in VR. These moments highlighted instances of SSD, where PSTs advanced both mentally and practically in their placement preparation. Within SSD, PSTs developed a deeper understanding of child development, classroom dynamics, and the effectiveness of various teaching strategies. They also practised strategies, including teaching techniques, classroom management, behavioural guidance, and adapting to the diverse needs of avatar children. The mental and practical development significantly contributed to PSTs’ preparation for professional experiences. The subjective nature of their reflections provided valuable insights into their development from their own perspectives. However, it is important to note that this paper is based on data collected from a single post-graduate course. Future research will aim to gather perspectives from PSTs at multiple institutions, as well as from placement mentors, regarding PSTs’ understanding of working in early childhood contexts in Australia. Full article

Decarbonizing production activities is a critical task in the transition towards carbon neutrality. Traditional carbon footprint accounting tools, such as life-cycle assessment (LCA) and the Greenhouse Gas Protocol, primarily quantify direct and indirect emissions but offer limited guidance on actionable reduction strategies. To address this gap, this study proposes a comprehensive life-cycle carbon footprint optimization framework that integrates LCA with a mixed-integer linear programming (MILP) model. The framework, while applicable to various production contexts, is validated using a university campus as a case study. In 2023, the evaluated university’s net carbon emissions totaled approximately 24,175.07 t CO₂-eq. Based on gross emissions (28,306.43 t CO₂-eq) before offsetting, electricity accounted for 66.09%, buildings for 15.55%, fossil fuels for 8.67%, and waste treatment for 8.46%. Seasonal analysis revealed that June and December exhibited the highest energy consumption, with emissions exceeding the monthly average by 19.4% and 48.6%, respectively, due to energy-intensive air conditioning demand. Teaching activities emerged as a primary contributor, with baseline emissions estimated at 5485.24 t CO₂-eq. Optimization strategies targeting course scheduling yielded substantial reductions: photovoltaic-based scheduling reduced electricity emissions by 7.00%, seasonal load shifting achieved a 26.92% reduction, and combining both strategies resulted in the highest reduction, at 45.95%. These results demonstrate that aligning academic schedules with photovoltaic generation and seasonal energy demand can significantly enhance emission reduction outcomes. The proposed framework provides a scalable and transferable approach for integrating time-based and capacity-based carbon optimization strategies across broader operational systems beyond the education sector. Full article

Interdisciplinary teaching is a pivotal strategy for deepening disciplinary theory and broadening students’ cognitive boundaries, crucial for the sustainability of education. By considering scientific knowledge’s humanistic background and technological evolution, this study proposes a novel interdisciplinary teaching framework based on the Source–Knowledge–Use (SKU) paradigm. Then, taking fuzzy mathematics as a case, the Humanities–Science–Technology Model (HSTM), based on a tripartite progression from humanistic foundations to scientific principles and then to technological applications, was established. This study systematically expounds the HSTM’s framework, contents, and implementation design, while critically examining potential challenges and corresponding mitigation strategies. The proposed SKU-based interdisciplinary teaching framework not only provides methodological guidance for interdisciplinary instruction in fuzzy mathematics but also offers transferable insights for cognate disciplines seeking to implement sustainable educational practices. Full article

Generative artificial intelligence (GenAI) is transforming various sectors, including education. This study investigates the integration of GenAI in higher education, focusing on its potential to enhance teaching and learning. Through a series of workshops and courses delivered to university professors, it examines opportunities such as improved resource creation and challenges like ethical AI usage, proposing best practices for the sustainable implementation of GenAI in the classroom. The main objective is to analyze how the use of GenAI tools such as ChatGPT, Gemini, and Claude can improve teachers’ professional skills and the overall educational experience while ensuring ethical and responsible use. The methodology comprised a literature review and practical experimentation with university professors. Data collection involved observations, surveys, discussion forums, cooperative activities, and exercises focused on evaluating AI-generated educational resources and analyzing forum insights to identify best practices. The results highlight several opportunities around the use of GenAI in education, including improving writing, creating educational resources, supporting lesson planning, and increasing teacher productivity. In addition, significant challenges were identified, such as the ethical use of AI and strategies for detecting AI-generated text. For instance, workshops demonstrated a 30% increase in teacher confidence with GenAI tools like ChatGPT, highlighting the effectiveness of these technologies in professional development. To address these challenges, best practices for the responsible integration of GenAI in education are presented, focusing primarily on ongoing training, the establishment of institutional policies, the encouragement of responsible use, and the ongoing evaluation of impact in the educational setting. Best practices include clear ethical guidelines, prompt development techniques, and continuous professional training to ensure teachers can effectively and responsibly integrate GenAI tools into their instructional practices. These practices for the effective use of GenAI tools in education aim to maximize benefits while mitigating risks. These include the development of effective prompts for various activities and guidance on the ethical use of AI to ensure a balanced and responsible approach to the integration of GenAI in higher education. Full article

This systematic review investigated how Higher Education Institutions (HEIs) optimise data analytics in postgraduate programmes to enhance student achievement. Existing research explores the theoretical benefits of data analytics but lacks practical guidance on strategies to effectively implement and utilise data analytics for student success. As such, this review aimed to identify data analytics approaches used by HEIs and explore challenges and best practices in their application. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Five databases were searched. Studies that examined data analytics in HEIs postgraduate programmes and their impact on student learning were included. Studies that were solely theoretical or in non-postgraduate settings were excluded. Twenty-six studies were included. Quality assessment using the Critical Appraisal Skills Programme (CASP) Checklist was employed. The review identified various data analytics approaches including descriptive, predictive, and prescriptive analytics, among others. These approaches can improve foundational skills, create supportive learning environments, and optimise teaching strategies. However, limitations (standardised tests, data integration) and privacy concerns were acknowledged. Recommendations include developing a comprehensive evaluation system, equipping educators with the skills to utilise diverse analytics to enhance student achievement, fostering open communication about data use, and cultivating a data-literate student body. While diverse approaches were explored, the review’s lack of specific contextual details may limit the generalisability of findings. To mitigate this, the review categorised techniques and provided references for further exploration. Full article