Search Results (267)

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

This work presents the development of NOVA, an educational virtual assistant designed for the Parallel Computing course, built using a Retrieval-Augmented Generation (RAG) architecture combined with Large Language Models (LLMs). The assistant operates entirely in Spanish, supporting native-language learning and increasing accessibility for students in Latin American academic settings. It integrates vector and relational databases to provide an interactive, personalized learning experience that supports the understanding of complex technical concepts. Its core functionalities include the automatic generation of questions and answers, quizzes, and practical guides, all tailored to promote autonomous learning. NOVA was deployed in an academic setting at Universidad Politécnica Salesiana. Its modular architecture includes five components: a relational database for logging, a vector database for semantic retrieval, a FastAPI backend for managing logic, a Next.js frontend for user interaction, and an integration server for workflow automation. The system uses the GPT-4o mini model to generate context-aware, pedagogically aligned responses. To evaluate its effectiveness, a test suite of 100 academic tasks was executed—55 question-and-answer prompts, 25 practical guides, and 20 quizzes. NOVA achieved a 92% excellence rating, a 21-second average response time, and 72% retrieval coverage, confirming its potential as a reliable AI-driven tool for enhancing technical education. Full article

This study investigates the implementation and impact of the Guided Personalised Learning (GPL) model, a structured pedagogical framework designed to operationalise personalised and student-centred learning in STEM higher education. The GPL model integrates three interconnected components: a three-dimensional knowledge and skill grid, Interactive Learning Progress Assessments (ILPA), and an adaptive learning resource pool. These components were embedded into two undergraduate engineering modules, Network Engineering and Software Engineering, at a UK university. A mixed-method evaluation, centred on student attainment data across two academic years, revealed statistically significant improvements among students who engaged with GPL, particularly those who completed ILPA activities. Participation was associated with higher mean grades, increased proportions of high achievers, and reduced failure rates. These findings demonstrate the GPL model’s effectiveness in supporting learner autonomy, formative assessment, and targeted feedback, while offering a scalable strategy for integrating personalised learning into mainstream STEM curricula. Full article

This paper investigates how sustainable STEM education is being shaped within the pre-university systems of the 22 Arab countries. By categorizing these systems into four groups based on the Global Knowledge Index and two analytical tracks, this study examines in detail the factors that enable—or hinder—the development of long-term, sustainability-oriented competencies in science, technology, engineering, and mathematics. Beyond pedagogical dimensions, this study emphasizes STEM education as a strategic tool for achieving national sustainable development goals (SDGs), promoting workforce readiness, and informing education policy reform. The analysis highlights the policy efforts, systemic limitations, and the need for localized strategies to integrate sustainability into the STEM curricula and teacher training. It is structured in six sections: (1) an introduction to STEM and sustainability concepts, the Global Knowledge Index, and the Arab-region education landscape; (2) research questions, methodology, and data sources; (3) analysis of Groups 1 and 2, assessing their experiences in implementing sustainability-driven STEM initiatives; (4) analysis of Groups 3 and 4, evaluating their readiness for adopting sustainable STEM programs; (5) discussion of findings in light of sustainability policy frameworks; and (6) a concluding overview with actionable recommendations to align national education systems with global sustainability goals. Full article

Science teachers continue to face decreased motivation, lower achievement levels, and decreased enrollment in post-secondary science programs. Teachers ask themselves this question: How do I motivate my students to achieve? Student-centered pedagogies, such as an in-depth pedagogy informed by Self-Determination Theory, can improve students’ motivation by addressing students’ basic psychological needs for autonomy, competency, and relatedness. Problem-based learning presents students with relevant situations and actively engages them in developing plausible solutions to problems. Environmental sustainability encompasses issues concerning our ecological and social environments. Teachers can focus on these issues to develop authentic problem-based learning units that offer a student-relevant pathway to improve motivation and scientific literacy. We propose a pedagogical framework, drawing on Self-Determination Theory, to promote students’ motivation to engage keenly with environmental sustainability education through problem-based learning. This framework is designed for secondary science classrooms to inform science teachers’ pedagogical practice. Full article

The integration of artificial intelligence (AI) into educational environments is fundamentally transforming the learning process, raising new questions regarding student engagement and motivation. This empirical study investigates the relationship between AI-based learning support and the experience of flow, defined as the optimal state of deep attention and intrinsic motivation, among university students. Building on Csíkszentmihályi’s flow theory and current models of technology-enhanced learning, we applied a validated, purposefully developed AI questionnaire (AIFLQ) to 142 students from two Hungarian universities: the Ludovika University of Public Service and Eszterházy Károly Catholic University. The participants used generative AI tools (e.g., ChatGPT 4, SUNO) during their academic tasks. Based on the results of the Mann–Whitney U test, significant differences were found between students from the two universities in the immersion and balance factors, as well as in the overall flow score, while the AI-related factor showed no statistically significant differences. The sustainability of the flow experience appears to be linked more to pedagogical methodological factors than to institutional ones, highlighting the importance of instructional support in fostering optimal learning experiences. Demographic variables also influenced the flow experience. In gender comparisons, female students showed significantly higher values for the immersion factor. According to the Kruskal–Wallis test, educational attainment also affected the flow experience, with students holding higher education degrees achieving higher flow scores. Our findings suggest that through the conscious design of AI tools and learning environments, taking into account instructional support and learner characteristics, it is possible to promote the development of optimal learning states. This research provides empirical evidence at the intersection of AI and motivational psychology, contributing to both domestic and international discourse in educational psychology and digital pedagogy. Full article

The implementation of new curricula presents significant challenges for teachers and necessitates the adaptation of their professional competencies. This study examines how the digital skills of Swiss teachers differ during and after the implementation of the Media and Information Literacy (MIL) modular curriculum. To achieve this, two independent samples totaling 617 teachers were surveyed at different stages of the reform. The analysis employed a multivariate analysis of variance (MANOVA) to identify differences in the four facets of MIL competence between the samples. The results indicate that MIL competencies did not improve uniformly. While no significant differences were found in the facets of ‘ICT knowledge and application’ and ‘media education knowledge and application’, significant differences were observed in the facets of ‘specialized didactics MIL’ and ‘interdisciplinary pedagogical and didactic knowledge’. The latter facet showed higher values after the reform, likely due to increased exposure to further training measures. These findings illustrate that teachers’ competencies can develop differently throughout a reform process, highlighting the importance of targeted professional development programs. The study provides valuable empirical findings for educational research and offers practical implications for the design of future reform processes. Full article

This study examines the use of digitalized educational solutions among students in higher education institutions (HEIs). Drawing upon theories of technology, digitalization, and education, we analyze the suitability of different digitalization solutions for students in HEIs. Educational organizations that apply different digitalized technologies provide customizable platforms for authoring and disseminating multimedia-rich e-education and smart education. However, pedagogical practices indicate several gaps between the level of HEI digitalization achieved and its suitability for HEI participants. Thus, we analyze the state of various digitalized technologies in HEIs and their suitability for meeting students’ expectations. The results of our research show that students most highly rate modern educational methods such as practical learning supported by access to digitized materials via websites, social networks, and smartphones while assigning a lower rating to the use of classic education, supported by digital textbooks and traditional technologies such as Skype, Zoom, podcasts, and online videos. This study has several theoretical implications, among which is the need to further develop highly digitized materials and purpose-designed digitized solutions for individual areas and specific educational purposes. The practical implications indicate the need to expand the use of website networks, smartphones, and smart table solutions in modern educational practices in HEIs. Full article

Low achievement in mathematics across educational levels has long been a concern for researchers. Recent evidence points to equipping teachers with skills and competencies that align with the demands of the modern, technology-rich world. This systematic review explored how technology-facilitated heutagogical practices contribute to the professional development of preservice and in-service mathematics teachers. Drawing on 21 empirical studies published between 2017 and 2024, this review identified three major findings. First, technology-enhanced heutagogical practices promote teaching skills by fostering learner autonomy, self-reflection, and professional identity development. Second, tools such as mobile apps, Massive Open Online Courses (MOOCs), adaptive learning platforms, and collaborative digital environments support the integration of heutagogical principles. Third, implementation is challenged by limited digital access, institutional constraints, and the need for gradual adaptation to self-determined learning models. These findings prove the need for policy and institutional investment in digital infrastructure, blended learning models, and teacher support. Theoretically, this study affirms heutagogy as a relevant pedagogical approach for preparing mathematics teachers in dynamic learning contexts. There is also a need for more empirical studies to investigate scalable models for technology-driven heutagogy, especially in resource-constrained settings. Full article

In today’s digitalized educational landscape, the intelligent use of information is essential for personalizing learning, improving assessment accuracy, and supporting data-driven pedagogical decisions. This systematic review examines the integration of Application Programming Interfaces (APIs) powered by Artificial Intelligence (AI) to enhance educational information management and learning processes. A total of 27 peer-reviewed studies published between 2013 and 2025 were analyzed. First, a general description of the selected works was provided, followed by a breakdown by dimensions in order to identify recurring patterns, stated interests and gaps in the current scientific literature on the use of AI-driven APIs in Education. The findings highlight five main benefits: data interoperability, personalized learning, automated feedback, real-time student monitoring, and predictive performance analytics. All studies addressed personalization, 74.1% focused on platform integration, and 37% examined automated feedback. Reported outcomes include improvements in engagement (63%), comprehension (55.6%), and academic achievement (48.1%). However, the review also identifies concerns about privacy, algorithmic bias, and limited methodological rigor in existing research. The study concludes with a conceptual model that synthesizes these findings from pedagogical, technological, and ethical perspectives, providing guidance for more adaptive, inclusive, and responsible uses of AI in education. Full article

Scientific culture in pedagogical work involves the integration of scientific principles, values, and practices into education to promote critical thinking, evidence-based reasoning, and curiosity. It fosters an environment where students develop as independent learners, problem-solvers, and informed citizens. Teachers play a key role in cultivating this culture, acting as facilitators and guides who equip students with the tools to think critically and engage with the world scientifically. While often associated with STEM disciplines, scientific literacy extends beyond these areas, emphasizing the integration of scientifically grounded knowledge into all subject areas. To achieve this, teachers must continually enhance their own understanding and skills in scientific thinking. Staying updated with the latest scientific discoveries, critically evaluating information, and applying innovative pedagogical methods are essential. Professional development can provide a vital avenue for teachers to acquire these competencies. Approaches such as reading scientific literature, collaborating with colleagues, and attending specialized training programs can improve teaching strategies and promote scientific thinking in the classroom. This study investigated professional development programs provided for in-service teachers to understand how they contribute to fostering a scientific culture. The researchers collected data from municipalities in Latvia and online resources to summarize the information on the professional development programs provided to in-service teachers. This study examined how elements of scientific culture are reflected in teacher professional development programs in Latvia, using Merton’s CUDOS norms as an analytical framework. The acronym CUDOS refers to four foundational principles of scientific ethos: communalism, universalism, disinterestedness, and organized skepticism. These norms guided the evaluation of whether and how scientific inquiry values are embedded in the design and delivery of training programs for in-service teachers. Using quantitative and qualitative methods for data analysis, it was found that in-service teacher training in Latvia is highly eclectic and often lacks alignment with scientifically grounded principles. There are a lot of programs provided to develop transversal competencies, but very few subject-specific programs are available. The findings highlight the need for more structured and cohesive professional development programs to support in-service teachers in developing competence in cultivating scientific inquiry, nurturing curiosity, and empowering students to navigate an increasingly complex and technology-driven society. These insights offer practical implications for education policymakers and program designers aiming to enhance the scientific orientation of teacher training. By identifying specific gaps in content and alignment with scientific culture, this study provides an original contribution to the discourse on evidence-informed teacher development and supports a more conceptually grounded and equitable approach to lifelong professional learning in Latvia. Full article

This systematic review synthesizes 82 peer-reviewed studies published between 2014 and 2024 on the use of audio features in educational research. We define audio features as descriptors extracted from audio recordings of educational interactions, including low-level acoustic signals (e.g., pitch and MFCCs), speaker-based metrics (e.g., talk-time and participant ratios), and linguistic indicators derived from transcriptions. Our analysis contributes to the field in three key ways: (1) it offers targeted mapping of how audio features are extracted, processed, and functionally applied within educational contexts, covering a wide range of use cases from behavior analysis to instructional feedback; (2) it diagnoses recurrent limitations that restrict pedagogical impact, including the scarcity of actionable feedback, low model interpretability, fragmented datasets, and limited attention to privacy; (3) it proposes actionable directions for future research, including the release of standardized, anonymized feature-level datasets, the co-design of feedback systems involving pedagogical experts, and the integration of fine-tuned generative AI to translate complex analytics into accessible, contextualized recommendations for teachers and learners. While current research demonstrates significant technical progress, its educational potential is yet to be translated into real-world educational impact. We argue that unlocking this potential requires shifting from isolated technical achievements to ethically grounded pedagogical implementations. Full article

Generative AI is an emerging tool in higher education; however, its connection with transversal competencies, as well as their sustainable adoption, remains underexplored. The study aims to analyze the scientific and conceptual development of generative artificial intelligence in higher education to identify the most relevant transversal competencies, strategic processes for its sustainable implementation, and global trends in academic production. A systematic literature review (PRISMA) was conducted on the Web of Science, Scopus, and PubMed, analyzing 35 studies for narrative synthesis and 897 publications for bibliometric analysis. The transversal competencies identified were: Academic Integrity, Critical Thinking, Innovation, Ethics, Creativity, Communication, Collaboration, AI Literacy, Responsibility, Digital Literacy, AI Ethics, Autonomous Learning, Self-Regulation, Flexibility, and Leadership. The conceptual framework connotes the interdisciplinary nature and five key processes were identified to achieve the sustainable integration of Generative AI in higher education oriented to the development of transversal competencies: (1) critical and ethical appropriation, (2) institutional management of technological infrastructure, (3) faculty development, (4) curricular transformation, and (5) pedagogical innovation. On bibliometric behavior, scientific articles predominate, with few systematic reviews. China leads in publication volume, and social sciences are the most prominent area. It is concluded that generative artificial intelligence is key to the development of transversal competencies if it is adopted from a critical, ethical, and pedagogically intentional approach. Its implications and future projections in the field of higher education are discussed. Full article

Knowledge and problem-solving approaches that span disciplinary boundaries and involve diverse communities are foundational aspects of transdisciplinarity. Transdisciplinary approaches in research efforts are needed to address complex problems of global importance. At the same time education systems should be preparing graduates to enter the workforce with complex problem-solving skills. Students need to have learning experiences that allow for the acquisition of cross-disciplinary systematic reasoning if they are expected to engage in addressing these complex problems. Recent reports have underscored the need to create university structures and incentives that allow for dynamic and responsive approaches to this global need for transdisciplinary discovery and learning efforts; however, little is known about the efficacy of the approaches and models that have been implemented to create large-scale change in higher education and how they help in achieving transdisciplinary goals. Through an ethnographic case study analysis, this paper examines how a faculty-led community of practice model is being used to build transdisciplinary research capacity and transdisciplinary curricula at a Research I university. Given the unique nature of this transdisciplinary community of practice model, this qualitative and descriptive study sought to examine what elements of the model facilitated faculty participation in transdisciplinary research and curricular efforts. More specifically, this study intended to respond to recent calls to better understand the systematic approach that would need to be employed by higher education institutions if they are adequately engaging faculty in addressing complex problems in their research efforts, as well as engaging faculty in the adequate development of the future workforce through pedagogical transdisciplinary approaches. The findings indicate that the transdisciplinary community of practice model is useful for initially motivating and incentivizing faculty participation. The results also indicate that the inclusive internal support networks that were part of the model facilitated faculty engagement. Full article

Aim: To understand dementia care knowledge, attitudes, and confidence among acute-care support staff following a dementia education intervention titled Dementia Workforce Excellence in Acute Care. Design: A convergent parallel mixed-methods pilot study. Methods: Data were collected from 30 participants using an online survey and three individual interviews between January and March 2024. Survey data were analysed using descriptive statistics, and a thematic analysis underpinned by Kirkpatrick’s framework was used to analyse the qualitative data from interviews. Results: The online survey established good levels of dementia knowledge, attitudes, and confidence among support staff with enhanced attitudes among staff who completed the training. Analysis of interviews resulted in three themes: dementia in the acute care setting; motivation for learning; and evaluation of the intervention on four levels [satisfaction, learning gains, behaviours, and results]. Conclusion: Findings suggest that the dementia education intervention used in this study is a comprehensive dementia training resource that promotes person-centred and compassionate dementia care across all stages of the dementia journey. Dementia is a public health priority with workforce education identified as a key response for capacity building. This pilot evaluation offers insight and new learning on the pedagogical approaches that are inclusive of peer-supported reflective learning in small groups that remain untapped for dementia workforce development. Dementia inclusive and enabling environments with a knowledgeable and skilled workforce are crucial to mitigate stigma and discrimination. This can be best achieved by raising awareness through targeted staff education and training to make hospital environments more dementia inclusive. Patient or Public Contributions: Dementia care scenarios used in this study were co-designed by experts with lived experience of dementia. Additionally, these experts along with family carers of people living with dementia were involved in the delivery of the training where appropriate. Full article