Search Results (777)

Despite the significant volume of empirical research found in student-authored academic theses—particularly in the social sciences—these works are often poorly documented and difficult to discover within institutional repositories. A key reason for this is the lack of appropriate metadata frameworks that balance descriptive richness with usability. General standards such as Dublin Core are too simplistic to capture critical research details, while more robust models like the Data Documentation Initiative (DDI) are too complex for non-specialist users and not designed for use with student theses. This paper presents the design and validation of a lightweight, web-based metadata framework specifically tailored to document empirical research in academic theses. We are the first to adapt existing hybrid Dublin Core–DDI approaches specifically for thesis documentation, with a novel focus on cross-methodological research and non-expert usability. The model was developed through a structured analysis of actual student theses and refined to support intuitive, structured metadata entry without requiring technical expertise. The resulting system enhances the discoverability, classification, and reuse of empirical theses within institutional repositories, offering a scalable solution to elevate the visibility of the gray literature in higher education. Full article

In an increasingly complex global context, higher education faces the challenge of preparing professionals who are innovative, committed, and socially responsible. Entrepreneurial competence is particularly prominent among the key skills required to meet this goal, given its significant personal and social impact. This study examines how final-year undergraduate students at the University of Deusto (Spain) perceive the importance of entrepreneurial competence—defined as a set of transversal skills, knowledge, and attitudes enabling initiative and opportunity recognition across various contexts—rather than entrepreneurial competence strictly understood as business creation. The sample included 267 students from different faculties. Descriptive, comparative, and ordinal logistic regression analyses (SPSS) were used. The results show that, while entrepreneurial competence was given significant importance, it was ranked comparatively low relative to other competencies. Significant differences by gender were observed, with women rating entrepreneurial competence more highly than men. The faculty variable showed slight disparities, and there were no relevant differences between campuses. These findings highlight the need to reinforce the integration of entrepreneurial competence into educational curricula on a transversal basis, adapting the teaching of this competence to the sociocultural context of students, as well as the need to increase students’ awareness of the importance of entrepreneurial competence. It is proposed that further research should focus on the relationships between intrapreneurship, gender, and academic disciplines, in order to enrich entrepreneurial competence education and its impact on the employability and social commitment of students. Full article

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

This paper investigates how governance frameworks shape sustainable tourism outcomes in protected areas by comparing Tara National Park (Serbia) and Triglav National Park (Slovenia). Both parks, established in 1981 and classified under IUCN Category II, exhibit rich biodiversity and mountainous terrain but differ markedly in governance structures, institutional integration, and local community engagement. Using a qualitative, indicator-based methodology, this research evaluates ecological, economic, and social dimensions of sustainability. The findings reveal that Triglav NP demonstrates higher levels of participatory governance, tourism integration, and educational outreach, while Tara NP maintains stricter ecological protection with less inclusive management. Triglav’s zoning model, community council, and economic alignment with regional development policies contribute to stronger sustainability outcomes. Conversely, Tara NP’s centralized governance and infrastructural gaps constrain its potential despite its significant conservation value. This study highlights the importance of adaptive, inclusive governance in achieving the Sustainable Development Goals (SDGs) within protected areas. It concludes that hybrid approaches, combining legal rigor with participatory flexibility, can foster resilience and sustainability in ecologically sensitive regions. Full article

Research highlights a growing demand for active, experiential learning methods in higher education, especially in teacher education. While the benefits of parental involvement (PI) are well-documented, Hungary lacks tools to effectively prepare teacher trainees for fostering family–school cooperation. This study addresses this gap by introducing a custom-designed board game as an innovative teaching tool. The game simulates real-world challenges in PI through a cooperative, scenario-based framework. Exercises are grounded in international and national research, ensuring their relevance and evidence-based design. Tested with 110 students, the game’s educational value was assessed via post-gameplay questionnaires. Participants emphasized the strengths of its cooperative structure, realistic scenarios, and integration of humor. Many reported gaining new insights into parental roles and strategies for effective home–school partnerships. Practical applications include integrating the game into teacher education curricula and adapting it for other educational contexts. This study demonstrates how board games can bridge theory and practice, offering an engaging, effective medium to prepare future teachers for the challenges of PI. Full article

Sport volunteering plays an important role in achieving the goals of sustainable development by supporting the social dimension of sustainability, fostering social integration, and promoting a healthy lifestyle. However, there is a lack of systematic research in Poland on the motivations of sport volunteers, particularly in the context of mass endurance events. This study employed a quantitative, cross-sectional design involving 148 sport volunteers engaged in mass endurance events in Poznan, Poland. To measure motivation, the Polish adaptation of the VMS-ISE scale was used. Data analysis was conducted using one-way analysis of variance (ANOVA). The results showed that volunteer motivations were relatively homogeneous regardless of gender and education level, with the exception of passion for sport, which was significantly stronger among men (p = 0.037). Significant differences were found based on place of residence: residents of medium-sized cities demonstrated the highest motivation for personal development (p < 0.001), whereas individuals from rural areas exhibited stronger patriotism, a greater need for interpersonal interaction, and a higher valuation of external rewards (p < 0.05). The motivations of sport volunteers in Poland are complex and sensitive to environmental factors. Understanding these differences allows for better alignment of recruitment and volunteer management strategies, which can enhance both the effectiveness and sustainability of volunteer engagement. It is recommended to develop volunteer programs that take into account the demographic and socio-cultural characteristics of participants. Full article

Sustainable education requires addressing the challenges posed by digital transformation, including technostress among university students. This study evaluates technostress levels in higher education through the validation of the TS4US scale and its implications for sustainable learning environments. A cross-sectional study was conducted with 328 university students from four districts in Lima, Peru, using an online survey to measure technostress. Confirmatory factor analysis (CFA) was performed to assess the psychometric properties of the TS4US scale, resulting in a refined model with two latent factors and thirteen validated items. Findings indicate that 28% of students experience high technostress levels, while 5% report very high levels, though no significant associations were found between technostress and sociodemographic variables such as campus location, employment status, gender, and academic level. The TS4US instrument had been previously validated in Chile; this study confirms its structure in a new sociocultural context, reinforcing its cross-cultural applicability. These results highlight the need for sustainable strategies to mitigate technostress in higher education, including institutional support, digital literacy programs, and policies fostering a balanced technological environment. Addressing technostress is essential for promoting sustainable education (SDG4) and enhancing student well-being (SDG3). This study directly contributes to the achievement of Sustainable Development Goals 3 (Good Health and Well-being) and 4 (Quality Education) by providing validated tools and evidence-based recommendations to promote mental health and equitable access to digital education in Latin America. Future research should explore cross-country comparisons and targeted interventions, including digital well-being initiatives and adaptive learning strategies, to ensure a resilient and sustainable academic ecosystem. Full article

This study presents the outcomes of the Erasmus+ European project Higher Education Classroom of the Future (HECOF), with a particular focus on chemical engineering education. In the digital era, the integration and advancement of artificial intelligence (AI) in higher education, especially in engineering, are increasingly important. The main goal of the HECOF project is to establish a system of new higher education teaching practices and national reforms in education. This system has been developed and tested through an innovative personalized and adaptive method of teaching that exploited digital data from students’ learning activity in immersive environments, with the aid of computational analysis techniques from data science. The unit operations—extraction process course—a fundamental component of the chemical engineering curriculum, was selected as the case study for the development of the HECOF learning system. A group of undergraduate students evaluated the system’s usability and educational efficiency. The findings showed that the HECOF system contributed positively to students’ learning—although the extent of improvement varied among individuals—and was associated with a high level of satisfaction, suggesting that HECOF was effective in delivering a positive and engaging learning experience. Full article

Blended and virtual learning have become an integral part in international higher education, especially in the wake of the COVID-19 pandemic and the European Union’s Digital Education Action Plan. These modalities have enabled more inclusive, flexible, and sustainable forms of international collaboration, such as Collaborative Online International Learning (COIL) and Blended Intensive Programs (BIPs), reshaping the landscape of global academic mobility. This paper introduces the INVITE Learning Design Framework (LDF), developed to support higher education instructors in designing high-quality, internationalized blended and virtual learning experiences. The framework addresses the growing need for structured, theory-informed approaches to course design that foster student engagement, intercultural competence, and motivation in non-face-to-face settings. The INVITE LDF was developed through a rigorous scoping review of existing models and frameworks, complemented by needs-identification analysis and desk research. It integrates Self-Determination Theory, Active Learning principles, and the ADDIE instructional design model to provide a comprehensive, adaptable structure for course development. The framework was successfully implemented in a large-scale online training module for over 1000 educators across Europe. Results indicate that the INVITE LDF enhances educators’ ability to create engaging, inclusive, and pedagogically sound international learning environments. Its application supports institutional goals of internationalization by making global learning experiences more accessible and scalable. The findings suggest that the INVITE LDF can serve as a valuable tool for higher education institutions worldwide, offering a replicable model for fostering intercultural collaboration and innovation in digital education. This contributes to the broader transformation of international higher education, promoting equity, sustainability, and global citizenship through digital pedagogies. 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

Differences exist between rural–urban migrants and urban–urban migrants in terms of human capital’s accumulation and pathways of social integration, yet few studies have systematically compared these distinctions. Based on the CMDS2017 survey data, this study constructed a comprehensive social integration index across four dimensions—economic integration, behavioral adaptation, identity recognition, and psychological assimilation—to analyze the influencing factors and decompose the disparities in social integration levels between the two groups from a human capital perspective. Using Oaxaca mean decomposition and Machado–Mata (MM) quantile decomposition, the results indicated that urban–urban migrants exhibited higher social integration levels than rural–urban migrants, with human capital significantly influencing integration outcomes. Better education, health status, longer migration duration, and more work experience positively enhanced migrants’ social integration. Human capital accounted for 38.35% of the social integration gap between the two groups, while coefficient differences were the primary driver of disparities. The returns to education diminish at higher integration levels, suggesting education played a stronger role for those with lower integration. The social integration gap between the two groups followed an inverted U-shaped trend, with smaller disparities at higher quantiles. As integration levels rose, characteristic differences declined continuously, indicating convergence toward homogeneity among high-integration migrants. These research findings indicated that the improvement in the social integration level of migrants still requires continuous investment in cultivating the human capital of migrants. Full article

This research analyzed university students’ perceptions of the use of generative artificial intelligence (hereafter Gen-AI) in a higher education context. Specifically, it addressed the potential benefits and challenges related to the application of these web-based resources. A mixed method was adopted and the sample consisted of 407 teacher training students enrolled in the Early Childhood and Primary Education Degrees in the Region of Murcia in Spain. The results indicated a clear recognition of the relevance of these technological tools for teaching and learning. Respondents highlighted the potential to engage them in academic tasks, increase their motivation, and personalize their learning pathways. However, participants identified some challenges related to technology dependency, ethical issues, and privacy concerns. By understanding learners’ beliefs and assumptions, educators and educational administrations can adapt Gen-AI according to learners’ needs and preferences to improve their academic performance. In learning practice, these adaptations could involve evidence-based interventions, such as AI literacy modules or hybrid assessment frameworks, to translate findings into practice. In addition, it is necessary to adjust materials, methodologies, and the assessment of the academic curriculum to facilitate student learning and ensure that all students have access to quality education and the adequate development of digital skills. Full article

Certain industries will bear a disproportionate share of the burden of climate change. Climate change risk perceptions can impact workers’ mental health and well-being; increased climate change risk perceptions are also associated with more favourable adaptive attitudes. It is, therefore, important to understand whether climate risk perceptions differ across workers between industries. We conducted an online survey of British Columbians (16+) in 2021 using social media advertisements. Participants rated how likely they believed their industry (Natural Resources, Science, Art and Recreation, Education/Law/Government, Health, Management/Business, Manufacturing, Sales, Trades) would be affected by climate change (on a scale from “Very Unlikely” to “Very Likely”). Ordinal logistic regression examined the association between occupational category and perceived industry vulnerability, adjusting for socio-demographic factors. Among 877 participants, 66.1% of Natural Resources workers perceived it was very/somewhat likely that climate change would impact their industry; only those in Science (78.3%) and Art and Recreation (71.4%) occupations had higher percentages. In the adjusted model, compared to Natural Resources workers, respondents in other occupations, including those in Art and Recreation, Education/Law/Government, Management/Business, Manufacturing, Sales, and Trades, perceived significantly lower risk of climate change-related industry impacts. Industry-specific interventions are needed to increase awareness of and readiness for climate adaptation. Policymakers and industry leaders should prioritize sectoral differences when designing interventions to support climate resilience in the workforce. Full article

Inclusive higher education requires educators who are not only willing to teach inclusively but who also have the skills to do so. This preliminary study offers a blueprint on how to examine the effectiveness of a three-day professional development program to strengthen the attitudes, self-efficacy, and inclusive didactics of educators. We propose a quasi-experimental design with pre-, post-, and follow-up measures, to measure the effect of the professional development program at three levels: attitudes (SACIE-R), self-efficacy (TEIP), and inclusive teaching practices (adapted Teaching Practices Questionnaire). The results, although preliminary, show a small but significant decrease in concerns toward inclusive education over time. Self-efficacy, on the other hand, showed a non-significant but consistent increase, especially at follow-up. In terms of teaching practices, significant improvements were observed in the teaching of basic skills, but not in dealing with diversity or differentiating for individual students. These preliminary findings seem to underline that short professional development programs, while contributing to increased confidence and certain didactic skills, are not sufficient to achieve lasting changes in attitudes and inclusive teaching strategies. This suggests that lasting impact likely requires structural follow-up, practical support, and strengthening the inclusive learning climate within higher education institutions. Full article

The integration of Lesson Study (LS) and Didactic Suitability Criteria (DSC) presents an innovative framework for enhancing mathematics teacher training in higher education. This study examines how LS-DSC fosters instructional refinement, professional growth, and pedagogical transformation among in-service teachers. Using a quasi-experimental mixed-methods approach, the study analyzed data from 520 mathematics educators participating in a six-month training program incorporating collaborative lesson planning, structured pedagogical assessment, and reflective teaching practices. Findings indicate significant improvements in instructional design, mathematical discourse facilitation, and adaptive teaching strategies, with post-training evaluations demonstrating a strong positive correlation (r = 0.78) between initial competency levels and learning gains. Participants reported increased confidence in implementing student-centered methodologies and sustained engagement in peer collaboration beyond the training period. The results align with prior research emphasizing the effectiveness of lesson study models and structured evaluation frameworks in teacher professionalization. This study contributes to higher education policy and practice by advocating for the institutional adoption of LS-DSC methodologies to promote evidence-based professional development. Future research should explore the long-term scalability of LS-DSC in diverse educational contexts and its impact on student learning outcomes. Full article