Keyword: analysis

ABSTRACT: The Higher Education sector is being revolutionized by AI, with machine learning, natural language processing and generative AI applications. Yet, with the fast pace of technological evolution, both the possibilities and threats of AI incorporation have been spread out. This study includes an integrated multimethod systematic review and bibliometric analysis of the dual landscape of AI in higher education. Following PRISMA 2020 guidelines, systematic searches of Web of Science (n=1,631), PubMed (n=1,349), and Scopus (n=1,439) yielded 4,419 records (2021-2025). Following screening for duplication and eligibility, 110 studies (85 high and 25 medium quality) were included for thematic synthesis. A concurrent bibliometric analysis of 1,227 documents in Scopus was performed to depict author citation networks, keyword co-occurrence, and bibliographic coupling by source and country, using VOSviewer. The study identified Jiao, Ouyang, and Zheng as the most cited authors; the most frequently used keywords are 'ChatGPT' (173 times) since late 2022. Academic support, automated grading, teacher development, perceived usefulness, personalization of learning, and prediction of performance. The perceived ease of use, 24/7 access to technology, administrative efficiency; and academic integrity, data issues, integration barriers, technological limitations, equity concerns. And attitudinal barriers were identified as opportunity and challenge domains, respectively, during the thematic synthesis process. This study offers a dual landscape framework which means that there are no opportunities without challenges, and no challenges without opportunities. It delivers an evidence-based typology for institutional AI strategy, and priority areas for policy intervention, practically. The methodologically, it shows the usefulness of the combination of bibliometric and systematic review for complete literature synthesis. This research aims to explore the opportunities and challenges in integrating AI into higher education, based on a systematic review and bibliometric analysis of relevant literature.

ABSTRACT: This study proposes an integrated, evidence-based model to explain the transition from artificial intelligence (AI)-related anxiety to acceptance and learning engagement in the context of Education 4.0. Drawing on a secondary data synthesis of four nationally representative South Korean datasets—KISTEP (2024), NIA (2024), KEEP (2024), and Statistics Korea (2024)—and grounded in Keller’s (1987) ARCS motivational model and Davis’s (1989) Technology Acceptance Model (TAM), the study develops a multi-layered framework linking AI anxiety, AI literacy, AI acceptance, and learning engagement. The analysis tests four hypotheses using a pseudo-SEM approach. Because the study relies on secondary data, proxy indicators are employed to operationalize key constructs, with the Digital Competency Index (DCI) used as a proxy for AI literacy. The findings show that AI anxiety is significantly and negatively associated with AI acceptance, whereas AI literacy exerts a direct positive effect and also weakens the anxiety-acceptance relationship, functioning as a cognitive buffer (moderating effect, β = .23, p < .05). In turn, AI acceptance emerges as a strong predictor of learning engagement (R² = .34). Most notably, this study reframes AI literacy not merely as a technical skill, but as a resilience mechanism that helps reduce technology-related anxiety. The theoretical contribution has direct implications for instructional design and age-sensitive policy interventions. In addition, the pseudo-SEM approach offers a replicable framework for building macro-level theory from national administrative data.

ABSTRACT: This study evaluates the efficiency of STEM departments at a Zimbabwean university using Data Envelopment Analysis and the Malmquist Productivity Index. Guided by Education 5.0 and persistent resource constraints, the analysis assesses performance using inputs (staff, budgets, programmes, teaching assistants) and outputs (graduates, publications, citations, grants). Overall efficiency improved from 24% in 2023 to 45% in 2024. Sensitivity tests show that programme offerings and graduate output are the strongest drivers of efficiency, while slack analysis highlights opportunities to optimise resource utilisation. Benchmarking identifies DMU14 and DMU33 as top performers that can serve as models for weaker departments. Unlike much of the global literature, this study emphasises resource reallocation rather than frontier shifts, reflecting the realities of low-funding environments. Recommendations include targeted budget adjustments, structured benchmarking, and policy measures to address STEM-specific costs. The findings support evidence-based decision-making and advance methodological debates on DEA in resource-constrained settings.

ABSTRACT: Amid the post‑COVID‑19 expansion of e‑learning, this study developed and validated a survey instrument to assess e‑learning competencies among science and mathematics supervisors in Kuwait. After expert review using the Lawshe–Tristan method, an exploratory factor analysis on data from 345 supervisors confirmed an eight‑factor structure encompassing computer literacy, computer skills, educational planning, program design, pedagogical practice, assessment, professional development, and ethical/legal awareness. Confirmatory factor analysis produced an acceptable fit for teachers but a weaker fit for students, underscoring the need for further refinement. Cronbach’s alpha and McDonald’s omega coefficients indicated excellent internal consistency. Descriptive analyses revealed that male supervisors, those with advanced technology expertise and higher English proficiency, and those at intermediate or secondary levels scored higher across domains. The validated instrument is a valuable tool for diagnosing training needs and guiding e‑learning professional development in Kuwait and similar contexts. The instrument thus provides a psychometrically robust tool for assessing readiness to learn and highlights demographic differences that can be used to target professional development. The study concludes with a recommendation to conduct larger sample sizes and additional validation phases to refine the instrument further and improve its applicability in different educational contexts.

ABSTRACT: This study compares the preprimary education systems of Singapore and Ethiopia, two nations with contrasting socioeconomic and legislative environments. Using a comparative analysis that integrates both quantitative and qualitative data, the research identifies discrepancies in accessibility, curriculum, teacher training, infrastructure, and policy efficacy. It fills a research gap by comparing ECE in a developed and developing nation, contextualizing global best practices for resource-limited settings. Ethiopia’s gradual progress (20% to 60% coverage, 2016–2025) reflects external collaboration and bottom-up approaches, but it lacks structural changes. The findings reveal significant gaps in essential resources, particularly textbook shortages, which hinder learning outcomes, and concerns about teacher expertise, highlighting the need for improved professional development programs. In contrast, Singapore’s coordinated curricula and nearly universal enrollment demonstrate astute investment and efficient regulation. Key findings reveal divergent long-term effects, values, and resource investments. While Singapore’s model offers insights into standards and technology utilization, recommendations emphasize Ethiopia’s need for additional funding and teacher professional development. The report underscores the importance of policy convergence with Sustainable Development Goal 4 to ensure inclusive early childhood care and education (ECCE) worldwide.

ABSTRACT: In this study, the relationships between Personality Trait (PT), Emotional Intelligence (EI), and Intrapreneurial Self-Capital (ISC) are examined. A modified version of the Intrapreneurial Self Capital and Trait Emotional Intelligence Questionnaire was administered to 200 first-year and 200 third-year undergraduate students. Hierarchical regression analysis was used to explore relationships. The study found that a significant change in one’s ISC occurs with a unit increase in EI and PT. Also, in reference to respondents aged less than 18, none of the age groups exhibited a positive EI. It was also established that females exhibited a positive EI as compared to their male counterparts, whereas level 300 students also exhibited a positive EI ahead of their juniors in level 100. This study recommends that students should take an interest in non-cognitive workshops and seminars to improve their EI to increase their capabilities to cope with their future careers and work life. It is also recommended that students understand their personality traits to interact well with their colleagues at the workplace after graduation. Lastly, this study recommends that the content of courses taught in our universities should be tailored to enhance EI since the current situation is not too good.

ABSTRACT: The paper highlighted the nexus between various research paradigms vis-a-viz mathematics education. Research in education entails basing the process on three fundamental philosophical traditions – epistemology, ontology, and methodology. The trio formed the core of the paradigm likely to be employed to explain the research problem – in which case, mathematics education research problems. Thus, the paper argues that employing a research paradigm in mathematics education research has far-reaching effect in giving an expository analysis of the problem. The research paradigms that were discussed in this paper comprised of positivism, post-positivism, interpretivism and critical theory. In the end, the paper suggested that, in mathematics education, applying a given research paradigm in explaining a particular research problem is imperative, and can help in viewing the problem from philosophical perspective.