Search Results (1,447)

Background: Children’s resilience is a dynamic, multisystemic process shaped by cultural context, supportive relationships, and family values. In Romania, research on children’s resilience and the role of parental values remains limited. Objective: This study examined the relationship between children’s resilience, socio-demographic factors (parental educational level, economic status, family structure, religious affiliation, and ethnicity), and parental values based on Schwartz’s value model. We hypothesized that socially oriented values (security, conformity, tradition, universalism, and benevolence) would be associated with higher resilience. Method: A cross-sectional correlational study was conducted with 245 children aged 8–15 years (M = 10.58, SD = 1.95) and one parent per child. Children’s resilience was assessed using the Child and Youth Resilience Measure (CYRM-28), parental values using the Short Schwartz’s Value Survey (SSVS), and socio-demographic data through a parental questionnaire. Results: Higher resilience was associated with higher parental educational level and with parental values reflecting conservation and self-transcendence. In the regression model, parental educational level, conservation values, Orthodox religious affiliation, and intact family structure were independently associated with resilience and jointly accounted for 22.6% of the variance. Younger children had higher resilience, but age was not a significant predictor in regression analysis. Conclusions: The findings support the socio-ecological perspective on resilience and provide among the first empirical evidence from Romania that parental values, together with family and cultural resources, are associated with children’s resilience. Full article

Explicit gender stereotypes are shifting toward neutrality, yet how widely a stereotype is endorsed and how consistently it appears across domains within those who endorse it are separable properties of a stereotype climate. The same Romanian adults, parents alongside teachers and school counsellors (N = 121), reported gender-stereotyped beliefs about children’s play and traits in the abstract, together with two applied judgements: gender-differentiated educational expectations and gender suitability of occupations. Stereotyped judgement covaried across all three domains, substantially among parents and moderately among teachers. This cross-domain covariation indexes consistency within respondents rather than a common latent construct, which the confirmatory models did not support. Stereotyping was voiced more strongly as abstract belief than in either applied domain. Explicitly gender-typing occupations was a minority position, yet those who did so held the strongest beliefs, a concentration that direction-cancelling coding rendered undetectable. Because gendered channelling is concentrated rather than diffuse, awareness work is better targeted than universal and better timed before high-stakes transitions such as allocation to high-school tracks. Teachers resembled parents, suggesting professional training does not by itself displace such judgement. Where access is no longer the binding constraint, monitoring that tracks only how prevalent gender bias is will miss what remains coherent in a shrinking minority. Full article

The relationship between public education spending and economic growth remains contested in developing economies, where quality, not just quantity, of investment may matter most. This study examines whether public education expenditure has driven economic growth in Egypt over 1980–2023, addressing the under-studied role of education quality and testing the robustness of the estimated relationship to structural breaks linked to major reforms. Using the Autoregressive Distributed Lag (ARDL) bounds testing approach together with Zivot–Andrews structural break unit root tests and CUSUM/CUSUMSQ parameter-stability tests, the study models short- and long-run effects of education expenditure on GDP growth, controlling for capital formation, labor participation, trade openness, and foreign investment, and incorporating quality proxies such as student–teacher ratios and completion rates. The bound F-statistic (6.23) exceeds the 5% upper-bound critical value of 3.83 (Narayan in 2005 on small-sample critical values), confirming long-run cointegration, and the error-correction coefficient (−0.835, p < 0.01) indicates rapid adjustment to equilibrium. Physical capital is the strongest driver of long-run growth, while education expenditure shows a weakly negative long-run association (significant at 10%), suggesting allocative inefficiency rather than absent returns; quality proxies (completion rate, student–teacher ratio) show a negative short-run and positive one-period-lagged effect, consistent with a delayed adjustment process. Stability tests confirm the long-run relationship is structurally invariant once short-run dynamics are accounted for. Thus, improving the efficiency and quality of education spending, rather than its volume, appears essential for Egypt to advance Vision 2030, SDG 4, and SDG 8. Full article

Higher Education Institutions are increasingly expected to connect teacher education with professional knowledge, institutional collaboration, and territorially situated educational challenges. However, less attention has been paid to the processes through which teacher professional knowledge is articulated, interpreted, represented, and communicated across institutional boundaries within Initial Teacher Education (ITE). This study examined an instrumental qualitative case developed by the University of Valladolid in collaboration with five educational institutions in rural and urban settings, educational authorities, and the Institute for Women. Sixty-nine first-year pre-service teachers participated in a structured knowledge mobilization process involving nine collective teacher interview-dialogue sessions with nine professional participants, collaborative analysis, the production of multimodal communication outputs, and a final public seminar. The qualitative corpus comprised records generated through the nine interview-dialogue sessions, including 69 individual field-note records and partial audio recordings, together with 12 analytical summaries, 12 infographics, 12 oral presentations, seminar records, institutional documentation, and a whole-cohort plenary discussion used as supplementary contextual evidence. Data were examined through reflexive thematic analysis supported by NVivo 15. Four interrelated themes were constructed: teacher professional knowledge as situated expertise; its transformation from individual experience into a shared professional resource; students’ representations of teaching and their interpretive and communication practices; and the interinstitutional coordination supporting knowledge mobilization. The findings document a bounded process through which situated professional knowledge moved from teacher articulation to student interpretation, multimodal representation, and institutional communication. They support claims concerning coordination, complementary institutional responsibilities, and knowledge communication, while providing more limited evidence concerning shared governance, long-term knowledge reuse, institutionalization, or territorial effects. Collaborative governance and Education for Sustainable Development (ESD) are therefore used as interpretive perspectives rather than as demonstrated outcomes of the partnership. The study contributes a relational conceptualization of university–school partnerships as infrastructures through which situated teacher professional knowledge can be made available for interpretation and communication within ITE. Full article

Predicting academic performance in face-to-face computing and software engineering courses is hindered by the limited availability of fine-grained process data. This study proposes STEAP, a camera-based static–temporal fusion framework that integrates longitudinal classroom behavior sensing with conventional educational records. Classroom videos from 375 undergraduates enrolled in four computing-related courses were collected over nine teaching weeks. Camera-derived observable behaviors were organized into student-level weekly sequences and transformed into outcome-independent longitudinal representations. Multiple machine-learning classifiers were subsequently applied to predict students’ academic performance. Checkpoint-specific predictions were conducted at Weeks 3, 6, and 9, with each prediction using only the classroom behavioral information available up to the corresponding time point. Using the complete nine-week Temporal representation together with the pre-course Background variables, XGBoost achieved the strongest classification performance among the evaluated models, with an Accuracy of 0.867, a Macro F1 of 0.862, and an At-risk Recall of 0.924. The checkpoint analyses further indicated that classroom behavioral information collected during the early course stage already provided useful predictive information without incorporating behavioral observations from subsequent weeks. After further integrating pre-course background variables and regular assessment information, the final fusion model achieved an Accuracy of 0.896 and a Macro F1 of 0.895. Overall, longitudinal camera-derived classroom behavior provides complementary predictive information beyond conventional educational information and supports the feasibility of earlier academic-risk identification at different course checkpoints. Full article

This article presents a scoping review of antigypsyism in compulsory education, with particular attention to the Catalan context. Its aim is to map the state of knowledge, identify the main mechanisms through which structural racism against Roma people is reproduced in education, and identify existing gaps in the research. Following the PRISMA-ScR guidelines, of the 282 records initially identified across five databases, 48 academic studies and institutional sources meeting the inclusion criteria were analysed. The conceptual framework brings together the definition proposed by the Alliance Against Antigypsyism with critical pedagogy, critical communicative methodology, critical race theory, and intersectionality. The analysis is organised around four levels: discourse, pedagogical practices, school organisation, and educational outcomes. The findings show that educational antigypsyism is a structural phenomenon, with both explicit and implicit manifestations, which produces profound gaps in achievement, graduation, and academic continuity. Its most persistent expressions are school segregation, low teacher expectations, and curricular invisibility. In Catalonia, despite an advanced legal framework, specific research remains virtually non-existent. These findings indicate that educational antigypsyism must be addressed as embedded structural racism, requiring Catalonia-specific research and policies that move beyond deficit-based thinking and recognise Roma agency. Full article

Background/Objectives: The integration of artificial intelligence (AI) and digital technologies into dental practice is reshaping clinical workflows, administrative processes, and, increasingly, the patient experience and the doctor–patient relationship. While prior research has documented the attitudes of clinicians and practice managers, the perspective of the patient remains comparatively underexplored. This study examined how dental patients perceive AI integration and digital tools across the dental care pathway, together with the associated implications for data security, cost, and the human dimension of care. Methods: A cross-sectional, questionnaire-based study was conducted among 200 dental patients in Bucharest, Romania, and the surrounding region. The instrument assessed perceived difficulty and availability regarding digital technology, current use of digital tools, demographic and educational characteristics (age, gender, practice environment, educational level), and two attitudinal dimensions, namely digital prudence and concern for technological sustainability, across five subdomains of the dental care pathway: scheduling, diagnosis, treatment planning, feedback, and follow-up (dispensarization). Responses were analyzed using non-parametric tests and exploratory principal component analysis with internal-consistency validation. Results: Patients expressed moderate-to-high interest in AI support during the diagnostic (median = 3.3, IQR = 2.7–3.9) and feedback (median = 3.11, IQR = 2.78–3.67) stages and the lowest interest in scheduling (median = 2.7, IQR = 2.0–3.3). A marked level of digital prudence was observed (median = 3.24, IQR = 2.82–3.61), reflecting concerns about data security, automation, and a possible weakening of the clinician–patient bond. Younger and academically educated patients reported lower perceived difficulty, higher availability, and greater current use of digital tools (all p ≤ 0.001); counterintuitively, the same patients scored significantly higher on digital prudence (Spearman’s ρ = −0.260, p < 0.001). Greater familiarity with digital tools was therefore accompanied by a more critical awareness of their informational risks rather than by uncritical acceptance. Conclusions: Dental patients approach AI through a dual lens of openness and informed caution, welcoming efficiency gains in the clinical and continuity-of-care stages while voicing measured concerns about data security, affordability, and the preservation of human contact. To interpret this profile, we propose two conceptual contributions: a mapping of patient needs onto Maslow’s hierarchy in the context of AI-mediated care and the Informational VUCA framework, which characterizes the volatility, uncertainty, complexity, and ambiguity that patients face when navigating AI-generated information. The findings point to a clear practical agenda of transparent communication, robust data governance, and education strategies adapted to patients’ educational and demographic profiles, so that AI-enhanced workflows strengthen rather than erode the doctor–patient relationship. Full article

Widening access has transformed who enters higher education but not who thrives once inside: inequality resurfaces as first-year failure and dropout, while the rapid spread of generative artificial intelligence (AI) threatens to open new divides. This article presents a whole-institution case study of how one Portuguese public university, Iscte—University Institute of Lisbon, has pursued equitable success in the first year through a single strategy with two reinforcing arms: a reactive arm that detects at-risk students and responds with personalized support, and a proactive arm that builds a baseline of critical AI literacy that is mandatory on one campus and optional on the other. Drawing on institutional records from 2016/17 to 2024/25, it combines a rule-based early warning alarm system, a comparison of machine learning models, and descriptive evidence on the literacy initiatives. The findings show that academic risk is concentrated in identifiable groups, that flagged students who engaged with support re-enrolled at higher rates than those who did not (though not distinguishably so once students who had already formalized a withdrawal are set aside, a group that took up no support and left without exception), and that a common literacy baseline is operationally achievable. The findings also expose the limits of each, as support reaches only a minority of those flagged and exploratory predictive models transfer poorly to the following cohort. Together, these observational results frame reactive support and proactive capacity-building as facets of one inclusion strategy. Full article

Objectives: The rapid digitalization of health communication has made electronic health (e-health) literacy an essential competency, while also increasing concerns about cyberchondria. This descriptive–correlational study examined perceived e-health literacy and cyberchondria among students enrolled in a Faculty of Sports Sciences and evaluated their associations with selected socio-demographic and behavioral factors. Methods: Data were collected between February and May 2025 using a Socio-Demographic Information Form, the e-Health Literacy Scale (eHEALS), and the Short-Form Cyberchondria Severity Scale (CSS-12). Quantitative data were analyzed using group comparison tests, correlation analyses, effect size estimates, and multivariable linear regression. Results: Students demonstrated eHEALS scores close to the upper end of the possible range, together with considerable cyberchondria severity scores. Cyberchondria severity was positively associated with perceived e-health literacy; this association remained statistically significant but weak after adjustment for daily internet use, gender, age, academic year, and department. Significant differences were also observed according to daily internet use, gender, academic year, and department. Conclusions: These findings highlight the dual role of digital health platforms among students engaged in health- and performance-related fields. Educational interventions should promote critical appraisal of online health information and psychologically safe digital health behaviors. Full article

Digital access occupies a prominent place in educational policy debates, yet its predictive contribution to mathematics achievement, relative to psychological and socioeconomic factors, remains insufficiently quantified. Existing evidence often relies on linear models, examines predictor blocks separately, or considers technological resources without placing them alongside psychological constructs within a common framework. This study compares the predictive importance of psychological, socioeconomic, demographic and ICT-access indicators for mathematics achievement in PISA 2022. The analysis used data from 141,563 students across 19 education systems and included seven predictors: mathematics self-efficacy, mathematics anxiety, sense of school belonging, economic, social and cultural status (ESCS), gender, ICT resources at home and ICT resources at school. Weighted gradient boosting models were fitted separately to each of the ten plausible mathematics values and interpreted using TreeSHAP; a weighted random forest with permutation importance was used as a robustness check. The full model explained 39.2% of the weighted test-set variance in the plausible-value outcomes (R² = 0.3922, ${SE}_{total}=0.0066$, and RMSE = 77.95 score points). Mathematics self-efficacy ranked first under both criteria (42.3% of SHAP importance; 57.8% of permutation importance), ahead of socioeconomic status (30.9%; 32.0%), while the ICT-access block contributed 6.9% and 2.1%, respectively and added 0.0122 to test-set R². The two importance rankings were identical (Spearman’s ρ = 1.000). The SHAP ranking was unchanged across all 800 plausible-value × replicate-weight runs, matched XGBoost permutation importance, and was reproduced under a school-grouped train–test split. The model also detected a non-monotonic association between school belonging and predicted achievement, together with a MATHEFF × ESCS interaction pattern, supported by a direct-outcome interaction model, in which the modelled association between self-efficacy and achievement was stronger at higher ESCS levels. Because PISA 2022 is cross-sectional and plausible values are designed for population-level inference, these findings should be interpreted as predictive and associational rather than causal. The results suggest that, in systems where ICT access is already widespread, reported access to technological resources contributes comparatively little to prediction once psychological and socioeconomic indicators are considered, although sensitivity analyses indicate that home ICT access is partly affected by missingness patterns. Full article

With the rise of artificial intelligence (AI), an increasing number of AI-based diagnostic tools are being developed. Before clinical implementation, these tools must be validated against existing gold standards. This requires trials that quantify the agreement between AI predictions and reference measurements. However, designing such agreement studies poses methodological challenges that differ substantially from classical superiority trials. This paper aims to provide statistical methods for assessing diagnostic agreement. Methods were categorized according to the measurement scale of the data (nominal, ordinal, continuous)—with a separate group for methods that apply across several scales—and according to the number of raters or measurements involved. A decision tree is provided as a simplified educational framework for method selection rather than as a general method-selection algorithm: design features such as repeated measurements, clustering, spectrum effects, dependence between raters, and an imperfect reference method are not encoded in it and are discussed separately, together with the circularity and confounding issues specific to the validation of AI-based tools. For each method, we summarized assumptions, appropriate use cases, interpretation of results, and available open-source software for sample size calculation and analysis, and we illustrate the sample size calculations in three fully worked examples covering binary, ordinal, and continuous outcomes. We further distinguish conditional inference about one fixed, frozen model version from the broader generalization to a class of algorithms or to future model versions, which require additional sources of algorithmic and dataset variability to be represented in the design and analysis. We conclude by outlining open methodological questions—including Bayesian approaches to agreement estimation, methods for complex AI outputs, agreement models for clustered and repeated-measures designs, and the limited software support for Gwet’s AC1/AC2 sample size planning—that warrant further work as diagnostic technologies and statistical methodology continue to evolve. Full article

Generative artificial intelligence (GenAI) is reshaping higher education, yet its educational value, governance, inclusion and responsible implementation remain contested. This structured integrative literature review used a PRISMA-informed search and selection process and thematic synthesis to examine pedagogical opportunities and risks, student–staff partnership, assessment and academic integrity, institutional governance, privacy, accessibility and digital equity, with particular attention to UK higher education. The detailed synthesis drew on a 27-report focused corpus formed through an investigator-dependent purposive selection process from 1011 broadly eligible reports; it is therefore neither exhaustive nor statistically or descriptively representative of the broad eligible register. The evidence suggests that GenAI may support explanation, writing, planning, language assistance and flexible academic engagement, but these benefits remain conditional on disciplinary context, user capability, verification and continued human support. The evidence base is stronger for reported use, perceptions and implementation concerns than for causal improvements in learning outcomes. Evidence of student–staff partnership was clearest where participation influenced defining curriculum, assessment or policy decisions; consultation alone should not be presented as co-creation. Responsible adoption also requires programme-level assessment review, clear permitted-use guidance, accessible alternatives, equitable access, data protection, meaningful human oversight and institutional accountability. Drawing these findings together, the review develops an evidence-informed TPACK–Responsible AI–Sociotechnical framework based on pedagogical and capability alignment, Responsible AI safeguards and sociotechnical readiness. The framework supports transparent decisions to approve, approve with controls, pilot with monitoring, redesign or reject proposed uses. Responsible GenAI integration should therefore be use-specific, participatory, proportionate and subject to continued review. Full article

This study examined parents’ and grandparents’ perspectives on intergenerational interactions within an innovative school–family partnership in early childhood education (ECE). Drawing on sociocultural concepts of learning and a ‘more-than-parents’ approach to intergenerational learning (IGL), this research project adopted a two-way partnership perspective, encouraging children and their family members to explore and reconstruct local cultural heritage sites together by utilising their lived experiences and cultural resources. A qualitative case study approach was employed, with data derived from an online open-ended questionnaire completed by 20 adult participants (i.e., 14 parents and six grandparents). Inductive thematic analysis revealed five core themes: (a) acknowledging children as capable agents; (b) identifying children’s learning; (c) adults as learners; (d) shared activities as a space for fostering family connection; and (e) challenges and suggestions for future implementation. The findings demonstrate that the shared exploration of cultural heritage can enhance mutual learning and foster vivid intergenerational communication, thereby strengthening family relationships. By observing the active role of young learners in intergenerational activities, participants acknowledged capabilities that they had previously overlooked in their children and/or grandchildren. However, challenges relating to time constraints, available resources and practical issues suggest that schools need to provide families with tailored information about such initiatives. Overall, this study emphasises the pivotal role of early childhood education in fostering IGL through reciprocal school–family partnerships. Full article

As higher education institutions respond to societal transformation and institutional reform, academic staff wellbeing is increasingly relevant to the conditions that sustain teaching quality, research productivity, and service performance. This study, the second phase of a research project, evaluated Academic Staff Wellbeing (ASW) as a reflective–reflective second-order construct and examined its relationships with Job Satisfaction (JS) and Academic Performance (AP), including JS mediation. A cross-sectional questionnaire with closed- and open-ended items was completed by 119 academic staff at a single Saudi university. Partial Least Squares Structural Equation Modelling (PLS-SEM) with 5000 bootstrap resamples was complemented by structured interviews with eight faculty members and a focus group with eight academic leaders; qualitative evidence was analyzed thematically and integrated at the interpretation stage. The measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity. ASW explained 74.3% of the variance in JS and, together with JS, 36.1% of the variance in AP. ASW was strongly associated with JS and significantly associated with AP, whereas the JS–AP relationship and the indirect ASW–JS–AP relationship were not significant (β = 0.125, 95% bias-corrected confidence interval [−0.185, 0.474]). Qualitative findings highlighted leadership, institutional support, workload, resources, professional development, workplace quality, and recognition as organizational mechanisms. Within the limitations of a cross-sectional, single-institution design and self-reported academic performance, these findings suggest that ASW may function as a multidimensional institutional condition supporting teaching, research, and service performance through multiple organizational pathways rather than primarily through job satisfaction alone. Full article

The rapid emergence of large language models (LLMs) is reshaping Computer Science (CS) education. Yet, little is known about how students engage with these tools as both technical and ethical learning partners in authentic project-based environments. This qualitative study investigates how 29 undergraduate CS students used LLMs while developing open-ended AI applications in a project-based course. Analysis of project documentation, reflection logs, and presentation transcripts revealed three interconnected forms of student–LLM interaction. First, we observed a technical partnership, in which students leveraged LLMs for code generation, debugging, architectural planning, and API integration while working on complex development challenges. Second, there was an ethical-reflective partnership, as students negotiated transparency, originality, bias, and the risks of over-reliance, demonstrating elements of critical AI literacy. Third, students reported perceived shifts in their learning practices, including increased confidence, more intentional problem-solving, and changes in how they sought support from peers and instructors, suggesting a reconfiguration of self-regulatory learning practices in interaction with LLMs. Together, these findings suggest that LLMs can be understood not merely as productivity tools but as multifaceted partners involved in the technical, ethical, and self-regulatory dimensions of learning in CS education. The study offers theoretical and practical implications for designing AI-enabled curricula that cultivate responsible, reflective, and critically engaged use of LLMs. Full article