Trends in Higher Education

Evidence regarding the relationships among metacognitive awareness, teacher digital competence, science teaching self-efficacy, attitudes toward science, and experimental science anxiety remains limited. The aim of this study was to examine, through a structural equation model, the relationships among metacognitive awareness, teacher digital competence, science teaching self-efficacy, attitudes toward science, and experimental science anxiety, with particular attention to the association between metacognitive awareness and experimental science anxiety in pre-service teachers. The sample comprised 325 undergraduate pre-service teachers enrolled in bachelor’s degree programmes in Early Childhood Education and Primary Education at the University of Jaén. Data were analyzed using structural equation modeling with robust maximum likelihood estimation and bootstrap procedures. The results indicated that metacognitive awareness and teacher digital competence were positively associated with science teaching self-efficacy, which was the strongest predictor of attitudes toward science, explaining 30.1% of their variance. Metacognitive awareness was negatively associated with experimental science anxiety (β = −0.217, p < 0.001). Mediation analyses indicated significant indirect associations through science teaching self-efficacy. In conclusion, the present study may inform the design of initial teacher education programs through the integration of metacognitive awareness, teacher digital competence, and active learning methodologies to strengthen scientific literacy and quality education in experimental science teaching. Full article

Teaching excellence has become an increasingly important but contested concept in higher education, shaped by institutional priorities, policy instruments, and changing understandings of pedagogical quality. Teaching awards are widely used to recognize outstanding teaching, although institutional schemes may also encompass broader forms of pedagogical and curricular innovation. This study examines the longitudinal evolution of teaching recognition schemes and pedagogical innovation initiatives at the University of Porto, a large Portuguese public university, from 2004 onwards. A qualitative documentary analysis was conducted of regulations, calls for applications, application forms, evaluation criteria, evidence requirements, jury arrangements, and dissemination mechanisms associated with successive institutional initiatives. The findings show a gradual expansion from an initial focus on excellence in e-learning to broader forms of pedagogical excellence, project-based innovation, curricular experimentation, and educational innovation. Over time, recognition shifted from individual teachers to pedagogical teams, funded projects, curricular units, and institutional innovation mechanisms. Evidence requirements also became more formalized, incorporating reflective accounts, evaluation grids, impact documentation, and dissemination expectations. The study concludes that pedagogical awards can operate not only as symbolic recognition mechanisms but also as institutional policy instruments that shape what counts as excellent teaching and how pedagogical knowledge circulates within the university. Full article

The curriculum is the vehicle that drives teaching and learning processes. Regarding the South African teacher education curriculum (SATEC), scholars and researchers are negatively disposed and have contrasting views about its responsiveness to learners’ diverse learning needs and skill development. This study, therefore, explored the perceptions of teacher educators at a public university in South Africa with regard to the teacher education curriculum’s responsiveness (TECR) and potential in developing pre-service teachers’ 21st century skills (PT21Cs). Underpinned by the constructivist theory (CT), via the interpretivist paradigm, this study utilised a phenomenological case study design of the qualitative research approach. This study’s population consisted of lecturers in the education faculty at the sampled university. The purposive sampling technique was used in selecting seven participants (five females and two males). A semi-structured interview guide was used to collect data for this study, and analysed using Inductive Thematic Analysis (ITA) via ATLAS.ti software version 24. The findings reveal that SATEC is somewhat culturally and disciplinarily responsive but highly structured and is not adequately developing pre-service teachers’ 21st century skills as it should. The challenges in implementing a responsive curriculum and the development of pre-service teachers’ 21st century skills, as well as strategies for addressing these, were discussed. This study recommends that further teacher education curriculum reviews and evaluations, as Education 5.0 dictates and per the Sustainable Development Goals 2030 and 2063, as well as teacher educators’ professional development and training, be pursued and actualised. Full article

Engineering literacy research has been dominated by static, competence-based frameworks that fail to explain how attributes evolve dynamically and systematically underrepresent two critical dimensions: engineering consciousness as a cognitive precursor to decision-making, and engineering culture as a value-laden, inherited dimension sustaining professional identity across generations. These gaps are particularly consequential for STEM teacher preparation, where cultivating comprehensive engineering literacy in future educators is hypothesized to create a multiplier effect on societal STEM engagement. This study develops a theoretical model through a systematized literature retrieval and critical synthesis of engineering literacy literature (2005–2025; N = 113 publications), combined with theoretical deduction grounded in system theory and synergy theory. Two research questions guide the study: (1) How is the connotation of engineering literacy systematically reconstructed under multiple transformations? (2) What is the synergistic evolution logic among its constituent elements, and how can this logic inform curriculum design for STEM teacher preparation? The design utility of the model is illustrated through a curriculum design case. The analysis yields a “multi-driver, five-dimension synergy” dynamic model comprising five interconnected elements (knowledge, competence, consciousness, ethics, and culture) that co-evolve through a “consciousness–action–culture” spiral cycle, in which culture functions as the slow variable governing the long-term evolution of the entire system. The curriculum design case (a 64 h course for pre-service teachers centered on the Hong Kong–Zhuhai–Macao Bridge) illustrates how the model guided curricular decisions in four areas: consciousness activation before skill training, distributed cultural integration, multidimensional assessment, and operationalization of the multiplier effect through a teaching transformation module. The study advances engineering literacy theory by upgrading it from a static competence inventory to a culturally embedded, consciousness-driven adaptive ecosystem and provides a potentially transferable curriculum design framework for STEM teacher preparation. As a design case, the curriculum blueprint awaits implementation and empirical validation; claims about learning outcomes, multiplier effects, and cross-cultural applicability are theoretical hypotheses rather than empirically validated findings. Full article

The convergence of decoloniality, artificial intelligence (AI), and university classroom practice has emerged as a pressing concern in African higher education. While decolonial movements such as Rhodes Must Fall and #FeesMustFall have reignited debates regarding epistemic justice within African universities, the rapid mainstreaming of generative AI has introduced new inquiries concerning whose knowledge is valued, which languages are acknowledged, and whose pedagogies are perpetuated through machine learning systems. This systematic review maps the trends in studies that engage with decoloniality, AI, and university classrooms, focusing specifically on African universities and African scholarship from 2010 to 2025. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the review examined Scopus, Web of Science, African Journals Online, Sabinet, ERIC, and Google Scholar, supplemented by citation chasing. A total of twenty-five studies of varying types and peer-review status met the inclusion criteria. The findings indicate a marked acceleration in scholarly output following 2020, the emergence of three dominant thematic clusters (epistemic injustice and curriculum, algorithmic coloniality and data extractivism, and pedagogical adaptation of generative AI), and a significant concentration of research in South Africa, alongside emerging Pan-African scholarship on African-language AI. The review concludes that African scholarship is formulating a coherent decolonial AI research agenda; however, the scarcity of empirical evidence from classroom settings, the marginalisation of African languages within AI corpora, and uneven regional representation hinder the advancement of the field. Implications are discussed for institutional policy, curriculum design, and future research directions. Full article

Family-related factors play a fundamental role in students’ development. However, the relationship between family-related factors and the acquisition of Learning to Learn (LtL) competence in higher education remains underexplored. This study aimed to examine the association between family-related factors and the development of LtL competence among undergraduate students enrolled in Education degree programmes at the University of Valencia (Spain). A sample of 723 students completed two validated questionnaires: one assessing family-related factors in university students’ learning and another assessing the acquisition and level of mastery of LtL competence. Data were analysed using Structural Equation Modeling (SEM) to examine the relationships between family structure, family dynamics, family support, and LtL competence. The final model demonstrated a satisfactory fit to the data and showed a strong, statistically significant positive association between Family Factors and LtL competence (γ = 0.71, p < 0.001), consistent with the hypothesised structural model. Family dynamics and family support made the greatest contributions to the Family Factors latent construct, whereas family structure showed a comparatively lower contribution. These findings highlight the relevance of considering the family as an ecological context associated with university students’ LtL competence and provide empirical support for incorporating family-related variables into research on this competence. Full article

Undergraduate residential construction programs face growing pressure to prepare graduates with competencies that reflect evolving workforce expectations. This study develops a preliminary evidence-informed framework for aligning undergraduate residential construction education with workforce expectations identified from selected U.S. residential construction markets and practitioner perspectives. Two complementary evidence streams were integrated. First, a systematic screening process informed by PRISMA reporting yielded 156 residential construction job postings collected from selected markets in the U.S. Southeast and Southwest (2024: 70; 2025: 86), which were analyzed using thematic coding to identify labor-market competency requirements. Second, a three-hour focus group involving 14 senior residential construction professionals representing 10 NAHB-affiliated firms provided practitioner perspectives on workforce competency expectations and educational preparation. Findings were integrated using Patton’s convergence model to examine convergence, complementarity, divergence, and source-specific signals across the two evidence streams. Five competency areas received cross-source support from labor-market evidence and practitioner perspectives. These findings were subsequently organized for educational interpretation within the following four domains: Technical; Professional; Management; and Safety, Quality, and Regulatory Compliance. Digital construction capability emerged as a cross-cutting practitioner-anticipated signal. The integrated findings informed six proposed educational modules and associated indicative learning outcomes. The study contributes a mixed-method approach for integrating labor-market and practitioner evidence and demonstrates how such evidence can inform preliminary curriculum design. Full article

Most computational work on emotional intelligence in education targets students; where the emotional states of teaching staff are modeled at all, the signal is typically a static questionnaire score rather than an input to an automated response. This study asks a narrower question: does routing a detected emotion through Self-Determination Theory—rather than naming the emotion, or using generic language—produce a message that university faculty themselves rate as better, and does that advantage survive once the underlying emotion classifier is realistically imperfect? Using two public emotion corpora (GoEmotions and ISEAR), a 75-item researcher-authored and independently unvalidated set of faculty-oriented stress vignettes, four classifier families spanning lexicon counting to large-language-model few-shot prompting, a four-level message-personalization ablation (generic, emotion-only, need-only, full situational context), and blind ratings from 30 higher-education faculty and researchers, two findings are reported. First, when classifiers are compared on a matched label space, the few-shot large-language-model classifier performs best on both out-of-domain evaluations without fine-tuning, including 0.893 accuracy on the synthetic faculty-oriented vignette set. Second, the human evaluation does not support a uniform monotonic benefit from progressively adding personalization. Need-only routing does not yield a consistent gain over emotion-only messages, whereas the full-context condition shows its clearest positive signal for relevance and motivational usefulness; these two contrasts are significant in the crossed mixed-effects analysis but are not uniformly significant under the rater-level paired tests. The automatic text-similarity proxy does not detect this contextualized-condition advantage. Because the faculty vignettes are synthetic and were not independently content-validated, the results should be interpreted as evidence of technical and methodological feasibility rather than ecological validity or deployment effectiveness. Full article

Smart industries base their development, maintenance, and operation on technologies compatible with what is known as Industry 4.0. In this context, the education sector needs the implementation of new learning schemes and environments for conceptual and practical learning, thereby supporting students’ training to help in the industrial digital transformation. The main contributions of this work are a learning environment for robotics that integrates software applications that quantitatively measure the student’s learning process; a competence framework focused on learning robotics, ROS background, and developing pickup/delivery tasks very common in the Industry 4.0 context; and a learning path implemented in step-by-step guided documentation that considers the competence framework proposed and progressively teaches ROS concepts. This work also describes the development of the learning environment and the software tools that support the student’s learning process. Two pilot tests were performed on iBotAcademy considering twenty students of the Universidad del Valle. The iBotAcademy mobile robotics and the manipulator robotics learning paths achieved an average of 82% positive answers, and 99% positive answers, respectively. The average coefficients of variation of these tests were 16.4% and 8.3%, showing high data consistency. Then, the iBotAcademy learning environment helps to train students for the industrial digital transformation. Full article

Artificial intelligence is converging with labor-market change, the proliferation of alternative credentials, and growing demands for public impact, placing the traditional university model under increasing pressure. This conceptual review argues that the central challenge is not whether universities will be replaced by AI, but whether they can move beyond a model organized around time-bound degree delivery. It proposes the Human-Agency Infrastructure Framework, which redefines the future university as a trusted system that helps people and societies build, verify, apply, and sustain human capabilities over time. The framework was developed abductively by integrating literature on human agency, the capability approach, higher education futures, AI in education, credentialing, innovation ecosystems, and public-purpose governance. It identifies four core institutional functions: building capabilities, verifying capabilities, applying capabilities, and sustaining belonging, enabled by governing trust as a foundation and boundary condition. The article then translates the framework into strategic shifts, governance questions, risks, and research propositions for universities seeking to remain relevant in an age of intelligence abundance. Full article

Research increasingly examines initial access to higher education (HE) in sub-Saharan Africa, yet less is known about the persistence of enrolled students in fragile contexts where costs are high and outcomes are uncertain. This study provides the first large-scale quantitative analysis of perceived HE worth in Guinea-Bissau, a resource-scarce Lusophone state. Drawing on Human Capital Investment (HCI), Behavioural Economics (BE), and Afrocentric perspectives on resilience and hope, we analysed survey data from 2255 students across six HE institutions (HEIs). A binary indicator of whether HE was worth the cost was used to assess ongoing valuation, and the data were analysed using sequential block-entry logistic regression after multiple imputation. Overall, 30.1% of students did not affirm that HE was worth the cost, indicating that enrolment does not guarantee sustained valuation. While cost-benefit reasoning remains important, perceptions were strongly shaped by campus climate and forward-looking expectations. Perceived advantages of HE, even without completion, outweighed the value of credentials alone, suggesting a pragmatic evaluation. We suggest anticipatory resilience as a lens for understanding students’ sustained valuations of HE amid uncertainty, grounded in anticipated benefits, institutional experience, and relational resources. Policy should prioritise campus climate, reduce financial strain, and reinforce the intrinsic value of HE. Full article

Adjustment to university unfolds over time, whereas a single-time-point survey can describe only concurrent patterns in students’ experience. This exploratory, descriptive study examined first-year students’ overall appraisal of group work (GW) in an elective, cross-faculty “University and Career” course at a Japanese private university. We assessed its association with four self-reported indicators: class satisfaction (a proximal, coursework-domain measure), university-life satisfaction and friendship satisfaction (broader measures of students’ experience), and career-goal clarity (a course-relevant vocational measure). A voluntary, anonymous survey was administered in the final (15th) session, before grades were assigned. The analytical sample comprised 564 students who had self-selected into the elective course. Each indicator was regressed on GW appraisal using ordinary least squares with heteroskedasticity-robust standard errors; complementary analyses used ordinal logistic models, and all models adjusted for demographic background, faculty, peer-network quantity, and friendship orientation. A more positive GW appraisal was associated with all four indicators (unstandardized b = 0.35 for class satisfaction, 0.33 for university-life satisfaction, 0.21 for friendship satisfaction, and 0.21 for career-goal clarity; all false-discovery-rate-adjusted p < 0.01), and the estimates were stable across sensitivity analyses of coding and missing-data handling; for friendship satisfaction and career-goal clarity, however, the associations were concentrated in the top rating category, and the career association fell below conventional significance when retrospectively reported prior attitude toward group work was conditioned on. Because all focal measures were single-item self-reports collected at one time point, these findings describe concurrent associations among student ratings, most proximally between GW appraisal and satisfaction with classes, rather than effects of group work on adjustment. Full article

Generative artificial intelligence (GenAI) has moved from an emerging educational tool to a structural challenge for science, technology, engineering, and mathematics (STEM) higher education. This narrative review argues that the most consequential effect of GenAI is not the automation of existing teaching practices but the need to redesign curricula, learning outcomes, pedagogies, and assessment around disciplinary judgment, critical verification, intellectual independence, and transparent, ethical use of GenAI. Its distinctive contribution is to frame GenAI as a problem of curriculum and assessment validity rather than primarily as a question of tool adoption or academic integrity. Because widely available systems can generate code, solve quantitative problems, summarize literature, draft laboratory reports, and produce fluent scientific prose, conventional submitted artifacts have become weaker indicators of the reasoning and competence they are intended to demonstrate. The review therefore examines the full programme-to-classroom pathway, connecting definitions of graduate competence with course design, classroom and laboratory practice, assessment, feedback, faculty capability, technology adoption, and iterative evaluation. The analysis integrates cognitive load theory, constructive alignment, constructivist perspectives, and frameworks of faculty capability and technology adoption. The biological sciences serve as a recurring disciplinary case because they combine conceptual knowledge, laboratory practice, computational analysis, and ethical decision-making, and are also being transformed by AI-based scientific methods. A worked cell biology example, structured using the Analysis, Design, Development, Implementation, and Evaluation model, operationalizes the review’s conceptual argument and demonstrates how GenAI integration can translate into needs analysis, outcome specification, resource development, blended laboratory implementation, assessment, and iterative redesign. The resulting design logic is generalized into a transferable five-step template for STEM curriculum redesign, with recommendations at programme, course, and institutional levels. Full article

Generative artificial intelligence (GenAI) has intensified concerns about academic integrity, yet little is known about discrepancies between students’ self-reported frequencies in AI-assisted academic cheating and the frequencies they attribute to their peers. Using an anonymous cross-sectional survey, this study collected responses from 863 undergraduates at a science and technology university in Vietnam. Participants rated ten AI-assisted academic cheating behaviors from two perspectives: the frequency they attributed such behaviors to their classmates, referred to as perceived peer cheating (PPC), and the frequency they reported such behaviors for themselves, referred to as self-reported cheating (SRC). Split-plot ANOVAs were conducted to compare PPC and SRC ratings and to examine whether the magnitude of the discrepancy varied by gender and student seniority. The results show that PPC ratings were significantly higher than SRC ratings for all ten behaviors, with partial eta-squared values ranging from 0.060 to 0.196. Cheating perspective × gender interactions and cheating perspective × student seniority interactions were each significant for three behaviors; however, all interaction effects were small (ηp² ≤ 0.025). Overall, the study reveals a consistent discrepancy between self-reported and peer-attributed frequencies of AI-assisted academic cheating. This discrepancy highlights the limitations of using either direct self-reports or peer-attributed frequencies alone to estimate the actual prevalence of AI-assisted academic cheating. Full article

First-year students in STEM programs face significant academic and personal challenges that can undermine retention and success, particularly for those navigating new institutional environments without prior college experience. While self- regulated learning (SRL) theory offers a well-established framework for understanding how students plan and reflect, less attention has been paid to the performance phase, the stage where students must translate plans into action amid real academic and social demands. This qualitative study examines the experiences of 15 first-year life science students across three institution types, a Hispanic-Serving Institution, a predominantly white institution, and a liberal arts college, to investigate what plans students formed at the end of their first semester and what factors facilitated or hindered implementation during their second semester. Using thematic analysis of semi-structured interviews, three major plan themes emerged: help-seeking, internal academic adjustments, and managing social and emotional well-being. Facilitating factors for these plans included small class sizes, anonymized participation tools, approachable instructors, peer and family support, counseling services, and structured planning tools, while hindering factors included fear of judgment, high instructor-student ratios, scheduling conflicts, academic burnout, and unsupportive living environments. The findings reveal that plan implementation depended on the interplay of intersecting psychological, social, and structural factors, which created unique conditions that influenced whether students were able to enact their plans. Importantly, the findings reveal that plan implementation unfolded not as a linear process but through nested micro-cycles of forethought, performance, and reflection within the performance phase, triggered by specific events throughout the semester. These findings have implications for how institutions design learner-centered support for STEM students not only at key transition points, but also throughout the semester, to address the conditions that influence whether students are able to successfully implement, adapt, or abandon their regulatory efforts. Full article

Emerging technologies provide promising opportunities to improve learning in construction and engineering education. In the current study, the interaction between students and AI-enhanced educational podcasts in undergraduate and postgraduate modules offered by two South African universities is analysed. Using a quantitative research design, the study noted a considerable increase in class average scores at the postgraduate level. However, the undergraduate class average scores remained at baseline, indicating no apparent improvement; therefore, no causation can be established based on these results. Furthermore, guided by the Cognitive Theory of Multimedia Learning, the Technology Acceptance Model (TAM), and Self-Determination Theory (SDT), student perceptions were assessed across learning effectiveness and engagement, accessibility and satisfaction, and motivation and intent to continue using podcasts. Rankings showed high satisfaction across all dimensions. Although access challenges were infrequent, PLS-SEM revealed a significant negative relationship between technical barriers and motivation and willingness to expand podcast use, highlighting human–technology interaction constraints. Findings underscore the importance of seamless access and curriculum alignment and conclude with strategies to strengthen adoption, engagement, and learning outcomes in AI-assisted podcast-based education. Full article

Students at creative higher education (CHE) institutions frequently report boundary violations and other forms of misconduct, while prevention efforts have been described as insufficient. Drawing on a capabilities-informed perspective attentive to the environmental and institutional conditions of flourishing, we examine what structural and cultural conditions stakeholders consider necessary for misconduct prevention in CHE. Using a complementary multimethod qualitative design, we report two studies: 16 interviews with stakeholder groups analyzed through reflexive thematic analysis, and 228 open-ended responses from a national student survey analyzed through structured tabular thematic analysis. Interview participants identified three interconnected areas of transformation: community, institutions, and knowledge and norms. Seven themes generated from the survey responses were classified as confirmatory, and one theme as expanding, foregrounding material stability and students’ aspirations for cultural and institutional reform, a dimension addressed only implicitly in the interviews. Two themes had no direct counterpart, foregrounding students’ skepticism about the possibility of change and the societal recognition of artists. Across both studies, participants described prevention efforts as often reactive and insufficient, and called for systemic approaches in which institutions recognize their societal role and prioritize students’ flourishing. Our findings identify participant-informed starting points for developing and evaluating prevention strategies. Full article

Motivation is a key predictor of success in second language (L2) acquisition, yet how culture and identity shape it among Latin American learners remains under-examined. This study used a quasi-experimental design with non-equivalent control and experimental groups (n = 100) to test the effect of an eight-week culturally responsive pedagogical intervention on the motivation of intermediate-level English-as-a-second-language learners at a public university in Ecuador, measured with the validated Spanish version of Gardner’s Attitude/Motivation Test Battery (AMTB). An independent-samples t-test revealed a statistically significant difference in post-intervention motivation scores between the experimental group (M = 156.1, SD = 31.2) and the control group (M = 134.5, SD = 28.4), t(98) = 3.91, p = 0.002, Cohen’s d = 0.72. The intervention was associated with a medium-to-large increase in motivation, consistent with sociocultural theory, the socio-educational model, the L2 Motivational Self System, and identity-investment theory; because the control group did not receive an equally novel activity, this finding should be read as preliminary evidence for the cultural/identity component specifically. Implications for culturally sensitive language teaching in diverse Hispanic contexts are derived. Full article

This study investigates the educational potential of instructor-designed Generative Pre-trained Transformers (GPTs) in a university-level Japanese course, drawing on self-determination theory (SDT) and the noticing hypothesis. Using a mixed-methods design, we examined how the continuous use of an instructor-developed Artificial Intelligence tutoring system (basic Japanese GPTs) relates to learners’ psychological needs satisfaction, cognitive noticing, and perceptions of Artificial Intelligence (AI)-assisted learning among 74 undergraduate students at a South Korean university. Quantitative data were analyzed using descriptive statistics and Pearson correlation; qualitative data from open-ended items and reflective writing underwent systematic content analysis. The findings revealed three key patterns. First, learners reported relatively high levels of satisfaction across all three SDT needs—autonomy, competence, and relatedness—particularly in relation to self-directed reviews and affective safety. Second, qualitative analysis identified three distinct noticing experiences: AI-supported clarification of linguistic form, noticing through intentional error generation and AI feedback, and metacognitive regulation of learning strategies. Third, the learners perceived the instructor-designed GPTs not merely as a convenience tool but as a structured learning environment that supported output-oriented, interaction-based practice. These findings suggest that the educational effectiveness of generative AI in foreign language education is not determined by frequency of use alone but also by the quality of pedagogical design underlying its deployment. This study contributes a practice-based model for AI integration in general education language courses while acknowledging limitations related to its single-course scope and reliance on self-reported data. Full article