Purpose This study examines structural patterns in pedagogical innovation across six Indonesian doctoral dissertations, five in chemistry education and one in physics education, using the Glocal Myths-Facts Analysis Framework (GMFAF) to diagnose whether an innovation is contextually relevant, resistant to adaptation, or likely to last. Design/methodology/approach A critical cross-case analysis combined rubric-anchored evaluation with fuzzy reasoning (Mamdani inference), modelling relevance, resistance and impact via triangular membership functions to derive a Glocal Fit Compliance Index (GFCI), a 0–1 score expressing structural alignment, not academic merit. Findings Adaptation resistance is the dominant moderating factor in sustainability, limiting transferability even when relevance and impact are strong. Dissertations incorporating contextual realities (D3, D4, D6) reached higher GFCI values than those dependent on infrastructure and facilitation (D1, D2, D5). Research limitations/implications The study analyses a small corpus and does not support statistical generalisation; its contribution is analytic generalisation: adaptation resistance moderates relevance and impact. Practical implications Assessing contextual relevance and adaptation resistance at the research-design stage may help doctoral studies produce more sustainable innovations. Social implications Infrastructural dependence, not a lack of pedagogical merit, most often limits the reach of promising innovations; addressing it directly may narrow the gap between policy and classroom practice. Originality/value This study introduces GMFAF, a framework combining qualitative evaluation with fuzzy reasoning to diagnose pedagogical innovations' contextual viability, a novel method for evaluating doctoral research design in higher education.
Purpose This study examines how post-secondary students' adjustment changes over time during prolonged wartime and identifies the psychological factors that predict adjustment at different stages of crisis. While prior research has explored student adjustment during short-term disruptions such as the COVID-19 pandemic, the effects of sustained armed conflict on post-secondary students' academic and psychological functioning remain underexplored. Design/methodology/approach A longitudinal design tracked 83 Israeli first-year post-secondary students across two consecutive academic years during the Iron Swords War. Participants completed self-report measures of adjustment, locus of control, well-being (Diener et al., 1985) and general anxiety at Time 1 (October 2024) and Time 2 (April–May 2025). Paired-samples t-tests and stepwise regression analyses were employed. Findings Results revealed a significant decline in total adjustment and institutional attachment over time. Psychological predictors shifted substantially: locus of control was the sole predictor at Time 1 (13.8%), whereas well-being, locus of control and anxiety jointly explained 46.7% of variance at Time 2. Originality/value This is among the first longitudinal studies to examine student adjustment during active, prolonged wartime, extending Tinto's (1975) integration model to conflict conditions. The findings demonstrate that prolonged crisis transforms the psychological landscape of post-secondary students' adjustment, with implications for designing timely, psychologically informed institutional support.
Purpose This study examines the relationship between gamified learning practices and undergraduate students' motivation and perceived academic performance in higher education. Design/methodology/approach A cross-sectional survey collected data from 311 undergraduates with prior exposure to gamification. The instrument assessed their experience with common game elements, alongside self-reported motivation and academic outcomes. Data were analyzed using correlation, regression and ANOVA techniques. Findings Students reported positive motivation and academic outcomes, with a strong correlation observed between the two. Notably, perceived performance did not differ significantly across academic years. Badges, points and quizzes were the most frequently experienced features. Research limitations/implications Reliance on cross-sectional, self-reported data from a convenience sample limits causal interpretation and generalizability, and the use of perceived rather than objective academic performance measures may not fully reflect actual achievement. Practical implications The findings provide practical guidance for educators and instructional designers seeking to integrate inclusive and effective gamification strategies. Originality/value This study provides applied empirical evidence on how specific game mechanics influence student engagement and success across different academic stages.
PurposeWhile existing studies have extensively examined AI-supported teaching and learning, limited attention has been paid to AI from an educational management perspective. This integrative conceptual review aims to synthesise recent research on the role of AI in educational management and explore its implications for educational leadership and governance. Design/methodology/approachDrawing on a structured literature search and selection process, this integrative conceptual review employs an iterative thematic synthesis to examine how AI is being applied across educational management functions. Following Torraco's (2016) framework for integrative reviews, the study prioritises theoretical development and conceptual insight over comprehensive coverage. The review analyses AI applications in strategic planning, administrative efficiency, and data-driven decision-making, whilst identifying key managerial challenges and leadership implications. FindingsAI technologies offer significant potential to enhance educational management through predictive analytics, operational automation, and sophisticated data analysis. However, effective integration requires more than technological adoption; it demands strong leadership, continuous professional development, and human-centred governance frameworks. This study proposes an integrative framework conceptualising AI's role across three interrelated dimensions: (1) Management Functions and AI Applications, (2) Governance Mechanisms and Decision Rights, and (3) Leadership Capabilities. Educational leaders face substantial challenges in developing AI literacy, addressing ethical concerns about bias and privacy, navigating power dynamics with commercial vendors, and managing organisational resistance to change. Research limitations/implicationsAs an integrative conceptual review, this study does not aim to provide exhaustive coverage of all empirical studies on AI in educational management. The synthesis is based on a selective but transparent evidence base and focuses on conceptual clarity rather than statistical generalisation. Future research should empirically test and refine the proposed framework across different institutional and national contexts. Practical implicationsThe study identifies essential leadership competencies for the AI era: critical technology evaluation, ethical reasoning, data stewardship, collaborative leadership across diverse expertise, and adaptive learning agility. Educational institutions must establish robust governance frameworks with clear decision-rights matrices specifying who decides what and through what processes, supported by professional development strategies integrating technical understanding with ethical reflection and by formal oversight processes for high-stakes AI use. Social implicationsBy highlighting governance, equity, and accountability concerns, this review underscores the broader societal implications of AI adoption in education. Responsible AI governance in educational institutions can help prevent the reproduction of social inequalities, protect student data, and foster public trust in AI-enabled educational systems. Originality/valueThis study contributes to the growing body of research on AI in education by shifting the focus toward management, leadership, and governance perspectives rather than pedagogical applications. It provides educational leaders with an original integrative framework for understanding AI's role across management functions, identifies critical competencies and governance considerations for responsible AI implementation, and offers a critical analysis of power dynamics, equity concerns, and commercialisation issues in AI deployment.
Purpose This study examines the effect of an edutainment-based flipped classroom model on the numeracy competence of prospective elementary school teachers. Design/methodology/approach This study employed a mixed-methods embedded design with a quasi-experimental approach. The participants consisted of 312 Primary School Teacher Education students divided into an experimental group (n = 156) and a control group (n = 156) using intact group sampling. Quantitative data were collected through a numeracy competence test and analyzed using pretest equivalence testing and ANCOVA to examine the effect of the Edutainment-Flipped Classroom model while controlling baseline differences. Qualitative data were collected through questionnaires and semi-structured interviews to explore students’ learning experiences, cognitive and emotional engagement, and perceptions of the learning model. The qualitative data were analyzed thematically using ATLAS.ti version 24 to support and enrich the quantitative findings. Findings The results indicate that students exposed to the edutainment-based flipped classroom demonstrated significantly higher numeracy competence than those in the conventional flipped classroom. Improvements were evident across three indicators: the use of basic mathematical numbers and symbols, the ability to analyze information in various representations and the ability to use analytical results for prediction and decision-making. Qualitative findings suggest that the integration of edutainment enhanced student engagement and conceptual understanding. Originality/value This study contributes empirical evidence on the effectiveness of integrating edutainment into the flipped classroom model to enhance numeracy competence in teacher education. It offers a novel instructional perspective for strengthening numeracy competence among prospective teachers.
Purpose Finding meaning in the artificial intelligence (AI) era is no longer a philosophical exercise – it is a survival strategy for doctoral students. This study examines how AI self-efficacy shapes doctoral persistence through the mediating role of doctoral meaning. Design/methodology/approach Grounded in social cognitive career theory, meaning theory and persistence theory, this study examined four AI self-efficacy dimensions alongside search for meaning, presence of meaning and doctoral persistence. Survey data from 372 doctoral students in East Java were analyzed using partial least squares structural equation modeling. Multi-group analysis tested invariance across gender, discipline and doctoral stage. Findings Both search for meaning and presence of meaning predicted doctoral persistence more strongly than any AI self-efficacy dimension. Among the four dimensions, only psychological comfort with AI-shaped persistence, operating entirely through presence of meaning (indirect-only mediation), while perceived AI usefulness exerted a negative direct effect on persistence. Multi-group analysis showed comfort fostered meaning-seeking among male students but slightly inhibited it among female students, while the mechanism held across discipline and stage. Research limitations/implications The cross-sectional design limits causal inference, and data relied on self-reported measures without the adviser's perspective. Small R2 values for the meaning constructs indicate that doctoral meaning is shaped by multiple determinants beyond AI self-efficacy. Future research should incorporate longitudinal designs, multi-informant approaches and cross-cultural validation to strengthen the meaning-mediated persistence framework. Practical implications Doctoral programs should cultivate students' psychological comfort with AI, not just technical skill, since comfort predicts the presence of meaning that sustains persistence. Institutions should embed reflective practices, purpose-oriented mentoring and responsible AI use into doctoral education. Originality/value This study advances doctoral persistence research by conceptualizing doctoral meaning as the mechanism that transforms AI self-efficacy into persistence. It introduces meaning-mediated persistence, showing that technological capability sustains doctoral completion only when it is converted into personal meaning.
Purpose The study aimed to examine the effect of university students’ prior knowledge in mathematics as a predictor of problem-solving confidence and self-efficacy. Design/methodology/approach The study adopted a quantitative research design. The study population comprised 1,600 university students pursuing a BSc in Mathematics Education at the University of Skills Training and Entrepreneurial Development (USTED), Kumasi campus. The sample size was 320, as calculated using Yamane’s formula. Two sampling techniques, purposive and stratified simple random sampling, were used. Data were collected using a structured questionnaire. SPSS (ver. 23) and Amos (ver. 23) were used for data analysis. SPSS (ver. 23) was used to perform the Exploratory Factor Analysis. Finally, Confirmatory Factor Analysis (CFA), discriminant validity analysis, and Structural Equation Modeling (SEM) were performed using Amos (ver. 23). Findings The study found that prior knowledge in mathematics has a significant positive effect on problem-solving confidence and self-efficacy. Originality/value Many studies have examined prior knowledge in mathematics and students’ self-efficacy, but very few have focused on how students’ prior mathematical knowledge influences their confidence in solving problems. However, problem-solving confidence plays an important role in students’ willingness to attempt mathematical tasks, persist through challenges, and succeed academically. To fill this gap, this study investigated the effect of prior knowledge in mathematics on students’ problem-solving confidence and provided a better understanding of how students’ existing knowledge can shape their confidence and performance in mathematics.
Purpose This study examines how university students' conceptions of learning shape their motivation, self-regulation and information processing throughout different academic stages, showing how these factors interact and transform as a function of academic progression. The objective, therefore, is to provide evidence on the mechanisms that sustain self-regulated learning and its impact on retention and academic success during higher education. Design/methodology/approach A formative structural model was developed and validated using Partial Least Squares Path Modeling (PLS-PM), with 5,000 bootstrap resamples from a large and diverse sample of 1,630 Colombian university students. This methodology helped to capture the complex dynamics among conceptions, motivation, self-regulation and information processing as well as to analyze variations across three cohorts at different stages of academic progression. Findings The results indicate that in the early semesters, learning conceptions drive motivation and indirectly influence information processing. In the intermediate stages, self-regulation emerges as a key mediator. By the later semesters, metacognitive self-regulation becomes the primary determinant of deep processing, overshadowing the direct influence of conceptions. These findings confirm the dynamic and evolving nature of learning patterns throughout university education and highlight the development of self-directed and deep learning. Research limitations/implications This study has some limitations that should be acknowledged. First, the cross-sectional design limits the possibility of establishing causal relationships between the variables, since the observed associations reflect patterns at a single point. Future research should adopt longitudinal designs that better capture the evolution of learning processes throughout academic progression. Second, the use of non-probability convenience sampling may limit the generalizability of the findings. Although the sample was large and diverse, it is recommended that future studies replicate the model in different institutional and cultural contexts. Finally, the use of self-report measures may introduce potential response biases. Practical implications The findings provide explicit guidelines for curriculum design and university policies aimed at promoting constructivist principles and intrinsic motivation through active methodologies in the early stages of education. In more advanced stages, it is essential to enhance metacognitive regulation, autonomy and reflective practices. The implementation of these strategies can result in enhanced academic performance, increased student retention rates and the development of self-reliant graduates equipped for lifelong learning. Originality/value This study provides a novel contribution by proposing a dynamic and developmental model of learning patterns, demonstrating how the relationships among conceptions, motivation, self-regulation and information processing reorganize across academic progression.
PurposeOnline learning communities on social media platforms can support peer learning, but educators often lack theoretically grounded and measurable approaches for monitoring how participation and discourse evolve across a semester. This study proposes an extended Community of Inquiry (CoI) evaluation framework that integrates Social, Teaching, and Cognitive Presence with a fourth behavioural dimension, Student Presence. Design/methodology/approachA sequential exploratory mixed-method design was adopted. Qualitative analysis of prior literature and semester-long observations of two large first-year engineering course Facebook groups (each enrolling 800–1000 students) informed an indicator-based coding scheme, applied quantitatively over Weeks 1–13. Predictive modelling used a persistence baseline, a multi-output Random Forest, and a multilayer perceptron under time-aware evaluation protocols. FindingsSocial Presence was enquiry-driven and peaked in Weeks 3–4; Teaching Presence was frontloaded and primarily reactive; Cognitive Presence was shallow, dominated by remembering and analysing. Student participation was consumption-oriented, with observers consistently outnumbering posters. Random Forest achieved consistent poster prediction (R2 ˜ 0.48–0.49), while observers and non-members remained difficult to forecast due to structural interdependence. Permutation importance identified remembering and evaluating as the most influential cognitive predictors. Research limitations/implicationsThe dataset comprises 13 weekly observations from a single platform and institution, limiting generalisability. Future work should collect multi-cohort data, introduce lagged predictors, and explore individual-level modelling. Practical implicationsThe framework provides instructors with an early-warning system for low poster activity, enabling timely, evidence-based interventions to support online peer learning communities. Originality/valueThis study makes three contributions: a multi-dimensional coding scheme grounded in the extended CoI framework; a data-driven analytics pipeline enabling descriptive monitoring and predictive modelling of participation roles; and an integrated evaluation framework that combines theory-grounded indicator coding with transparent machine learning to produce actionable insights from social media learning data.
Purpose Few researchers have formulated the competencies of entrepreneurship pre-service teachers. This paper aims to clarify the competencies that entrepreneurship pre-service teachers should possess. Design/methodology/approach This study employs an ex post facto survey method, and proceeds with a quantitative analysis model using the Structural Equation Modeling (SEM) technique to examine the constructs that form the variable of entrepreneurship pre-service teacher competencies. The sample was selected using a random sampling technique. The study involved 163 pre-service teachers as respondents. Findings Entrepreneurial, intrapersonal, organizational, professional competences and communication are constructs of pre-service entrepreneurship teachers. Entrepreneurial, intrapersonal, and organizational competencies constitute the highest construct of pre-service teacher competencies in entrepreneurship. In other words, an entrepreneurship pre-service teacher should have Entrepreneurial, intrapersonal, organizational and professional competencies, as well as communication competency, to be considered competent. Originality/value Developing countries suffer from teacher shortages including entrepreneurship teachers. Only few research explores entrepreneurship pre-service teacher competencies. The contribution of this study is to construct and test the competencies of entrepreneurship pre-service teachers, providing a reference for policy in recruiting entrepreneurship teachers.
Purpose The integration of artificial intelligence (AI) into higher education has significantly transformed academic writing by enhancing students' capabilities in terms of organization, coherence, and critical thinking. This study examined the predictive relationships of AI-based writing tools on Academic Writing Performance (AWPS). Additionally, it explores the mediating role of the Frequency of AI Usage (AUF) in the relationships among students' cognitive and affective factors, specifically Technological Pedagogical and Content Knowledge (TPACK) and Self-Regulated Learning (PSRL). Design/methodology/approach A quantitative cross-sectional survey involving 629 university students from various parts of Indonesia was conducted, and the data were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM) to confirm the model's reliability and validity. Findings The results revealed that AUF significantly mediated the connections between TPACK and PSRL with AWPS, demonstrating that consistent AI tool use is vital for improving academic writing. Although Technology Anxiety (TA) did not exhibit a significant moderating effect, it remains an essential factor that may influence students' confidence and engagement with AI tools. Research limitations/implications These findings highlight the need for instructional approaches that integrate AI responsibly while promoting critical thinking, digital literacy, and self-regulated learning in classrooms. Originality/value Ultimately, this study offers meaningful insights into how AI adoption in higher education can support reflective, innovative, and globally competitive academic development in the future.
Purpose This study aims to test the impact of green human resource management (HRM) on talent retention in Malaysian private higher education institutions. The study also tested the mediation effect of a sustainable work environment in this context Design/methodology/approach This study is quantitative in nature. A total of 167 academic staff members from private higher education institutions in the northern area of Malaysia were involved in this study. Data was collected through a survey questionnaire. Data was analysed through Structural Equation Modelling using Smart Partial Least Square (PLS). Findings The findings revealed that green HRM and a sustainable work environment significantly affected talent retention. A sustainable work environment is also influenced by green HRM, thus playing the role as a mediator between green HRM and the talent retention relationship Originality/value The study also addresses the issue of the limited research on green HRM and talent retention in the PHEIs of Malaysia under the mediation of sustainable work environment. Moreover, it addresses the opportunities for the synergic application of green HRM and AI in the context of sustainable workplace management and decision making in higher education
PurposeThe main goal of this meta-analytic study was to explore the effects of the gamified flipped classroom (GFC) in higher education, considering both cognitive (i.e. academic achievement) and non-cognitive (i.e. motivation and engagement) learning outcomes. Design/methodology/approachThe search in the Scopus, Web of Science and EBSCO databases yielded 26 relevant experimental and quasi-experimental studies. The meta-analysis followed the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), and CMA (Comprehensive Meta-analysis version 4) was used for statistical analyses. Overrepresentation of studies reporting multiple outcomes was controlled by using one effect size per outcome category per study. FindingsThe overall pooled effect sizes indicate positive GFC effects on both cognitive (g = 0.921) and non-cognitive outcomes (g = 0.735). These estimates are interpreted cautiously, given the substantial heterogeneity across studies and small-study effects for cognitive outcomes. Moderator analyses for cognitive outcomes indicate that incorporating gamification in pre-class activities was associated with higher effects. Other examined moderators failed to explain the variation across studies. Tests for publication bias, such as funnel plots and Egger's regression, did not indicate substantial bias overall, although some asymmetry was observed. Originality/valueThe study provides additional evidence that GFC is a promising pedagogical approach in higher education, especially when gamification is employed pre-class. It uniquely investigates variations in GFC effectiveness by the type of comparison condition (traditional lecture or non-gamified flipped classroom), and contextual factors (intervention length, gender composition and geographic location), as well as gamification timing, issues that are seldomly explored in previous studies, but highly relevant for instructional design.
Purpose This paper aims to examine university students' choice of ChatGPT, Grammarly and Gemini for academic tasks. It contributes to debates on artificial intelligence (AI) in higher education by analysing students' differentiated engagement with generative artificial intelligence (GenAI) tools and proposing guidelines for GenAI literacy (GenAIL) development. Design/methodology/approach The study used a mixed-methods online survey combining Likert-scale, multiple-choice and open-ended items. In total, 441 students completed the survey. Quantitative data were analysed using a chi-square test and qualitative data through descriptive thematic categorisation. Findings The paper provides empirical insights into the relationship between AI tool choice and academic task. ChatGPT was strongly linked to idea generation and planning, Grammarly to writing and feedback, while Google Gemini appeared comparatively diffuse across task categories. Research limitations/implications The contextualised sample limits generalisability, and the study would have been strengthened by interviews or focus groups. Future research could adopt longitudinal mixed-methods designs. Practical implications The paper highlights the need to embed critical digital literacy development to support purposeful and informed GenAI use in higher education and provides guidelines for implementation. Originality/value By examining the statistical association between academic tasks and tool selection, the paper contributes to emerging understandings of how students choose specific AI tools and proposes guidelines for GenAIL development.
Purpose This study integrated the SD-R and CLT to explain how resources, demands, and cognitive load shape engagement and satisfaction in AI-mediated higher education. Design/methodology/approach A quantitative, non-experimental survey was conducted with 432 Indonesian university students. Data were analysed using PLS-SEM to test the effects of study demands, study resources, personal resources, and cognitive load dimensions on engagement and satisfaction. Findings Lecturer support, self-efficacy, self-compassion, and AI use increased engagement and indirectly supported satisfaction, with personal resources emerging as the strongest predictors. The burnout classroom climate reduced satisfaction through cognitive load. Intrinsic and germane loads were positively related to satisfaction, whereas extraneous loads were negatively related. Practical implications Lecturers should provide clear task structures, guidance on responsible AI use, and feedback that supports productive cognitive efforts. Reducing burnout classroom climate is also recommended. Originality/value This study extends SD-R beyond motivational pathways by embedding differentiated cognitive load, while extending CLT beyond task-level explanations by situating cognitive effort within study demands and resources in AI-supported learning.
Purpose This study investigates research clusters in innovative teaching and learning in business, economics, and management using co-occurrence analysis of author keywords. It offers a wide overview and a dedicated analysis of clusters associated with SDGs 3, 4, and 9, offering new insights into the integration of research themes across these SDGs. Design/methodology/approach This study adopts a multi-stage bibliometric approach based on author keywords occurrence analysis to examine the intellectual structure of the literature across the overall set of publications, as well as within publications related to the three SDGs. Findings Research on innovative teaching and learning, notably in education, health, and innovation aligned with SDGs 3, 4, and 9, is predominantly published in journal papers and conference proceedings. The main topics are e-learning, pedagogical innovation, blended and online learning, technology-assisted learning, management systems, and user-centered design. SDG 4 research is quite advanced and technical, SDG 3 deals with practical health issues, and SDG 9 links education to infrastructure. Emerging directions are digitalization, data analytics, industrial integration, technology-based healthcare education, and sustainability. Put differently, education and infrastructure are mostly related to learning platforms and pedagogies (SDGs 4 and 9), whereas change and intervention are mostly related to health and well-being (SDG 3). Research limitations/implications This study uses only Web of Science Core Collection publications and selected SDGs. However, it provides useful insights into global research trends and supports researchers and policymakers in fostering innovation, sustainability, and SDG integration in higher education. Originality/value This study provides a bibliometric analysis of innovative teaching and learning for the SDGs in business, economics, and management. Moreover, this study offers research clusters for each SDG separately and offers a combined analysis between the research themes and the SDGs.
Purpose The study aimed to examine the overall total effect size of academic buoyancy and academic self-concepts on mathematics performance utilizing meta-analysis. Design/methodology/approach This study employed a meta-analysis design. Drawing on 20 peer-reviewed studies published from 2017 to 2026, the research applied strict inclusion criteria to identify relevant quantitative studies across global educational contexts. Data were extracted using a structured coding sheet, and statistical analyses were conducted in JASP, incorporating heterogeneity tests, forest plots, and publication bias assessments. Moreover, the study drew heavily on secondary data. Findings The study revealed that the overall effect of academic buoyancy on mathematics achievement was positive and statistically significant. Finally, the study findings revealed that the overall positive and statistically significant effect of academic self-concept on mathematics achievement was substantial. Originality/value This study examined the overall effect size of academic buoyancy and self-concept on mathematics achievement using meta-analysis.
Purpose This study examines how generative artificial intelligence functions as a governance stressor within higher education institutions and investigates how ethical boundary ambiguity, perceived academic risk and institutional policy clarity shape support for governance reform. Design/methodology/approach A mixed methods design was employed. The quantitative phase involved 228 participants comprising 142 undergraduate students and 86 lecturers. A structured survey measured perceptions across four governance related domains. Multiple regression analysis identified predictors of reform support. The qualitative phase included semi structured interviews to explore implementation level tensions and policy interpretation dynamics. Findings Ethical boundary ambiguity and perceived academic risk significantly predicted support for governance reform. Institutional policy clarity was negatively associated with reform demand, indicating a stabilising effect. Qualitative findings revealed that inconsistent assessment expectations and uneven policy communication translated abstract governance into everyday uncertainty. Results suggest that reform pressure emerges from misalignment between policy articulation and pedagogical practice rather than from misconduct alone. Originality/value The study proposes a governance alignment model that reframes academic integrity governance as a problem of communicative coherence linking policy clarity, ethical boundary definition and perceived academic risk. The findings contribute empirical evidence to institutional level debates on sustainable AI integration in higher education.
Purpose This study aims to examine the impact of integrating digital storytelling (DST) into English instruction within a vocational college English as a Foreign Language (EFL) context in southwestern China. Specifically, it investigates the effects of DST on students' English language performance and affective engagement, with particular attention to foreign language speaking anxiety (FLSA) and willingness to communicate (WTC). Design/methodology/approach A six-week DST intervention was implemented with 50 students in a natural class setting. A mixed-methods approach was adopted, involving pre- and post-test writing assessments, affective factor questionnaires and a focus group discussion. Findings Quantitative analysis revealed a statistically significant improvement in students' writing performance following the intervention. Although changes in FLSA and WTC were not statistically significant, effect size analysis indicated slight positive trends in affective engagement. Qualitative findings further highlighted increased learner motivation, confidence and willingness to express themselves in English, particularly through collaborative task completion and multimodal composition. The results suggest that DST creates a supportive yet cognitively demanding environment that facilitates both language production and emotional engagement. Originality/value This study extends DST research by exploring its application in the underexplored EFL context of vocational colleges. By linking DST with affective factors like FLSA and WTC, it offers fresh insights into the connection between multimodal writing and emotional engagement. The findings suggest practical ways to boost learner motivation and confidence, particularly in low-resource EFL settings.
Purpose Blended learning has become a defining instructional approach in higher education (HE), particularly after the COVID-19 pandemic. It integrates online and face-to-face (F2F) components to provide flexibility and personalization. However, the effectiveness of blended learning depends on the pedagogical decisions that shape its design. While blended learning has a longer historical trajectory, this study does not aim to map its evolution over time. Instead, it investigates how pedagogy has been articulated in influential blended learning research during a period of systemic disruption in HE. Design/methodology/approach A bibliometric analysis was conducted using articles published between 2019 and 2024 in the Web of Science database, covering the period immediately preceding, during, and following the COVID-19 pandemic. The study examined leading journals publishing blended learning research, the academic fields represented, and how researchers approached the pedagogical dimension of blended learning. Ninety-eight articles were reviewed to identify trends, disciplinary patterns, and the presence or absence of pedagogical frameworks. Findings The analysis revealed that only 23.5% of studies explicitly mentioned blended learning models, with flipped learning being the most frequent. More than half of the publications (58.2%) did not address any learning theory, while 41.8% incorporated a pedagogical perspective, mainly constructivism and cognitivism. These findings emphasize the need to ground blended learning practices in pedagogical theory rather than technological preference. Originality/value This study provides a comprehensive overview of blended learning pedagogy in HE during the pre- and post-COVID-19 period, rather than a historical mapping of the field. It identifies a persistent gap between technological design and pedagogical reasoning, underlining the importance of maintaining theoretical integrity in blended HE. The paper contributes insights for researchers and practitioners seeking to strengthen the pedagogical grounding of blended learning during periods of institutional transformation.
