This study synthesizes quantitative evidence on experiential marketing mechanisms in educational metaverse environments for STEM-oriented educational technology (EdTech). Following PRISMA, 29 publications were included in the review; 25 primary empirical studies (total N = 4988) each contributed one effect size to a random-effects (REML) meta-analysis, and four review articles were used only as background. The pooled effect was large (d = 0.79, 95% CI [0.75, 0.83]) with no detectable between-study heterogeneity (Q(24) = 7.78, p = 0.999; I2 = 0%). Estimates were similar for STEM-labelled and general higher-education contexts (d = 0.78 and 0.79), and for VR, mixed-reality, or immersive-simulation studies and other metaverse platforms (d = 0.75 and 0.80). Leave-one-out estimates ranged from 0.77 to 0.79. Egger's test indicated small-study asymmetry (p = 0.014), whereas Begg's test did not (p = 0.835); the asymmetry runs opposite to the direction expected under publication bias. Because most primary studies are survey-based structural-equation studies whose standardized coefficients were approximately converted to d, the pooled estimate reflects the strength of association rather than a causal treatment effect. The findings suggest that immersive interaction and emotional engagement are central components of effective instructional design, and they encourage EdTech providers and institutions to align experiential design with pedagogical objectives, institutional trust, and sustainable adoption.
Foresight informs collective action not only through signal detection and modelling, but through the institutional processes by which uncertain evidence is interpreted, contested, authorized, and revised. This article reconstructs the meso-level evidential basis of decision-ready foresight in the DeflyCompass Framework (DCF) through a retrospective integrative synthesis of peer-reviewed studies from the cumulative DeflyCompass programme, distinguishing direct evidence, corroborative evidence, and architectural inference. Four bounded findings emerge: explicit criteria made trend delimitation traceable, 276 to 69 to 30 trends, with high rating convergence among evaluators but no direct validation of stakeholder viability; hybrid analysis broadened evidential coverage when heterogeneous procedures remained mutually checkable; classifying 55 institutions across 16 variables supported institutional singularity as a classificatory, not early-warning, problem; and a documented 24-professional process evidenced intervention and reconsideration. Originality lies in separating construct from evidence and specifying an auditable meso-level pathway. Practically, its artefacts and review points can be implemented independently; academically, the study frames end-to-end prospective validation as the next research task.
Mathematics performance in Tanzanian secondary schools remains critically low, with form four national examination pass rates averaging 20.87% between 2018 and 2023. Majority of existing studies attribute such poor performance to resource deficits, psychological, social, economic, and instructional dimensions. However, up to now a few studies have applied machine learning techniques to rigorously identify the structural determinants of mathematics performance in Tanzanian secondary schools. This study employed Lasso Regression and Principal Component Analysis (PCA) on data collected from three thousand and two (3,002) form three students across five geographical zones in Tanzania. From 88 initial variables, Lasso Regression identified 35 significant predictors grouped into four categories: psychological factors, instructional quality, household assets, and demographic and environmental factors. The results reveal that Math Career Utility and Math Confidence are the strongest positive predictors, while Smartphone ownership, Commute Time, and Chore Hours significantly hinder performance. PCA identified Teacher Clarity, Adaptability, and Encouragement as a unified instructional construct. These findings challenge resource-centred assumptions and offer evidence-based recommendations for policy intervention in mathematics education in Tanzania and across sub-Saharan Africa.
This study designs and validates a Work-Integrated Learning (WIL)-based Collaborative Teacher Inquiry (CTI) model to address persistent gaps in vocational teacher professional development (PD) in Indonesia. Through a sequential explanatory mixed-methods approach with 270 vocational teachers, we examined the relationships between Learning Goal Orientation (LGO), readiness, and systemic barriers. Quantitative results revealed that LGO significantly predicts CTI readiness (β = .317, p < .001) and WIL readiness (β = .237, p = .024). Counterintuitively, barriers positively predicted both readiness outcomes (β = .708–.758), a finding interpreted through Activity Theory (AT) as heightened awareness of systemic contradictions among engaged teachers. Gender showed a small, non-significant direct association with CTI readiness (β = −.066, p = .190), although it was positively associated with barrier perceptions. Qualitative findings highlighted strong teacher motivation for collaboration despite time, facility, and bureaucratic constraints. Integrating Self-Determination Theory (SDT) and AT, this study positions LGO as a resilience mechanism in resource-constrained contexts and offers a validated framework for fostering equity-sensitive, industry-aligned professional learning communities (PLCs), with implications for vocational education systems facing similar school-to-work transition challenges.
Higher education institutions play a key role in advancing the 2030 Agenda and its 17 Sustainable Development Goals (SDGs) through Education for Sustainable Development (ESD). However, Spanish universities rarely incorporate SDGs into their curricula, and sustainability is not systematically integrated into the curricula of Business Analytics degree programs in the country. This pilot study examines whether integrating ESD through project-based service-learning activities with cross-disciplinary projects is associated with differences in academic performance and how students appreciate the experience. A post-test quasi-experimental design with a historical control group was applied. The experimental group (Business Analytics students (n = 86 after exclusions)), developed SL projects focused on the SDGs. The control group (n = 38 after exclusions) completed the same project structure but on topics unrelated to sustainability. Academic performance was measured through final grades and a written exam; student appreciation was analyzed through 13 team-authored reflections. The final-grade comparison showed a small descriptive difference between groups; the written exam also showed no significant difference (d = −0.14). Because the study was underpowered, these results do not establish non-inferiority or equivalence. Students in the 13 teams expressed positive appreciation, with eight teams expressing an orientation toward social action, seven reporting increased knowledge of specific SDGs, and six expressing self-reported personal impact. This low-barrier approach suggests that SDG-focused service-learning can be introduced into existing academic frameworks without a detectable difference in performance on the common course content in this sample, although further research with stronger designs is needed to confirm causality and long-term effects.
