Teacher turnover undermines school stability and student outcomes. Guided by the job demands–resources model, this study used random forest analysis to examine 36 potential antecedents of turnover intentions. Data were collected from 830 Czech primary and lower secondary school teachers with a mean age of 45.9 years (SD = 10.9) across 44 schools. Twenty-four predictors exceeded the random-noise benchmark, with physical fatigue and job satisfaction emerging as the strongest. Work engagement, cognitive weariness, work ability and work–life conflict also played major roles, alongside personal resources such as resilience, teacher self-efficacy and locus of control. Several predictors exhibited non-linear or threshold patterns. Conservation of resources (COR) theory provided a complementary lens for interpreting U-shaped resource-related patterns in terms of resource conservation and investment. By evaluating a broad set of antecedents, the study extends existing research beyond isolated predictors and provides a more integrated account of the factors shaping teachers’ turnover intentions. It also reveals patterns that conventional linear analyses may obscure. Together, these findings provide school leaders and policymakers with a basis for retention strategies. Such strategies should prioritise early burnout detection, strengthen work engagement and job satisfaction, and be tailored to teachers at different career stages.
This new restriction forces me to rearrange the entire Abstract, which the platform does not allow. If this 200 word limit is mandatory, please use this new Abstract: "Structural dynamics, resonance, and seismic isolation are usually taught through complex mathematical formulations that students struggle to relate to observable physical behaviour. This paper presents and technically validates a reproducible 20-h project-based learning module, ebedded in a 75-h Structural Dynamics and Earthquake Engineering course, in which teams of three to five students integrate material characterization, numerical modelling, specimen fabrication, structural identification, base isolator design, shake-table testing, and data interpretation. Flexural tests on 12 cantilever balsa specimens gave a mean modulus of elasticity of 2637 MPa. An ETABS model predicted a fundamental period of 0.172 s, closely matching the measured 0.174 s of the fixed-base physical model. A 3D-printed pendulum base isolation system extended the measured period to 0.36 s. Under a 5.50 Hz sine-beat excitation (nominal peak base acceleration 0.122 g), five repeat runs gave mean peak absolute roof accelerations of 0.95 g (fixed-base) and 0.27 g (isolated), a 72.1% reduction. The study contributes a pedagogical framework, a weighted student assessment framework, replication protocols, and a critical limitation analysis. Only technical validation is reported; educational effectiveness has not been measured and will be evaluated with student cohorts once the module is formally integrated into the curriculum.
Drawing on the existing literature in educational leadership, we conducted a scoping review of research on gender-justice-centered educational leadership in Tanzania, Uganda, and Kenya. We specifically conducted a systematic search on Web of Science, EBSCOhost, and Google Scholar for journal articles published for a period spanning between January 2014 and January 2025. Following a multi-stage screening process to exclude duplicates and irrelevant studies, 39 relevant articles were selected for in-depth review. Our analysis identified three broad areas of barriers to educational leadership for gender justice. These are personal (e.g. self-doubt, lack of mentorship), systemic (e.g. institutional bias, limited policy support), and societal (e.g. gender norms, cultural expectations) as barriers to gender-just educational leadership in Tanzania, Uganda, and Kenya. By synthesizing region-specific literature, this paper addresses a significant gap in research on gender equity in educational leadership in East Africa. The findings offer context-sensitive recommendations for policy and practice, contributing to the development of more inclusive and locally informed leadership frameworks. Our findings offer evidence-based insights to help policymakers and educational institutions in East Africa design more inclusive leadership pathways and gender-responsive policies. By identifying context-specific barriers and strategies, the study supports targeted interventions that can advance gender equity in educational leadership across the region.
The study aimed to: (i) analyze the impact of the teaching modality on the employability expectations of students in business master’s programs, seeking to identify whether there are statistically significant differences according to the learning environment; and (ii) analyze the segmentation of business master’s students based on their employability expectations to identify differentiated profiles. To this end, the study focused on students in business master’s programs at a prestigious business school in Ecuador. Under both online and in-person modalities. After controlling for age and gender, instructional modality was associated with only modest multivariate differences, suggesting that employability expectancy is related to a broader combination of demographic and educational characteristics rather than to instructional modality alone. Furthermore, students in business master’s programs are segmented according to high, medium-high, and low employability expectancy. These findings serve to strengthen student employability, personalize training, and support institutional decision-making.
As generative artificial intelligence (GenAI) becomes integrated into academic writing, understanding how second language (L2) English students revise AI-assisted writing remains underexplored; therefore, this inductive content analysis explores students’ revision strategies for academic writing to propose a conceptual framework . The study employs an open-ended free listing task with 100 students, who listed their five major strategies for revising AI-assisted writing assignments. Additionally, semi-structured interviews were conducted with 10 students experienced in revising AI-assisted writing. The interview transcripts were open-coded to develop categories. Meanwhile, the free-listing responses were directly categorized because participants listed strategies as explicit codes. The researcher then compared, consolidated, and synthesized the strategy categories into broader themes to form a conceptual framework. The analysis found key revision strategies like incorporating personal ideas and expressions, simplifying the language, consulting additional sources, paraphrasing and rewriting content, integrating personal experience, contextualizing AI content, and cross-checking AI information. It suggested that L2 English students primarily personalize and improve AI-assisted writing rather than passively accepting AI-generated text, while also adapting, evaluating, and responsibly managing AI-generated content. This final synthesis yielded six overarching themes: supportive utilization, ethical engagement, creative reform, critical evaluation, contextual revision, and functional adjustment. The proposed framework with six themes serves as an empirically grounded taxonomy of student revision strategies in AI-assisted writing.
