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In the discussion of social capital and social equality, gender is still one of the most important variables that may navigate once's opportunity to pursue higher education. Despite numerous studies having examined the influence of digital literacy, socioeconomic status (SES), social capital, and their subsequent impact on access to higher education (HE), investigating the inputs and output paths of graduates using gender as a theme is yet to be done. Viewing the input path as secondary education certification and admission into university as the output path, 623 secondary school graduates for Study-1 and 635 secondary school graduates for Study-2 were examined using the quantitative method. Primary data was collected through a questionnaire to assess one independent variable (social capital), while secondary data stored in the institutions serve to measure the independent (SES), mediator, moderator and dependent variables. Multiple regression, interaction terms and KHB (Karlson-Holm-Breen method) analysis were utilized to analyse the data. Results indicated that SES-driven social capital predicts the digital literacy, and digital literacy mediates the relationship between graduates' social capital and access to HE. Furthermore, gender does not moderate the correlations between digital literacy and access to higher education. This paper contributes to the discourse on technology in societies and inequalities in higher education. The study concludes that digital discrimination increases inequality in terms of people's access to higher education. However, gender does not pose any technological discrimination for graduates in China.
PurposeIn 2021, China implemented the double reduction policy, aiming to promote more equitable and sustainable learning environments. This study aims to investigate whether the policy reshaped over time the relationship between family socioeconomic status (SES) and academic achievement and whether its effects varied when comparing public and private schools.Design/methodology/approachUsing a quasi-experimental longitudinal research design and relying on naturally occurring variations across time (before vs during policy implementation) and school type (public vs private), students' performance data were observed before 2020 and after 2022. Regression analysis, moderation models and heterogeneity tests were conducted. Academic achievement was measured using standardized composite scores in Chinese, mathematics and English; SES was constructed from household income and parental education.Findings SES remained a significant predictor of academic achievement in both public schools (beta = 0.37, R & sup2; = 0.14 in 2020) and private schools (beta = 0.42, R & sup2; = 0.18 in 2020). However, its influence weakened significantly in public schools (beta = 0.16 in 2022), while in private schools it slightly increased (beta = 0.45, R & sup2; = 0.20 in 2022).Originality/valueFrom a sustainable education perspective, the findings suggest that regulatory reforms can contribute to educational sustainability by reducing students' reliance on family background rather than by increasing short-term performance levels.
3D concrete printing is increasingly being adopted in construction, yet limited knowledge exists on the creep behaviour of printed elements, particularly in the hardened state. This study presents an investigation into the short-term creep behaviour of 3D printed concrete (3DPC) under compressive stress. Printed samples reinforced with micro-synthetic polypropylene fibers were compared with conventionally cast samples of the same mix. A sustained load of 35
Equatorial Spread F (ESF) is a well-documented phenomenon characterised by electron density perturbations in the F-layer, significantly impacting the propagation of electromagnetic waves. The automatic identification of ESF and its statistical analysis are crucial for understanding the physical mechanisms that drive ionospheric irregularities and for improving predictive models. This study implements a convolutional neural network (CNN), a deep learning model, for the automatic detection of ESF using ionosonde data from a Digisonde (DPS-4) at Ilorin (Geographic: 8.5 degrees N, 4.5 degrees E; Geomagnetic: 4.5 degrees S), an equatorial station in the African sector. We used a dataset of 3,000 manually labelled ionograms randomly selected from the period 2019 to 2021. The dataset was categorised into clear, ESF, and unidentified, with 1,000 images per category. Two models were trained using raw ionograms (RGB-model) and gray-scale preprocessed ionograms (Gray-model). The models achieve overall accuracies of 95.17% and 99.33% for raw and preprocessed ionograms, respectively, when tested on an unseen dataset. Evaluation using three complementary skill scores: F1-score, True Skill Score (TSS) and Heidke Skill Score (HSS) indicates that while both methods enable efficient CNN-based ionogram classification tasks, the grayscale approach requires lower computational load and obtains the largest gains compared to the RGB-image approach. For the first time, we provide classification of 60,326 ionograms across diverse geophysical conditions in the African sector, using both manual classification and machine-learning methods. The main contribution lies in the systematic evaluation and application of the models for ionogram classification tasks. The improved classification performance and robustness of the models under both quiet and disturbed geomagnetic conditions demonstrate their potential for large-scale, automated ionogram analysis.
This study examined the leadership capacity of principals in ensuring equitable resource distribution and its influence on teacher productivity in secondary schools in Kebbi State, Nigeria. Two research objectives and research questions were raised to guide the study. Adopting a descriptive survey design, the population comprised 104 principals and 4,272 teachers, from which a sample size of 357 was drawn using research advisor table 2006, respondents were selected through stratified and simple random sampling techniques, data were collected using the Principals' Resource Management Questionnaire (PRMQ) which was validated by experts and a Cronbach’s alpha reliability coefficient of 0.73 was achieved. Findings indicate that principals demonstrate strong capacity in key administrative areas, including needs-based budgeting, ensuring access to facilities, and managing core staffing logistics like recruitment and assignment by expertise. However, significant gaps were identified in strategic leadership domains: principals showed limited effectiveness in distributing experienced teachers equitably, providing targeted support for struggling students, fostering collaborative mentorship, ensuring adequate physical working conditions, and building inter-school partnerships. The study concludes that while principals are proficient operational managers, their capacity for transformative, equity-focused leadership that directly enhances teacher productivity through collaborative culture and strategic resource responsiveness requires substantial development. Recommendations include targeted training and policy reforms to address these identified gaps.
