http://bdigital2.ula.ve:8080/xmlui/654321/26919 2026-08-05T07:41:52Z http://bdigital2.ula.ve:8080/xmlui/654321/26940 Data Science and Machine Learning in Secondary Education: A Case Study of Barriers and Opportunities at Cambine Secondary School, Mozambique This study examines the potential of Data Science (DS) and Machine Learning (ML) to transform teaching and learning at Cambine Secondary School, a rural institution in Inhambane Province, Mozambique, characterized by limited technological infrastructure. Using a concurrent mixed-methods case study design, data were collected through semi-structured interviews with two school directors and nine teachers, alongside structured questionnaires administered to sixty students and nine teachers, analyzed thematically and through descriptive and inferential statistics. The findings reveal substantial barriers, including unreliable or absent internet connectivity, a shortage of computers, near-total absence of virtual learning environments, and low teacher familiarity with DS/ML concepts, with only a quarter of teachers reporting that they understand them. Nevertheless, participants expressed strong interest: most students want to learn more about DS and ML, and most teachers believe such tools could support personalized learning. Inferential comparisons between the two groups showed significant differences in perceived internet adequacy and in familiarity with DS/ML concepts (p < .05), while perceptions of resource adequacy and optimism about the personalizing potential of these tools were broadly shared. The study concludes that, despite significant infrastructural and training gaps, a coordinated strategy combining infrastructure investment, continuous teacher training, curriculum integration, and public-private partnerships could enable DS and ML to meaningfully improve teaching quality and school management. These findings contribute to the broader debate on digital transformation in resource-constrained settings and offer practical recommendations aligned with Mozambique’s national education strategy and international frameworks such as Sustainable Development Goal 4. http://bdigital2.ula.ve:8080/xmlui/654321/26939 New Pathways to Explore for Updating Engineering Curricula in Venezuela The concepts and facts involved in university education are evolving rapidly, just as they are in all aspects of daily life. Recently, Industry 4.0 has emerged alongside Artificial Intelligence (AI)—elements that impose the need for capable, cutting-edge professionals who can face the challenges presented by highly technified organizations with self-governing cybernetic elements. One tool available to universities is the implementation of programs oriented toward Competency-Based Education (CBE), which argue that the knowledge to be taught goes beyond the mere transmission of knowledge; instead, they also seek to develop a set of skills, attitudes, aptitudes, and abilities that can ensure graduates achieve successful job performance. On the other hand, Venezuela requires university professionals called upon to fill the positions that will open given the expected economic recovery; hence, the need to evaluate aspects related to the accreditation of prior learning and the existing possibilities as alternatives for developing Special Degree Projects (SDP). Following the philosophical current of positivism and framed within qualitative evaluative research, the study presented here seeks to clearly establish the advantages of the CBE model and the aspects established in Official Gazette No. 42,903 regarding the accreditation of prior learning. http://bdigital2.ula.ve:8080/xmlui/654321/26942 Domain Adaptation for CI/CD Workload Prediction: Transferring Seasonality Patterns Across Non-Overlapping Time Windows Machine learning models for workload prediction typically require training data from the same time period as deployment. In CI/CD environments, obtaining representative real-world training data is often difficult due to privacy concerns, data availability constraints, or the need to predict for newly established infrastructure. This paper presents a transfer learning methodology that enables transferring seasonality patterns and workload characteristics from real billing data collected during one-time period to real workflow execution logs from a completely non-overlapping period. The article approach combines two techniques: (1) Quantile Mapping to align duration distributions between the target- and source-domain data, preserving structural characteristics (shape, variability, skewness), and (2) Cross-Temporal Seasonality Transfer to map weekly activity patterns across time windows. Validated on GitHub Actions data spanning two distinct quarters (January-April 2023 billing data transferred to June-October 2023 real execution logs), our method achieved a strong correlation (r=0.8739, p=0.0101) for weekly patterns, explaining 76.4% of the variance in seasonality (R2=0.764), and aligned the duration distribution, reducing the CV error from 0.26 to 0.01, skewness from 1.06 to 0.03, and kurtosis from 2.33 to 0.07 relative to the source domain – all without overlapping temporal coverage between the two data sources. http://bdigital2.ula.ve:8080/xmlui/654321/26941 The Systemic Impact of Monitoring and Evaluation on the Performance of Development Projects or Programs in Burkina Faso This article analyzes the contribution of the monitoring and evaluation (M&E) system to the operational and strategic performance of development projects or programs in Burkina Faso. By relying on a mixed-method approach combining quantitative data from a questionnaire and qualitative interviews, the research explores the tension between administrative accountability toward donors and organizational learning for field actors. The results demonstrate that while the M&E framework is formally standardized, its actual effectiveness is constrained by a passive compliance bias, logistical constraints in rural environments, and a deficit in technical skills. The article proposes an optimization model focused on the simplification of indicators, the co-construction of tools, and decentralized digitalization to transform M&E into a genuine lever for territorial resilience.