Rubén Baena-Navarro

Purpose: This study examines how teaching practices, student experience, family support and institutional conditions align to sustain future-oriented integral learning in primary education. Design/methodology/approach: A convergent mixed-methods secondary analysis was conducted using an anonymised dataset from three Colombian primary schools. The quantitative strand calculated instrument-specific descriptive summaries, while the qualitative strand synthesised pre-assigned multilabel themes from de-identified records. A school-by-dimension joint display distinguished cross-method convergence from cross-stakeholder and within-stakeholder/self-report consistency, while also retaining complementarity, divergence, single-source findings, absence of evidence and source asymmetries. Findings: Across the three cases, the same EESSAX-18 form yielded generally favourable student self-reports; teacher and family records were also favourable within their respective instruments. Mixed-source integration nevertheless identified contextual constraints in resources, ICT, monitoring and school-family communication. Because leadership evidence comprised only six records from non-equivalent instruments, evidence on institutional implementation conditions was treated as a limited contextual signal and interpreted alongside teacher, qualitative and documentary sources rather than as a cross-school estimate. The integrated pattern indicated that favourable pedagogical intention could coexist with constraints in enabling ecosystem conditions. Originality/value: The study contributes a preliminary, traceability-oriented analytical framework for integrating heterogeneous multi-stakeholder evidence while preserving source-specific limits and asymmetries. Practically, the framework can help schools identify misalignments among teaching practice, student experience, family support, institutional implementation conditions and socio-emotional and values-based learning before selecting improvement actions. It contributes situated evidence from Latin American primary education on how schools may move from valuable practices to sustainable integral learning innovation.

Este artículo analiza la evolución de la política de ciencia, tecnología e innovación en Colombia y sus efectos sobre el desarrollo científico y las asimetrías territoriales entre 2000 y 2022. Se emplea un estudio descriptivo con enfoque cuantitativo basado en fuentes oficiales, complementado con normativa estratégica (Conpes, leyes, decretos y planes nacionales de desarrollo). Los resultados evidencian un fortalecimiento sostenido de las capacidades nacionales: el país registró un crecimiento promedio anual de 280 grupos de investigación, 624 investigadores reconocidos, 11 revistas indexadas, 151 patentes concedidas y 822 publicaciones en Scopus; en el ámbito económico, se observaron incrementos medios anuales de 12,2% en el PIB nominal, 17% en las exportaciones, 30% en la inversión extranjera directa, 8% en el PIB per cápita y 28,45% en las importaciones. Aun así, el 63% de los grupos y el 68,4% de los investigadores se concentran en cinco departamentos, confirmando la persistencia de brechas territoriales. El estudio concluye que, aunque la política de ciencia, tecnología e innovación ha dinamizado el sistema nacional, persiste el desafío de avanzar hacia una distribución más equitativa de capacidades en el país.

This study examines the gap between digitalization agendas and school realities, where connectivity constraints, limited devices, and uneven support restrict pedagogical innovation. It evaluates the Model for Integrative and Predictive Smart Teaching Adaptation, integrating artificial intelligence, learning analytics, and Internet of Things components, within a holopraxic cycle of diagnosis, design, implementation, evaluation, and readaptation driven by field feedback. Over a semester, 120 rural teachers participated in a quasi-experimental study combining a competence questionnaire, interviews, and system usage logs. Baseline competence was comparable between groups, and gain was defined on a zero to one hundred scale as post minus pre. The experimental group showed a median gain of 6.25 points, whereas the control group remained at 0.00; the common-language effect size was 0.765. Engagement was sustained with peaks (twenty-five to thirty-seven sessions per week), indicating selective appropriation rather than linear growth. Results support improvement and emphasize adoption conditions: teacher agency, ethical trust, and institutional sustainability, operationalized through pseudonymization and bias auditing.

Rural accounting systems operate under persistent constraints - fragmented records, heterogeneous data sources, and intermittent connectivity - that undermine the reliability of both financial reporting and sustainability-related disclosures. This study addresses these limitations by designing and internally validating the AI-Blockchain Accounting Framework for Rural Entrepreneurship (ABAF-RE), a modular architecture that integrates distributed-ledger mechanisms, explainable machine-learning analytics, and rule-based verification logic for ESG-oriented controls. The research combines a PRISMA-guided systematic review (2019-2025) with a structured conceptual modeling procedure to extract recurrent design requirements for verifiable provenance, offline-first evidence capture, deferred notarization, oracle-aware data ingestion, and rule-as-code validation under low-infrastructure conditions. Across the reviewed literature, hybrid artificial intelligence/machine-learning and deep-learning approaches are consistently discussed as enabling stronger auditability and evidence continuity through tamper-evident records, traceable data lineage, and programmable control enforcement; however, reported effects are typically task- and context-specific, with heterogeneous baselines and evaluation protocols that limit cross-study comparability and discourage the use of universal performance percentages. Building on these convergent mechanisms, ABAF-RE consolidates an asynchronously synchronized, offline-first blueprint intended to preserve auditable information flows, semantic coherence across sources, and multi-actor traceability in rural value chains. The study argues that distributed computational trust - grounded in cryptographic lineage, explainability-oriented governance, and programmable assurance - provides a defensible pathway to strengthen the quality of financial and ESG disclosures and to support continuous auditing workflows under real-world connectivity constraints, while framing quantitative impacts as hypotheses to be tested through pilots with pre-registered metrics and explicit baselines.

The growing need to preserve cognitive health in aging populations has intensified interest in adaptive digital interventions that provide personalized and interpretable support. This study presents a web-based cognitive stimulation system for older adults integrating a multilayer perceptron (MLP) classifier, expert-derived symbolic rules, and explainable artificial intelligence (XAI) techniques, including Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME). The platform was evaluated through a 24-week intervention involving 150 participants aged 65 years and older, combining baseline cognitive profiling, rule-guided recommendation logic, and neural prediction to support individualized task allocation. Compared with a control group, participants in the intervention arm showed statistically significant improvements in cognitive outcomes (p <0.05), with measurable gains in memory- and attention-related tasks. The explainability component enabled examination of model behavior at the level of individual features through feature attribution analysis and symbolic consistency checks, supporting interpretation beyond aggregate performance metrics. Unlike approaches dependent on high-end extended reality (XR) infrastructures or game centered interaction, the system was implemented to operate under low connectivity conditions and was tested with participants from diverse educational backgrounds. This hybrid configuration provides an interpretable basis for cognitive support initiatives adaptable to community settings contexts.

Mental health problems affect nearly half of university students worldwide, with around 20% reporting depressive symptoms and over 40% showing signs of anxiety. This burden is particularly acute in low-resource universities, where limited infrastructure and minimal investment in mental health restrict access to effective care. To address this gap, this study applies a projective research approach, defined as the design of evidence-based solutions without immediate empirical implementation. A systematic review of 402 scientific articles was carried out across major databases, from which 15 met strict inclusion criteria. The analysis identified recurrent barriers such as unstable internet connectivity, devices with less than 2 GB RAM, and the absence of regulatory frameworks governing AI in education. Based on these findings, an adaptive intervention model was proposed, integrating artificial intelligence (AI), machine learning (ML), and deep learning (DL) to deliver personalized psychological support directly on local devices, without requiring permanent connectivity. The proposed system demonstrated potential to reduce anxiety and depression scores by 15–25% in controlled studies and achieved prediction accuracies above 80% for stress and loneliness detection. This framework provides a scalable foundation for universities in developing contexts, contributing to equity in access to digital mental health services.

RuralTech: AI-Driven Innovation for Transforming Rural Entrepreneurship is an essential work for researchers, educators, policymakers, and entrepreneurs interested in sustainable development and digital transformation in rural territories. The book offers a comprehensive and up-to-date perspective on how artificial intelligence, the Internet of Things, digital governance, and renewable energy are redefining rural entrepreneurship—not only from a technological standpoint, but also from social, economic, and environmental perspectives. This work stands out for its systemic approach and logical sequence, articulated through five interconnected chapters: from the foundations of innovation and digitalization in rural ecosystems, through the adoption of emerging technologies in agricultural production, the impact of artificial intelligence on financial inclusion and decision-making, the relevance of social innovation and sustainability in rural communities, and finally, the analysis of public policy and governance models for the future of rural entrepreneurship. The authors interpret the field from a multidisciplinary and comparative perspective, integrating experiences from Latin America, Europe, and Africa, with a deep focus on the Colombian case. The book highlights the importance of local technology appropriation, community participation, and co-creation of solutions adapted to territorial realities. Its logical sequence allows readers to understand that rural innovation is not a linear process, but a dynamic evolution that depends on the integration of actors, connectivity, resilience, and sustainability. The innovative character of the book lies in its proposal of isometric models that integrate hardware, software, and governance, validated in contexts of low connectivity and high environmental vulnerability. It also addresses topics such as algorithmic ethics, blockchain-based traceability, digital and energy inclusion, and the transition toward circular and sustainable economies. The relevance of the results is reflected in the empirical evidence presented: improvements in agricultural productivity, cost reduction, strengthening of community resilience, advances in financial and digital inclusion, and the consolidation of ethical and participatory governance frameworks. RuralTech not only diagnoses the asymmetries and challenges of the rural world, but also offers methodological and policy pathways for building inclusive and sustainable innovation ecosystems, positioning rural territories as laboratories for modernization and equity.

This study investigates how the use of information and communication technologies (ICT) influences the academic resilience of high school students in Soacha, Colombia, an area with significant socioeconomic challenges. The objective of the research is to analyze whether greater access to and proper use of ICT can improve students’ ability to face academic and emotional adversities. A mixed methodology was applied, combining quantitative surveys and qualitative interviews with 300 students from three educational institutions, measuring ICT use and academic resilience through validated scales. The findings revealed a significant positive correlation (r=0.95) between ICT use and academic resilience, demonstrating that students who used ICT more frequently showed higher levels of self-efficacy, better stress management, and more efficient academic organization. These results highlight the importance of integrating ICT into educational policies in vulnerable contexts, given their impact on both academic performance and students’ emotional well-being. It is recommended to prioritize digital competence training in educational institutions to strengthen students' abilities to face academic and emotional challenges.