Carolina Gonzalez

Global agricultural innovation and food security increasingly hinge on access to plant genetic resources that transcend national borders. Utilizing Colombia’s recent ratification of the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA) as a case study, this research explores how participation in the Treaty’s Multilateral System (MLS) can strengthen national agricultural innovation by mitigating legal and transactional barriers to germplasm exchange and conservation. We analyze four case studies encompassing cassava, forage grasses, bananas, and beans to quantify both realized and potential benefits derived from international germplasm flows. Two ex-post cases demonstrate that the adoption of foreign-derived materials has already yielded considerable economic gains in Colombia, including US$1.62 million from the TAI-8 cassava variety and significant productivity improvements from Brachiaria forages, which now cover 32% of the nation’s pastures. Two ex-ante cases illustrate that continued access to foreign genetic resources is crucial for addressing major emerging threats, with potential losses exceeding US$9 billion from cassava brown streak disease in Africa and up to US$700 million annually for Colombia’s banana sector under a scenario of full Fusarium TR4 spread. The analysis further reveals that Colombia is already deeply integrated into global germplasm exchange through CGIAR collections governed by Article 15 of the Treaty, accounting for approximately 87% of all MLS distributions worldwide. However, prior to ratification, national institutions faced considerable legal and institutional constraints concerning the exchange and safe duplication of their own collections, including an inability to deposit materials in the Svalbard Global Seed Vault. Collectively, these findings underscore that germplasm exchange has long been a pivotal driver of agricultural advancement in Colombia and that Treaty participation offers essential institutional infrastructure to secure, govern, and amplify these benefits within a more regulated and equitable global framework. Despite these advances, important challenges remain in ensuring fair and equitable benefit-sharing, particularly for farmers, highlighting the need for continued institutional and policy development within the MLS.

Objectively evaluating STEM education projects is essential for ensuring fairness, consistency, and evidence-based instructional decisions. Recent interest in data-informed approaches highlights the use of standardized frameworks, rubric-based assessment, and computational techniques to support more transparent evaluation practices. This systematic review examines how machine learning (ML) techniques—within the broader field of Artificial Intelligence in Education (AIED)—contribute to the evaluation of STEM projects. Following Kitchenham’s guidelines and PRISMA 2020, searches were conducted across Scopus, Web of Science, ScienceDirect, and IEEE Xplore, resulting in 39 studies published between 2020 and 2025. The findings show that current STEM frameworks emphasize disciplinary integration, inquiry, creativity, and collaboration, while rubrics operationalize these principles through measurable criteria. ML techniques have been applied to classification, prediction, and multidimensional analysis; however, these computational approaches remain largely independent from established frameworks and rubric structures. Existing ML models demonstrate feasibility for modeling evaluative indicators but do not yet integrate pedagogical constructs within automated assessment pipelines. By synthesizing evidence across frameworks, rubrics, and ML techniques, this review clarifies the methodological landscape and identifies opportunities to advance scalable, transparent, and pedagogically aligned evaluation practices. The results provide a conceptual foundation for future work aimed at developing integrative and trustworthy ML-supported evaluation systems in STEM education.

The Amazon—the most biodiverse biome in the world—faces huge conservation challenges. Multiple institutional and technological actors, organized in environmental networks, collaborate to support improved environmental governance. However, these efforts are understudied and underrecognized, particularly in the case of the Colombian and Peruvian Amazon. Employing a social network analysis, this study evaluates the actors, the institutional–technological relationships, and the network addressing socio-environmental challenges. We identified 392 development actors (nodes) with different levels of international, national, and regional participation. They use transnational technologies, but have limited integration of local actors where environmental challenges are most pressing. To combat the biome's rapid degradation, these countries could strengthen their environmental agendas, governance, and policy design—for greater environmental impact—by effectively integrating cooperation between international and local partners. Both countries should develop their local networks, create participatory collaborative spaces to enhance national capacities, and promote diverse and coordinated coalitions.

Este estudio explora el uso de redes neuronales de memoria larga a corto plazo para el reconocimiento del la lengua de señas colombiana. Abarca tanto señas estáticas (letras) como dinámicas (palabras). Los resultados muestran que el modelo alcanzó una precisión del 90 % en el reconocimiento de letras y del 82 % en palabras, y se logró identificar en promedio 27 señas independientes. Se analizaron, además, distintas estrategias de extracción de características espaciotemporales por medio de MediaPipe y se encontró que para detectar señas estáticas solo bastan los puntos de control de manos y para señas dinámicas se necesitan los puntos de control de manos y postura. Sin embargo, los resultados no superaron el 90 % de precisión alcanzado en estudios internacionales, lo que sugiere que la calidad y cantidad del conjunto de datos utilizado podría mejorarse. Como trabajo futuro, se plantea evaluar el desempeño del modelo en tiempo real, con el fin de facilitar la comunicación entre personas sordas y oyentes. También se recomienda explorar arquitecturas de aprendizaje profundo más avanzadas, como redes convolucionales de gráficos, redes neuronales transformer o combinaciones de red neuronal convolucional con memoria larga a corto plazo; estas han mostrado buenos resultados en el reconocimiento de signos dinámicos.

La evaluación formativa contribuye en la mejora del proceso educativo, ya que recopila, interpreta y usa información de forma sistemática, con el fin de verificar y proveer retroalimentación sobre el logro de los objetivos de aprendizaje. La retroalimentación, es un componente fundamental durante la evaluación formativa, ya que, a través de ella, es posible recibir información inmediata y personalizada, que permite conocer de manera oportuna los avances en términos del conocimiento adquirido, contribuyendo a la mejora en los resultados de aprendizaje y por consiguiente aportando al logro de las competencias. En la actualidad, existen soluciones que contribuyen al proceso evaluativo, pero enfocadas en campos de estudio específico, las cuales no incluyen elementos del contexto con uso de información sensórica de los dispositivos móviles para enriquecer y personalizar la retroalimentación. Este artículo presenta un Modelo Conceptual de Evaluación Formativa en Sistemas de Aprendizaje Móvil, el cual, define de manera explícita los componentes conceptuales, que deben ser considerados para soportar procesos de evaluación formativa. Durante la investigación se desarrolló un prototipo de Servicio Web, basado en el modelo propuesto e integrado en la plataforma LMS Google Classroom que permitió brindar retroalimentación en tiempo real basada en contexto sensible. La aplicación práctica del modelo evidenció un incremento significativo en el nivel de aprendizaje de los estudiantes, sin embargo, es importante ampliar la población de estudio para generalizar los resultados.

Considering the relevance of the coffee industry for Colombia and specifically for the department of Cauca and because of the sustainability challenges that the industry presents in the department, the article presents the design process, using a UCD methodology, a digital platform that aims to contribute to improving the quality of coffee in the department of Cauca, Colombia. An agile and iterative software design methodology is used to provide effective solutions to the needs of the different identified users. As a result, a software design is obtained that contains user history, use cases, sequence of use, data structure and evaluation. Using modern and flexible technologies such as TypeScript, React and React DO, a prototype was developed to be evaluated with coffee tasters in Cauca who expressed an understanding and interest in the exposed platform. In conclusion, the agile UCD Software design methodology proved to be effective. Furthermore, it is concluded that the implementation of this platform would represent an important contribution to the Cauca coffee industry, promoting best practices to increase the profits of coffee growers and strengthen regional competitiveness. It is suggested that future research explore the adoption of the platform in other coffee-growing regions of Colombia and internationally

Jàmbá: Journal of Disaster Risk Studies aims to engage, inform, and catalyse scholarly discourse within research and real-world practices that work towards the minimisation of vulnerabilities and disaster risks throughout developing communities with special emphasis on Africa.

Home gardens have been an integral part of the recent food‐based interventions aimed at stimulating changes in dietary patterns and improving nutrition. However, evidence of their effects on food security, dietary quality, child anthropometry and incomes is limited, particularly among vulnerable populations groups. Using panel data from a sample of approximately 1900 households from vulnerable population groups in Odisha, India, difference‐in‐differences and other econometric techniques, we analyse the effects of home gardens on food security, dietary quality, child anthropometry and income. On average, home gardens contribute to better household food security, higher dietary quality of men and women but do not contribute to higher children's dietary quality and anthropometry. Also, home gardens increase monthly per adult equivalent incomes by 37% and reduce the prevalence of poverty by 11.7 percentage points. Quantile regression results suggest that home gardens enhance food security and incomes in all quantiles, but richer farmers benefit more than poorer farmers. Overall, home gardens can enhance household food security, dietary quality of men and women, and income gains among vulnerable farming population groups, but they may not suffice to improve child dietary quality and anthropometry.

This article presents the SmartFC mobile application, an application aimed at students which allows the implementation of the flipped classroom model compatible with the use of ICT (Information and Communications Technologies) even in conditions of low or no connectivity. This proposal not only allows the visualization of open educational resources but also capturing the record of the effort made by students during the development of an academic activity, which represents a significant advantage over the majority of applications used to deliver thematic content. Besides, this application allows two-way communication between students and teachers.