A Collaborative Networked Organization (CNO) is a set of entities that operate in heterogeneous contexts and aim to collaborate to take advantage of a business opportunity or solve a problem. Big data allows CNOs to be more competitive by improving their strategy, management and business processes. To support the development of big data ecosystems in CNOs, several frameworks have been reported in the literature. However, these frameworks limit their application to a specific CNO manifestation and cannot conduct intelligent processing of big data to support decision making at the CNO. This paper makes two main contributions: (1) the proposal of a metaframework to analyze existing and future frameworks for the development of big data ecosystems in CNOs and (2) to show the Collaborative Networked Organizations–big data (CNO-BD) framework, which includes guidelines, tools, techniques, conceptual solutions and good practices for the building of a big data ecosystem in different kinds of Collaborative Networked Organizations, overcoming the weaknesses of previous issues. The CNO-BD framework consists of seven dimensions: levels, approaches, data fusion, interoperability, data sources, big data assurance and programmable modules. The framework was validated through expert assessment and a case study.

This study explores the relationship among student college attainment and the variables mathematical thinking, analytical thinking, language structure, reading comprehension, mathematics result, physics result, written language (Spanish), and English as a foreign language. Method: The participants were 130 students enrolled in an undergraduate 4-year program at a University who started in 2014. Mann–Whitney, $t$ , and chi-square tests were used, to compare the attributes of the students in two groups: 1) completed students and 2) students who dropped out. Additionally, a logistic regression model was carried out to predict the probability of student academic success based on the variables measured by the admission exam EXANI-II of CENEVAL. Results and Discussion: The findings indicate that in all the variables analyzed there were differences in favor of the group formed by students who did graduate compared to those who did not. On the other hand, only the mathematics report ( $p = 0.013$ ) and the written language report ( $p = 0.029$ ) were significant to predict the probability of graduation of the students. The authors identified that mathematics and written language are variables that affect students’ academic attainment. Conclusion: It is necessary to recognize that there are inequalities in the conditions of students entering university. For this reason, consideration should be given to the creation of academic support and inclusion programs that stimulate the integral development of the student from before entering the first semester. It is also important to periodically quantify and analyze student progress to implement the necessary strategies and continually follow up their academic attainment.

The representing of a student’s knowledge in an academic discipline plays an important role in boosting the student’s skills. To support stakeholders in the educational domain, it is necessary to provide them with robust assessment strategies that facilitate the teaching-learning process. Student´s mastery is determined by the degree of knowledge, which demonstrates objectively, on the topics included in the different areas that make up an academic discipline. Although there is a wide variety of techniques to represent knowledge, particularly Knowledge Graph technique is becoming relevant due to the structured approach and benefits it offers. This paper proposes a method that classifies and weights the nodes (topics) of a Knowledge Graph of a disciplinary area, which is analyzed through a case study. The method has two approaches: avoid exhaustive evaluation of the nodes and weight the nodes with adequate precision. Method´s application is illustrated by a case study. As results, a Knowledge Graph is obtained with its classified and weighted nodes through the application of the proposed method, in which 100% of the topics have been impacted through the objective evaluation of 20.8% representing 10 nodes. It is concluded that the proposed method has potential to be used in the representation and management of knowledge, being necessary to improve phases’ iteration to condition number of objective nodes.

El objetivo de esta investigación fue identificar los factores de mayor incidencia que potencializaron la educación virtual en la Universidad Autónoma de Tamaulipas, durante la pandemia por Covid-19. Se trabajó en un enfoque cuantitativo con diseño transeccional descriptivo con alcance correlacional por el enfoque analítico y tipos de datos usados. Se utilizó la escala de Satisfacción, Interacción, Autosuficiencia y Aprendizaje Autorregulado en cursos virtuales de Kuo et al. (2014), la cual analiza: Interacción estudiante-estudiante, Interacción estudiante-instructor, Interacción estudiante-contenido, Autoeficacia en el uso de internet, Aprendizaje autorregulado y Satisfacción sobre el curso virtual. Los resultados mostraron que la mayor parte de los estudiantes (muestra estratificada n = 3604, Edad M = 20.49, DE ± 0.552, Min = 18, Max = 28) presentaron niveles altos de satisfacción en los cursos en línea y más del 80% de los participantes manifestaron su disposición de continuar estudiando bajo la modalidad virtual. Los factores de autoeficacia en el uso de internet y la interacción del estudiante-instructor mostraron correlaciones positivas y significativas (p < 0.05) respecto a la satisfacción estudiantil bajo la modalidad virtual, además, los participantes de mayor edad presentaron una mayor satisfacción que los sujetos menor edad sin importar su estado de empleabilidad en paralelo a su escolaridad. Por otra parte, no se encontraron diferencias significativas (p > 0.05) entre el género de los participantes, el estado de su empleabilidad y su rango etario respecto a los puntajes obtenidos.

The communication of e-Health has been transformed with the advancement of information technologies, therefore it is feasible to carry out studies in the context of health professionals’ interactions. Objective: This study aimed to design and validate a preliminary questionnaire to investigate the context of the communications of health professionals through information technologies considering three significant dimensions. Method: The stages provided by Hernández Sampieri guided the building, validation through Cronbach’s alpha and factorial analysis. The questionnaire was applied to 43 participants who simulated health professionals. Results: We obtained an instrument that includes a demographic data section and 20 items distributed into three factors. Internal consistency reliability with Cronbach’s alpha values generally of 0.848 and higher than 0.811 was obtained in each dimension. Kaiser-Meyer-Olkin’s measure of sampling adequacy was regular, with 0.781, and Bartlett’s test of sphericity was significant ( p < 0.001). Conclusion: It is necessary to apply in real-world environments to reaffirm the results obtained.

El artículo aborda el análisis de opiniones de estudiantes sobre las clases en línea utilizando procesamiento de lenguaje natural (PLN). Se destacan respuestas diversas obtenidas mediante un enfoque mixto. El estudio se centra en comprender las percepciones estudiantiles hacia la enseñanza remota. El PLN se emplea para procesar y analizar los datos recopilados, permitiendo identificar patrones y tendencias en las respuestas. Se exploran diferentes aspectos de las experiencias estudiantiles, incluyendo desafíos, beneficios y sugerencias de mejora. La aplicación de un enfoque mixto en el análisis garantiza una comprensión integral y contextualizada de las opiniones estudiantiles, brindando información valiosa para la optimización de las clases en línea.