Imagine a platform in which the teacher can access to identify patterns in the learning styles of students attached to their course, and in turn this will allow you to know which pedagogical techniques to use in the teaching process-learning to increase the probability of success in your classroom?. What if this tool could be used by students to identify the teacher that best suits their learning style?. Yes, was the tool able to improve its prediction regarding academic performance as time passes? It is obvious that this would require specialized software in the handling of large data. This research-development aims to answer these questions, proposing a design methodology of a student pattern recognition tool to facilitate the teaching-learning process through Knowledge Data Discovery (Big Data). After an extensive document review and validation of experts in various areas of knowledge, the methodology obtained was structured in four phases: identification of patterns, analysis of the teaching-learning process, Knowledge Data Discovery and Development, implementation and validation of software.

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The study analyzes the factors that contribute to the technical efficiency of the visibility of the universities included in the Top100 of the Latin American Universities Ranking Web published by Webometrics database in January, 2017. Data Envelopment Analysis (DEA) was used to calculate the contributions of input variables to efficiency. As data sources for inputs, the study considers the academic data published on the web of each university, the content and profiles displayed from Google Scholar (GS), data by university published in ResearchGate as a scientific network, and finally, data from social networks as Twitter and Facebook accounts of the respective institutions. The postgraduate offer, visibility in GS, and the use of scientific and social networks contribute favorably to the web positioning of Latin American universities.

<p>A partir del cálculo de indicadores de género (distribución horizontal, distribución vertical, índice de feminidad, índice de masculinidad, índice de Duncan, índice de segregación e índice de contribución al sexismo) y el uso de representaciones artográficas temáticas, se presenta una aproximación cuantitativa sobre la brecha de género y la segregación departamental en el liderazgo de los grupos de investigación colombianos en las áreas de Economía y Administración, de acuerdo con la información disponible hasta junio de 2012. Se halló que el liderazgo femenino de grupos de investigación en las dos disciplinas es inferior al promedio nacional; las tasas de graduación mujeres de pregrado y posgrado entre 2000 y 2010 no se reflejan en la composición de los grupos de investigación; la mayor parte de grupos de investigación liderados por mujeres se encuentran categoría D; en la mayoría de los departamentos hay líderes de grupo de ambos sexos, pero superan en cantidad los hombres a las mujeres; al igual que en el contexto nacional los grupos de investigación, para ambos casos, se concentran en Bogotá, Antioquia y Valle del Cauca.</p>

Due to the processes of internationalization, competitiveness and other related elements, universities have implemented policies and management systems that allow them to monitor and measure their world ranking position of presence on the web. The purpose of this paper is to perform a conglomerate analysis of the Top100 of the Latin American universities positioned in Webometrics database in January 2017. For this reason, information on postgraduate programs and social networks included in the websites of these institutions, and on the professors and documents found in Google Scholar was obtained. The correlation between the variables is observed, and the clusters are identified. The variable with the highest correlation with the ranking is the number of postgraduate programs. Three conglomerates are formed, with 11 being the least number of postgraduate programs guaranteeing universities to be among the Top50 positions.

Due to the processes of internationalization, competitiveness and other related factors, universities have implemented policies and management systems that allow them to monitor and measure their world ranking position. The present work analyzes a group of manageable visibility factors corresponding to universities present in the Top100 of Latin American Webometrics database published in January 2017 for the identification of profiles. For this purpose, information was collected about: the academic offer and scientific journals published on each university website, figures on documents and profiles found in Google Scholar, activity on social networks, and the institutional score reported by ResearchGate as a scientific network. Clusters were formed by quartiles to characterize the visibility profiles of Latin American universities considering the variables studied. The high offer of postgraduate degrees and presence in scientific networks and Google Scholar characterize the best positioned universities.

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This paper aims to establish the participation behavior of residents in the city of Bogotá between 25 and 44 years of age, to finance or seek funding for entrepreneurial projects through crowdfunding? In order to meet the proposed objective, the focus of this research is quantitative, non-experimental and transactional (2017). Through data collection and data analysis, we seek patterns of behavior of the target population. Two machine learning techniques will be used for the analysis: supervised learning (using the learning algorithm of the decision tree) and unsupervised learning (clustering). Among the main findings are that (i) most of the people who would participate as an entrepreneur and donor and entrepreneur simultaneously belong to stratum 3; (ii) Crowdfunding projects based on donations do not have a high interest on the part of Bogotans, but those in which they aspire to recover the investment.