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The internationalization and positioning of universities offers relative comparisons, studied according to different teaching and research criteria. Among the recognized rankings are the Academic Ranking of World Universities (ARWU) or Ranking of Shanghai, QS World University Ranking, SCimago Institutions Rankings SIR and the Web Ranking of Universities-Webometrics. Comparatively, characteristics of each one are presented in terms of scope, volume of universities positioned and evaluation criteria. The indicators associated with research and institutional capacity stand out as common criteria in the revised evaluation methodologies. Regarding Latin America, Brazil leads with its presence in the four rankings. There is a greater number of Latin American universities in QS (40%) and Webometrics (31%), in the other two rankings it does not exceed 8%.

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The automatic clustering differential evolution (ACDE) is one of the clustering methods that are able to determine the cluster number automatically. However, ACDE still makes use of the manual strategy to determine k activation threshold thereby affecting its performance. In this study, the ACDE problem will be ameliorated using the u-control chart (UCC) then the cluster number generated from ACDE will be fed to k-means. The performance of the proposed method was tested using six public datasets from the UCI repository about academic efficiency (AE) and evaluated with Davies Bouldin Index (DBI) and Cosine Similarity (CS) measure. The results show that the proposed method yields excellent performance compared to prior researches.

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.

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.