Web visibility profiles of top100 Latin American universities

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

Efficiency analysis of the visibility of Latin American universities and their impact on the ranking web

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

Determinants of researchgate (rg) score for the top 100 of Latin American universities at webometrics

This paper has the purpose of establishing the variables that explain the behavior of ResearchGate for the Top100 Latin American universities positioned in Webometrics database for January 2017. For this purpose, a search was carried out to get information about postgraduate courses and professors at the institutional websites and social networks, obtaining documents registered in Google Scholar. For the data analysis, the econometric technique of ordinary least squares was applied, a cross-sectional study for the year 2017 was conducted, and the individuals studied were the first 100 Latin American universities, obtaining a coefficient of determination of 73.82%. The results show that the most significant variables are the number of programs, the number of teacher’s profiles registered in Google Scholar, the number of subscribers to the institutional YouTube channel, and the GDP per capita of the university origin country. Variables such as (i) number of undergraduate programs, (ii) number of scientific journals; (iii) number of documents found under the university domain; (iv) H-index of the 1st profile of researcher at the university; (vi) number of members of the institution; (v) SIR Scimago ranking of Higher Education Institutions; (vi) number of tweets published in the institutional account; (vii) number of followers in the Twitter institutional account; (vii) number of “likes” given to the institutional count, were not significantCorporación Universitaria Minuto de Dios, Fundación Universitaria Konrad Lorenz, Universidad Nacional Experimental Politécnica, Universidad Centroccidental “Lisandro Alvarado, Universidad de la Costa

Comparison of websites based on webometrics index, Alexa\u27s traffic rating and estimated value of Sinium Case study: Islamic Azad University (IAU), five units

The purpose here is to compare the status of website of five IAU unites based on webometrics, Alexa\u27s traffic rating and estimated value of Sinium. This applied research is run through a descriptive method, where Webometrics index, Alexa\u27s traffic rating and estimated value of Sinium are applied. The statistical population consists of five: IAU Kerman, Shiraz, Bushehr, Bandar Abbas and Ahwaz units’ websites. The results reveal that Ahvaz unit with the average of 10356 pages is ranked the highest and the first and Bushehr unit with the lowest 4441 pages is ranked the last. As to the enriched files, Kerman and Ahwaz units are ranked first and last, with 2248 and 459 files, respectively. As to visibility (internal linkage), Kerman and Ahwaz units rank first and last with 9th and 5th rankings, respectively. As to Sinium, Shiraz and Bandar-Abbas units have the highest and the lowest estimated values of $18144 and$3780 respectively. In general, based on the webometrics database (size, visibility, formatted files and count of articles in Google Scalar) and the traffic rating of Alexa\u27s website and the estimated value of the web site, Shiraz unit has the highest performance among IAU units. It is assumed that national and global universities in terms of having characteristics and elements like: the active presence of professors and researchers, graduate programs promotion, credibility, up-to-date, user-friendly, free access to articles, popularity, etc. are of higher status. As to their websites in terms of visibility, size, rank, monitoring, traffic rating, and increased value, they are ranked higher

Comparison of websites based on webometrics index, Alexa\u27s traffic rating and estimated value of Sinium Case study: Islamic Azad University (IAU), five units

The purpose here is to compare the status of website of five IAU unites based on webometrics, Alexa\u27s traffic rating and estimated value of Sinium. This applied research is run through a descriptive method, where Webometrics index, Alexa\u27s traffic rating and estimated value of Sinium are applied. The statistical population consists of five: IAU Kerman, Shiraz, Bushehr, Bandar Abbas and Ahvaz units’ websites. The results reveal that Ahvaz unit with the average of 10356 pages is ranked the highest and the first and Bushehr unit with the lowest 4441 pages is ranked the last. As to the enriched files, Kerman and Ahvaz units are ranked first and last, with 2248 and 459 files, respectively. As to visibility (internal linkage), Kerman and Ahvaz units rank first and last with 9th and 5th rankings, respectively. As to Sinium, Shiraz and Bandar-Abbas units have the highest and the lowest estimated values of $18144 and$3780 respectively. In general, based on the webometrics database (size, visibility, formatted files and count of articles in Google Scalar) and the traffic rating of Alexa\u27s website and the estimated value of the web site, Shiraz unit has the highest performance among IAU units. It is assumed that national and global universities in terms of having characteristics and elements like: the active presence of professors and researchers, graduate programs promotion, credibility, up-to-date, user-friendly, free access to articles, popularity, etc. are of higher status. As to their websites in terms of visibility, size, rank, monitoring, traffic rating, and increased value, they are ranked higher

Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis

In the academic world, a large amount of data is handled each day, ranging from student’s assessments to their socio-economic data. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. However, Data Warehouses are not able to perform predictive analysis by themselves, so machine intelligence techniques can be used for sorting, grouping, and predicting based on historical information to improve the analysis quality. This work describes a Data Warehouse architecture to carry out an academic performance analysis of students

Data mining to identify risk factors associated with university students dropout

. This paper presents the identification of university students dropout patterns by means of data mining techniques. The database consists of a series of questionnaires and interviews to students from several universities in Colombia. The information was processed by the Weka software following the Knowledge Extraction Process methodology with the purpose of facilitating the interpretation of results and finding useful knowledge about the students. The partial results of data mining processing on the information about the generations of students of Industrial Engineering from 2016 to 2018 are analyzed and discussed, finding relationships between family, economic, and academic issues that indicate a probable desertion risk in students with common behaviors. These relationships provide enough and appropriate information for the decision-making process in the treatment of university dropout.Universidad Peruana de Ciencias Aplicadas, Universidad de la Costa, Universidad Libre Seccional Barranquilla, Corporación Universitaria Latinoamericana

Integration of data technology for analyzing university dropout

Dropout, defined as the abandonment of a career before obtaining the corresponding degree, considering a significant time period to rule out the possibility of return. Higher education students´ dropout generates several issues that affect students and universities. The results obtained from the data provided by the Engineering departments of the University of Mumbai, in India, determine that the variables that best explain a student's dropout are the socioeconomic factors and the income score provided by the University Admission Test (UAT). According to the decision tree technique, it is concluded that the retention is 78.3%. The quality of the classifiers allows to ensure that their predictions are correct, with statistical levels of ROC curve are 76%, 75%, and 83% successful for Bayesian network classifiers, decision tree, and neural network respectively

Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles

The choice of academic itineraries and/or optional subjects to attend is not usually an easy decision since, in most cases, students lack the information, maturity, and knowledge required to make right decisions. This paper evaluates the support of Collaborative Systems for helping and guiding students in this decision-making process, considering the behavior and impact of these systems on the use of data different from the formal information the students usually use. For this purpose, the research applied the clustering based Multi-dimension Tensor Factorization approach to build a recommendation system and confirm that the increment in tensors improves the recommendation accuracy. As a result, this approach permits the user to take advantage of the contextual information to reduce the sparsity issue and increase the recommendation accuracy

Website information architecture of latin american universities in the rankings

En las ediciones 2019 de los rankings universitarios SIR, QS, ARWU and Webometric, luego de evaluarlas y posicionarlas mediante criterios de calidad académica y de investigación establecidos en sus metodologías, coinciden 22 instituciones universitarias latinoamericanas ubicadas en Brasil, México, Chile, Argentina y Colombia. En este trabajo se caracteriza el menú principal de los portales web de estas universidades para describir y enlistar la información accesible y disponible a los usuarios Destaca que el 18% de las opciones del menú principal se refieren a biblioteca, vida académica, servicios de correo institucional, traductor de idioma, buscador e información a los visitantes.In the 2019 editions of SIR, QS, ARWU and Webometric university rankings, after the evaluating and positioning through academic and research quality criteria established in their methodologies, 22 Latin American university institutions located in Brazil, Mexico, Chile, Argentina, and Colombia coincide in their positions in all the rankings considered for the study. This paper intends to characterize the website main menu design of these universities to describe and list the information available to users. Results highlight that 18% of the options of the main menu refer to library, academic life, institutional mail services, language translator, search engine, and information to visitors