Cluster of the Latin American Universities Top100 According to Webometrics 2017

Vásquez, Carmen; Torres-Samuel, Maritza; Viloria, Amelec; Lis-Gutiérrez, Jenny-Paola; Borrero, Tito Crissien; Varela, Noel; Cabrera, Danelys

doi:10.1007/978-3-319-93803-5_26

Cited by 14 publications

(8 citation statements)

References 3 publications

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“…After analyzing the recommendation systems based on proposed collaborative filtering, it has been proved that their use can be useful for making personalized recommendations to students about educational itineraries [25] when choosing optional subjects and foreseeing which common subjects will present greater learning difficulties or specific needs of reinforcement in the student.…”

Section: Discussionmentioning

confidence: 99%

Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles

Silva

Varela

Lezama

et al. 2019

Advances in Neural Networks – ISNN 2019

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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.

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Section: Discussionmentioning

confidence: 99%

Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles

Silva

Varela

Lezama

et al. 2019

Advances in Neural Networks – ISNN 2019

Self Cite

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“…Particularly, the K-means clustering algorithm and the A priori algorithm were used for the association rules. To carry out the data analysis process, the KDD method was used, consisting of the following steps [14,20,21,25]:…”

Section: Methodsmentioning

confidence: 99%

“…In the Preprocessing/Cleaning stage (Data Cleaning), the data quality is analyzed, and basic operations are applied such as the removal of noisy data. Strategies are selected for the management of unknown data (missing and empty), null data, duplicated data, and statistical techniques for its replacement [21].…”

Section: Data Mining and Classifiersmentioning

confidence: 99%

“…The objective of this stage is the search, extraction, and discovery of unsuspected and relevant patterns. Data mining consists of different tasks, each of which can be considered as a type of problem to be solved by a data mining algorithm as Adamo and Hernández et al assert, where the main tasks are Classification, Association, and Clustering [21,22].…”

Section: Data Mining and Classifiersmentioning

confidence: 99%

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RETRACTED CHAPTER: Data Mining to Identify Risk Factors Associated with University Students Dropout

Silva

Sarmiento

Santodomingo

et al. 2019

Data Mining and Big Data

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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.

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“…This research firstly states that Brazil occupies the first positions in the four rankings; and secondly, that there is a greater number of Latin American universities in QS (40%) and Webometrics (31%), while in the other two rankings it does not exceed 8%. [9] carried out a cluster analysis of the Top100 Latin American universities positioned in the Webometrics database in January 2017. The research included information about postgraduate programs and social networks on the web sites of these institutions and teachers.…”

Section: Introductionmentioning

confidence: 99%

Determinants of ResearchGate (RG) Score for the Top100 of Latin American Universities at Webometrics

Henao-Rodríguez

Lis-Gutiérrez

Gaitán-Angulo

et al. 2019

Communications in Computer and Information Science

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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 significant.

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Cluster of the Latin American Universities Top100 According to Webometrics 2017

Cited by 14 publications

References 3 publications

Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles

Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles

RETRACTED CHAPTER: Data Mining to Identify Risk Factors Associated with University Students Dropout

Determinants of ResearchGate (RG) Score for the Top100 of Latin American Universities at Webometrics

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