Brazil's university ranking a prediction study with machine learning

1. 1
Brazil's University Ranking:A knowledge prediction
study with MachineLearning
Sérgio Nicolau da Silva
Departamento Sistemas
Instituto Federal de Educação, Ciência e Tecnologia de Santa Catarina -
IFSC
University Address:Rua 15 de Julho, - Coqueiros. Florianópolis, SC.
Brazil. CEP 88070-010
E-mail: sergio.silva@ifsc.edu.br
CleversonTabajara Vianna
Departamento de Saúde e Serviço
Instituto Federal de Educação, Ciência e Tecnologia de Santa Catarina -
IFSC
University Address:Av.Mauro Ramos, 950 - Centro. Florianópolis,
SC. Brazil. CEP 88020-300.
E-mail: tabajara@ifsc.edu.br
Fernando Alvaro Ostuni Gauthier
Departamento de Engenharia e Gestão do Conhecimento
Universidade Federal de Santa Catarina - UFSC
University Address: Campus Reitor João David Ferreira Lima, s/n - Trindade,
Florianópolis - SC. Brazil, CEP 88040-900
E-mail: gauthier@egc.ufsc.br
Antônio Pereira Cândido
Departamento de Saúde e Serviço
Instituto Federal de Educação, Ciência e Tecnologia de Santa Catarina -
IFSC

2. 2
University Address:Av. Mauro Ramos, 950 - Centro. Florianópolis,
SC. Brazil. CEP 88020-300
E-mail: apec@ifsc.edu.br
Structured Abstract
How to distinguish the best orworst institutions ofhigher education? This is a question that
permeates the minds and hearts of parents, students and teachers,because education is an
investment in the personaland nation's future. As a source of information for the response
to ask, the University Ranking of Folha - RUF appears.Known for its traditional evaluation,
the Folha's Ranking is considered an independent evaluation tooland provides a ranking of
the best Brazilian universities. Knowledge puts in evidence that 74% of the data are related
to research areas and postgraduate programs. Who regulates and supervises the
postgraduate programs in Brazil is CAPES (Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior), authorizing or not the program, assigning score from 1 to 7, with 7
being the best score. Your data to this evaluation is an open data published. In this article,
are user machine learning techniques based in Naïve Bayes algorithms. CAPES data and
the Folha's Ranking of previous years are used as the training mass for the machine Naïve

3. 3
Bayes algorithm. After the training, CAPES data from 2015 was applied to predict the 2016
Ranking with a hit rate of 61.5%. A percentage above 60 of the Folha's Ranking shows that
it is possible, with a more detailed study and analysis of the techniques, to predict with a
certain confidence. It should be noted that according to the Folha's Ranking roles, the
Scientific Research (mostly postgraduate) corresponds to a weight of 42% in the ranking.
Purpose – With focus on Knowledge Engineering and the use of Machine Learning
techniques to predict the Ranking Universitário da Folha (RUF), we used previous year's
history to train the Naïve Bayes algorithm
Design/methodology/approach – Applied research,descriptive, exploratory objective and
qualitative and quantitative abortion. Data were extracted from the RUF, CAPES,
homogenized, and engineering methods were applied using several tools (WEKA, KDD,
Data Mining, Postgres, ETL Pentaho)
Originality/value – The use of knowledge engineering and machine learning techniques
togetherin purpose to gauge / predict the quality of Brazilian Universities, an index that is
inserted in a complex and interdisciplinary context.
Practical implications – Proposes a statistical-based model to determine the quality of an
educational institution, considering only Open Data and built ontologies.
Keywords – Brazilian Universities Ranking, Naïve Bayes, Machine Learning, Knowledge
Engineering.
Please for the full paper submission category indicate the nature of the proposed paper:
Academic Research Paper
References
Berners-Lee, T. (1989) Information management: A proposal.

4. 4
Witten, I. H. and Frank, E. (2011) Data Mining: Practical machine learning tools and techniques, ed
Morgan Kaufmann
Sobrinho, J. D. and Vessuro, H. (2008) Quality, evoluation: from sinaes to indexes, In Avaliação da
Educação Superior Magazine.
Fulmari, A. and Chandak, M. B. (2014) An approch for word sense disambiguation using modified
naïve bayes classifier, In International Journal of Innovative Research in Computer and
Communication Engineering.
Vianna, C. T. (2014) Avaliação institucional e o desafio da cultura da autoavaliação e cpa, In
conference's publications of regional seminar about institutionalself-evaluation and evaluations
committees
Authors’ short bio
Sérgio Nicolau da Silva is graduate at Computer Science from the Universidade do
Vale do ITAJAI (2005), specialist in Engineering of Software Projects Specialist at
Universidade do Sul de Santa Catarina (2012) and currently pursuing a master's degree in
Knowledge Engineering at Universidade Federal de Santa Catarina. Curriculum in
http://lattes.cnpq.br/1089544465003007
Cleverson Tabajara Vianna is graduate at Business Administration from Fundação
de Estudos Sociais do Paraná (1984). Has experience in Administration, focusing on
System Administration and System Auditing. Is also a Instituto Federal of Santa Catarina
States' teacher of several Management contents and Entrepreneurship. Currently pursuing
a doctor's degree in Knowledge Engineering at Universidade Federal de Santa Catarina
(UFSC). Curriculum in http://lattes.cnpq.br/0554360822978236
Fernando Alvaro Ostuni Gauthier is graduate at Engenharia Química from
Universidade Federal do Rio Grande do Sul (1981), master's at Production Engineering
from Universidade Federal de Santa Catarina (1988) and ph.d. at Production Engineering
from Universidade Federal de Santa Catarina (1993). Has experience in Computer Science,
focusing on Computer Science, acting on the following subjects: entrepreneurship,
semantic web, environment web, educations and multimedia. Curriculum in
http://lattes.cnpq.br/1282804646377460
Antônio Pereira Cândido is graduate at in Computer Science from Universidade
Federal de Santa Catarina (1984), master's at Production Engineering from Universidade
Federal de Santa Catarina (1999), doctor at Production Engineering from Universidade
Federal de Santa Catarina (2005) and ph.d at Inovation Tecnology from Universidad
Politécnica de Madrid e ph.d in Linked Open Date from Universidade Federal de Santa
Catarina. Curriculum in http://lattes.cnpq.br/6350499806542489