Educational Data Mining: A Profile Analysis of Brazilian Students

This paper presents an analysis of data referring to the profile of Brazilian students in the year 2016, according to the Higher Education Census. The information provided by this census is used to carry out various analyses related to the current situation of Brazilian education, as well as the profile of students and institutions. In this work, we analyze the modality of courses offered by educational institutions, investigating the profile and the quantitative of students who have scholarship, and how they enter the courses. We also investigate the distribution of students according to the informed skin color/race. The method used to conduct this research involved the application of the steps proposed by the CRISP-DM Reference Model and the use of the Apriori association algorithm to identify and analyze the data set. The results show that 30% of students choose not to report their skin color; Of those who reported, 37.4% are white and 6% are black. Approximately 82% of the students entered higher education through the traditional method (entrance examination), and 82.3% of the male and 77.2% of the female opted for face-to-face courses.

[1]  Anastasios A. Economides,et al.  Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence , 2014, J. Educ. Technol. Soc..

[2]  Osman Ibrahim,et al.  Educational Data Mining Rule based Recommender Systems , 2017, CSEDU.

[3]  Wahidah Husain,et al.  A Review on Predicting Student's Performance Using Data Mining Techniques , 2015 .

[4]  Juan Alfonso Lara,et al.  A system for knowledge discovery in e-learning environments within the European Higher Education Area - Application to student data from Open University of Madrid, UDIMA , 2014, Comput. Educ..

[5]  Corlane Barclay,et al.  Application of the CRISP-DM Model in Predicting High School Students’ Examination (CSEC/CXC) Performance , 2015 .

[6]  Tutut Herawan,et al.  A Systematic Review on Educational Data Mining , 2017, IEEE Access.

[7]  Ramesh Sharda,et al.  Progression analysis of signals: Extending CRISP-DM to stream analytics , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[8]  Mykola Pechenizkiy,et al.  Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data , 2018, IEEE Transactions on Cybernetics.

[9]  Anjan Mukherjee,et al.  A density-based clustering algorithm and experiments on student dataset with noises using Rough set theory , 2016, 2016 IEEE International Conference on Engineering and Technology (ICETECH).

[10]  Hosam Al-Samarraie,et al.  Educational data mining and learning analytics for 21st century higher education: A review and synthesis , 2019, Telematics Informatics.

[11]  Syed Abbas Ali,et al.  Analyzing undergraduate students' performance using educational data mining , 2017, Comput. Educ..

[12]  Ig Ibert Bittencourt,et al.  Visualizing Learning Analytics and Educational Data Mining Outputs , 2018, AIED.

[13]  R. R.Kabra,et al.  Performance Prediction of Engineering Students using Decision Trees , 2011 .

[14]  Ryan S. Baker,et al.  Educational Data Mining and Learning Analytics , 2014 .

[15]  B. M. Monjurul Alom,et al.  Educational Data Mining: A Case Study Perspectives from Primary to University Education in Australia , 2018 .

[16]  Edna Dias Canedo,et al.  Analysis of the teaching-learning methodology adopted in the introduction to computer science classes , 2017, 2017 IEEE Frontiers in Education Conference (FIE).

[17]  Steven M. Crooks,et al.  Examining Online Learning Patterns with Data Mining Techniques in Peer-Moderated and Teacher-Moderated Courses , 2009 .

[18]  Rommel N. Carvalho,et al.  Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil , 2019, Journal of Business Research.

[19]  Anderson Amendoeira Namen,et al.  MINERAÇÃO EM BASES DE DADOS DO INEP: UMA ANÁLISE EXPLORATÓRIA PARA NORTEAR MELHORIAS NO SISTEMA EDUCACIONAL BRASILEIRO , 2016 .

[20]  Ganesan Kavitha,et al.  Educational Data Mining and Learning Analytics - Educational Assistance for Teaching and Learning , 2017, ArXiv.

[21]  Erman Yukselturk,et al.  Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program , 2014 .

[22]  Alex Sandro Gomes,et al.  Mineração de dados educacionais na análise das interações dos alunos em um Ambiente Virtual de Aprendizagem , 2015 .

[23]  Alaa M. El-Halees,et al.  Data mining in higher education: university student dropout case study , 2015 .

[24]  Se Hwan Ahn,et al.  Comparison of flavivirus using datamining-Apriori, K-means, and decision tree algorithm , 2017, 2017 19th International Conference on Advanced Communication Technology (ICACT).

[25]  S. Jang,et al.  Comparison of H5N1, H5N8, and H3N2 Using Decision Tree and Apriori Algorithm , 2015 .

[26]  Rui Guo,et al.  Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory , 2015, Comput. Hum. Behav..

[27]  Mohammed Saeed,et al.  Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education , 2018, Applications of Big Data Analytics.