Fingerprints in a Computer Science Course Profile: Early results from the IFNMG DataWarehouse
Resumo
This manuscript reports the implementation of an Educational Data Warehouse (EDW) at the Federal Institute of North of Minas Gerais by using data from the academic system called Cajuí. The logical model of the system is the Fact Constellation with data persisted into the relational DBMS PostgreSQL. After the loading and setting of the EDW, we ran a set of analytic queries regarding courses from the Computer Science bachelor course at the IFNMG campus of Montes Claros for the 2013/2020 timespan. The data analysis indicates: (i) there were no significant differences in the academic performances of students enrolled by either standard entrance or Brazilian SISU exams, (ii) the number of unofficial dropouts reached up to 19% of students, (iii) the 19.51% of students that took a leave of absence and 15.38% of dropout bachelor candidates had completed at least 1/3 of courses from the entire graduation process, (iv) the first-year courses had more failing students than final-year courses, and the average grade of final-year courses was higher than those of other years, and (v) nearly 60% of students had at least one failure in either Algorithms and Data Structures or Calculus courses.
Palavras-chave:
Information Systems for Education, Educational Data Warehouse, Data Analytics, Dropout, Computer Science
Referências
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J. Han, M. Kamber, and J. Pei. 2012. Data mining concepts and techniques. Elsevier.
W. Inmon, J. Welch, and K. Glassey. 2005. Building the Data Warehouse. Sons Inc, New York.
M. Júnior, M. Mendonça, and F. Rodrigues. 2009. Data warehousing in an industrial software development environment. In Software Engineering Workshop. IEEE, 131–135.
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O. Moscoso-Zea, J. Paredes-Gualtor, and S. Luján-Mora. 2018. A holistic view of data warehousing in education. IEEE Access 6, 64659–64673.
F. Nunes, M. Júnior, J. Junior, L. Costa, and E. Recchi. 2019. Galactus – Um ambiente inteligente para apoio à tomada de decisão no âmbito do Ministério Público de S.E.. In Simpósio Brasileiro de Sistemas de Informação. SBC, 153–156.
J. Oliveira Jr., L. Bastos, and C. Kaestner. 2015. Uma Abordagem de Data Warehouse Educacional para Apoio à Tomada de Decisão. In Anais do Congresso Brasileiro de Informática na Educação. SBC, 1064–1073.
F. Pfeffer. 2008. Persistent inequality in educational attainment and its institutional context. European Sociological Review 24, 5, 543–565.
B. Rance, V. Canuel, H. Countouris, P. Laurent-Puig, and A. Burgun. 2016. Integrating heterogeneous biomedical data for cancer research: the CARPEM infrastructure. Applied Clinical Information 7, 2, 260.
F. Ravat and Y. Zhao. 2019. Data lakes: Trends and perspectives. In International Conference on Database and Expert Systems Applications Conference. Springer, 304–313.
C. Romero, S. Ventura, M. Pechenizkiy, and R. Baker. 2010. Handbook of educational data mining. CRC press.
G. Santos, A. Bordignon, D. Haddad, D. Brandão, L. Tarrataca, and K. Belloze. 2019. DataWarehouse Educacional: Uma visão sobre a Evasão no Ensino Superior. In Simpósio Brasileiro de Banco de Dados. SBC, 235–240.
G. A. S. Santos, K. T. Belloze, L Tarrataca, DB Haddad, AL Bordignon, and DN Brandao. 2020. EvolveDTree: Analyzing Student Dropout in Universities. In International Conference on Systems, Signals and Image Processing. IEEE, 173–178.
D. Saraiva, S. Pereira, R. Braga, and C. Oliveira. 2021. Análise de Agrupamentos para Caracterização de Indicadores de Evasão. In WEI. SBC, 238–247.
B. Shin. 2002. A case of data warehousing project management. Information & Management 39, 7, 581–592.
V. Theodorou, A. Abelló, M. Thiele, and W. Lehner. 2017. Frequent patterns in ETL workflows: An empirical approach. Data & Knowledge Eng. 112, 1 – 16.
William Villegas-Ch, Xavier Palacios-Pacheco, and Sergio Luján-Mora. 2020. A business intelligence framework for analyzing educational data. Sustainability
E. Fernandes, M. Holanda, M. Victorino, V. Borges, R. Carvalho, and G. Erven. 2019. Educational Data Mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research 94, 1, 335 – 343.
H. Garcia-Molina. 2008. Database systems: The complete book. Pearson.
R. Gouveia and C. Freitas. 2018. Implementação de um Data Warehouse para Análise de Dados Abertos Governamentais da Educação a Distância. Tear: Revista de Educação Ciência e Tecnologia 7, 2, 1 – 15.
B. Griesemer. 2009. Oracle Warehouse Builder 11g. Packt Publishing Ltd.
J. Guan, W. Nunez, and W. 2002. Institutional strategy and information support: The role of data warehousing in higher education. Campus-wide Information Systems.
J. Han, M. Kamber, and J. Pei. 2012. Data mining concepts and techniques. Elsevier.
W. Inmon, J. Welch, and K. Glassey. 2005. Building the Data Warehouse. Sons Inc, New York.
M. Júnior, M. Mendonça, and F. Rodrigues. 2009. Data warehousing in an industrial software development environment. In Software Engineering Workshop. IEEE, 131–135.
R. Kimball and M. Ross. 2002. The Data Warehouse Toolkit: The complete guide to dimensional modeling. Wiley, New York.
L. Manhães, S. Cruz, and G. Zimbrão. 2014. WAVE: an architecture for predicting dropout in undergraduate courses using EDM. In Symposium On Applied Computing. ACM, 243–247.
O. Moscoso-Zea and S. Luján-Mora. 2016. Datawarehouse design for educational data mining. In International Conference on Information Technology Based Higher Education and Training. IEEE, 1–6.
O. Moscoso-Zea, J. Paredes-Gualtor, and S. Luján-Mora. 2018. A holistic view of data warehousing in education. IEEE Access 6, 64659–64673.
F. Nunes, M. Júnior, J. Junior, L. Costa, and E. Recchi. 2019. Galactus – Um ambiente inteligente para apoio à tomada de decisão no âmbito do Ministério Público de S.E.. In Simpósio Brasileiro de Sistemas de Informação. SBC, 153–156.
J. Oliveira Jr., L. Bastos, and C. Kaestner. 2015. Uma Abordagem de Data Warehouse Educacional para Apoio à Tomada de Decisão. In Anais do Congresso Brasileiro de Informática na Educação. SBC, 1064–1073.
F. Pfeffer. 2008. Persistent inequality in educational attainment and its institutional context. European Sociological Review 24, 5, 543–565.
B. Rance, V. Canuel, H. Countouris, P. Laurent-Puig, and A. Burgun. 2016. Integrating heterogeneous biomedical data for cancer research: the CARPEM infrastructure. Applied Clinical Information 7, 2, 260.
F. Ravat and Y. Zhao. 2019. Data lakes: Trends and perspectives. In International Conference on Database and Expert Systems Applications Conference. Springer, 304–313.
C. Romero, S. Ventura, M. Pechenizkiy, and R. Baker. 2010. Handbook of educational data mining. CRC press.
G. Santos, A. Bordignon, D. Haddad, D. Brandão, L. Tarrataca, and K. Belloze. 2019. DataWarehouse Educacional: Uma visão sobre a Evasão no Ensino Superior. In Simpósio Brasileiro de Banco de Dados. SBC, 235–240.
G. A. S. Santos, K. T. Belloze, L Tarrataca, DB Haddad, AL Bordignon, and DN Brandao. 2020. EvolveDTree: Analyzing Student Dropout in Universities. In International Conference on Systems, Signals and Image Processing. IEEE, 173–178.
D. Saraiva, S. Pereira, R. Braga, and C. Oliveira. 2021. Análise de Agrupamentos para Caracterização de Indicadores de Evasão. In WEI. SBC, 238–247.
B. Shin. 2002. A case of data warehousing project management. Information & Management 39, 7, 581–592.
V. Theodorou, A. Abelló, M. Thiele, and W. Lehner. 2017. Frequent patterns in ETL workflows: An empirical approach. Data & Knowledge Eng. 112, 1 – 16.
William Villegas-Ch, Xavier Palacios-Pacheco, and Sergio Luján-Mora. 2020. A business intelligence framework for analyzing educational data. Sustainability
Publicado
24/04/2022
Como Citar
ELEUTERIO, Igor; BEDO, Marcos; OLIVEIRA, Daniel de; OLMES, Luiz; SANTOS, Lucio.
Fingerprints in a Computer Science Course Profile: Early results from the IFNMG DataWarehouse. In: SIMPÓSIO BRASILEIRO DE EDUCAÇÃO EM COMPUTAÇÃO (EDUCOMP), 2. , 2022, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 57-66.
DOI: https://doi.org/10.5753/educomp.2022.19199.
