Intelligent Vocational Guidance Based on Machine Learning Applied to POSCOMP Microdata
DOI:
https://doi.org/10.5753/isys.2024.3612Keywords:
Vocational Guidance, Educational Data Analysis, Graduate Studies in Computer Science, Clustering Algorithms, Data MiningAbstract
The National Examination for Admission to Graduate Studies in Computing (POSCOMP) is applied by the Brazilian Society of Computing (SBC) to assess the knowledge of candidates to graduate programs in Computing in Brazil. As one of the main assessment instruments, the results of the POSCOMP exam, i.e., the database, have the potential to reveal relevant patterns about candidates. In this sense, this article aims to propose a clustering-based intelligent vocational guidance system from the exploratory analysis of the POSCOMP results between the years 2016 to 2019. This vocation system guides the candidate to follow a research area in post-graduation based on the performance obtained in the subjects of the test. The proposed approach aims to guide and support students in their academic decisions.
Downloads
References
Ahmed, M. R., Tahid, S. T. I., Mitu, N. A., Kundu, P., and Yeasmin, S. (2020). A comprehensive analysis on undergraduate student academic performance using feature selection techniques on classification algorithms. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pages 1–6.
Amazona, M. V. and Hernandez, A. A. (2019). Modelling student performance using data mining techniques: Inputs for academic program development. In Proceedings of the 2019 5th International Conference on Computing and Data Engineering, ICCDE’ 19, page 36–40, New York, NY, USA. Association for Computing Machinery.
Arcanjo Augusto, A. L., Viana, B. M. d. F., Oliveira, D. E. C., Bezerra, G. A., Neto, J. M. L., Maciel, K. W. d. S., Ferraz, P. C., Silva, R. S., and da Costa, U. S. (2021). Comparação dos conteúdos do poscomp com o currículo de referência dos cursos de computação da sbc. Revista ComInG - Communications and Innovations Gazette, 5(3):14–23.
Awat, K. A. S. and Ballera, M. A. (2018). Applying k-means clustering on questionnaires item bank to improve students’ academic performance. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pages 1–6.
Batista, E. J. S., dos Santos Batista, J., de Souza, R. S., and Primo, W. M. (2014). Desenvolvimento de um aplicativo para android com questões do POSCOMP como um objeto de aprendizagem para o auxílio no ingresso a programas de pós-graduação. In Workshops do Congresso Brasileiro de Informática na Educação. Sociedade Brasileira de Computação - SBC.
Brasil, C. I. d. (2023). População do Brasil passa de 203 milhões, mostra censo 2022.
Carrillo, J. M. and Parraga-Alava, J. (2018). How predicting the academic success of students of the ESPAM MFL?: A preliminary decision trees based study. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM), pages 1–6.
Carvalho, H. P. d., Soares, M. V., Carvalho, S. M. d. L., and Telles, T. C. K. (2021). O professor e o ensino remoto: tecnologias e metodologias ativas na sala de aula. Revista Educação Pública, 21(28).
Digitais, M. (2023). Women in information technology.
Droescher, F. D. and Silva, E. L. d. (2014). O pesquisador e a produção científica. Perspectivas em Ciência da Informação, 19(1):170–189.
Faceli, K., Lorena, A. C., Gama, J., de Almeida, T. A., and de Leon Ferreira de Carvalho, A. C. P. (2023). Inteligência artificial : uma abordagem de aprendizado de máquina. LTC, Rio de Janeiro, second edition.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3):37.
Fernando Raguro, M., Carpio Lagman, A., P. Abad, L., and S. Ong, P. L. (2022). Extraction of lms student engagement and behavioral patterns in online education using decision tree and k-means algorithm. In 2022 4th Asia Pacific Information Technology Conference, APIT 2022, page 138–143, New York, NY, USA. Association for Computing Machinery.
Fraley, C. and Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458):611–631.
Hui, H., Ming-jie, T., Qing-tao, Z., and Xiao-liang, Z. (2020). Application of student achievement analysis based on apriori algorithm. In 2020 2nd International Conference on Information Technology and Computer Application (ITCA), pages 19– 22.
Imdad, U., Ahmad, W., Asif, M., and Ishtiaq, A. (2017). Classification of students results using knn and ann. In 2017 13th International Conference on Emerging Technologies (ICET), pages 1–6.
Islam, R., Sazid, M. T., Mahmud, S. R., Ferdous, C. N., Reza, R., and Hossain, S. A. (2019). Parametric study of student learning in it using data mining to improve academic performance. In 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pages 286–290.
Junior, F. D. S. and Brancher, J. (2014). Uma pesquisa de opinião sobre a relevância dos conteúdos abrangidos pelo poscomp. In Anais do XXII Workshop sobre Educação em Computação, pages 90–99, Porto Alegre, RS, Brasil. SBC.
Lloyd, S. P. (1982). Least squares quantization in pcm. IEEE Trans. Inf. Theory, 28(2):129–136.
MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Cam, L. M. L. and Neyman, J., editors, Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1, pages 281–297. University of California Press.
Marbouti, F., Ulas, J., and Wang, C.-H. (2021). Academic and demographic cluster analysis of engineering student success. IEEE Transactions on Education, 64(3):261–266.
Marcelo Mendes, F., P. Mendonça, A., and B. Guedes, E. (2018). Poscomp coach: Plataforma web para apoio ao ingresso na pós-graduação em computação. Revista Novas Tecnologias na Educação, 16(1).
Moura, N., Gordiano, R. S., Silva, R. K. J., and Santos, S. S. (2012). Poscomp e a importância da pós-graduação para aprimoramento profissional. Centro Federal de Educação Tecnológica de Minas Gerais.
Nabil, A., Seyam, M., and Abou-Elfetouh, A. (2021). Prediction of students’ academic performance based on courses’ grades using deep neural networks. IEEE Access, 9:140731–140746.
PPGCC/UFAC (2023). Programa de pós-graduação em ciência da computação.
Ribeiro, M. A. and Uvaldo, M. d. C. C. (2007). Frank parsons: Trajetória do pioneiro da orientação vocacional, profissional e de carreira. Revista Brasileira de Orientação Profissional, 8(1):19–31.
Russell, S. and Norvig, P. (2022). Inteligência Artificial - Uma Abordagem Moderna. GEN LTC.
Silva Guerra, M., Asseiss Neto, H., and Azevedo Oliveira, S. (2018). A case study of applying the classification task for students’ performance prediction. IEEE Latin America Transactions, 16(1):172–177.
Silveira, M. E. R. d., Boll, D. C. C., Matozinho, F. L., Negrizoli, I. F., Vanzin, L., Camara, M. K., Ismael, M. N., Alcantara, R. A. d. S., and Oyamada, M. S. (2021). Classificação por matérias das questões do enade e poscomp. Revista ComInG - Communications and Innovations Gazette, 5(2):9–19.
Sociedade Brasileira de Computação (2022). Exame nacional para ingresso na pós-graduação em computação (poscomp).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 iSys - Brazilian Journal of Information Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
