Intelligent Analysis of Students Profile about Dropout Factors: A Study in Information System Course Context
Resumo
Student dropout from higher education is still a challenge, imposing a financial and human burden and refusing students to learn. Brazil witnessed a university dropout rate of almost 55%. This work aims to analyze the factors that lead to student dropout from Information System courses, exploring the profile of students, using intelligent techniques. The information obtained can help reduce the evasion rate and identify key actions to control the problem. We used the Design Science Research methodology to conduct our study. An analysis with data from a university, considering the LGPD was conducted to verify the proposal's feasibility. Our results show that the solution can help identify key factors that lead to dropping out.
Palavras-chave:
Evasão estudantil, Cursos de Sistemas de Informação, Ensino superior, Fatores de evasão, Taxa de evasão, Design Science Research, Ontologia, Regras SWRL, Desempenho acadêmico, Cotas universitárias, As
Referências
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da Cruz, R. C., Juliano, R. C., Monteiro Souza, F. C., & Correa Souza, A. C. (2023, May). A Score approach to identify the risk of students dropout: an experiment with Information Systems Course. In Proceedings of the XIX Brazilian Symposium on Information Systems (pp. 120-127).
da Silva, Claiton. A holistic profile ontology for undergraduate students. 2021. [link]. accessed in: ago. 2023. (in Portuguese)
da Silva, L. M., Dias, L. P. S., Rigo, S., Barbosa, J. L. V., Leithardt, D. R., & Leithardt, V. R. Q. (2021). A literature review on intelligent services applied to distance learning. Education Sciences, 11(11), 666.
De Oliveira, P., da Silva, G., Dourado, R., & Rodrigues, R. L. (2021). Linking Engagement Profiles to Academic Performance Through SNA and Cluster Analysis on Discussion Forum Data. In LALA (pp. 39-47).
El-Rady, Alla Abd . An Ontological Model to Predict Dropout Students Using Machine Learning Techniques . Arab Academy for Science , Technology & Maritime Transport. Egypt, 2020. [link].
Fernández-López, M., Gómez-Pérez, A. and Juristo, N. (1997). “Methontology: from ontological art towards ontological engineering”. Spring Symposium Series, 1997. Facultad de Informática (UPM).
General Data Protection Law (LGPD) - Brazil. Effective Date. Brasília, DF: Presidency of the Republic, [year of publication]. Available at: [link]
Gonzalez-Nucamendi, A., Noguez, J., Neri, L., Robledo-Rella, V., García-Castelán, R. M. G., & Escobar-Castillejos, D. (2022). Learning Analytics to Determine Profile Dimensions of Students Associated with Their Academic Performance. Applied Sciences, 12(20), 10560.
Gruber, Thomas R. . A Translation Approach to Portable Ontology Specifications . 1993. [link].
MAPA do Ensino superior no Brasil. Brasil, 8 maio 2024. Disponível em: [link]. Acesso em: 5 jun. 2024.
Ozkaya I., Application of Large Language Models to Software Engineering Tasks: Opportunities, Risks, and Implications," in IEEE Software, vol. 40, no. 3, pp. 4-8, May-June 2023, DOI: 10.1109/MS.2023.3248401,
Peffers, Ken et al. A design science research methodology for information systems research. Journal of management information systems, v. 24, n. 3, p. 45-77, 2007.
Priyambada, S. A., Er, M., Yahya, B. N., & Usagawa, T. (2021). Profile-based cluster evolution analysis: Identification of migration patterns for understanding student learning behavior. IEEE Access, 9, 101718-101728.
Saqr, M., López-Pernas, S., Helske, S., & Hrastinski, S. (2023). The longitudinal association between engagement and achievement varies by time, students’ profiles, and achievement state: A full program study. Computers & Education, 199, 104787.
Vinker, E., & Rubinstein, A. (2022, March). Mining code submissions to elucidate disengagement in a computer science MOOC. In LAK22: 12th international learning analytics and knowledge conference (pp. 142-151).
Wieringa, Roel. Design science as nested problem solving. In: Proceedings of the 4th international conference on design science research in information systems and technology. 2009. p. 1-12.
World Wide Web Consortium. (2012). OWL 2 Web Ontology Language Document Overview. W3C Recommendation. Retrieved from [link]
da Cruz, R. C., Juliano, R. C., Monteiro Souza, F. C., & Correa Souza, A. C. (2023, May). A Score approach to identify the risk of students dropout: an experiment with Information Systems Course. In Proceedings of the XIX Brazilian Symposium on Information Systems (pp. 120-127).
da Silva, Claiton. A holistic profile ontology for undergraduate students. 2021. [link]. accessed in: ago. 2023. (in Portuguese)
da Silva, L. M., Dias, L. P. S., Rigo, S., Barbosa, J. L. V., Leithardt, D. R., & Leithardt, V. R. Q. (2021). A literature review on intelligent services applied to distance learning. Education Sciences, 11(11), 666.
De Oliveira, P., da Silva, G., Dourado, R., & Rodrigues, R. L. (2021). Linking Engagement Profiles to Academic Performance Through SNA and Cluster Analysis on Discussion Forum Data. In LALA (pp. 39-47).
El-Rady, Alla Abd . An Ontological Model to Predict Dropout Students Using Machine Learning Techniques . Arab Academy for Science , Technology & Maritime Transport. Egypt, 2020. [link].
Fernández-López, M., Gómez-Pérez, A. and Juristo, N. (1997). “Methontology: from ontological art towards ontological engineering”. Spring Symposium Series, 1997. Facultad de Informática (UPM).
General Data Protection Law (LGPD) - Brazil. Effective Date. Brasília, DF: Presidency of the Republic, [year of publication]. Available at: [link]
Gonzalez-Nucamendi, A., Noguez, J., Neri, L., Robledo-Rella, V., García-Castelán, R. M. G., & Escobar-Castillejos, D. (2022). Learning Analytics to Determine Profile Dimensions of Students Associated with Their Academic Performance. Applied Sciences, 12(20), 10560.
Gruber, Thomas R. . A Translation Approach to Portable Ontology Specifications . 1993. [link].
MAPA do Ensino superior no Brasil. Brasil, 8 maio 2024. Disponível em: [link]. Acesso em: 5 jun. 2024.
Ozkaya I., Application of Large Language Models to Software Engineering Tasks: Opportunities, Risks, and Implications," in IEEE Software, vol. 40, no. 3, pp. 4-8, May-June 2023, DOI: 10.1109/MS.2023.3248401,
Peffers, Ken et al. A design science research methodology for information systems research. Journal of management information systems, v. 24, n. 3, p. 45-77, 2007.
Priyambada, S. A., Er, M., Yahya, B. N., & Usagawa, T. (2021). Profile-based cluster evolution analysis: Identification of migration patterns for understanding student learning behavior. IEEE Access, 9, 101718-101728.
Saqr, M., López-Pernas, S., Helske, S., & Hrastinski, S. (2023). The longitudinal association between engagement and achievement varies by time, students’ profiles, and achievement state: A full program study. Computers & Education, 199, 104787.
Vinker, E., & Rubinstein, A. (2022, March). Mining code submissions to elucidate disengagement in a computer science MOOC. In LAK22: 12th international learning analytics and knowledge conference (pp. 142-151).
Wieringa, Roel. Design science as nested problem solving. In: Proceedings of the 4th international conference on design science research in information systems and technology. 2009. p. 1-12.
World Wide Web Consortium. (2012). OWL 2 Web Ontology Language Document Overview. W3C Recommendation. Retrieved from [link]
Publicado
04/11/2024
Como Citar
AZY, Wallyce; BRAGA, Regina; STRÖELE, Victor; DAVID, José Maria N.; CAMPOS, Fernanda; CHAVES, Luciano J.; CAMPOS, Luciana.
Intelligent Analysis of Students Profile about Dropout Factors: A Study in Information System Course Context. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 35. , 2024, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 3038-3048.
DOI: https://doi.org/10.5753/sbie.2024.244921.
