Predição de Desempenho de Estudantes: Uma Revisão Sistemática de Literatura
Resumo
A Inteligência Artificial (IA) na Educação tem sido utilizada para lidar com problemas de evasão e desempenho acadêmico de estudantes. Este artigo tem por objetivo apresentar uma Revisão Sistemática de Literatura (RSL) de trabalhos que abordam a predição de desempenho de estudantes. O trabalho buscou responder as seguintes questões: (a) Como os modelos de predição de estudante são utilizados no processo de ensino-aprendizagem; (b) Qual o conjunto de variáveis que melhor explica a predição de desempenho dos estudantes; (c) Como é um modelo de predição do desempenho dos estudantes. Os resultados mostram que embora haja trabalhos que apresentam altos valores de acurácia na predição, ainda existe uma grande oportunidade em relacionar e evidenciar quais são os benefícios para os estudantes e/ou professores.
Palavras-chave:
Predição de Desempenho de Estudantes, Revisão Sistemática de Literatura
Referências
ADNAN, M. et al. Predicting at-risk students at different percentages of course length for early intervention using machine learning models. Ieee Access, IEEE, v. 9, p. 7519–7539, 2021.
AKCAPINAR, G.; ALTUN, A.; A，SKAR, P. Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, Springer, v. 16, n. 1, p. 1–20, 2019.
ALAMRI, R.; ALHARBI, B. Explainable student performance prediction models: a systematic review. IEEE Access, IEEE, v. 9, p. 33132–33143, 2021.
ALMASRI, A.; CELEBI, E.; ALKHAWALDEH, R. S. Emt: Ensemble meta-based tree model for predicting student performance. Scientific Programming, Hindawi, v. 2019, 2019.
ALSHANQITI, A.; NAMOUN, A. Predicting student performance and its influential factors using hybrid regression and multi-label classification. IEEE Access, IEEE, v. 8, p. 203827–203844, 2020.
ALYAHYAN, E.; DU，STEGOR, D. Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, Springer, v. 17, n. 1, p. 1–21, 2020.
ASIAH, M. et al. A review on predictive modeling technique for student academic performance monitoring. In: EDP SCIENCES. MATEC Web of Conferences. [S.l.], 2019. v. 255, p. 03004.
AZCONA, D.; HSIAO, I.-H.; SMEATON, A. F. Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints. User Modeling and User-Adapted Interaction, Springer, v. 29, n. 4, p. 759–788, 2019.
BLOOM, B. S. The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, Sage Publications Sage CA: Thousand Oaks, CA, v. 13, n. 6, p. 4–16, 1984
BUJANG, S. D. A. et al. Multiclass prediction model for student grade prediction using machine learning. IEEE Access, IEEE, v. 9, p. 95608–95621, 2021.
COOK, D. J.; MULROW, C. D.; HAYNES, R. B. Systematic reviews: synthesis of best evidence for clinical decisions. Annals of internal medicine, American College of Physicians, v. 126, n. 5, p. 376–380, 1997.
DEO, R. C. et al. Modern artificial intelligence model development for undergraduate student performance prediction: An investigation on engineering mathematics courses. IEEE Access, IEEE, v. 8, p. 136697–136724, 2020.
DIGIAMPIETRI, L. A.; NAKANO, F.; LAURETTO, M. de S. Mineracao de dados para identificacao de alunos com alto risco de evasao: Um estudo de caso. Revista de Graduacao USP, v. 1, n. 1, p. 17–23, 2016.
DIOGO, M. F. et al. Percepcoes de coordenadores de curso superior sobre evasao, reprovacoes e estrategias preventivas. Avaliacao: Revista da Avaliacao da Educacao Superior (Campinas), SciELO Brasil, v. 21, p. 125–151, 2016.
GAEEVIC, D. et al. Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, Elsevier, v. 28, p. 68–84, 2016.
HELLAS, A. et al. Predicting academic performance: a systematic literature review. In: Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education. [S.l.: s.n.], 2018. p. 175–199.
IMRAN, M. et al. Student academic performance prediction using supervised learning techniques. International Journal of Emerging Technologies in Learning, v. 14, n. 14, 2019.
KHAN, I. et al. An artificial intelligence approach to monitor student performance and devise preventive measures. Smart Learning Environments, SpringerOpen, v. 8, n. 1, p. 1–18, 2021.
LAGUS, J. et al. Transfer-learning methods in programming course outcome prediction. ACM Transactions on Computing Education (TOCE), ACM New York, NY, USA, v. 18, n. 4, p. 1–18, 2018.
LOPES, A. Algumas reflexões sobre a questão do alto índice de reprovação nos cursos de cálculo da ufrgs. Sociedade Brasileira de Matemática. Rio de Janeiro, n. 26/27, p. 123–146, 1999.
MARBOUTI, F.; DIEFES-DUX, H. A.; MADHAVAN, K. Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education, Elsevier, v. 103, p. 1–15, 2016.
MUSSO, M. F.; HERNANDEZ, C. F. R.; CASCALLAR, E. C. Predicting key educational outcomes in academic trajectories: a machine-learning approach. Higher Education, Springer, v. 80, n. 5, p. 875–894, 2020.
POLYZOU, A.; KARYPIS, G. Grade prediction with models specific to students and courses. International Journal of Data Science and Analytics, Springer, v. 2, n. 3, p. 159–171, 2016.
POPESCU, E.; LEON, F. Predicting academic performance based on learner traces in a social learning environment. IEEE Access, IEEE, v. 6, p. 72774–72785, 2018.
SANTOS, H. G. d. et al. Machine learning para analises preditivas em saude: exemplo de aplicacao para predizer obito em idosos de sao paulo, brasil. Cadernos de Saude Publica, SciELO Public Health, v. 35, p. e00050818, 2019.
SEKEROGLU, B.; DIMILILER, K.; TUNCAL, K. Student performance prediction and classification using machine learning algorithms. In: Proceedings of the 2019 8th International Conference on Educational and Information Technology. [S.l.: s.n.], 2019. p. 7–11.
WAKELAM, E. et al. The potential for student performance prediction in small cohorts with minimal available attributes. British Journal of Educational Technology, Wiley Online Library, v. 51, n. 2, p. 347–370, 2020.
WANG, X. et al. Student performance prediction with short-term sequential campus behaviors. Information, MDPI, v. 11, n. 4, p. 201, 2020.
WEI, H. et al. Predicting student performance in interactive online question pools using mouse interaction features. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. [S.l.: s.n.], 2020. p. 645–654.
YU, C.-H.; WU, J.; LIU, A.-C. Predicting learning outcomes with mooc clickstreams. Education sciences, MDPI, v. 9, n. 2, p. 104, 2019.
ZHAO, L. et al. Academic performance prediction based on multisource, multifeature behavioral data. IEEE Access, IEEE, v. 9, p. 5453–5465, 2020.
ZOHAIR, A.; MAHMOUD, L. Prediction of student’s performance by modelling small dataset size. International Journal of Educational Technology in Higher Education, SpringerOpen, v. 16, n. 1, p. 1–18, 2019.
ZOLLANVARI, A. et al. Predicting students’ gpa and developing intervention strategies based on self-regulatory learning behaviors. IEEE Access, IEEE, v. 5, p. 23792–23802, 2017.
AKCAPINAR, G.; ALTUN, A.; A，SKAR, P. Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, Springer, v. 16, n. 1, p. 1–20, 2019.
ALAMRI, R.; ALHARBI, B. Explainable student performance prediction models: a systematic review. IEEE Access, IEEE, v. 9, p. 33132–33143, 2021.
ALMASRI, A.; CELEBI, E.; ALKHAWALDEH, R. S. Emt: Ensemble meta-based tree model for predicting student performance. Scientific Programming, Hindawi, v. 2019, 2019.
ALSHANQITI, A.; NAMOUN, A. Predicting student performance and its influential factors using hybrid regression and multi-label classification. IEEE Access, IEEE, v. 8, p. 203827–203844, 2020.
ALYAHYAN, E.; DU，STEGOR, D. Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, Springer, v. 17, n. 1, p. 1–21, 2020.
ASIAH, M. et al. A review on predictive modeling technique for student academic performance monitoring. In: EDP SCIENCES. MATEC Web of Conferences. [S.l.], 2019. v. 255, p. 03004.
AZCONA, D.; HSIAO, I.-H.; SMEATON, A. F. Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints. User Modeling and User-Adapted Interaction, Springer, v. 29, n. 4, p. 759–788, 2019.
BLOOM, B. S. The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, Sage Publications Sage CA: Thousand Oaks, CA, v. 13, n. 6, p. 4–16, 1984
BUJANG, S. D. A. et al. Multiclass prediction model for student grade prediction using machine learning. IEEE Access, IEEE, v. 9, p. 95608–95621, 2021.
COOK, D. J.; MULROW, C. D.; HAYNES, R. B. Systematic reviews: synthesis of best evidence for clinical decisions. Annals of internal medicine, American College of Physicians, v. 126, n. 5, p. 376–380, 1997.
DEO, R. C. et al. Modern artificial intelligence model development for undergraduate student performance prediction: An investigation on engineering mathematics courses. IEEE Access, IEEE, v. 8, p. 136697–136724, 2020.
DIGIAMPIETRI, L. A.; NAKANO, F.; LAURETTO, M. de S. Mineracao de dados para identificacao de alunos com alto risco de evasao: Um estudo de caso. Revista de Graduacao USP, v. 1, n. 1, p. 17–23, 2016.
DIOGO, M. F. et al. Percepcoes de coordenadores de curso superior sobre evasao, reprovacoes e estrategias preventivas. Avaliacao: Revista da Avaliacao da Educacao Superior (Campinas), SciELO Brasil, v. 21, p. 125–151, 2016.
GAEEVIC, D. et al. Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, Elsevier, v. 28, p. 68–84, 2016.
HELLAS, A. et al. Predicting academic performance: a systematic literature review. In: Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education. [S.l.: s.n.], 2018. p. 175–199.
IMRAN, M. et al. Student academic performance prediction using supervised learning techniques. International Journal of Emerging Technologies in Learning, v. 14, n. 14, 2019.
KHAN, I. et al. An artificial intelligence approach to monitor student performance and devise preventive measures. Smart Learning Environments, SpringerOpen, v. 8, n. 1, p. 1–18, 2021.
LAGUS, J. et al. Transfer-learning methods in programming course outcome prediction. ACM Transactions on Computing Education (TOCE), ACM New York, NY, USA, v. 18, n. 4, p. 1–18, 2018.
LOPES, A. Algumas reflexões sobre a questão do alto índice de reprovação nos cursos de cálculo da ufrgs. Sociedade Brasileira de Matemática. Rio de Janeiro, n. 26/27, p. 123–146, 1999.
MARBOUTI, F.; DIEFES-DUX, H. A.; MADHAVAN, K. Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education, Elsevier, v. 103, p. 1–15, 2016.
MUSSO, M. F.; HERNANDEZ, C. F. R.; CASCALLAR, E. C. Predicting key educational outcomes in academic trajectories: a machine-learning approach. Higher Education, Springer, v. 80, n. 5, p. 875–894, 2020.
POLYZOU, A.; KARYPIS, G. Grade prediction with models specific to students and courses. International Journal of Data Science and Analytics, Springer, v. 2, n. 3, p. 159–171, 2016.
POPESCU, E.; LEON, F. Predicting academic performance based on learner traces in a social learning environment. IEEE Access, IEEE, v. 6, p. 72774–72785, 2018.
SANTOS, H. G. d. et al. Machine learning para analises preditivas em saude: exemplo de aplicacao para predizer obito em idosos de sao paulo, brasil. Cadernos de Saude Publica, SciELO Public Health, v. 35, p. e00050818, 2019.
SEKEROGLU, B.; DIMILILER, K.; TUNCAL, K. Student performance prediction and classification using machine learning algorithms. In: Proceedings of the 2019 8th International Conference on Educational and Information Technology. [S.l.: s.n.], 2019. p. 7–11.
WAKELAM, E. et al. The potential for student performance prediction in small cohorts with minimal available attributes. British Journal of Educational Technology, Wiley Online Library, v. 51, n. 2, p. 347–370, 2020.
WANG, X. et al. Student performance prediction with short-term sequential campus behaviors. Information, MDPI, v. 11, n. 4, p. 201, 2020.
WEI, H. et al. Predicting student performance in interactive online question pools using mouse interaction features. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. [S.l.: s.n.], 2020. p. 645–654.
YU, C.-H.; WU, J.; LIU, A.-C. Predicting learning outcomes with mooc clickstreams. Education sciences, MDPI, v. 9, n. 2, p. 104, 2019.
ZHAO, L. et al. Academic performance prediction based on multisource, multifeature behavioral data. IEEE Access, IEEE, v. 9, p. 5453–5465, 2020.
ZOHAIR, A.; MAHMOUD, L. Prediction of student’s performance by modelling small dataset size. International Journal of Educational Technology in Higher Education, SpringerOpen, v. 16, n. 1, p. 1–18, 2019.
ZOLLANVARI, A. et al. Predicting students’ gpa and developing intervention strategies based on self-regulatory learning behaviors. IEEE Access, IEEE, v. 5, p. 23792–23802, 2017.
Publicado
16/11/2022
Como Citar
SILVA, Bruno João da; PIMENTEL, Edson P.; BOTELHO, Wagner Tanaka.
Predição de Desempenho de Estudantes: Uma Revisão Sistemática de Literatura. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 33. , 2022, Manaus.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 1040-1052.
DOI: https://doi.org/10.5753/sbie.2022.225093.
