Qual Técnica de Learning Analytics Usar para Prever o Desempenho Acadêmico de Estudantes? Uma Análise Comparativa Experimental com Dados de MOOCs
Resumo
A previsão do desempenho acadêmico de estudantes é um dos principais tópicos de pesquisa em Learning Analytics, para o qual diferentes técnicas foram aplicadas. De modo a facilitar a escolha de uma técnica, este estudo apresenta uma análise comparativa entre técnicas de regressão e classificação, considerando diferentes cenários de aplicação. Foram utilizados dados do MITx/HarvardX contendo logs de atividades e participação de 15 turmas de 12 MOOCs. Os resultados obtidos, a partir das métricas de avaliação de performance, sugerem a escolha de Árvores de Decisão como técnica para criação de modelos para regressão e uma escolha entre Árvores de Decisão e Support Vector Machines para a criação de modelos de classificação.
Palavras-chave:
Learning Analytics, Desempenho Acadêmico, MOOCs
Referências
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Gaševi?, D., Dawson, S., Rogers, T., Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84.
Gaševi?, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
Ho, A., Reich, J., Nesterko, S., Seaton, D., Mullaney, T., Waldo, J., Chuang, I. (2013) HarvardX and MITx: The First Year of Open Online Courses, fall 2012-summer 2013. HarvardX and MITx Working Paper No. 1. Available at SSRN: [link].
Jayaprakash, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R., Baron, J. D. (2014). Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative. Journal of Learning Analytics, 1(1), 6–47.
Liyanagunawardena, T. R., Parslow, P., Williams, S. A. (2017). Exploring ‘success’ in MOOCs: Participants’ perspective. In Massive Open Online Courses and Higher Education, (pp. 106-122). Routledge.
Munafò, R.; Nosek, B.; Bishop, D.; Button, K.; Chambers, C.; Percie du Sert, N., Simonsohn, U. & Wagenmakers, E. e J. Ware, Jennifer & P. A. Ioannidis, John. (2017). A manifesto for reproducible science. Nature Human Behaviour. 1. 0021.
Naif, A. D., Rabeeh R. A., Miltiadis, A. A., Farhat D. L, Abbas, J. S. A. (2017). Predicting student performance using Advanced Learning Analytics. In Proceedings of the 26th International Conference on World Wide Web Companion, (pp. 415-421).
Okubo, F., Yamashita, T., Shimada, A., Ogata, H. (2017) A Neural Network Approach for Students’ Performance Prediction. In Proceedings of the 7th International Conference on Learning Analytics & Knowledge, (pp. 598-599). ACM.
Pardo, A., Mirriahi, N., Martinez, R., Jovanovic, J., Dawson, S., & Gaševi?, D. (2016). Generating actionable predictive models of academic performance. In Proceedings of the 6th International Conference on Learning Analytics & Knowledge, (pp. 474-478). ACM.
Romero, C., López, M. I., Luna, J. M., Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion forums. Computers & Education, 68, 458-472.
Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422.
Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural computing and applications, 24(1), 175-186.
Yadav, S.K., Pal, S. (2012). Data mining: A prediction for performance improvement of engineering students using classification. World of Computer Science and Information Technology Journal. (ISSN: 2221-0741), Vol. 2, No. 2, 51-56, 2012.
Barber, R., Sharkey, M. (2012). Course correction: Using Analytics to Predict Course Success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, (pp. 259-262). ACM.
Gaševi?, D., Dawson, S., Rogers, T., Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84.
Gaševi?, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
Ho, A., Reich, J., Nesterko, S., Seaton, D., Mullaney, T., Waldo, J., Chuang, I. (2013) HarvardX and MITx: The First Year of Open Online Courses, fall 2012-summer 2013. HarvardX and MITx Working Paper No. 1. Available at SSRN: [link].
Jayaprakash, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R., Baron, J. D. (2014). Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative. Journal of Learning Analytics, 1(1), 6–47.
Liyanagunawardena, T. R., Parslow, P., Williams, S. A. (2017). Exploring ‘success’ in MOOCs: Participants’ perspective. In Massive Open Online Courses and Higher Education, (pp. 106-122). Routledge.
Munafò, R.; Nosek, B.; Bishop, D.; Button, K.; Chambers, C.; Percie du Sert, N., Simonsohn, U. & Wagenmakers, E. e J. Ware, Jennifer & P. A. Ioannidis, John. (2017). A manifesto for reproducible science. Nature Human Behaviour. 1. 0021.
Naif, A. D., Rabeeh R. A., Miltiadis, A. A., Farhat D. L, Abbas, J. S. A. (2017). Predicting student performance using Advanced Learning Analytics. In Proceedings of the 26th International Conference on World Wide Web Companion, (pp. 415-421).
Okubo, F., Yamashita, T., Shimada, A., Ogata, H. (2017) A Neural Network Approach for Students’ Performance Prediction. In Proceedings of the 7th International Conference on Learning Analytics & Knowledge, (pp. 598-599). ACM.
Pardo, A., Mirriahi, N., Martinez, R., Jovanovic, J., Dawson, S., & Gaševi?, D. (2016). Generating actionable predictive models of academic performance. In Proceedings of the 6th International Conference on Learning Analytics & Knowledge, (pp. 474-478). ACM.
Romero, C., López, M. I., Luna, J. M., Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion forums. Computers & Education, 68, 458-472.
Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422.
Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural computing and applications, 24(1), 175-186.
Yadav, S.K., Pal, S. (2012). Data mining: A prediction for performance improvement of engineering students using classification. World of Computer Science and Information Technology Journal. (ISSN: 2221-0741), Vol. 2, No. 2, 51-56, 2012.
Publicado
11/11/2019
Como Citar
VEIGA, Welington; MACHADO, Marcelo de O. C.; SIQUEIRA, Sean W..
Qual Técnica de Learning Analytics Usar para Prever o Desempenho Acadêmico de Estudantes? Uma Análise Comparativa Experimental com Dados de MOOCs. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1391-1400.
DOI: https://doi.org/10.5753/cbie.sbie.2019.1391.
