As Tecnologias de Análise de Aprendizagem e os Desafios de Prever Desempenhos de Estudantes de Programação
Resumo
Prever o futuro de aprendizagem de alunos, antecipando-se às possibilidades de fracassso escolar para planejar ações de reorientação do processo de aprendizagem, representa um desafio para as tecnologias do futuro. A Análise de Aprendizagem aparece como uma possibilidade de contemplar esse desafio, pois é uma técnica educacional que visa reconhecer perfis e tendências de aprendizagem a partir da coleta e da análise de dados de estudantes em ambientes online. Sabendo que os principais processos da Análise de Aprendizagem são Selecionar, Capturar, Agregar e Relatar, Predizer, Usar, Refinar e Compartilhar, este trabalho traz à discussão o processo de Predizer, destacando metodologias, tecnologias, práticas, desafios e caminhos de pesquisa da análise de aprendizagem preditiva. Contemplando essa discussão dentro do domínio da aprendizagem de programação, este trabalho tem como objetivos apresentar o estado da arte da análise de aprendizagem preditiva, propor um framework para previsão de desempenhos em programação e apontar caminhos para avançar nessas pesquisas.
Referências
Agudo-Peregrina, A., Hernández-García, A., and Iglesias-Pradas, S. (2012). Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications. In Computers in Education (SIIE), 2012 International Symposium on, pages 1–6.
Badgley, R. F., Hetherington, R. W., and Macleod, J. W. (1962). Social characteristics and prediction of academic performance of saskatchewan medical students. Canadian Medical Association Journal, 86(14):624.
Bauer, R. et al. (1968). Predicting performance in a computer programming course.
Butcher, D. F. and Muth, W. A. (1985). Predicting performance in an introductory computer science course. Commun. ACM, 28(3):263–268.
Corbett, A. T. and Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 4(4):253–278.
Curtis, B., Sheppard, S. B., Milliman, P., Borst, M. A., and Love, T. (1979). Measuring the psychological complexity of software maintenance tasks with the halstead and mccabe metrics. IEEE Trans. Softw. Eng., 5(2):96–104.
DeNELSKY, G. Y. and McKEE, M. G. (1974). Prediction of computer programmer training and job performance using the aabp test1. Personnel Psychology, 27(1):129–137.
Hostetler, T. R. (1983). Predicting student success in an introductory programming course. SIGCSE Bull., 15(3):40–43.
Hung, S.-l., Kwok, L.-f., and Chung, A. (1993). New metrics for automated programming assessment. In Proceedings of the IFIP WG3.4/SEARCC (SRIG on Education and Training) Working Conference on Software Engineering Education, pages 233–243, Amsterdam, The Netherlands, The Netherlands. North-Holland Publishing Co.
Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A., and Hall, C. (2016). NMC Horizon Report: 2016. Higher Education Edition. Learning Analytics and Adaptative Learning: Time–to–Adoption Horizon: One Year or Less. The New Media Consortium, Austin, Texas.
Kotsiantis, S., Patriarcheas, K., and Xenos, M. (2010). A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education. Knowledge-Based Systems, 23(6):529–535.
Lias, T. E. and Elias, T. (2011). Learning analytics: The definitions, the processes, and the potential.
Mat, U. B., Buniyamin, N., Arsad, P. M., and Kassim, R. (2013). An overview of using academic analytics to predict and improve students’achievement: A proposed proactive intelligent intervention. In Engineering Education (ICEED), 2013 IEEE 5th Conference on, pages 126–130.
MCNAMARA, W. J. and HUGHES, J. L. (1961). A review of research on the selection of computer programmers. Personnel Psychology, 14(1):39–51.
Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., and Punch, W. F. (2003). Predicting student performance: an application of data mining methods with an educational webbased system. In Frontiers in Education, 2003. FIE 2003 33rd Annual, volume 1, pages T2A–13.
Motley, R. and Brooks,W. (1977). Statistical prediction of programming errors. Technical report, DTIC Document.
Naser, S. A., Zaqout, I., Ghosh, M. A., Atallah, R., and Alajrami, E. (2015). Predicting student performance using artificial neural network: in the faculty of engineering and information technology. International Journal of Hybrid Information Technology, 8(2):221–228.
Oliveira, M., Jiménez, N., Daher, P., and Oliveira, E. (2015a). Representação da diversidade de componentes latentes em exercícios de programação para classificação de perfis. In Anais do Simpósio Brasileiro de Informática na Educação, volume 26, page 1177.
Oliveira, M., Nogueira, M. A., and Oliveira, E. (2015b). Sistema de Apoio à Prática Assistida de Programação por Execução em Massa e Análise de Programas. In XXIII Workshop sobre Educação em Computação (WEI) - CSBC 2015, Recife, PE. SBC.
Oliveira, M. G. (2013). Núcleos de Avaliações Diagnóstica e Formativa para Regulação da Aprendizagem de Programação. Tese de doutorado, Universidade Federal do Espírito Santo.
Oliveira, M. G., Ciarelli, P. M., and Oliveira, E. (2013). Recommendation of programming activities by multi-label classification for a formative assessment of students. Expert Systems with Applications, 40(16):6641–6651.
Oliveira, M. G. and Oliveira, E. (2015). Abordagens, práticas e desafios da avaliação automática de exercícios de programação. In CSBC 2015 - DesafIE 2015, Recife.
Pieterse, V. (2013). Automated assessment of programming assignments. In Proceedings of the 3rd Computer Science Education Research Conference on Computer Science Education Research, CSERC ’13, pages 4:45–4:56, Open Univ., Heerlen, The Netherlands, The Netherlands. Open Universiteit, Heerlen.
Qiu, J., Tang, J., Liu, T. X., Gong, J., Zhang, C., Zhang, Q., and Xue, Y. (2016). Modeling and predicting learning behavior in moocs. Science, 45(50):55.
Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., and Ventura, S. (2013). Web usage mining for predicting final marks of students that use moodle courses. Computer Applications in Engineering Education, 21(1):135–146.
Romero, C. and Ventura, S. (2010). Educational data mining: a review of the state of the art. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(6):601–618.
Sorour, S. E., Goda, K., and Mine, T. (2015). Estimation of student performance by considering consecutive lessons. In Advanced Applied Informatics (IIAI-AAI), 2015 IIAI 4th International Congress on, pages 121–126. IEEE.
Strang, K. D. (2016). Beyond engagement analytics: which online mixed-data factors predict student learning outcomes? Education and Information Technologies, pages 1–21.
Suchithra, R., Vaidhehi, V., and Iyer, N. E. (2015). Survey of learning analytics based on purpose and techniques for improving student performance. International Journal of Computer Applications, 111(1).
Watson, C., Li, F.W. B., and Godwin, J. L. (2013). Predicting performance in an introductory programming course by logging and analyzing student programming behavior. In 2013 IEEE 13th International Conference on Advanced Learning Technologies, pages 319–323.
Werth, L. H. (1986). Predicting student performance in a beginning computer science class. In Proceedings of the Seventeenth SIGCSE Technical Symposium on Computer Science Education, SIGCSE86, pages 138–143, New York, NY, USA. ACM.
Badgley, R. F., Hetherington, R. W., and Macleod, J. W. (1962). Social characteristics and prediction of academic performance of saskatchewan medical students. Canadian Medical Association Journal, 86(14):624.
Bauer, R. et al. (1968). Predicting performance in a computer programming course.
Butcher, D. F. and Muth, W. A. (1985). Predicting performance in an introductory computer science course. Commun. ACM, 28(3):263–268.
Corbett, A. T. and Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 4(4):253–278.
Curtis, B., Sheppard, S. B., Milliman, P., Borst, M. A., and Love, T. (1979). Measuring the psychological complexity of software maintenance tasks with the halstead and mccabe metrics. IEEE Trans. Softw. Eng., 5(2):96–104.
DeNELSKY, G. Y. and McKEE, M. G. (1974). Prediction of computer programmer training and job performance using the aabp test1. Personnel Psychology, 27(1):129–137.
Hostetler, T. R. (1983). Predicting student success in an introductory programming course. SIGCSE Bull., 15(3):40–43.
Hung, S.-l., Kwok, L.-f., and Chung, A. (1993). New metrics for automated programming assessment. In Proceedings of the IFIP WG3.4/SEARCC (SRIG on Education and Training) Working Conference on Software Engineering Education, pages 233–243, Amsterdam, The Netherlands, The Netherlands. North-Holland Publishing Co.
Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A., and Hall, C. (2016). NMC Horizon Report: 2016. Higher Education Edition. Learning Analytics and Adaptative Learning: Time–to–Adoption Horizon: One Year or Less. The New Media Consortium, Austin, Texas.
Kotsiantis, S., Patriarcheas, K., and Xenos, M. (2010). A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education. Knowledge-Based Systems, 23(6):529–535.
Lias, T. E. and Elias, T. (2011). Learning analytics: The definitions, the processes, and the potential.
Mat, U. B., Buniyamin, N., Arsad, P. M., and Kassim, R. (2013). An overview of using academic analytics to predict and improve students’achievement: A proposed proactive intelligent intervention. In Engineering Education (ICEED), 2013 IEEE 5th Conference on, pages 126–130.
MCNAMARA, W. J. and HUGHES, J. L. (1961). A review of research on the selection of computer programmers. Personnel Psychology, 14(1):39–51.
Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., and Punch, W. F. (2003). Predicting student performance: an application of data mining methods with an educational webbased system. In Frontiers in Education, 2003. FIE 2003 33rd Annual, volume 1, pages T2A–13.
Motley, R. and Brooks,W. (1977). Statistical prediction of programming errors. Technical report, DTIC Document.
Naser, S. A., Zaqout, I., Ghosh, M. A., Atallah, R., and Alajrami, E. (2015). Predicting student performance using artificial neural network: in the faculty of engineering and information technology. International Journal of Hybrid Information Technology, 8(2):221–228.
Oliveira, M., Jiménez, N., Daher, P., and Oliveira, E. (2015a). Representação da diversidade de componentes latentes em exercícios de programação para classificação de perfis. In Anais do Simpósio Brasileiro de Informática na Educação, volume 26, page 1177.
Oliveira, M., Nogueira, M. A., and Oliveira, E. (2015b). Sistema de Apoio à Prática Assistida de Programação por Execução em Massa e Análise de Programas. In XXIII Workshop sobre Educação em Computação (WEI) - CSBC 2015, Recife, PE. SBC.
Oliveira, M. G. (2013). Núcleos de Avaliações Diagnóstica e Formativa para Regulação da Aprendizagem de Programação. Tese de doutorado, Universidade Federal do Espírito Santo.
Oliveira, M. G., Ciarelli, P. M., and Oliveira, E. (2013). Recommendation of programming activities by multi-label classification for a formative assessment of students. Expert Systems with Applications, 40(16):6641–6651.
Oliveira, M. G. and Oliveira, E. (2015). Abordagens, práticas e desafios da avaliação automática de exercícios de programação. In CSBC 2015 - DesafIE 2015, Recife.
Pieterse, V. (2013). Automated assessment of programming assignments. In Proceedings of the 3rd Computer Science Education Research Conference on Computer Science Education Research, CSERC ’13, pages 4:45–4:56, Open Univ., Heerlen, The Netherlands, The Netherlands. Open Universiteit, Heerlen.
Qiu, J., Tang, J., Liu, T. X., Gong, J., Zhang, C., Zhang, Q., and Xue, Y. (2016). Modeling and predicting learning behavior in moocs. Science, 45(50):55.
Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., and Ventura, S. (2013). Web usage mining for predicting final marks of students that use moodle courses. Computer Applications in Engineering Education, 21(1):135–146.
Romero, C. and Ventura, S. (2010). Educational data mining: a review of the state of the art. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(6):601–618.
Sorour, S. E., Goda, K., and Mine, T. (2015). Estimation of student performance by considering consecutive lessons. In Advanced Applied Informatics (IIAI-AAI), 2015 IIAI 4th International Congress on, pages 121–126. IEEE.
Strang, K. D. (2016). Beyond engagement analytics: which online mixed-data factors predict student learning outcomes? Education and Information Technologies, pages 1–21.
Suchithra, R., Vaidhehi, V., and Iyer, N. E. (2015). Survey of learning analytics based on purpose and techniques for improving student performance. International Journal of Computer Applications, 111(1).
Watson, C., Li, F.W. B., and Godwin, J. L. (2013). Predicting performance in an introductory programming course by logging and analyzing student programming behavior. In 2013 IEEE 13th International Conference on Advanced Learning Technologies, pages 319–323.
Werth, L. H. (1986). Predicting student performance in a beginning computer science class. In Proceedings of the Seventeenth SIGCSE Technical Symposium on Computer Science Education, SIGCSE86, pages 138–143, New York, NY, USA. ACM.
Publicado
04/07/2016
Como Citar
DE OLIVEIRA, Márcia G..
As Tecnologias de Análise de Aprendizagem e os Desafios de Prever Desempenhos de Estudantes de Programação. In: WORKSHOP DE DESAFIOS DA COMPUTAÇÃO APLICADA À EDUCAÇÃO (DESAFIE!), 5. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 636-645.
DOI: https://doi.org/10.5753/desafie.2016.9164.
