Avaliação de Desempenho de Estudantes em Cursos de Educação a Distância Utilizando Mineração de Dados
Resumo
Para o desenvolvimento de cursos a distância a utilização de um Ambiente Virtual de Aprendizado (AVA) tornou-se fundamental. Estes ambientes armazenam grandes volumes de dados relativos às atividades desenvolvidas pelos estudantes. Estes dados podem ser utilizados juntamente com técnicas de Mineração de Dados para a descoberta de informações que auxiliem professores na gestão do processo de ensino. Neste trabalho propõe se a definição de um conjunto de atributos amplo e generalizável a ser utilizado para realizar inferências relativas ao desempenho de estudantes. Experimentos realizados demonstraram a viabilidade desta proposta, atingindo-se índices de 76% de acerto na previsão de desempenho.
Referências
Baker, R.S.J.D., Corbett, A.T., Koedinger, K.R., Evenson, S.E., Roll, I., Wagner, A.Z., Naim, M., Raspat, J., Baker, D.J., Beck, J. (2006) “Adapting to When Students Game an Intelligent Tutoring System”. In Proceedings of the International Conference on Intelligent Tutoring Systems, p. 392-401.
Baker, R.S.J.D., Isotani, S., Carvalho, A.M.J.B.D. (2011) “Mineração de Dados Educacionais: Oportunidades para o Brasil”. Revista Brasileira de Informática na Educação, vol. 19, no. 2, p. 2-13.
D'mello, S.K., Craig, S.D., Witherspoon, A.W., Mcdaniel, B.T., Graesser, A.C. ( 2008) “Automatic Detection of Learner‟s Affect from Conversational Cues”. User Modeling and User-Adapted Interaction, p. 45-80.
Fayyad, U. M., Irani, K.B. (1993) “Multi-interval Discretization of Continuous Valued Attributes for Classification Learning”. In: Thirteenth International Joint Conference on Articial Intelligence, p. 1022-1027.
Fayyad, U.M., Piatesky-Shapiro, G., Smyth, P. (1996) “The KDD Process for Extracting Useful Knowledge from Volumes of Data”. Comunications of ACM, vol. 39, no. 11, p. 27-34.
Hall, M. A. (1998) “Correlation-based Feature Subset Selection for Machine Learning”. The University of Waikato, Hamilton, New Zealand.
Hämäläinen, W., Vinni, M. (2011) “Classifiers for Educational Data Mining”. In: Romero et al. Handbook of Educational Data Mining. Flórida, CRC Press, p. 57-71.
Holliman, R. Scanlon, E. (2006) “Investigating Cooperation and Collaboration in Near Synchronous Computer Mediated Conferences”. Computers & Education, no. 46, p. 322-335.
Ibrahim, Z., Rusli, D. (2007) “Predicting students‟ Academic Performance: Comparing Artificial Neural Network, Decision Tree and Linear Regression”, In Proceedings of the 21º Annual SAS Malaysia Forum, Kuala Lumpur, Malaysia, p. 1–6.
Kotsiantis, S.B. (2012) “Use of Machine Learning Techniques for Educational Proposes: A Decision Support System for Forecasting Student's Grades”. Artificial Intelligence Review, vol.37, no. 4, p. 331-344.
Li, Y. Huang, R. (2008) “Analyzing Peer Interaction in Computer-Supported Collaborative Learning: Model, Method and Tool”. Lecture Notes in Computer Science (LNCS). no. 5169, p. 125-136.
Macfadyen, L.P., Dawson, S. (2010) “Mining LMS Data to Develop an „Early Warning System‟ for Educators: A Proof of Concept”. Computers & Education, no. 54, p. 588-599.
Manhães, L.M.B., Cruz, S.M.S., Costa, R.J.M, Zavaleta, J., Zimbrão, G. (2011) “Previsão de Estudantes com Risco de Evasão Utilizando Técnicas de Mineração de Dados”. Anais do XXII SBIE-XVII WIE, p. 150-159.
Mcquiggan, S., Mott, B., Lester, J. (2008) “Modeling Self-Efficacy in Intelligent Tutoring Systems: An Inductive Approach”. User Modeling and User-Adapted Interaction, vol.18, p. 81-123.
Merceron, A., Yacef, K. (2005) “Educational Data Mining: a Case Study”. Artificial Intelligence in Education. In Proceedings of the 12th International Conference on Artificial Intelligence, p. 467-474.
Minaei-Bidgoli, B., Kashy, A.D., Kortemeyer, G., Punch, F.W. (2003) “Predicting Student Performance: An Application of Data Mining Methods with the Educational Web-Based System LON-CAPA”. 33ª ASEE/IEEE Frontiers in Education Conference, p. 1-6.
Moodle (2012), disponível em [link]. Acesso em 24/03/2012.
Rabbany, R.K,. Takaffoli M., Zaïane, O.R. (2011) “Analyzing Participation of Students in Online Courses Using Social Network Analysis Techniques”. In Proceedings of the Fourth International Conference on Educational Data Mining, p. 22-30.
Romero, C. Ventura, S., García, E. (2008) “Data Mining in Course Management Systems: Moodle Case Study and Tutorial”. Computers & Education, no. 51, p. 368-384.
Romero, C. Ventura, S. (2010) “Educational Data Mining: A Review of the State of Art”. IEEE Transactions on Systems , Man, and Cybernetics – Part C: Application and Reviews. vol. 40, no. 6, p. 601-618.
Romero-Zaldivar, V.A., Pardo, A., Burgos, D., Kloos, C.D. (2012) “Monitoring Student Progress Using Virtual Appliances: A Case Study”. Computers & Education, no. 58, p. 1058-1067.
Schrire, S. Knowledge. (2006) “Building in Asynchronous Discussion Groups: Going Beyond Quantitative Analysis”. Computers & Education, vol. 46, no. 1, p. 49–70.
Witten, I.H., Frank E., Hall, M.A. (2011) Data Mining: Practical Machine Learning Tools and Techniques. San Francisco: Morgan Kaufmann, 3 ed.
Baker, R.S.J.D., Isotani, S., Carvalho, A.M.J.B.D. (2011) “Mineração de Dados Educacionais: Oportunidades para o Brasil”. Revista Brasileira de Informática na Educação, vol. 19, no. 2, p. 2-13.
D'mello, S.K., Craig, S.D., Witherspoon, A.W., Mcdaniel, B.T., Graesser, A.C. ( 2008) “Automatic Detection of Learner‟s Affect from Conversational Cues”. User Modeling and User-Adapted Interaction, p. 45-80.
Fayyad, U. M., Irani, K.B. (1993) “Multi-interval Discretization of Continuous Valued Attributes for Classification Learning”. In: Thirteenth International Joint Conference on Articial Intelligence, p. 1022-1027.
Fayyad, U.M., Piatesky-Shapiro, G., Smyth, P. (1996) “The KDD Process for Extracting Useful Knowledge from Volumes of Data”. Comunications of ACM, vol. 39, no. 11, p. 27-34.
Hall, M. A. (1998) “Correlation-based Feature Subset Selection for Machine Learning”. The University of Waikato, Hamilton, New Zealand.
Hämäläinen, W., Vinni, M. (2011) “Classifiers for Educational Data Mining”. In: Romero et al. Handbook of Educational Data Mining. Flórida, CRC Press, p. 57-71.
Holliman, R. Scanlon, E. (2006) “Investigating Cooperation and Collaboration in Near Synchronous Computer Mediated Conferences”. Computers & Education, no. 46, p. 322-335.
Ibrahim, Z., Rusli, D. (2007) “Predicting students‟ Academic Performance: Comparing Artificial Neural Network, Decision Tree and Linear Regression”, In Proceedings of the 21º Annual SAS Malaysia Forum, Kuala Lumpur, Malaysia, p. 1–6.
Kotsiantis, S.B. (2012) “Use of Machine Learning Techniques for Educational Proposes: A Decision Support System for Forecasting Student's Grades”. Artificial Intelligence Review, vol.37, no. 4, p. 331-344.
Li, Y. Huang, R. (2008) “Analyzing Peer Interaction in Computer-Supported Collaborative Learning: Model, Method and Tool”. Lecture Notes in Computer Science (LNCS). no. 5169, p. 125-136.
Macfadyen, L.P., Dawson, S. (2010) “Mining LMS Data to Develop an „Early Warning System‟ for Educators: A Proof of Concept”. Computers & Education, no. 54, p. 588-599.
Manhães, L.M.B., Cruz, S.M.S., Costa, R.J.M, Zavaleta, J., Zimbrão, G. (2011) “Previsão de Estudantes com Risco de Evasão Utilizando Técnicas de Mineração de Dados”. Anais do XXII SBIE-XVII WIE, p. 150-159.
Mcquiggan, S., Mott, B., Lester, J. (2008) “Modeling Self-Efficacy in Intelligent Tutoring Systems: An Inductive Approach”. User Modeling and User-Adapted Interaction, vol.18, p. 81-123.
Merceron, A., Yacef, K. (2005) “Educational Data Mining: a Case Study”. Artificial Intelligence in Education. In Proceedings of the 12th International Conference on Artificial Intelligence, p. 467-474.
Minaei-Bidgoli, B., Kashy, A.D., Kortemeyer, G., Punch, F.W. (2003) “Predicting Student Performance: An Application of Data Mining Methods with the Educational Web-Based System LON-CAPA”. 33ª ASEE/IEEE Frontiers in Education Conference, p. 1-6.
Moodle (2012), disponível em [link]. Acesso em 24/03/2012.
Rabbany, R.K,. Takaffoli M., Zaïane, O.R. (2011) “Analyzing Participation of Students in Online Courses Using Social Network Analysis Techniques”. In Proceedings of the Fourth International Conference on Educational Data Mining, p. 22-30.
Romero, C. Ventura, S., García, E. (2008) “Data Mining in Course Management Systems: Moodle Case Study and Tutorial”. Computers & Education, no. 51, p. 368-384.
Romero, C. Ventura, S. (2010) “Educational Data Mining: A Review of the State of Art”. IEEE Transactions on Systems , Man, and Cybernetics – Part C: Application and Reviews. vol. 40, no. 6, p. 601-618.
Romero-Zaldivar, V.A., Pardo, A., Burgos, D., Kloos, C.D. (2012) “Monitoring Student Progress Using Virtual Appliances: A Case Study”. Computers & Education, no. 58, p. 1058-1067.
Schrire, S. Knowledge. (2006) “Building in Asynchronous Discussion Groups: Going Beyond Quantitative Analysis”. Computers & Education, vol. 46, no. 1, p. 49–70.
Witten, I.H., Frank E., Hall, M.A. (2011) Data Mining: Practical Machine Learning Tools and Techniques. San Francisco: Morgan Kaufmann, 3 ed.
Publicado
16/07/2012
Como Citar
GOTTARDO, Ernani; KAESTNER, Celso; NORONHA, Robinson Vida.
Avaliação de Desempenho de Estudantes em Cursos de Educação a Distância Utilizando Mineração de Dados. In: WORKSHOP DE DESAFIOS DA COMPUTAÇÃO APLICADA À EDUCAÇÃO (DESAFIE!), 1. , 2012, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2012
.
p. 20-29.
