Abstract
This paper depicts a research work that uses neural networks to predict academic performance in mathematics, focusing on students enrolled in a public school in Chile. This proposal identifies social, knowledge and psychological issues that impact upon successful learning in a meaningful way. The experience considers different instruments used to gather the necessary information for training the neural network. This information includes the level of knowledge, the logical-mathematical intelligence, the students’ self-esteem and about 80 factors considered as relevant in an international project known as PISA. The most adequate network configuration can be found with different experiments. Results show a good predictive level and point out the importance of using local data for fine tuning.
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References
Bekele, R., Manzel, W.: A Bayesian Approach to Predict Performance of a Student (APPS): A Case with Ethiopian Students. In: Artificial Intelligence Applications, AIA 2005 (February 2005)
Cripps, A.: Using Artificial Neural Nets to Predict Academic Performance. In: SAC 1996: Proceedings of the 1996 ACM Symposium on Applied Computing, Philadelphia, Pennsylvania, United States, pp. 33–37 (1996)
Mengel, S., Lively, W.: Using a Neural Network to Predict Student Responses. In: SAC 1992: Proceedings of the 1992 ACM/SIGAPP symposium on Applied Computing, Kansas City, Missouri, United States, pp. 669–676 (1992)
Oladokun, V.O., Adebanjo, A.T., Charles-Owaba, O.E.: Predicting Students Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course. The Pacific Journal of Science and Technology (May-June 2008)
Pinninghoff, J.M.A., Salcedo, L.P., Contreras, A.R.: Neural Networks to Predict Schooling Failure/Success. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4528, pp. 571–579. Springer, Heidelberg (2007)
OECD, PISA 2006 Science Competencies for Tomorrow’s World. Unesco (2006)
Salcedo, L.P., Pinninghoff, J.M.A., Contreras, A.R.: MISTRAL: A knowledge-based system for distance education that incorporates neural network techniques for teaching decisions. In: Mira, J., Álvarez, J.R. (eds.) IWANN 2003. LNCS, vol. 2687, pp. 726–733. Springer, Heidelberg (2003)
Salcedo, L.P., Pinninghoff, J.M.A., Contreras, A.R.: Computerized adaptive tests and item response theory on a distance education platform. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 613–621. Springer, Heidelberg (2005)
Salcedo, L.P., Pinninghoff, J.M.A., Contreras, A.R.: Putting Artificial Intelligence Techniques into Distance Education. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) Research in Computer Science. Special Issue: Advances in Artificial Intelligence, November 2006, vol. 26 (2006) ISSN: 1870-6049
Stites, R.L., Ward, B., Walters, R.V.: Defect Prediction With Neural Networks. In: ANNA 1991: Proceedings of the Conference on Analysis of neural network applications. Fairfax, Virginia (1991)
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A., R.C., L., P.S., Pinninghoff J., M.A. (2009). Performance of High School Students in Learning Math: A Neural Network Approach. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds) Bioinspired Applications in Artificial and Natural Computation. IWINAC 2009. Lecture Notes in Computer Science, vol 5602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02267-8_55
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DOI: https://doi.org/10.1007/978-3-642-02267-8_55
Publisher Name: Springer, Berlin, Heidelberg
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