Abstract
This paper depicts an already developed experience in search for a predictable mechanism with respect to the future performance of a student considering the numerous factors that influence in its failure/success. The use of different neural networks configurations in conjunction with a large data volume on top of detailed attributes consideration for each student makes for an adequate base for the results obtained to be analyzed. The idea behind this paper is to arrange a mechanism that allows us to estimate before hand taking into consideration data from the student in reference to family, social and wealth surroundings for the student future performance identifying those factors that favors the tendency to failure or success.
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References
Begg, R., Kamruzzaman, J., Sarkar, R. (eds.): Neural Network in Healthcare: Potential and Challenges. Idea Group Publishing, Hershey (2006)
Bekele, R., Manzel, W.: A Bayesian Approach to Predict Performance of a Student (BAPPS): A Case with Ethiopian Students. In: The IASTED Conference Artificial Intelligence and Applications, AIA 2005, Innsbruck, Austria (February 2005)
Nukoolkit, C., Chen, H.-C., Brown, D.: A Data Transformation Technique for Car Injury Prediction. In: 39th ACM-SE, Athens, Georgia, USA (2001)
Cripps, A.: Using Artificial Neural Nets to Predict Academic Performance. In: Proceedings of the ACM Symposium on Applied Computing, Philadelphia, PA, USA (February 1996)
Karamouzis, S.T., Katsiyannis, A., Archwamety, T.: An Application of Neural Networks for Predicting Juvenile Recidivism. In: The IASTED Conference on Artificial Intelligence and Applications, Innsbruck, Austria (February 2006)
Kroese, B., van der Smagt, P.: An Introduction to Neural Networks. University of Amsterdam (1996)
Linstrom, K., Boye, J.: A Neural Network Prediction Model for a Psychiatric Application. In: Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Application (ICCIMA’05), Washington, DC, USA, IEEE Computer Society Press, Los Alamitos (2005)
Liu, T.-Y., Li, G.-Z., Liu, Y., Wu, G.-F., Wang, W.: Estimation of the Future Earthquake Situation by Using Neural Networks Ensemble. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 1231–1236. Springer, Heidelberg (2006)
Nebot, A., Castro, F., Mugica, F., Vellido, A.: Identification of Fuzzy Models to Predict Students Performance in a E-Learning Environment. In: The IASTED Conference on Web-based Education, Puerto Vallarta, Mexico (January 2006)
OECD: Knowledge and Skills for Life. First Results from PISA 2000. Unesco (2001)
OECD: PISA 2000 Technical Report. Unesco (2001)
OECD: Education at a Glance, OECD Indicators. Unesco (2002)
Wang, T., Mitrovic, A.: Using Neural Networks to Predict Student’s Performance. In: International Conference on Computers in Education, ICCE’02, Auckland, New Zealand (December 2002)
Wu, T.-K., Meng, Y.-R., Huang, S.-C.: Application of Artificial Neural Network to the identification of Students with Learning Disabilities. In: Proceedings of the 2006 International Conference on Artificial Intelligence, ICAI 2006, Las Vegas, Nevada (June 2006)
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Pinninghoff Junemann, M.A., Salcedo Lagos, P.A., Contreras Arriagada, R. (2007). Neural Networks to Predict Schooling Failure/Success. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_59
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DOI: https://doi.org/10.1007/978-3-540-73055-2_59
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