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Neural Networks to Predict Schooling Failure/Success.

Authors :
Hutchison, David
Kanade, Takeo
Kittler, Josef
Kleinberg, Jon M.
Mattern, Friedemann
Mitchell, John C.
Naor, Moni
Nierstrasz, Oscar
Rangan, C. Pandu
Steffen, Bernhard
Sudan, Madhu
Terzopoulos, Demetri
Tygar, Doug
Vardi, Moshe Y.
Weikum, Gerhard
Mira, José
Álvarez, José R.
Pinninghoff Junemann, María Angélica
Salcedo Lagos, Pedro Antonio
Contreras Arriagada, Ricardo
Source :
Nature Inspired Problem-Solving Methods in Knowledge Engineering; 2007, p571-579, 9p
Publication Year :
2007

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540730545
Database :
Complementary Index
Journal :
Nature Inspired Problem-Solving Methods in Knowledge Engineering
Publication Type :
Book
Accession number :
33041425
Full Text :
https://doi.org/10.1007/978-3-540-73055-2_59