Universidad Autonoma de Sinaloa (MEXICO)
About this paper:
Appears in: INTED2014 Proceedings
Publication year: 2014
Pages: 4584-4589
ISBN: 978-84-616-8412-0
ISSN: 2340-1079
Conference name: 8th International Technology, Education and Development Conference
Dates: 10-12 March, 2014
Location: Valencia, Spain
The implementation of mathematical methods for the solution of social problems has been doing for many years. Just like there still few studies where the data mining techniques are applied or artificial neural networks for obtaining, analysis and modeling of data, the tendency of the used of these tools in combination of computer science, has been increased through softwares such as SAS, SPSS, Mathlab among others that were manufactured particularly by researchers to solve specific problems.

This paper describes the methodology to apply techniques of artificial neural networks for the prediction of academic performance. This is intended to make a classification of the aspirants to enter to a university career in different levels according to the probability of reaching a given performance category (e. i. excellent, good, acceptable, insufficient, and poor).

Increase discharge indicators, titling and terminal efficiency; decrease the dropout rate, especially the first and second year of the career, are areas of opportunity in any educational institution, regardless of its prestige, nationality and area of knowledge. This paper proposes a methodology based on a rational technique to improve these indicators.

Implement artificial neural networks for the prediction of academic performance is a field that still little explored, there are some isolated pieces of work with this application. In this work is carried out an exhaustive review of the literature, It is a methodological proposal and concludes with a comparison with other methods of prediction as multiple linear regression and data mining.

This research aims to expand in future work to predict shortcomings of the students in various aspects of the courses during the academic career and suggest actions to avoid them.
Neural Networks, Prediction, Academic achievement, Higher Education.