Journal of Computer Sciences and Applications
ISSN (Print): 2328-7268 ISSN (Online): 2328-725X Website: Editor-in-chief: Minhua Ma, Patricia Goncalves
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Journal of Computer Sciences and Applications. 2013, 1(4), 75-79
DOI: 10.12691/jcsa-1-4-3
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An Analysis of Students Performance Using Genetic Algorithm

T. Miranda Lakshmi1, A. Martin2, and V. Prasanna Venkatesan2

1Department of Computer Science, Bharathiyar University, Coimbatore, India

2Department of Banking Technology, Pondicherry University, Puducherry, India

Pub. Date: May 31, 2013

Cite this paper:
T. Miranda Lakshmi, A. Martin and V. Prasanna Venkatesan. An Analysis of Students Performance Using Genetic Algorithm. Journal of Computer Sciences and Applications. 2013; 1(4):75-79. doi: 10.12691/jcsa-1-4-3


Genetic algorithm plays a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. The genetic processes on the natural evolution principles of populations have been fairly successful at solving problems and produce optimized solution from generation to generation. This is applied in students’ quantitative data analysis to identify the most impact factor in their performance in their curriculum. The results will help the educational institutions to improve the quality of teaching after evaluating the marks achieved by the students’ in academic career. This student analysis model considers the quantitative factors such as theoretical, mathematical, practical, departmental and other departmental marks to find the most impacting factor using real genetic algorithm.

students’ performance quantitative factors real genetic algorithm impact factor evaluation results

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[1]  A. Martin, V. Prasanna Venkatesan et al, “To find the most impact financial features for bankruptcy model using genetic algorithm”, International Conference on Advances in Engineering and Technology, (ICAET-2011), May 27-28, 2011.
[2]  Ramjeet Singh Yadav, “Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach”, International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 2 Feb 2011.
[3]  O.K Chaudhari, P.G khot, “Soft computing model for academic performance of teachers using fuzzy logic”, British journal of applied science and technology, 2(2):213-226, 2012.
[4]  V.O. Oladokun, “Predicting Students’ Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course”, The Pacific Journal of Science and Technology, Volume 9. Number 1. May-June 2008 (Springer).
[5]  Osman Taylan, Bahattin Karagozog, “An adaptive neuro-fuzzy model for prediction of student’s academic performance computers & Industrial Engineering”, 57 (2009) 732-741
[6]  Emmanuel N. Ogor “Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques” published on Fourth Congress of Electronics, Robotics and Automotive Mechanics, 2009
[7]  Tossapon boongoe, Qiang shen and Chris price, , International Journal of Uncertainty, fuzzy qualitative link analysis for academic performance evaluation Fuzziness and Knowledge-Based Systems Vol. 19, No. 3 (2011) 559-585.
[8]  Dimitris Kalles, Analyzing student performance in distance learning with genetic algorithms and decision trees, Hellenic Open University, Greece.
[9]  Harvinder Singh Atwal, Predicting Individual StudentPerformance by Mining Assessment and Exam Data, 2010.
[10]  Aavo Luuk Kersti Luuk, Predicting students’ academic performance in Aviation College from their admission test results, 2011
[11]  Mukta Paliwal, Usha A. Kumar, A study of academic performance of business school graduates using neural network and statistical techniques, Expert Systems with Applications 36 (2009) 7865-7872.