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A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance

DOI: 10.1155/2013/179097

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Abstract:

Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions. 1. Introduction Accurately predicting student performance is useful in many different contexts in educational environments. When admission officers review applications, accurate predictions help them to distinguish between suitable and unsuitable candidates for an academic program. The failure to perform an accurate admission decision may result in an unsuitable candidate being admitted to the university. Since the quality of an educational institution is mainly reflected in its research and training, the quality of admitted candidates affects the quality level of an institution. Accurate prediction enables educational managers to improve student academic performance by offering students additional support such as customized assistance and tutoring resources. The results of prediction can also be used by lecturers to specify the most suitable teaching actions for each group of students and provide them with further assistance tailored to their needs. Thus, accurate prediction of student achievement is one way to enhance quality and provide better educational services. As a result, the ability to predict students’ academic performance is important for educational institutions. A very promising tool to achieve this objective is the use of data mining. Data mining processes large amounts of data to discover hidden patterns and relationships that support decision-making. Data mining in higher education is forming a new research field called educational data mining [1, 2]. The application of data mining to education allows educators to discover new and useful knowledge about students [3]. Educational data mining develops techniques

References

[1]  A. Merceron and K. Ycef, “Educational data mining: a case study,” in Proceedings of the 12th International Conference on Artificial Intelligence in Education (AIED '05), IOS Press, Amsterdam, The Netherlands, 2005.
[2]  C. Romero and S. Ventura, “Educational data mining: a survey from 1995 to 2005,” Expert Systems with Applications, vol. 33, no. 1, pp. 135–146, 2007.
[3]  K. Barker, T. Trafalis, and T. R. Rhoads, “Learning from student data,” in Proceedings of IEEE Systems and Information Engineering Design Symposium, pp. 79–86, 2004.
[4]  A. Sharma, R. Kumar, P. K. Varadwaj, A. Ahmad, and G. M. Ashraf, “A comparative study of support vector machine, artificial neural network and bayesian classifier for mutagenicity prediction,” Interdisciplinary Sciences, Computational Life Sciences, vol. 3, no. 3, pp. 232–239, 2011.
[5]  B. K. Bhardwaj and S. Pal, “Data mining: a prediction for performance improvement using classification,” International Journal of Computer Science and Information Security, vol. 9, no. 4, pp. 1–5, 2011.
[6]  N. T. Nghe, P. Janecek, and P. Haddawy, “A comparative analysis of techniques for predicting academic performance,” in Proceedings of the 37th ASEE/IEEE Frontiers in Education Conference (FIE '07), pp. T2–G7, Milwaukee, Wis, USA, October 2007.
[7]  S. Huang and N. Fang, “Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models,” Computers & Education, vol. 61, pp. 133–145, 2013.
[8]  Y. Norazah, B. A. Nor, S. O. Mohd, and C. N. Yeap, “A concise fuzzy rule base to reason student performance based on rough-fuzzy approach,” in Fuzzy Inference System-Theory and Application, InTech, 2010.
[9]  R. Ata and Y. Kocyigit, “An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines,” Expert Systems with Applications, vol. 37, no. 7, pp. 5454–5460, 2010.
[10]  J.-S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993.
[11]  K. S. Pal and S. Mitra, Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing, John Wiley & Sons, New York, NY, USA, 1999.
[12]  F. K. Nauck and R. Kruse, Foundation of Neuro-Fuzzy Systems, John Wiley & Sons, New York, NY, USA, 1997.
[13]  R. Singh, A. Kainthola, and T. N. Singh, “Estimation of elastic constant of rocks using an ANFIS approach,” Applied Soft Computing Journal, vol. 12, no. 1, pp. 40–45, 2012.
[14]  A. Keles, A. Samet Hasiloglu, A. Keles, and Y. Aksoy, “Neuro-fuzzy classification of prostate cancer using NEFCLASS-J,” Computers in Biology and Medicine, vol. 37, no. 11, pp. 1617–1628, 2007.
[15]  S. K. Sinha and P. W. Fieguth, “Neuro-fuzzy network for the classification of buried pipe defects,” Automation in Construction, vol. 15, no. 1, pp. 73–83, 2006.
[16]  B. Ceti?li and A. Barkana, “Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training,” Soft Computing, vol. 14, no. 4, pp. 365–378, 2010.
[17]  S. Abe, Support Vector Machines for Pattern Classification, Springer, New York, NY, USA, 2010.
[18]  J. Han and M. Kamber, Data Mining: Concepts and Techniquespublishers, Morgan Kaufmann, San Francisco, Calif, USA, 2006.
[19]  S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, London, UK, 2003.
[20]  A. J. Myles, R. N. Feudale, Y. Liu, N. A. Woody, and S. D. Brown, “An introduction to decision tree modeling,” Journal of Chemometrics, vol. 18, no. 6, pp. 275–285, 2004.
[21]  M. Debeljak and S. Dzeroski, Decision Trees in Scological Modelling in Modelling Complex Ecological Dynamics, Springer, Berlin, Germany, 2011.
[22]  C. T. Sun and J.-S. Jang, “Neuro-fuzzy classifier and its applications,” in Proceedings of the 2nd IEEE International Conference on Fuzzy Systems, vol. 1, pp. 94–98, San Francisco, Calif, USA, April 1993.
[23]  J. S. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Saddle River, NJ, USA, 1997.
[24]  S. Theoridis and K. Koutroumbas, Pattern Recognition, Academic Press, London, UK, 2003.
[25]  S. Haykin, Kalman Filtering and Neural Networks, John Wiley & Sons, New York, NY, USA, 2001.
[26]  J.-S. R. Jang and E. Mizutani, “Levenberg-Marquardt method for ANFIS learning,” in Proceedings of the Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS '96), pp. 87–91, Berkeley, Calif, USA, June 1996.
[27]  M. R. Mustafa, R. B. Rezaur, S. Saiedi, H. Rahardjo, and M. H. Isa, “Evaluation of MLP-ANN training algorithms for modeling soil pore-water pressure responses to rainfall,” Journal of Hydrologic Engineering, vol. 18, pp. 50–57, 2013.
[28]  M. F. M?ller, “A scaled conjugate gradient algorithm for fast supervised learning,” Neural Networks, vol. 6, no. 4, pp. 525–533, 1993.

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