We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Modeling Academic Performance Evaluation using Fuzzy C-Means Clustering Techniques

by Ramjeet Singh Yadav, Vijendra Pratap Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 8
Year of Publication: 2012
Authors: Ramjeet Singh Yadav, Vijendra Pratap Singh
10.5120/9711-4174

Ramjeet Singh Yadav, Vijendra Pratap Singh . Modeling Academic Performance Evaluation using Fuzzy C-Means Clustering Techniques. International Journal of Computer Applications. 60, 8 ( December 2012), 15-23. DOI=10.5120/9711-4174

@article{ 10.5120/9711-4174,
author = { Ramjeet Singh Yadav, Vijendra Pratap Singh },
title = { Modeling Academic Performance Evaluation using Fuzzy C-Means Clustering Techniques },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 8 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 15-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number8/9711-4174/ },
doi = { 10.5120/9711-4174 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:06:00.289596+05:30
%A Ramjeet Singh Yadav
%A Vijendra Pratap Singh
%T Modeling Academic Performance Evaluation using Fuzzy C-Means Clustering Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 8
%P 15-23
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we explore the applicability of Fuzzy C-Means clustering technique to student allocation problem that allocates new students to homogenous groups of specified maximum capacity, and analyze effects of such allocations on the academic performance of students. This paper also presents a Fuzzy set and Regression analysis based rules based Fuzzy Expert System model which is capable of dealing with imprecision and missing data that is commonly inherited in the student academic performance evaluation. This model automatically converts crisp sets into fuzzy sets by using C-Means clustering technique for academic performance evaluation.

References
  1. Mankad, K. , Sajja, P. S. , & Akerkar, R. (2011). Evolving Rules Using Genetic Fuzzy Approach: An educational case study. International Journal on Soft Computing. 2(1), 35-46.
  2. Biswas, R. (1995). An Application of fuzzy sets in Students' Evaluation. Fuzzy sets and System, ELSEVIER, 187-194.
  3. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-354.
  4. Wang, H. Y. , & Chen, S. M. (2007). Artificial Intelligence Approach to Evaluate Students' Answerscripts Based on the Similarity Measure Between Vague Sets. Educational Technology and Society, 10(4), 224-241.
  5. Gau, W. L. & Buehrer, D. J. (1993). Vague Sets. IEEE Transactions on System. Man and Cybernatics, 23(2), 610-614.
  6. Bai, S. M. , & Chen, S. M. (2008). Evaluating Students' Learning Achievement Using Fuzzy membership functions and Fuzzy rules. Expert System with Applications, ELSEVIER, 34, 399-410.
  7. Law, C. K. (1996). Using Fuzzy Numbers in Educational Grading system. Fuzzy sets and System 83, 311-323.
  8. Chen, S. M. , & Lee, C. H. (1999). New Methods for Students' Evaluation Using Fuzzy Sets. Fuzzy Sets and System, 104, 209-218.
  9. Wang, H. Y, & Chen, S. M. (2006). New Methods for Evaluating Students Answerscripts Using Fuzzy Numbers Associated with Degrees of Confidence. 2006 IEEE International Conference on Fuzzy Systems, 1004-1009.
  10. Stathacopoulou, R. , Magoulas, G. D. , Grigoriadou, M. & Samarakou (2005). Neuro-Fuzzy Knowledge Processing in Intelligent Learning Environments for Improved Student Diagnosis. Information Science, ELSEVIER, 170(2-4), 273-307.
  11. Guh, Y. Y. , Yang, M. S. , Po, R. W. , & Lee, E. S. (2008). Establishing Performance Evaluation Structures by Fuzzy Relation Based Cluster Analysis. Computers and Mathematics Applications, 56, 572-582.
  12. Gokmen, E. , Akinci, T. C. , Tektas, M. , Onat, N. , Kocyigit, G. , & Tektas, N. (2010). Evaluation of Student Performance in Laboratory Applications Using Fuzzy Logic. Procedia Social and Behavioral Science, 2, 902-909.
  13. Hameed, I. A. (2011). Using Gaussian Membership Functions for Improving the Reliability and Robustness of Students' Evaluation System. International Journal of Expert System with Applications, 38 (6), 7135-7142.
  14. Baylari, A. , & Montazer, G. A. (2009). Design a Personalized E-learning System Based on Item Response Theory and Artificial Neural Network Approach. Expert System with Applications, 36, 8013-8021.
  15. Posey, C. L. , & Hawkes, L. W. (1996). Neural Networks Applied in the Student Model. Intelligent Systems, 88, 275-298.
  16. Stathacopoulou, R. , Grigoriadou, M. , Samarakou, M. , & Mitoropoulou , D. (2007). Monitoring Students' Action and Using Teachers' Expertise in Implementing and Evaluating the Neural Network-based Fuzzy Diagnostic Model. Expert Systems with Applications, 32, 955-975.
  17. Bhatt, R. , & Bhatt, D. (2011). Fuzzy Logic Based Student Performance Evaluation Model for Practical Components of Engineering Institutions Subjects. International Journal of Technology and Engineering Education,8(1), 1-7.
  18. Gupta, C. R. , & Dhawan, A. K. (2012). Diagnosis, Modeling and Prognosis of Learning System Using Fuzzy Logic and Intelligent Decision Vectors. International Journal of Computer Applications, 37(6), (975-987).
  19. Ma, J. , & Zhou, D. (2000). Fuzzy Set Approach to the Assessment of student Centered Learning. IEEE Transaction on Education, 43(2), 112-120.
  20. Krzysztof, J. , Cios, Pedrycz, W. , Swiniarski, R. W. , Lukasz, A. , & Kurgan (2007). Data Mining: A Knowledge Discovery Approach. Springer, 263-265.
  21. Gagula-Palalic, S. , & Can, M. (2008). Fuzzy Clustering Models and Algorithms for Pattern Recognition. Master Thesis, 13-17.
  22. Yen, J. , & Langari, R. (1999). Fuzzy Logic: Intelligence, Control and Information. Center for Fuzzy logic. Robotics and Intelligent Systems. Texas A & M University, 375-401.
  23. S. S. Sansgiry, M. Bhosle and K. Sail (2006). Factors that Affect Academic Performance among Pharmacy Students. American Journal of Pharmaceutical Education, (231-243).
  24. Oyelade, O. J. , Oladipupo, O. O. , & I. C. Obagbua (2010). Application of K-Means Clustering Algorithm for prediction of students' Academic Performance. International Journal of Computer Science and Information Security. 7(1), 292-295.
  25. Zukhri, Z. , & Omar, K. (2008). Solving New Student Allocation Problem with Genetic Algorithm: A Hard Problem for Partition Based Approach. International Journal of Soft Computing Applications. Euro Journal Publishing Inc. , 6-15.
  26. Pavani, S. , Gangadhar, P. V. S. S. , & Gulhare, K. K. (2012). Evaluation of Teacher's Performance Evaluation Using Fuzzy Logic Techniques. International Journal of Computer Trends and Technology. 3(2), 200-205.
  27. Sreenivasarao, V. , & Yohannes, G. (2012). Improving Academic Performance of Students of Defense University Based on data Warehousing and Data Mining. Global Journal of Computer Science and Technology. 12(2), 201-209.
  28. Afoayan, O. , & El-Shamir Absalom, E. (2010). Design and implementation of Student's Information System for Tertiary Institutions Using Neural Network Techniques. International Journal of Green Computing. 1(1), (1-15).
  29. Chaudhari O. K. , Khot, P. G. , & Deshmukh, K. C. (2012). Soft Computing Model for Academic Performance of Teachers Using Fuzzy Logic. British Journal of Applied Science and Technology. 2(2), 213-226.
  30. Neogi, A. , Mondal, A. C. , & Mandal, S. (2011). A Cascaded Fuzzy Inference System for University Non-Teaching Staff Performance Appraisal. Journal of Information Processing Systems, 7(4), 595-612.
  31. Yadav, R. S. , & Singh, V. P. (2011). Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach. International Journal on Computer Science and Engineering, 3(2), 676-686.
  32. Daud, W. S. W. , Aziz, K. A. A. , & Sakib, E. (2011). An Evaluation of Students' Performance in Oral Presentation Using Fuzzy Approach. Empowering Science, Technology and Innovation towards a Better Tomorrow. UMTAS 2011, (MO36), 157-162.
  33. Upadhyay, M. S. (2012). Fuzzy Logic Based of Performance of Students in College. Journal of Computer Applications (JCA), 5(1), 6-9.
  34. White, H. (1989). Learning in Artificial Neural Networks: A Statistical Perspective. Neural Computation, 1, 425-464.
  35. Giarratano, J. C. & Riley, G. (2005). Expert System: Principles and Programming. Fourth ed. , PWS Publishing Com. Boston, MA, USA.
  36. Schneider, M. , Langholz, G. , Kandel, A. , & Chew, G. (1996). Fuzzy Expert System Tools, Jhon Willy and Sons, USA.
  37. www. brighthub. com, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Logic Clustering Fuzzy C-Means Clustering Technique Rule based Fuzzy Expert Systems Membership Function and Academic Performance Evaluation