Fuzzy Logic Modeling for Student Evaluation Systems

Student evaluation plays a crucial role in academic institutions for assessing learning outcomes and overall performance. Traditional evaluation methods often rely on rigid grading systems that may not accurately represent a student's capabilities. Fuzzy Logic provides an effective alternative by handling uncertainty and imprecision in academic assessment. This paper presents a fuzzy logic-based student evaluation system that considers multiple performance parameters such as attendance, assignment scores, internal assessments, and examination marks. The proposed model uses linguistic variables and fuzzy inference rules to generate a comprehensive performance score. Experimental analysis demonstrates that the fuzzy logic approach provides more realistic and flexible evaluations compared to conventional grading methods.

Fuzzy Logic, Student Evaluation, Academic Performance, Fuzzy Inference System, Educational Assessment.

1. Abdullah S. and Rahman M. M. (2018), “Student Performance Evaluation Using Fuzzy Logic Approach,” International Journal of Computer Applications, vol. 179, no. 7, 1–6. [Google Scholar] [Crossref]

2. Abraham A. (2005), “Rule-Based Expert Systems,” Handbook of Measuring System Design, Wiley. [Google Scholar] [Crossref]

3. Chai S., Liu Y., and Yue J. (2020), “An Intelligent Student Evaluation Framework Based on Fuzzy Inference Systems,” International Journal of Educational Technology in Higher Education, vol. 17, no. 45, 1–15. [Google Scholar] [Crossref]

4. Chen S. M. and Lee C. H. (1999), “New Methods for Students’ Evaluations Using Fuzzy Sets,” Fuzzy Sets and Systems, vol. 104, no. 2, 209–218. [Google Scholar] [Crossref]

5. Dominic P. D. D. and Khan S. A. (2019), “Application of Fuzzy Logic in Educational Assessment and Decision Making,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 4, 221–227. [Google Scholar] [Crossref]

6. Jang J.-S. R., Sun C.-T., and Mizutani E.(1997), “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence”, Prentice Hall. [Google Scholar] [Crossref]

7. Kaufmann A. and Gupta M. M. (1991), “Introduction to Fuzzy Arithmetic: Theory and Applications”, Van Nostrand Reinhold. [Google Scholar] [Crossref]

8. Klir G. J. and Yuan B. (1995), “Fuzzy Sets and Fuzzy Logic: Theory and Applications”, Prentice Hall. [Google Scholar] [Crossref]

9. Lee C. C. (1990), “Fuzzy Logic in Control Systems: Fuzzy Logic Controller—Part I,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, no. 2, 404–418. [Google Scholar] [Crossref]

10. Lee C. C. (1975), “Fuzzy Logic in Control Systems: Fuzzy Logic Controller—Part II,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, Machine Studies, vol. 7, no. 1, 1–13. [Google Scholar] [Crossref]

11. Mendel J. M. (2017), “Uncertain Rule-Based Fuzzy Systems”, Springer. [Google Scholar] [Crossref]

12. Mitra S. and Hayashi Y. (2000), “Neuro-Fuzzy Rule Generation,” IEEE Transactions on Neural Networks, vol. 11, no. 3, 748–768. [Google Scholar] [Crossref]

13. Passino K. M. and Yurkovich S. (1998), “Fuzzy Control”, Addison-Wesley. [Google Scholar] [Crossref]

14. Ross T. J. (2010), “Fuzzy Logic with Engineering Applications”, 3rd ed., Wiley. [Google Scholar] [Crossref]

15. Wang L. X. (1997),” A Course in Fuzzy Systems and Control”, Prentice Hall. [Google Scholar] [Crossref]

16. Yager R. R.and Filev D. P. (1994), “Essentials of Fuzzy Modeling and Control”, New York: Wiley. [Google Scholar] [Crossref]

17. Zadeh L. A. (1965), “Fuzzy Sets, Information and Control”, vol. 8, no. 3, 338–353. [Google Scholar] [Crossref]

18. Zarandi M. H. Fazel, Turksen I. B., and Rezaee A. (2009), “A Type-2 Fuzzy Rule-Based Expert System Model for Student Academic Evaluation,” Expert Systems with Applications, vol. 36, no. 10, 11999–12008. [Google Scholar] [Crossref]

19. Zimmermann H.-J. (2001), “Fuzzy Set Theory and Its Applications”, 4th ed., Springer. [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study