Using the Fuzzy Logic in Assessing the Programming Performance of Students

Abstract

The overall objective of this study is to understand how the fuzzy logic theory can be used in measuring the programming performance of the undergraduate students, as well as proving the advantages of using fuzzy logic in evaluation of students’ performance. 336 students were involved in the sample of this quantitative study. The first group was consisted of 150 students, whereas the second group was consisted of 186 students. Cluster analysis was also conducted in order to ensure the neutrality of sample. The rule-based intelligent fuzzy logic assessment logic (FLAL) system was developed. This system has a flexible database in order to assess the academic programming performances of students. Therefore, an absolute evaluation system was used in order to calculate the second group’s performance. On the other hand, FLAL system was applied to the first group to determine their programming performance. A Mamdani-type fuzzy logic algorithm mechanism having two inputs and one output was utilized. An independent sample T test was used in analyzing the data sets. As a result, there was a significant difference between first and second groups’ results in favor of the first group. While 29 students comprised of 19.3% of all the students failed in the flexible percentage system, 41 students comprised of 22% of all the students failed in the absolute evaluation system evaluating their grades via fuzzy logic system. By increasing the input parameters of the fuzzy logic rules, the results can be addressed more efficiently.

Keywords

• Absolute evaluation system
• flexible percentage system
• fuzzy logic

Using the Fuzzy Logic in Assessing the Programming Performance of Students

Öz

The overall objective of this study is to understand how the fuzzy logic theory can be used in measuring the programming performance of the undergraduate students, as well as proving the advantages of using fuzzy logic in evaluation of students’ performance. 336 students were involved in the sample of this quantitative study. The first group was consisted of 150 students, whereas the second group was consisted of 186 students. Cluster analysis was also conducted in order to ensure the neutrality of sample. The rule-based intelligent fuzzy logic assessment logic (FLAL) system was developed. This system has a flexible database in order to assess the academic programming performances of students. Therefore, an absolute evaluation system was used in order to calculate the second group’s performance. On the other hand, FLAL system was applied to the first group to determine their programming performance. A Mamdani-type fuzzy logic algorithm mechanism having two inputs and one output was utilized. An independent sample T test was used in analyzing the data sets. As a result, there was a significant difference between first and second groups’ results in favor of the first group. While 29 students comprised of 19.3% of all the students failed in the flexible percentage system, 41 students comprised of 22% of all the students failed in the absolute evaluation system evaluating their grades via fuzzy logic system. By increasing the input parameters of the fuzzy logic rules, the results can be addressed more efficiently.

Anahtar Kelimeler

• Absolute evaluation system
• Flexible percentage system
• Fuzzy logic
• Programming

References

1. Altrock, V., C. (1995). Fuzzy Logic Applications in Europe, In J. Yen, R. Langari, and L. A.Zadeh (Eds.) Industrial Applications of Fuzzy Logic and Intelligent Systems, Chicago:. IEEE Press.Anderson, R. S. (1998). Why Talk About Different Ways to Grade? The Shift from Traditional Assessment to Alternative Assessment, New Directions for Teaching and Learning, 74, 5-16.
2. Baba, A. F., Kuşcu, D., & Han, K. (2009). Developing a software for fuzzy group decisionsupport system: A case study. The Turkish Online Journal of Educational Technology, TOJET, 3(8), 22-29
3. Bai, S. M., & Chen, S. M. (2008). Automatically constructing grade membership functions of fuzzy rules for students’ evaluation. Expert Systems with Applications, 35(3), 1408–1414.
4. Biswas, R. (1995). An application of fuzzy sets in students’ evaluation. Fuzzy Sets and Systems, 74, 187-194.
5. Bowers, P.S. (1987). The Effects of the 4MAT System on Achievement and Attitudes inScience. Unpublished PhD thesis, The University of Noith Caroîina at Chapel Hill. (http://www.eric.ed.gov)
6. Butt, G. (2010). Making Assessment Matter, NewYork, USA: Continuum International Publishing Group.
7. Chen, S. M. (1999). Evaluating weapon systems using fuzzy arithmetic operations. Fuzzy Sets and Systems, 77, 265-276.
8. CTL, (2001). Teaching at Carolina. Center for Teaching and Learning, University of North Carolina at Chapel Hill. http://ctl.unc.edu/he2.html (2.2.2018)

9. Çekiç, H. (1991). Matching learning and teaching styles in a Turkish EFL university classroom and its effect on language development. Unpublished master thesis, Bilkent Üniversitesi, Ankara
10. Echauz, J. R., & Vachtsevanos, G. J. (1995). Fuzzy Grading System. IEEE Transactions on Education , 38(2), 158–165.
11. Ertuğrul, İ. (2006). Akademik performans değerlendirmede bulanık mantık yaklaşımı. Atatürk Üniversitesi İİBF Dergisi, 20, 1, 155-156.
12. Gawronski, J. D. (1971). An investigation of the effect of selected learning styles on achievement in eighth grade mathematics. Unpublished PhD thesis, University of Minnesota.
13. J. Mendel. Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83 (3): 345–377, Mar 1995.
14. K. A. Rasmani and Q. Shen. Data-Driven Fuzzy Rule Generation and its Application forStudent Academic Performance Evaluation. International Journal of Applied Intelligence, 25(3), pp. 305-319, 2006
15. Kerka, S., & Wonacott, M.E. (2000). Assessing learners online: Practitioner file. Washington, D.C.: Office of Educational Research and Improvement.
16. Keskin, M. & Ertan, H. (2001). İstanbul Üniversitesi’nin Bağıl Değerlendirme Sistemi, İstanbul
17. Kılıç, E. (2002), Web temelli öğrenmede baskın öğrenme stilinin öğrenme etkinlikleri tercihi ve akademik başarıya etkisi. Yayınlanmamış doktora tezi, Ankara Üniversitesi, Ankara.
18. Kibby, M (1999). Assessing Students On-line. The University of Newcastle Retrieved June 3, 2008, from: http://www.newcastle.edu.au/discipline/sociolanthrop/staff/kibbymarj/online/assess.html
19. Kwok, R. C.W., Ma, J., Vogel, D., & Zhou, D. (2001). Collaborative assessment in Education: An application of a Fuzzy GSS. Information Management, 39, 243- 253.
20. Lin, H. F. (2010). An application of fuzzy ahp for evaluating course website quality. Computers & Education, 54(4), 877-888.
21. Özdemir, O., & Tekin, A. (2016). Evaluation of the presentation skills of the pre-service teachers via fuzzy logic. Computers in Human Behavior, 61, 288-299.
22. Rasmani, K. A., & Shen, Q. (2005). Subsethood-Based fuzzy rule models and their application to student performance classification. IEEE International Conference on Fuzzy Systems.
23. Rockman, I. F. (2002). The importance of assessment, Reference Services Review, 30(3), 181-182.
24. R.S. Yadav, S. Kumar. The Effective Utilizations of Fuzzy Logic Approach for Student Academic Performance Evaluation. M.Phil. Dissertation, YCMOU, Nashik, India, pp 6-7, 2009.
25. Rutkowski, L. (2004). Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation, Bostan: Kluwer Academic Publisher.
26. Semerci, Çetin. "The Influence of Fuzzy Logic Theory on Students' Achievement." TOJET: The Turkish Online Journal of Educational Technology3.2 (2004).
27. S.M. Bai and S.M. Chen. A new method for students’ learning achievement evaluation using fuzzy membership functions. Proceeding of the 11th Conference of Artificial Intelligence and Applications, Kaohsiung, Taiwan, Republic of China, pp. 177-184, 2006.
28. Sönmez, V. (1994). Program geliştirmede öğretmen el kitabı, Ankara: PEGEM Yayınları. No :12, 7. Basım.
29. S. Pavani, P.V.S.S. Gangadhar and K. K. Gulhare. Evaluation of Teacher’s Performance Evaluation Using Fuzzy Logic Techniques. International Journal of Computer Trends and Technology, 3(2), pp. 200-205, 2012
30. Tamilselvan, G. M., and A. Shanmugam. "Fuzzy-Logic Based Medium Access ControlModel for Battery Lifetime Enhancement in Wireless Body Area Networks." Journal of Engineering and Technology 4.2 (2014): 135.
31. Ward, A., Stoker, H. W., & Murray-Ward, M. (1996). Educational measurement: Origins, theories, and explications Volume 2. Maryland: University Press of America
32. Wu, M. H. (2003). Research on applying fuzzy set theory and ıtem response theory to evaluate learning performance. Master Thesis, Department of Information Management, Chaoyang University of Technology, Wufeng, Taichung County, Republic of China.
33. Zadeh, “Fuzzy sets. Information and Control”, 8, pp. 338-354, 1965