A Novel Student Performance Evaluation Model Based on Fuzzy Logic for Distance Learning

Öz

Distance learning is an education model in which the educator and the student come together independently of time and place and the learning process is continued. Although it has positive aspects in terms of time and space, it has limitations such as weak interaction and poor functioning of evaluation processes. Assessment systems often include multiple-choice or open-ended questions. In other words, the result is evaluated in solving a given problem, and the student's actions can be ignored until the result is reached. In this study, while evaluating student performance, the student's behavior during the semester and the distractor weight coefficient for multiple-choice exams were added, and a performance evaluation was made on the student's incorrect answers. The proposed model was created based on fuzzy logic, and the uncertainties in the evaluation were attempted to be eliminated.

Anahtar Kelimeler

• distance learning
• fuzzy logic
• performance evaluation
• e-learning
• e-evaluation

Kaynakça

1. S. Ertürk, ‘Eğitimde program geliştirme (4. Baskı) Ankara: Yelken Yayınları’, 1974.
2. F. Ulutaş and B. Ubuz, ‘Research and Trends in Mathematics Education: 2000 to 2006.’, Elementary Education Online, vol. 7, no. 3, 2008.
3. M. F. Doğ, ‘Usability metrics on e-learning systems’, Master Thesis, Bahçeşehir University, 2012.
4. K. Khawar, S. Munawar, and N. Naveed, ‘Fuzzy Logic-based Expert System for Assessing Programming Course Performance of E-Learning Students’, Journal of Information Communication Technologies and Robotic Applications, pp. 54–64, Jun. 2020.
5. B. Tütmez, ‘Bulanık Mantık ve Eğitim Bilimlerinde Kullanılabilirliği’, Eğitim Dergisi, no. 18, 2018.
6. M. Annabestani, A. Rowhanimanesh, A. Mizani, and A. Rezaei, ‘Descriptive evaluation of students using fuzzy approximate reasoning’, arXiv:1905.02549 [cs], May 2019, Accessed: Jan. 10, 2021. [Online]. Available: http://arxiv.org/abs/1905.02549
7. Y. Altun Türker, ‘Selection of distance education learning management system with fuzzy multi-criteria decision making methods’, Master’s Thesis, Kocaeli University, 2012.
8. D. Herand and Z. A. Hatipoğlu, ‘E-learning and Comparison of Commonly Used E-learning Platforms’, Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 18, no. 1, 2014.

9. A. H. Işık, A. Karacı, O. Özkaraca, and S. Biroğul, ‘Web tabanlı eş zamanlı (senkron) uzaktan eğitim sistemlerinin karşılaştırmalı analizi’, Akademik Bilişim, pp. 10–12, 2010.
10. S. İzmirli and H. İ. Akyüz, ‘Examining synchronous virtual classroom software’, Journal of Theory and Practice in Education, vol. 13, no. 4, pp. 788–810, 2017.
11. E. Lavolette, M. A. Venable, E. Gose, and E. Huang, ‘Comparing synchronous virtual classrooms: Student, instructor and course designer perspectives’, TechTrends, vol. 54, no. 5, pp. 54–61, 2010.
12. S. Schullo, A. Hilbelink, M. Venable, and A. E. Barron, ‘Selecting a virtual classroom system: Elluminate live vs. Macromedia breeze (adobe acrobat connect professional)’, MERLOT Journal of Online Learning and Teaching, vol. 3, no. 4, pp. 331–345, 2007.
13. D. Yıldırım, H. Tüzün, M. Çınar, A. Akıncı, E. Kalaycı, and H. G. Bilgiç, ‘Comparison of Synchronous Virtual Classroom Tools Used In Distance Learning’, Akademik Bilişim, pp. 451–456, 2011.
14. H. Baran, ‘Measurement and evaluation in open and distance education’, Açıköğretim Uygulamaları ve Araştırmaları Dergisi, vol. 6, no. 1, pp. 28–40, 2020.
15. J. R. Echauz and G. J. Vachtsevanos, ‘Fuzzy Grading System’, IEEE Transactions on Education, vol. 38, no. 2, pp. 158–165, May 1995.
16. S. Kotsiantis, C. Pierrakeas, and P. Pintelas, ‘Predicting Students’ Performance in Distance Learning Using Machine Learning Techniques’, Applied Artificial Intelligence, vol. 18, no. 5, pp. 411–426, May 2004.
17. Ç. Ölmez, ‘Analysis of question banks in distance learning with fuzzy logic method’, Master Thesis, Afyon Kocatepe University, 2010.
18. S. N. Ingoley and J. W. Bakal, ‘Students’ performance evaluation using fuzzy logic’, in 2012 Nirma University International Conference on Engineering (NUiCONE), Dec. 2012, pp. 1–6.
19. S. S. Jamsandekar and R. R. Mudholkar, ‘Performance Evaluation by Fuzzy Inference Technique’, International Journal of Soft Computing and Engineering, vol. 3, no. 7, 2013.
20. O. Yildiz, A. Bal, and S. Gulsecen, ‘Improved fuzzy modelling to predict the academic performance of distance education students’, The International Review of Research in Open and Distributed Learning, vol. 14, no. 5, Dec. 2013.
21. G. Jyothi, M. C. Parvathi, M. P. Srinivas, and M. S. Althaf, ‘Fuzzy Expert Model for Evaluation of Faculty Performance in Technical Educational Institutions’, vol. 4, no. 5, p. 10, 2014.
22. K. Salmi, H. Magrez, and A. Ziyyat, ‘A fuzzy expert system in evaluation for E-learning’, in 2014 Third IEEE International Colloquium in Information Science and Technology (CIST), Oct. 2014, pp. 225–229.
23. O. Yıldız, ‘Evaluating distance learning students' performance by machine learning’, PhD Thesis, Istanbul University, 2014.
24. N. Ghatasheh, ‘Knowledge Level Assessment in e-Learning Systems Using Machine Learning and User Activity Analysis’, ijacsa, vol. 6, no. 4, 2015, doi: 10.14569/IJACSA.2015.060415.
25. J. Azimjonov, İ. H. Selvi̇, and U. Özbek, 'Evaluation of distance learning students performance using fuzzy logic’, Yönetim Bilişim Sistemleri Dergisi, vol. 2, no. 2, Art. no. 2, Oct. 2016.
26. A. Barlybayev, A. Sharipbay, G. Ulyukova, T. Sabyrov, and B. Kuzenbayev, ‘Student’s Performance Evaluation by Fuzzy Logic’, Procedia Computer Science, vol. 102, pp. 98–105, Jan. 2016.
27. T. Mahboob, S. Irfan, and A. Karamat, ‘A machine learning approach for student assessment in E-learning using Quinlan’s C4.5, Naive Bayes and Random Forest algorithms’, in 2016 19th International Multi-Topic Conference (INMIC), Dec. 2016, pp. 1–8.
28. J. C. S. Silva, J. L. C. Ramos, R. L. Rodrigues, A. S. Gomes, F. D. F. D. Souza, and A. M. A. Maciel, ‘An EDM Approach to the Analysis of Students’ Engagement in Online Courses from Constructs of the Transactional Distance’, in 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT), Jul. 2016, pp. 230–231.
29. S. Sisovic, M. Matetic, and M. B. Bakaric, ‘Clustering of imbalanced moodle data for early alert of student failure’, in 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Jan. 2016, pp. 165–170.
30. Y. Abubakar and N. B. H. Ahmad, ‘Prediction of Students’ Performance in E-Learning Environment Using Random Forest’, International Journal of Innovative Computing, vol. 7, no. 2, Art. no. 2, Dec. 2017.
31. A. Cebi and H. Karal, ‘An application of fuzzy analytic hierarchy process (FAHP) for evaluating students project’, Educ. Res. Rev., vol. 12, no. 3, pp. 120–132, Feb. 2017.
32. R. Umer, T. Susnjak, A. Mathrani, and S. Suriadi, ‘On predicting academic performance with process mining in learning analytics’, Journal of Research in Innovative Teaching & Learning, vol. 10, no. 2, pp. 160–176, Jan. 2017.
33. M. Hussain, W. Zhu, W. Zhang, and S. M. R. Abidi, ‘Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores’, Computational Intelligence and Neuroscience, Vol. 2018, Oct. 02, 2018. https://www.hindawi.com/journals/cin/2018/6347186/ (accessed Jan. 10, 2021).
34. C. Turan, Z. A. Reis, and S. Gülseçen, ‘Bakış Takibi ile E-Öğrenme Materyalinde Konu Odağı ve Öğrenci Bakış Reflekslerinin İlgisini Değerlendirme’, in 2018 5th International Management Information Systems Conference, pp. 34-37 Dec. 2018.
35. S.-U. Hassan, H. Waheed, N. R. Aljohani, M. Ali, S. Ventura, and F. Herrera, ‘Virtual learning environment to predict withdrawal by leveraging deep learning’, International Journal of Intelligent Systems, vol. 34, no. 8, pp. 1935–1952, 2019.
36. V. Ivanova and B. Zlatanov, ‘Implementation of Fuzzy Functions Aimed at Fairer Grading of Students’ Tests’, Education Sciences, vol. 9, no. 3, p. 214, Aug. 2019.
37. I. G. Ndukwe, B. K. Daniel, and C. E. Amadi, ‘A Machine Learning Grading System Using Chatbots’, in Artificial Intelligence in Education, Cham, 2019, pp. 365–368.
38. S. Slater and R. Baker, ‘Forecasting future student mastery’, Distance Education, vol. 40, no. 3, pp. 380–394, Jul. 2019.
39. P. Sokkhey and T. Okazaki, ‘Comparative Study of Prediction Models on High School Student Performance in Mathematics’, in 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), Jun. 2019, pp. 1–4.
40. N. Abu Bakar, S. Rosbi, and A. A. Bakar, ‘Robust Estimation of Student Performance in Massive Open Online Course using Fuzzy Logic Approach’, International Journal of Engineering Trends and Technology, pp. 143–152, Oct. 2020.
41. Y. Dashko, O. Vitchenko, and M. Kadomtsev, ‘Soft models of competence assessment in professional education’, E3S Web Conf., vol. 210, p. 18011, 2020.
42. S. Raval and B. Tailor, ‘Mathematical Modelling of Students’ Academic Performance Evaluation Using Fuzzy Logic’, International Journal of Statistics and Reliability Engineering, vol. 7, no. 1, Art. no. 1, Jul. 2020.
43. H. M. Ünver, ‘Design of a Fuzzy Logic Based Custom Exam Production System for High Performance’, International Journal of Engineering Research and Development, vol. 12, no. 2, pp. 745–752, Jun. 2020.
44. H. Waheed, S.-U. Hassan, N. R. Aljohani, J. Hardman, S. Alelyani, and R. Nawaz, ‘Predicting academic performance of students from VLE big data using deep learning models’, Computers in Human Behavior, vol. 104, p. 106189, Mar. 2020.
45. R. Wardoyo and W. D. Yuniarti, ‘Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning’, IJID (International Journal on Informatics for Development), vol. 9, no. 1, Art. no. 1, Nov. 2020.
46. S. Gocheva-Ilieva, H. Kulina, and A. Ivanov, ‘Assessment of Students’ Achievements and Competencies in Mathematics Using CART and CART Ensembles and Bagging with Combined Model Improvement by MARS’, Mathematics, vol. 9, no. 1, Art. no. 1, Jan. 2021.
47. Ö. Bursalıoğlu, ‘Uzaktan eğitime uygun mobil destekli çevirimiçi sınav sistemi’, Master’s Thesis, Kırıkkale Üniversitesi, 2016.
48. K. Almohammadi, H. Hagras, B. Yao, A. Alzahrani, D. Alghazzawi, and G. Aldabbagh, ‘A type-2 fuzzy logic recommendation system for adaptive teaching’, Soft Comput, vol. 21, no. 4, pp. 965–979, Feb. 2017, doi: 10.1007/s00500-015-1826-y.
49. N. Arora and J. R. Saini, ‘A fuzzy probabilistic neural network for student’s academic performance prediction’, International Journal of Innovative Research in Science, Engineering and Technology, vol. 2, no. 9, pp. 4425–4432, 2013.
50. N. A. Kumari, D. N. Rao, and M. S. Reddy, ‘Indexing student performance with fuzzy logics evaluation in engineering education’, International Journal of Engineering Technology Science and Research, vol. 4, no. 9, pp. 514–522, 2017.
51. S. Maitra, S. Madan, and P. Mahajan, ‘An Adaptive Neural Fuzzy Inference System for prediction of student performance in Higher Education’, in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2018, pp. 1158–1163.
52. H. Salvi Akansha, M. Khairnar Medha, R. Shaikh Samina, and T. Kokani Shital, ‘Prediction and Evaluation of Students Academic Performance using Fuzzy Logic’, International Research Journal of Engineering and Technology (IRJET), e-ISSN, vol. 5, no. 2, pp. 2395–0056, 2018.
53. T. Kim, E. Sotirova, A. Shannon, V. Atanassova, K. Atanassov, and L.-C. Jang, ‘Interval valued intuitionistic fuzzy evaluations for analysis of a student’s knowledge in university e-learning courses’, International Journal of Fuzzy Logic and Intelligent Systems, vol. 18, no. 3, pp. 190–195, 2018.
54. N. A. Bakar, S. Rosbi, and A. A. Bakar, ‘Evaluation of Students Performance using Fuzzy Set Theory in Online Learning of Islamic Finance Course.’, International Journal of Interactive Mobile Technologies, vol. 15, no. 7, 2021.
55. N. A. M. Nor, A. Azizan, B. Moktar, A. A. Aziz, and D. S. M. Nasir, ‘Modeling Mathematics Performance Between Rural and Urban School Using a Fuzzy Logic Approach’, Journal of Computing Research and Innovation, vol. 6, no. 1, pp. 77–87, 2021.
56. E. A. Laksana, B. T. Munajat, K. Permana, S. Aisyah, S. F. Wijawanto, and T. R. Soleh, ‘Student Grade 0Using Fuzzy Logic’, Review of International Geographical Education Online, vol. 11, no. 5, pp. 1073–1081, 2021.
57. H. J. Zimmermann, ‘Fuzzy set theory-and its applications’, Kluwer, 1991.
58. Ö. Mehmet Nuri, Bulanık Mantık Yöntem ve Uygulamaları. Türkiye: İKSAD, 2019.
59. İ. Gültaş, ‘A fuzzy AHP solution approach to the determination of the mathematics courses syllabuses in the industrial engineering education’, Master’s Thesis, Istanbul Technical University, 2007. Accessed: Jan. 10, 2021. [Online]. Available: https://polen.itu.edu.tr/handle/11527/5845
60. Shyi-Ming Chen and Chia-Hoang Lee, ‘New methods for students’ evaluation using fuzzy sets’, Fuzzy Sets and Systems, vol. 104, no. 2, pp. 209–218, Jun. 1999, doi: 10.1016/S0165-0114(97)00208-X.