Öz
The availability of Internet has accorded people with the opportunity to get education via Distance Learning. Universities, companies even people have their own online teaching tools. Indeed, learners or people who have business with these institutions want to know the quality of their services and how successful they are. This reason is vital to the institutions in getting ranked and rated in our competitive and challenging world. According to many research works measurement of student performance has a great value to rate and rank educational institutions. In this approach we develop a new measuring methodology of student performance for Distance Learning Institutions based on fuzzy logic. We divided Student Performance into major and additional factors. Major factor contains four sub-parameters and additional consists of three sub-parameters. In our view, it is very necessary to scale these seven factors to be able to get accurate results. The nature of fuzzy logic makes scaling easy to the above mentioned parameters which could lead to the achievement of the expected outcomes and result. However, essence of fuzzy logic cannot be over emphasized.
Keywords: Student Performance Evaluation, Fuzzy Logic, Fuzzy Operators and Reasoning, Linguistic Variables and Rules, Membership Function.
Kaynakça
- Arbaiy N., Z. bt Suradi and N. H. bt Yusoff “Fuzzy Approach for Student’s Performance Evaluation” International Conference on Islamic World, Information Technology and Information Society (IC3I 2006)
- Chen, G. and T. T. Pham (2001) “Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems.
- Dernoncourt, F. (2013), “Introduction to fuzzy logic”, franck.dernoncourt@gmail.com
- Gokmen G, T. Ç. Akinci, M. Tekta, N. Onat, G. Kocyigit and N. Tekta “Evaluation of student performance in laboratory applications using fuzzy logic” Procedia Social and Behavioral Sciences 2 (2010), 902–909.
- https://en.wikipedia.org/wiki/Membership_function_(mathematics) Wikipedia.
- Ibrahim Z. and D. Rusli “Predicting Students’ Academic Performance: Comparing Artificial Neural Network, Decision Tree And Linear Regression”, 21st Annual SAS Malaysia Forum.
- Kotsiantis S., C. Pierrakeas and P. Pintelas (2004) “Predicting Students' Performance In Distance Learning Using Machine Learning Techniques”, Applied Artificial Intelligence, 18:5, 411-426.
- Saxena N., K. K. Saxena,” Fuzzy Logic Based Students Performance Analysis Model for Educational Institutions”, IMS Engg College, Ghaziabad (UP)-INDIA *Corresponding Author’s email: neetesh.saxena@gmail.com.
- Yadav R. S. and V. P. Singh, (2011) ”Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach” International Journal on Computer Science and Engineering (IJCSE), 676-686.
- Yildiz O., A. Bal, and S. Gulsecen (2013) ”Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students” International Review of Research in Open and Distance Learning, 144-165
Öz
Kaynakça
- Arbaiy N., Z. bt Suradi and N. H. bt Yusoff “Fuzzy Approach for Student’s Performance Evaluation” International Conference on Islamic World, Information Technology and Information Society (IC3I 2006)
- Chen, G. and T. T. Pham (2001) “Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems.
- Dernoncourt, F. (2013), “Introduction to fuzzy logic”, franck.dernoncourt@gmail.com
- Gokmen G, T. Ç. Akinci, M. Tekta, N. Onat, G. Kocyigit and N. Tekta “Evaluation of student performance in laboratory applications using fuzzy logic” Procedia Social and Behavioral Sciences 2 (2010), 902–909.
- https://en.wikipedia.org/wiki/Membership_function_(mathematics) Wikipedia.
- Ibrahim Z. and D. Rusli “Predicting Students’ Academic Performance: Comparing Artificial Neural Network, Decision Tree And Linear Regression”, 21st Annual SAS Malaysia Forum.
- Kotsiantis S., C. Pierrakeas and P. Pintelas (2004) “Predicting Students' Performance In Distance Learning Using Machine Learning Techniques”, Applied Artificial Intelligence, 18:5, 411-426.
- Saxena N., K. K. Saxena,” Fuzzy Logic Based Students Performance Analysis Model for Educational Institutions”, IMS Engg College, Ghaziabad (UP)-INDIA *Corresponding Author’s email: neetesh.saxena@gmail.com.
- Yadav R. S. and V. P. Singh, (2011) ”Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach” International Journal on Computer Science and Engineering (IJCSE), 676-686.
- Yildiz O., A. Bal, and S. Gulsecen (2013) ”Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students” International Review of Research in Open and Distance Learning, 144-165
Ayrıntılar
| Bölüm | Makaleler |
|---|---|
| Yazarlar | |
| Yayımlanma Tarihi | 19 Ekim 2016 |
| Yayımlandığı Sayı | Yıl 2016 Cilt: 2 Sayı: 2 |