Fuzzy Grading for Adaptability in a Learning Platform

ABSTRACT

Due to the increasing global competition and technology's constant progress, the usage of online training platforms has grown rapidly. One of the problems that these platforms face is the lack of a physical evaluator. Given that flexible evaluating methods are considered necessary in training platforms this is an obstacle we must overcome. In this paper we describe the implementation of a fuzzy grading system that is going to be used in the Competence Oriented Multilingual Adaptive Language Assessment and Training (COMALAT) system as an intelligent evaluating system of the platform. First, we present a literature review on fuzzy evaluation methods and afterwards we describe the technique that we used in order to apply it in the COMALAT platform. Aside from the COMALAT platform, the fuzzy grading system is an innovative evaluation method that can be deployed in other online learning platforms as well, due to its adaptability and capability to automatically set the difficulty of a quiz, based on the learners' performance.

References

1. C. Law, "Using fuzzy numbers in educational grading system," Fuzzy Sets and Systems, vol. 83, pp. 311--323, 1996.
2. L. Zadeh, "Fuzzy sets," Information and Control, vol. 8, no. 3, p. 338--352, 1965.
3. R. Biswas, "An application of fuzzy sets in students' evaluation," Fuzzy Sets and System, vol. 74, no. 2, p. 187--194, 1995.
4. E. Wilson, C. L. Karr and L. M. Freeman, "Flexible, adaptive, automatic fuzzy-based grade assigning system," in In Proceedings of the North American fuzzy information processing society conference, 1998.
5. S. Chen and C. Lee, "New methods for students' evaluating using fuzzy sets," Fuzzy Sets and System, vol. 104, no. 2, p. 209--218, 1999.
6. H. Wang and S. Chen, "Evaluating students' answerscripts using fuzzy numbers associated with degrees of confidence," IEEE Transactions on Fuzzy System, vol. 16, no. 2, p. 403--415, 2008.
7. S. Weon and J. Kim, "Learning achievement evaluation strategy using fuzzy membership function," in In Proceedings of the 31st ASEE/IEEE frontiers in education conferenc, Reno, NV, 2001.
8. S.-M. Bai and S.-M. Chen, "Evaluating students' learning achievement using fuzzy membership functions and fuzzy rules," Expert Systems with Applications, vol. 34, p. 399--410, 2008.
9. S.-M. Bai and S.-M. Chen, "Automatically constructing grade membership functions of fuzzy rules for students' evaluation," Expert Systems with Applications, vol. 35, no. 3, pp. 1408--1414, 2008.
10. I. Saleh and S. Kim, " A fuzzy system for evaluation students' learning achievement," Expert Systems with Applications, vol. 36, no. 3, p. 6234--6236, 2009.
11. E. Mamdani, "Applications of fuzzy algorithms for simple dynamic plants," in Proceedings of the IEEE, 1974.
12. S.-M. Chen, "Evaluating students' learning achievement based on fuzzy rules with fuzzy reason-ing capability," Expert Systems with Applications, vol. 38, no. 4, p. 4368--4381, 2011.

Index Terms

• Applied computing

  • Education

    • Computer-assisted instruction