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Obtaining ABET Student Outcome Satisfaction from Course Learning Outcome Data Using Fuzzy Logic
| 1 | Umm Al-Qura University |
| 2 | Prince Sultan University |
CORRESPONDING AUTHOR
Imran Ali Tasadduq
Umm Al-Qura University, Dept. of Computer Engineering, College of Computer & Information Systems, Umm Al-Qura University, Abdia, 21955 Makkah, Saudi Arabia
Umm Al-Qura University, Dept. of Computer Engineering, College of Computer & Information Systems, Umm Al-Qura University, Abdia, 21955 Makkah, Saudi Arabia
Publication date: 2017-06-15
EURASIA J. Math., Sci Tech. Ed 2017;13(7):3069–3081
KEYWORDS
TOPICS
ABSTRACT
Background:
Transformation of CLO satisfaction data to ABET SO satisfaction data using crisp values of the parameters with a 0/1 mapping between CLOs and SOs has been presented earlier. No fuzzy logic application has been presented for transforming CLO satisfaction data to SO satisfaction data considering the fuzzy nature of the parameters.
Material and methods:
Considering the fuzzy nature of metrics of CLOs and SOs, a Fuzzy Logic algorithm has been proposed to extract SO satisfaction data from the CLO satisfaction data for any given course. The membership functions for the fuzzy variables namely CLOs, SOs and CLO-SO relationship have been defined with an implementable procedure to suit the problem. A set of 24 rules form the rule base of the fuzzy logic algorithm.
Results:
The algorithm has been implemented and tested in MATLAB. An application example of a real-world problem has been presented.
Conclusions:
The idea presented will help the instructors and administrators of academic programs seeking ABET accreditation. The presented work is a unique application of fuzzy set theory not presented before in the literature. Further research is required in finding the best membership functions and the effect of variations of the lexical parameters on the output.
Transformation of CLO satisfaction data to ABET SO satisfaction data using crisp values of the parameters with a 0/1 mapping between CLOs and SOs has been presented earlier. No fuzzy logic application has been presented for transforming CLO satisfaction data to SO satisfaction data considering the fuzzy nature of the parameters.
Material and methods:
Considering the fuzzy nature of metrics of CLOs and SOs, a Fuzzy Logic algorithm has been proposed to extract SO satisfaction data from the CLO satisfaction data for any given course. The membership functions for the fuzzy variables namely CLOs, SOs and CLO-SO relationship have been defined with an implementable procedure to suit the problem. A set of 24 rules form the rule base of the fuzzy logic algorithm.
Results:
The algorithm has been implemented and tested in MATLAB. An application example of a real-world problem has been presented.
Conclusions:
The idea presented will help the instructors and administrators of academic programs seeking ABET accreditation. The presented work is a unique application of fuzzy set theory not presented before in the literature. Further research is required in finding the best membership functions and the effect of variations of the lexical parameters on the output.
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