Paper Info
Title
A simplified implementation of interval type-2 fuzzy system and its application in students' academic evaluation
Abstract
Assessment and grading practices have the potential not only to measure and report learning but also to promote it. Student assessment is integral to learning experience and to curriculum design. It is typically used to evaluate learning outcomes and provide the basis for certification of individual students. Conventional grading system is largely based on human judgments, which tend to be subjective and have high degrees of errors and uncertainties. Because of the traditional pressure in assessment towards objectivity, conformity, consistency and certainty and due to the increasing trends in class sizes and limited resources for teaching, examiners and lectures are always challenging their abilities to deliver timely and fair assessments. To cope with the aforementioned challenges, the need arises for exploring innovation and technology to facilitate assessment and to incorporate other dimensions, which could not be considered in conventional grading system, to ensure and promote deep learning and critical thinking. In this paper a fuzzy grading system, which considers complexity, difficulty and importance of exam questions, is presented. A simplified implementation of interval type-2 fuzzy system using the basic knowledge of type-1 fuzzy is presented. A comparison between the use of type-1 and interval type-2 fuzzy systems in reducing uncertainties and providing more transparent and fair assessment that can reflect needs of individual students and foster development is presented.
Year
DOI
Venue
2016
10.1109/FUZZ-IEEE.2016.7737748
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Keywords
Field
DocType
interval type-2 fuzzy sets (IT2FSs),footprint-of-uncertainty (FOU),fuzzy grading system,intelligent evaluation,learning achievement,transparent assessment
Grading (education),Computer science,Fuzzy cognitive map,Fuzzy logic,Fuzzy set,Critical thinking,Curriculum,Artificial intelligence,Fuzzy control system,Certification,Machine learning
Conference
ISSN
ISBN
Citations 
1544-5615
978-1-5090-0627-4
0
PageRank 
References 
Authors
0.34
14
1
Name
Order
Citations
PageRank
I. A. Hameed1299.45