Access the full text.
Sign up today, get DeepDyve free for 14 days.
L.A. Zadeh (1965)
Fuzzy setInf. Control, 8
Robert Pryor, B. Hesketh, Melanie Gleitzman (1989)
Making Things Clearer by Making Them Fuzzy: Counseling Illustrations of a Fuzzy Graphic Rating ScaleCareer Development Quarterly, 38
Anthony Freeling (1980)
Fuzzy Sets and Decision AnalysisIEEE Transactions on Systems, Man, and Cybernetics, 10
J. Merriënboer, R. Clark, M. Croock (2002)
Blueprints for complex learning: The 4C/ID-modelEducational Technology Research and Development, 50
Establish the evaluation set
K.M. Sheehan (1997)
A tree-based approach to proficiency scaling and diagnostic assessmentJ. Educ. Meas., 34
Shyi-Ming Chen (1996)
Evaluating weapon systems using fuzzy arithmetic operationsFuzzy Sets Syst., 77
(2001)
A fuzzy approach to select the select the location of the distribution center
Membership function, single factor evaluation matrix R. For example, the Chinese score of 89 has the grade placement in A and
K. Sheehan (1997)
A TREE‐BASED APPROACH TO PROFICIENCY SCALING & DIAGNOSTIC ASSESSMENTETS Research Report Series, 1997
Chiu-Keung Law (1996)
Using fuzzy numbers in educational grading systemFuzzy Sets Syst., 83
Theodore Frick, Rajat Chadha, Carol Watson, E. Zlatkovska (2010)
Improving course evaluations to improve instruction and complex learning in higher educationEducational Technology Research and Development, 58
E. Nick (1973)
The dependability of behavioral measurements: theory of generalizability for scores and profiles, 25
H.J. Zimmerman (1991)
Fuzzy Set Theory and Its Applications
Yuan Huang, Jian-xun Qi, Jun-hua Zhou (2005)
Method of Risk Discernment in Technological Innovation Based on Path Graph and Variable Weight Fuzzy Synthetic Evaluation
Carlo Bagnoli, Halbert Smith (1998)
The Theory of Fuzzy Logic and its Application to Real Estate ValuationJournal of Real Estate Research, 16
K. Tatsuoka (1983)
RULE SPACE: AN APPROACH FOR DEALING WITH MISCONCEPTIONS BASED ON ITEM RESPONSE THEORYJournal of Educational Measurement, 20
F. Herrera, Enrique López, Cristina Mendaña, M. Rodríguez (2001)
A linguistic decision model for personnel management solved with a linguistic biobjective genetic algorithmFuzzy Sets Syst., 118
C. Carlsson, R. Fullér (2000)
Multiobjective linguistic optimizationFuzzy Sets Syst., 115
Sunghyun Weon, Jinil Kim (2001)
Learning achievement evaluation strategy using fuzzy membership function31st Annual Frontiers in Education Conference. Impact on Engineering and Science Education. Conference Proceedings (Cat. No.01CH37193), 1
G. Liang, Mao-Jiun Wang (1991)
A fuzzy multi-criteria decision-making method for facility site selectionInternational Journal of Production Research, 29
R. Biswas (1995)
An application of fuzzy sets in students' evaluationFuzzy Sets Syst., 74
Chitrasen Samantra (2012)
Decision-making in fuzzy environment
A fuzzy rule based approach for decision making in commercial food chains
(1995)
Fuzzy Sets and Fuzzy Logic: Theory and Application
C. Zopounidis, M. Doumpos (1998)
Developing a multicriteria decision support system for financial classification problems: the finclas systemOptimization Methods & Software, 8
P. Pirnay-Dummer, Dirk Ifenthaler, J. Spector (2010)
Highly integrated model assessment technology and toolsEducational Technology Research and Development, 58
Y. Chen, Wen-June Wang, C. Chiu (2000)
New estimation method for the membership values in fuzzy setsFuzzy Sets Syst., 112
Traditional method of student achievement evaluation only use arithmetic mean and convert them to rankings, but this does not provide further explanatory information to proceed with more reasonable evaluations, decisions, and interpretations for the learning achievements of students, and provide a fair and appropriate consideration of the evaluation results. Therefore, this study attempts to introduce four types of fuzzy synthetic decision methods in actual scores for evaluating and ranking student’s academic achievement. Using the synthetic decision method of fuzzy theory, the four types of composite operations are used in conjunction with the membership function, and finally fuzzy means are used to express the diverse evaluation results of students. This study uses junior high school first year students in central Taiwan as research subjects, selecting the actual mid-term exam results of the gifted class and general class as research data. The fuzzy synthetic decision method is applied through four types of composite operations, proposing a ranking system that is more diverse and precise than the traditional average method. Finally, this study proposes the characteristics of the four types of fuzzy composite operations, considering the most suitable composite operations for classes with different characteristics. Results of this study can be used as a reference for educators in the field and future researchers. The contribution of this study is to provide the fuzzy grade calculation methods that are suited to students with different characteristics, in order to achieve diverse and precise ranking evaluations.
Quality & Quantity – Springer Journals
Published: Nov 27, 2010
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.