Abstract This article reports on the design and development of an expert fuzzy classification scoring system for grading student writing samples. The growing use of written response tests in the education sector provides fertile domain areas for the application of soft computing and expert systems technology. The main function of the expert fuzzy classification scoring system is to support teachers in the evaluation of student writing samples by providing them with a uniform framework for generating ratings based on the consistent application of scoring rubrics. The system has been tested using actual student response data. A controlled experiment demonstrated that teachers using the expert fuzzy classification scoring system can make assessments in less time and with a level of accuracy comparable to the best teacher graders. The article introduces fuzzy classification techniques as a basis for constructing rule-based scoring models that can encapsulate knowledge needed for consistent scoring results. This increased consistency in the application of the scoring rubrics allows for more valid individual and group assessment.
[1]
Dennis F. Kibler,et al.
Symbolic Nearest Mean Classifiers
,
1997,
AAAI/IAAI.
[2]
Nikola Kasabov,et al.
Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief]
,
1996,
IEEE Transactions on Neural Networks.
[3]
Christof Ebert,et al.
Fuzzy classification for software criticality analysis
,
1996
.
[4]
Martin T. Hagan,et al.
Neural network design
,
1995
.
[5]
E. B. Page,et al.
The Computer Moves into Essay Grading: Updating the Ancient Test.
,
1995
.
[6]
Hans-Jürgen Zimmermann,et al.
Fuzzy Set Theory - and Its Applications
,
1985
.
[7]
M. Goldstein,et al.
Multivariate Analysis: Methods and Applications
,
1984
.