Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
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This paper 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 new and innovative applications 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 paper introduces fuzzy classification techniques that can encapsulate knowledge about imprecise qualities needed for constructing rule-based scoring models that provide consistent, uniform scoring results. This increased consistency in the application of the scoring rubrics allows for more valid individual and group assessment.