Evaluating students' learning achievement using fuzzy membership functions and fuzzy rules
Expert Systems with Applications: An International Journal
A fuzzy system for evaluating students' learning achievement
Expert Systems with Applications: An International Journal
Identification of sport talents using a web-oriented expert system with a fuzzy module
Expert Systems with Applications: An International Journal
Evaluating students' answerscripts based on interval-valued fuzzy grade sheets
Expert Systems with Applications: An International Journal
A new approach for evaluating students' answerscripts based on interval-valued fuzzy sets
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Evaluating students' learning achievement based on fuzzy rules with fuzzy reasoning capability
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Evaluating students' learning achievement based on the eigenvector method
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A user evaluation framework for web-based learning systems
MTDL '11 Proceedings of the third international ACM workshop on Multimedia technologies for distance learning
Application of high-level fuzzy Petri nets to educational grading system
Expert Systems with Applications: An International Journal
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In this paper, the authors suggest a new learning achievement evaluation strategy in student's learning procedure. They call this fuzzy evaluation. They may assign fuzzy lingual variables to each question pertaining to its importance, complexity and difficulty by using fuzzy membership functions. Then one can evaluate a score depending on the membership degree of uncertainty factors in each question. In addition, they consider the time consuming element for solving a question. They adapt an inverse sigmoid function to consider time consuming elements, fuzzy concentration and dilation function for importance, a sigmoid function for complexity, and fuzzy square method for difficulty.