Modeling and Verification of Time Dependent Systems Using Time Petri Nets
IEEE Transactions on Software Engineering
Using fuzzy numbers in educational grading system
Fuzzy Sets and Systems
Knowledge Representation Using Fuzzy Petri Nets
IEEE Transactions on Knowledge and Data Engineering
A fuzzy set approach to the evaluation of journal grades
Fuzzy Sets and Systems - Special issue: Soft decision analysis
The Implementation of an Adaptive Test on the Computer
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Plan Specification of Multi-agent based on Coloured Petri Nets
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 02
Learning achievement evaluation strategy using fuzzy membership function
FIE '01 Proceedings of the Frontiers in Education Conference, 2001. on 31st Annual - Volume 01
Learning Performance Assessment Approach Using Web-Based Learning Portfolios for E-learning Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Supervised and Unsupervised Learning by Using Petri Nets
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A reasoning algorithm for high-level fuzzy Petri nets
IEEE Transactions on Fuzzy Systems
An educational tool for fuzzy control
IEEE Transactions on Fuzzy Systems
Evaluating Students' Answerscripts Using Fuzzy Numbers Associated With Degrees of Confidence
IEEE Transactions on Fuzzy Systems
Self-assessment in a feasible, adaptive web-based testing system
IEEE Transactions on Education
IEEE Transactions on Education
Constructing concept maps for adaptive learning systems based on data mining techniques
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
For the purpose of understanding the students' learning achievement, the most direct way is to implement a test. Due to the rapid development of information technology, all kinds of combination of information technology with the adaptive test have been incessantly noted by many scholars. In general, the computerized adaptive test includes the item response theory that tests the students' learning ability of subjects. However, the results based only on the dichotomy of correct answers and wrong answers are not so comprehensive judgments. Situations of correct answers and wrong answers should be different in their degrees; for example, completely correct, partially correct, completely wrong, and partially wrong. But the partially correct or partially wrong is vague and difficult to define. Thus it is appropriate to use fuzzy theory to solve the vagueness problem. Therefore, this study presents a novel learning evaluation model which applies high-level fuzzy Petri net (HLFPN) and infers via a fuzzy reasoning method the different answering performances generated by different examinee's abilities corresponding to the test items in different degrees of difficulty. Finally, we synthesize the answering performance of every test item and make a reasonable evaluation.