The aim of this study is to develop a diagnostic system for mathematical concepts. By adopting a Bayesian network for its high recognition rate in artificial intelligence and diagnosis, and combining and applying deduction methods in computerized tests, this system helps students to understand the difficulties they encounter in mathematical learning, and subsequently helps in implementing immediate remedies. The computerized diagnostic tests established in this research module can diagnose the types of mistakes students make; and in addition to helping students realize their erroneous concepts, this system also helps teachers to grasp the types of mistakes students make, and to implement group remedial teaching accordingly. The study result indicates that the mean recognition rates of the computerized diagnostic system developed in this study are 95.72 %, 99.10 %, 98.73 %, 99.02 %, and 98.96 %; this system can effectively and automatically detect the types of mistakes that students make.
['Lin Jung Wu', 'Hsin Hao Chen', 'Yao Ting Sung', 'Ko En Chang']
Developing Cognitive Diagnostic Assessments System for Mathematics Learning