TY - GEN
T1 - Developing cognitive diagnostic assessments system for mathematics learning
AU - Wu, Lin Jung
AU - Chen, Hsin Hao
AU - Sung, Yao Ting
AU - Chang, Ko En
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Baysian network
KW - cognitive diagnostic
KW - formative evaluation
KW - mathematics learning
UR - http://www.scopus.com/inward/record.url?scp=84866977212&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866977212&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2012.182
DO - 10.1109/ICALT.2012.182
M3 - Conference contribution
AN - SCOPUS:84866977212
SN - 9780769547022
T3 - Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012
SP - 228
EP - 229
BT - Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012
T2 - 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012
Y2 - 4 July 2012 through 6 July 2012
ER -