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 -