TY - GEN
T1 - Predicting Math performance of children with special needs based on serious game
AU - Yuhana, Umi Laili
AU - Mangowal, Remy G.
AU - Rochimah, Siti
AU - Yuniarno, Eko M.
AU - Purnomo, Mauridhi H.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/5
Y1 - 2017/6/5
N2 - Predicting and classifying student's performance using data mining techniques have been gaining an enormous amount of attention from researchers and practitioners. However, the use of games for the classification of student's ability level is still slightly. This study focuses on identification of important factors for determining student level performance on Math. The best classification algorithm is observed as part of intelligent game development research for assessment of children with special needs. The real dataset from randomly selected of elementary school is taken to construct a dataset. About 135 normal students and 25 children with special needs played the game and did a manual test. Our study shows that the age, gender, grade, and mark of each level became important factors in determining the level of math skill for the normal student. However age, gender, and grade don't have a correlation with math level of children with special needs. Six classification methods, Naive Bayes, Multilayer Perceptron (MLP), SMO, Decision Table, JRip, and J48, were performed to predict math skill performance level of normal students and children with special needs. JRip with 10 fold cross validation gives the highest percentage of accuracy of 64.12.
AB - Predicting and classifying student's performance using data mining techniques have been gaining an enormous amount of attention from researchers and practitioners. However, the use of games for the classification of student's ability level is still slightly. This study focuses on identification of important factors for determining student level performance on Math. The best classification algorithm is observed as part of intelligent game development research for assessment of children with special needs. The real dataset from randomly selected of elementary school is taken to construct a dataset. About 135 normal students and 25 children with special needs played the game and did a manual test. Our study shows that the age, gender, grade, and mark of each level became important factors in determining the level of math skill for the normal student. However age, gender, and grade don't have a correlation with math level of children with special needs. Six classification methods, Naive Bayes, Multilayer Perceptron (MLP), SMO, Decision Table, JRip, and J48, were performed to predict math skill performance level of normal students and children with special needs. JRip with 10 fold cross validation gives the highest percentage of accuracy of 64.12.
KW - children with special needs
KW - math game
KW - student performance prediction
UR - http://www.scopus.com/inward/record.url?scp=85021751379&partnerID=8YFLogxK
U2 - 10.1109/SeGAH.2017.7939276
DO - 10.1109/SeGAH.2017.7939276
M3 - Conference contribution
AN - SCOPUS:85021751379
T3 - 2017 IEEE 5th International Conference on Serious Games and Applications for Health, SeGAH 2017
BT - 2017 IEEE 5th International Conference on Serious Games and Applications for Health, SeGAH 2017
A2 - Rodrigues, Nuno
A2 - Vilaca, Joao L.
A2 - Dias, Nuno
A2 - Wong, Kevin
A2 - de Freitas, Sara
A2 - Duque, Duarte
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Serious Games and Applications for Health, SeGAH 2017
Y2 - 2 April 2017 through 4 April 2017
ER -