TY - GEN
T1 - A pupil data based classification model for education learning states
AU - Lee, Jungjin
AU - Hong, Eunjae
AU - Park, Hyunggon
N1 - Funding Information:
This research was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1A2B4005041), in part by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A5B6036244) and in part by the MSIT, under the ITRC (Information Technology Research Center) support program (IITP-2017-2012-0-00559) supervised by the IITP (Institute for Information & communications Technology Promotion).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/12
Y1 - 2017/12/12
N2 - In this paper, we propose a classification model for learning state based on individual biometric data. In particular, we use the pupil size as a biometric data and the data has been collected from 72 participants. We also deploy the support vector machine (SVM) in conjunction with k-fold validation as an analysis tool. In order to improve the performance of the SVM, the we remove outliers from the data set and normalize it. Our experiment results show that the accuracy of the proposed classification model is up to 68.8% and thus confirm the effectiveness of the proposed classification model using the pupil data.
AB - In this paper, we propose a classification model for learning state based on individual biometric data. In particular, we use the pupil size as a biometric data and the data has been collected from 72 participants. We also deploy the support vector machine (SVM) in conjunction with k-fold validation as an analysis tool. In order to improve the performance of the SVM, the we remove outliers from the data set and normalize it. Our experiment results show that the accuracy of the proposed classification model is up to 68.8% and thus confirm the effectiveness of the proposed classification model using the pupil data.
KW - Support Vector Machine
KW - classification model
KW - k-fold cross validation
KW - machine learning
KW - pupil
UR - http://www.scopus.com/inward/record.url?scp=85046887585&partnerID=8YFLogxK
U2 - 10.1109/ICTC.2017.8190960
DO - 10.1109/ICTC.2017.8190960
M3 - Conference contribution
AN - SCOPUS:85046887585
T3 - International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
SP - 141
EP - 143
BT - International Conference on Information and Communication Technology Convergence
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 October 2017 through 20 October 2017
ER -