A pupil data based classification model for education learning states

Jungjin Lee; Eunjae Hong; Hyunggon Park

doi:10.1109/ICTC.2017.8190960

A pupil data based classification model for education learning states

Jungjin Lee, Eunjae Hong, Hyunggon Park

Electronic and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	International Conference on Information and Communication Technology Convergence
Subtitle of host publication	ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	141-143
Number of pages	3
ISBN (Electronic)	9781509040315
DOIs	https://doi.org/10.1109/ICTC.2017.8190960
State	Published - 12 Dec 2017
Event	8th International Conference on Information and Communication Technology Convergence, ICTC 2017 - Jeju Island, Korea, Republic of Duration: 18 Oct 2017 → 20 Oct 2017

Publication series

Name	International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
Volume	2017-December

Conference

Conference	8th International Conference on Information and Communication Technology Convergence, ICTC 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	18/10/17 → 20/10/17

Bibliographical note

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.

Keywords

Support Vector Machine
classification model
k-fold cross validation
machine learning
pupil

Access to Document

10.1109/ICTC.2017.8190960

Cite this

Lee, J., Hong, E., & Park, H. (2017). A pupil data based classification model for education learning states. In International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017 (pp. 141-143). (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTC.2017.8190960

Lee, Jungjin ; Hong, Eunjae ; Park, Hyunggon. / A pupil data based classification model for education learning states. International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 141-143 (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017).

@inproceedings{5fddb7f44fe54b8a8ef3f001aaaeb52c,

title = "A pupil data based classification model for education learning states",

abstract = "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.",

keywords = "Support Vector Machine, classification model, k-fold cross validation, machine learning, pupil",

author = "Jungjin Lee and Eunjae Hong and Hyunggon Park",

note = "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: {\textcopyright} 2017 IEEE.; 8th International Conference on Information and Communication Technology Convergence, ICTC 2017 ; Conference date: 18-10-2017 Through 20-10-2017",

year = "2017",

month = dec,

day = "12",

doi = "10.1109/ICTC.2017.8190960",

language = "English",

series = "International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "141--143",

booktitle = "International Conference on Information and Communication Technology Convergence",

}

Lee, J, Hong, E & Park, H 2017, A pupil data based classification model for education learning states. in International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017, vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 141-143, 8th International Conference on Information and Communication Technology Convergence, ICTC 2017, Jeju Island, Korea, Republic of, 18/10/17. https://doi.org/10.1109/ICTC.2017.8190960

A pupil data based classification model for education learning states. / Lee, Jungjin; Hong, Eunjae; Park, Hyunggon.
International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 141-143 (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017; Vol. 2017-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

T2 - 8th International Conference on Information and Communication Technology Convergence, ICTC 2017

Y2 - 18 October 2017 through 20 October 2017

ER -

Lee J, Hong E, Park H. A pupil data based classification model for education learning states. In International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 141-143. (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017). doi: 10.1109/ICTC.2017.8190960

A pupil data based classification model for education learning states

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this