A pupil data based classification model for education learning states

Jungjin Lee, Eunjae Hong, Hyunggon Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationInternational Conference on Information and Communication Technology Convergence
Subtitle of host publicationICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages141-143
Number of pages3
ISBN (Electronic)9781509040315
DOIs
StatePublished - 12 Dec 2017
Event8th International Conference on Information and Communication Technology Convergence, ICTC 2017 - Jeju Island, Korea, Republic of
Duration: 18 Oct 201720 Oct 2017

Publication series

NameInternational Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
Volume2017-December

Conference

Conference8th International Conference on Information and Communication Technology Convergence, ICTC 2017
Country/TerritoryKorea, Republic of
CityJeju Island
Period18/10/1720/10/17

Keywords

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

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