Analysis of Students’ Learning Emotions Using EEG

Ting Mei Li, Wei Xiang Shen, Han Chieh Chao, Sherali Zeadally

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


Advances in Internet technologies have enabled students to improve their learning experiences by making use of diverse sources of information and knowledge. The teaching model of the twenty-first century is gradually changing. The learning center of the student is no longer a teacher but the student himself/herself. Today, teachers are no longer the soul of teaching but the student is increasingly involved in the learning process. But students are easily affected by emotions in learning efficiency. Research has shown that learning experience can be good when students are in positive moods whereas negative emotions have the opposite effect. To improve the student’s learning efficiency, teachers who are students must understand their learning emotions. When students’ emotions are negative, teachers must step in to provide assistance to the students in their studies. This study analyzed brain wave signals and classified brain wave emotions by using the support vector machine (SVM) method. Brain wave emotional analysis results provide the teacher’s emotions during the learning process. When students are in negative moods, teachers can intervene to assist students during learning in due course.

Original languageEnglish
Title of host publicationInnovative Technologies and Learning - 2nd International Conference, ICITL 2019, Proceedings
EditorsLisbet Rønningsbakk, Ting-Ting Wu, Frode Eika Sandnes, Yueh-Min Huang
Number of pages7
StatePublished - 2019
Event2nd International Conference on Innovative Technologies and Learning, ICITL 2019 - Tromsø, Norway
Duration: Dec 2 2019Dec 5 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11937 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Conference on Innovative Technologies and Learning, ICITL 2019

Bibliographical note

Funding Information:
Acknowledgments. This research was partly funded by the Ministry of Science and Technology of the R.O.C. under grants MOST 106-2511-S-259-001-MY3 and 107-2221-E-259-005-MY3.

Publisher Copyright:
© Springer Nature Switzerland AG 2019.


  • EEG
  • Emotional analysis
  • Learning emotions

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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