Combining EEG Feedback on Student Performance and Self-efficacy

Astrid Tiara Murti, Ting Ting Wu, Yueh Min Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study aims to investigate whether the feedback designed based on EEG (electroencephalography) signals and mind-mapping contributes to student attention, performance, and self-efficacy. The EEG headset was used to collect and measure the participant’s attention levels. This study uses a mixed-methods of quasi-experimental design. The participants were 30 graduate students that randomly assigned to the control (non-feedback) group and experimental (with-feedback) group. A random grouping was used to divide the participants into two groups, control and experimental. The participants in experimental group will receive both negative and positive audio feedback. The research finding shows that the participants who receive the feedback had higher attention state and significant influence of self-efficacy compared to those in the groups without feedback. And the feedback does not influence the participant’s performance. Meanwhile, participant’s mind-maps score and performance between the two groups showed no significant influence. This study suggest for future studies, to explore the effect of different types of feedback on students attention.

Original languageEnglish
Title of host publicationInnovative Technologies and Learning - Third International Conference, ICITL 2020, Proceedings
EditorsTien-Chi Huang, Ting-Ting Wu, João Barroso, Frode Eika Sandnes, Paulo Martins, Yueh-Min Huang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages13-22
Number of pages10
ISBN (Print)9783030638849
DOIs
Publication statusPublished - 2020
Event3rd International Conference on Innovative Technologies and Learning, ICITL 2020 - Porto, Portugal
Duration: 2020 Nov 232020 Nov 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12555 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Innovative Technologies and Learning, ICITL 2020
CountryPortugal
CityPorto
Period20-11-2320-11-26

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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