The effects of attention monitoring with EEG biofeedback on university students' attention and self-efficacy: The case of anti-phishing instructional materials

Chih-Yuan Sun*, Katherine Pin Chen Yeh

*Corresponding author for this work

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

This study investigated the effects of attention monitoring with EEG biofeedback on university students' attention and self-efficacy for learning. This study employed a quasi-experimental design, collecting both qualitative and quantitative data. The instruments included an EEG sensor to collect learners' attention levels, the self-efficacy scale from the MSLQ, an anti-phishing achievement test, and an open-ended questionnaire. The participants were 80 university students. A stage-random grouping was adopted to divide the participants into a control group and an experimental group, with 40 participants in each group. The attention monitoring system was developed to monitor anti-phishing learners' attention levels for both the control and the experimental group, and to provide EEG biofeedback for the experimental group when low attention was detected. The results showed no significant differences in learners’ self-efficacy and achievement between the two groups. However, there were significant effects on overall attention when EEG biofeedback was provided. This study found that the types (e.g., auditory or visual, positive or negative) of EEG biofeedback and the selection of the course topics may influence learner's attention, self-efficacy, and learning achievement. This study suggests that when conducting similar studies, it is important to consider the types of EEG biofeedback, the selection of the course topics, and integrating encouraging elements into negative feedback to increase learning outcomes using such a system.

Original languageEnglish
Pages (from-to)73-82
Number of pages10
JournalComputers and Education
Volume106
DOIs
StatePublished - 1 Mar 2017

Keywords

  • Architectures for educational technology systems
  • Intelligent tutoring systems
  • Interactive learning environments
  • Teaching/learning strategies

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