Detecting Students' Flow States and Their Construct Through Electroencephalogram: Reflective Flow Experiences, Balance of Challenge and Skill, and Sense of Control. (January 2021)

• Record Type: Journal Article
• Authors: Wu, Shu-Fen; Lu, Yu-Ling; Lien, Chi-Jui
• Is Part Of: Journal of educational computing research. Volume 58:Number 8(2021)
• Page Start: 1515
• Page End: 1540
• Publication Date: 2021-01
• Subjects: attention -- electroencephalogram (EEG) -- engagement -- flow state -- sense of control; Computer literacy -- Periodicals; Computer-assisted instruction -- Periodicals; Computer managed instruction -- Periodicals; Education -- Data processing -- Periodicals; 371.334
• DOI: 10.1177/0735633120944084
• Languages: English
• ISSNs: 0735-6331

Abstract

Previous studies measured flow states using students' self-reported experiences, resulting in issues regarding nonobjective and nonreal-time data. Thus, this study used an electroencephalogram (EEG) to measure the EEG-detected real-time flow states (EEG-Fs) of 30 students from the 4th and 5th grades. Their EEG measurements, self-reported reflective flow experiences (SR-Fs), grade levels (GLs), balance of challenge and skill (BCS), and sense of control, represented by their overall test performance (OA-tp) and momentary test performance (MOM-tp), were analyzed to establish their EEG-F's construct. Based on the results of a chi-square test, the EEG-F correlates significantly with SR-F, BCS, OA-tp, and MOM-tp. A J48 decision tree analysis and logistic regression further revealed that in-flow experiences (in-EEG-F) were detected when students had high SR-Fs, where the BCS contributed to flow states. In particular, students with a low-challenge/high-skill BCS demonstrated an in-EEG-F state upon having a high OA-tp. For high-challenge/high-skill, the in-EEG-F state was determined through their MOM-tp. Through the EEG and flow state construct, this study revealed a whole-part association between students' momentary and overall reflective flow experiences and identified viable paths for inducing students' EEG-Fs, which can contribute to future e-learning development when integrated with a brain-computer interface for e-learning or e-evaluation systems.