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Detecting Students’ Flow States and Their Construct Through Electroencephalogram: Reflective Flow Experiences, Balance of Challenge and Skill, and Sense of Control
Journal of Educational Computing Research ( IF 4.345 ) Pub Date : 2020-07-30 , DOI: 10.1177/0735633120944084
Shu-Fen Wu, Yu-Ling Lu, Chi-Jui Lien

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.

中文翻译:

通过脑电图检测学生的流动状态及其构造:反射性流动经验,挑战与技能的平衡以及控制感

以前的研究使用学生的自我报告经验来测量流量状态,从而导致有关非客观和非实时数据的问题。因此,本研究使用脑电图(EEG)来测量30名来自4年级和5年级的学生的EEG检测到的实时血流状态(EEG-Fs)。他们的脑电图测量,自我报告的反射流经验(SR-Fs),成绩水平(GLs),挑战与技巧的平衡(BCS)和控制感,以整体测试性能(OA-tp)和瞬时测试表示性能(MOM-tp),进行分析以建立其EEG-F的结构。根据卡方检验的结果,EEG-F与SR-F,BCS,OA-tp和MOM-tp显着相关。J48决策树分析和逻辑回归进一步显示,当学生的SR-F较高时(BCS有助于流动状态),可以检测到流入经验(in-EEG-F)。特别是,具有低挑战/高技能BCS的学生在OA-tp较高的情况下表现出EEG-F状态。对于高挑战/高技能,通过他们的MOM-tp确定了EEG-F状态。通过脑电图和流动状态的构建,本研究揭示了学生的瞬时和整体反射流体验之间的全部分关联,并确定了诱导学生的脑电图F的可行路径,当与脑电整合时可以为未来的电子学习发展做出贡献电子学习或电子评估系统的人机界面。具有低挑战/高技能BCS的学生在OA-tp较高时表现出EG-F状态。对于高挑战/高技能,通过他们的MOM-tp确定了EEG-F状态。通过脑电图和流动状态的构建,本研究揭示了学生的瞬时和整体反射流体验之间的全部分关联,并确定了诱导学生的脑电图F的可行路径,当与脑电整合时可以为未来的电子学习发展做出贡献电子学习或电子评估系统的人机界面。具有低挑战/高技能BCS的学生在OA-tp较高时表现出EG-F状态。对于高挑战/高技能,通过他们的MOM-tp确定了EEG-F状态。通过脑电图和流动状态的构建,本研究揭示了学生的瞬时和整体反射性流经验之间的全部分关联,并确定了诱导学生的脑电图F的可行途径,当与脑电整合时可以为未来的电子学习发展做出贡献电子学习或电子评估系统的人机界面。
更新日期:2020-07-30
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