Although game-based interactive technology has long enhanced flow experiences crucial for learning, its effects have been unclear. Thus, this study gathered students’ electroencephalogram (EEG) information during their work in game-based learning environments with different levels of technological interactivity (LTIs; low, mid, and high LTIs). Multiple measurements were used in a relatively small sample, and 3 9th graders (age 15) of different learning environments worked on 360 test items. The EEG data were analyzed with the LTI, balance of challenge and skill (BCS), and sense of control (SC) to establish the flow state construct. A chi-square test showed a significant association between flow states and the LTI, whereas a J48 decision tree analysis and logistic regression demonstrated that inflow experiences would likely emerge in students with high short-term SC (ST-SC), high BCS, and high-LTI learning environments. Furthermore, in high ST-SC and high BCS cases, the odds ratio (OR) of emerging inflow experiences with a high LTI is eight times more than the rest, suggesting that instructional designers (and teachers) use high-LTI game-based learning environments while ensuring students’ learning with adequate SC and BCS.

中文翻译：

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用脑电图测量技术交互水平对流动的影响

尽管基于游戏的交互技术长期以来一直增强对学习至关重要的心流体验，但其效果尚不清楚。因此，本研究收集了学生在具有不同技术交互水平（LTI；低、中和高 LTI）的基于游戏的学习环境中工作期间的脑电图 (EEG) 信息。在相对较小的样本中使用了多次测量，不同学习环境的 3 名 9 年级学生（15 岁）完成了 360 个测试项目。使用 LTI、挑战与技能平衡 (BCS) 和控制感 (SC) 分析 EEG 数据以建立流动状态结构。卡方检验显示流动状态和 LTI 之间存在显着关联，而 J48 决策树分析和逻辑回归表明，流入体验可能会出现在具有高短期 SC (ST-SC)、高 BCS 和高 LTI 学习环境的学生中。此外，在高 ST-SC 和高 BCS 的情况下，具有高 LTI 的新兴流入体验的优势比 (OR) 是其他体验的八倍，这表明教学设计者（和教师）使用高 LTI 基于游戏的学习环境，同时确保学生有足够的 SC 和 BCS 学习