Measuring Effects of Technological Interactivity Levels on Flow with Electroencephalogram

Abstract

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.