Flow refers to a state of deep immersion in an activity, characterized by factors such as challenge–skill balance. Although flow research often focuses on sports and games, daily learning activities can also elicit flow. We investigated whether English vocabulary learning on a web application elicits flow by testing whether matched difficulty enhances flow. Participants completed six levels of English vocabulary quizzes on a web application over six days, one per day in random order. Each session consisted of a 10-minute quiz with immediate feedback and a post-quiz questionnaire including the Flow State Scale (Yoshida et al., 2013) and a 7-point rating of subjective difficulty (easy – moderate – difficult). We analyzed the data using mixed-effects models. Flow scores were separately modeled as a function of accuracy and subjective difficulty, using both linear and quadratic models. For accuracy, the linear effect was significant (β = 2.38, p < .001), whereas the quadratic effect was marginally non-significant (β = 2.01, p = .052), and the two models showed comparable fit (ΔAIC = 1.74). For subjective difficulty, both the linear (β = −0.19, p < .001) and quadratic (β = −0.07, p < .001) terms were significant. The quadratic difficulty model markedly outperformed the linear model (ΔAIC = 28.47), and also fit better than the linear accuracy model (ΔAIC = 19.90). The negative quadratic term indicates that application-based learning elicited flow, with flow peaking at moderate subjective difficulty. Additionally, model comparisons indicated that flow was better explained by subjective difficulty than by accuracy.

Authors:
Mitsuki Niida, Center for Human Nature, Artificial Intelligence, and Neuroscience, Hokkaido University, Japan
Masahiro Yoshihara, Center for Human Nature, Artificial Intelligence, and Neuroscience, Hokkaido University, Japan
Keisuke Suzuki, Center for Human Nature, Artificial Intelligence, and Neuroscience, Hokkaido University, Japan
Hiroyuki Iizuka, Center for Human Nature, Artificial Intelligence, and Neuroscience, Hokkaido University, Japan
Masahiro Shiraishi, Fujitsu Limited, Japan
Takuya Kamimura, Fujitsu Limited, japan

About the Presenter(s)
Dr. Mitsuki Niida is a Specially Appointed Assistant Professor at the Center for Human, AI, and Neuroscience (CHAIN), NAME University, Japan, studying human temporal processing and currently exploring flow in cognitive tasks.

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