Predicting students' flow experience through behavior data in gamified educational systems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU144723" target="_blank" >RIV/00216305:26230/21:PU144723 - isvavai.cz</a>
Result on the web
<a href="https://www.fit.vut.cz/research/publication/12735/" target="_blank" >https://www.fit.vut.cz/research/publication/12735/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1186/s40561-021-00175-6" target="_blank" >10.1186/s40561-021-00175-6</a>

Result language
angličtina
Original language name
Predicting students' flow experience through behavior data in gamified educational systems
Original language description
The flow experience (i.e., challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, concentration, sense of control, loss of self-consciousness, transformation of time, and autotelic experience) is an experience highly related to the learning experience. One of the current challenges is to identify whether students are managing to achieve this experience in educational systems. The methods currently used to identify students flow experience are based on self-reports or equipment (e.g., eye trackers or electroencephalograms). The main problem with these methods is the high cost of the equipment and the impossibility of applying them massively. To address this challenge, we used behavior data logs produced by students during the use of a gamified educational system to predict the students flow experience. Through a data-driven study (N = 23) using structural equation modeling, we identified possibilities to predict the students flow experience through the speed of students actions. With this initial study, we advance the literature, especially contributing to the field of student experience analysis, by bringing insights showing how to step towards automatic students flow experience identification in gamified educational systems.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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