Understanding the relationship between physiological signals and digital game-based learning outcome

Autor: Chih-Hung Wu, Yueh-Min Huang, Yi-Lin Tzeng
Rok vydání: 2014
Předmět:
Zdroj: Journal of Computers in Education. 1:81-97
ISSN: 2197-9995
2197-9987
DOI: 10.1007/s40692-014-0006-x
Popis: Digital game-based learning (DGBL) has been regarded as an effective method to drive learners’ motivation and positive emotions. Recent studies have mostly focused on exploring and determining motivational factors in digital games that support intrinsic motivation by means of questionnaire surveys. Investigating students’ emotions while they are learning has been an interesting and challenging issue as emotion is a vital factor that associates with learning attention. Furthermore, a few studies have focused on examining the effectiveness of DGBL on learning or relationships between DGBL and learners’ physiological state based on solid objective evidence, such as physiological signals. Therefore, this study designs an experiment that includes two learning methods to learn the Newton’s law of motion: one is a traditional e-learning method, and the other is DGBL by using SURGE physics game. Students’ eye movements, brain waves, and heart-beating data were measured during learning and then analyzed to possibly derive their attention and emotions. After learning, all participants were asked to take a posttest to evaluate their outcome. This study aims to adopt statistical analysis methods to examine the effectiveness of DGBL versus e-learning on learning achievement. Furthermore, the relationship between physiological signals and DGBL outcomes are tested in this study. Results showed that the relationship between physiological signals and DGBL learning outcome is significant. The learning outcomes in DGBL are positively influenced by cognitive load, however, negatively influenced by emotion. The findings of this study have a number of important implications for future practice. It is possible to obtain solid objective data such as physiological signals by using scientific sensing devices. The relationship between affective learning and academic achievement can be put into further research perspective by using more diverse data obtained from physiological sensors other than those employed in this study. In addition, the balance between high learning motivation and low cognitive load should be maintained to avoid learners’ cognitive capacity being overloaded but to positively influence learning outcomes.
Databáze: OpenAIRE
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