The relations between interface design of digital game-based learning systems and flow experience and cognitive load of learners with different levels of prior knowledge

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Abstract

Many studies have indicated that game-based learning has become an effective teaching strategy and a teaching tool. In the past decades, empirical studies showed that digital game-based could enhance learners' cognitive, affective and skills as learning objectives. Many studies have shown that learning process in which the flow experience for learning has a significant impact. In addition, researches have also pointed out that the multimedia materials presentation and system interface design would affect the learning of learners carrying out their cognitive load. Researchers indicated that learner's prior knowledge and learning strategies would influence the flow experience, learning outcomes and cognitive load of the learners. According to previous studies, the interface design of learning system would affect learner's learning, motivation, satisfaction, learning efficiency, and quality of interaction and so on. There are many studies have shown the system visibility, cognitive support, efficiency, user control, joyfulness are the important aspects of learning system design. Therefore, the purpose of this study was to explore digital game-based learning systems' visibility, cognitive support, efficiency, user control, and joyfulness for different prior knowledge learners' flow experience and cognitive load. The results show that there are significant differences on flow experience and cognitive load between learners with different prior knowledge. The usability of game-based learning system has significant effect on flow experience but not on cognitive load except joyfulness. Discussion and conclusion are provided in this study. © 2014 Springer International Publishing.

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APA

Shen, C. Y., & Chu, H. P. (2014). The relations between interface design of digital game-based learning systems and flow experience and cognitive load of learners with different levels of prior knowledge. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8528 LNCS, pp. 574–584). Springer Verlag. https://doi.org/10.1007/978-3-319-07308-8_55

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