Visual behavior, flow and achievement in game-based learning

被引:87
|
作者
Tsai, Meng-Jung [1 ]
Huang, Li-Ju [1 ]
Hou, Huei-Tse [2 ]
Hsu, Chung-Yuan [3 ]
Chiou, Guo-Li [1 ]
机构
[1] Natl Taiwan Univ Sci & Technol, Grad Inst Digital Learning & Educ, Taipei, Taiwan
[2] Natl Taiwan Univ Sci & Technol, Grad Inst Appl Sci & Technol, Taipei, Taiwan
[3] Natl Pingtung Univ Sci & Technol, Dept Child Care, Pingtung 91201, Taiwan
关键词
Eye-tracking; Game-based learning; Visual patterns; Visual attention; Flow; Conceptual learning; EYE-MOVEMENTS; SCIENCE TEXT; COMPUTER; TRACKING; STUDENTS; COMPREHENSION; PERFORMANCE; ENGAGEMENT; STRATEGIES; MOTIVATION;
D O I
10.1016/j.compedu.2016.03.011
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This study utilized eye-tracking technology to explore the differences between high- and low-conceptual-comprehension players' visual behaviors and game flows in game-based learning (GBL). A total of 22 university students participated in this study and their eye movements while playing a physics game were recorded by an eye tracker. Along with eye tracking measures, the participants' prior knowledge, flow and comprehension test scores were collected. Multiple data analysis methods including MWU tests, correlation analyses, heat map analyses and lag sequential analyses were employed in this study. The results indicated that the players in the high comprehension group demonstrated an efficient text-reading strategy and better metacognitive controls of visual attention during game plays; while those in the low comprehension group could have some difficulties in decoding the conceptual representations. In addition, the players with higher comprehension expressed a higher level of game flow in two aspects: the sense of control and concentration. Furthermore, the percentages of fixation for the main task and prompt messages were associated with the players' game flow experience, especially the time distortion feeling. This study successfully applied eye-tracking technology to find learners' visual behavioral patterns in GBL environment and confirmed the flow construct for GBL, which may provide some insights for the learning mechanism of GBL. Future studies have been suggested in this paper. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:115 / 129
页数:15
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