Digital Game-Supported Learning for Energy Knowledge: A Human Factor and Interactive Behavioral Pattern Approach

• Main Authors: Yi-Lung Lin, 林逸農
• Other Authors: Jie-Chi Yang
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/pqwzj6

博士 === 國立中央大學 === 網路學習科技研究所 === 104 === A growing body of research on digital game-based learning (DGBL) in recent years has shown that educational games may enhance learners’ learning motivation and performance. Many learning programs have begun to assist learning through educational games. Energy Education is the most important one of the many subjects. Learning activities of energy education, such as energy knowledge acquisition is gradually to increase the learning effectiveness through games. Some studies, however, indicated potential disadvantages exist in the digital game-supported learning environment. For example, learners may focus on gaming rather than learning or the complexity of game creates learning difficulties. Such mixed results suggested that although the educational game is highly effective for some learners, it may not suit every learner, especially when the effects of human factors, such as habits, preferences and experiences on learning are factored in. By examining learners’ learning histories, this study attempts to identify why DGBL benefits some learners while fails others. The objectives of this study are to discover how human factors influence learners’ learning performance on energy knowledge, and interactions throughout the entire gaming history. To address these issues, the purposes of this study are to design and develop educational game for energy knowledge which provided joyful learning environments to benefit students learn energy knowledge and conduct two empirical studies to examine how human factors influence students’ reactions within the digital game-supported learning environments for energy knowledge and discover their interactive behavioral patterns. The StudyⅠaims to examine effects of human factors, including locus of control, behavioral intention and diffusion of innovation within GLG (Green life game) environment. It also discusses how these factors affect energy knowledge; the findings indicate that that learners with internal locus of control (ILC) outperformed external locus of control (ELC) learners in energy knowledge after interacting with the game. Although the ILC and ELC learners progress fairly in eco-management and consumerism, the proposed educational game can reasonably reduce the differences in the behavioral intention, especially external behavioral intention in the aspects of persuasion, legal action, and political action. More specifically, the game may change ELC learners’ passive thoughts become active. Moreover, results also show that the educational game can enhance learning effectiveness and particular in learners with high behavior intention had better learning effectiveness. It is also found that learners have positive acceptance on the digital game-supported learning for energy knowledge. Furthermore, the result demonstrate that the complexity of innovation diffusion is another key factor to affect learning effectiveness; learners with high acceptance level of complexity have better learning effectiveness. The Study Ⅱ employed data mining techniques to investigate how learners interact during gameplay based on the impact of human factors, cognitive style with a focus being placed on how cognitive style influence learners’ interactive behaviors patterns, including gaming behaviors and learning behaviors in gaming history and how these factors relate to learning effectiveness. To this end, an energy knowledge-themed digital game, GEG (green energy game) was designed to record learners’ gaming factor indexes (trial-and-error, hint-helper and hint-exchange) and learning factor indexes (video-repetition, answer-matching and choice-rectification), and to enable the modeling of these learning behaviors by LCA (latent class analysis) clustering technology of data mining. In this research, the model can be clustered in three groups of different interactive behavioral patterns. The results indicate that these three clusters of learners in cognitive style had significant differences exist. More specifically, cognitive style strongly influences interactive behavioral patterns and learning performance. Moreover, the interactive behavioral patterns also show that field-dependent (FD) learners needed a greater number of hints to complete the game tasks (high frequency in hint-helper and hint-exchange) than field-independent (FI) earners. Furthermore, FD learners employ trial-and-error more frequently than FI learners. Most learners manage to escape learning and select random answers to complete the questions (high frequency in answer-matching and choice-rectification) rather than review the learning content by watching the video (low frequency in video-repetition), especially learners with lower learning performance and majority of FD learners. This thesis will make contributions to the field of the digital game-supported learning for energy knowledge. Firstly, this will provide a deeper understanding of the learners’ reactions to the assistance of the game for energy knowledge from the perspectives of human factors, regarding locus of control, behavioral intention, innovation diffusion and learning achievement. Secondly, this will take into account different cognitive styles, which affect interactive behavior pattern, including gaming pattern and learning pattern and the effects of different interactive behavior pattern on learning achievement. For further studies are also suggested in three aspects: theory, method and application. More effects of different human factors and factors of interactive behaviors should be taken into account for designing the educational game for energy knowledge, such as attitude or willingness for energy conservation. Moreover, the researchers should provide more chances for students’ learning based on the digital game-supported learning for energy knowledge, such as MMORG (Massively Multiplayer Online Role-Playing Game) or ARBL (Augmented Reality Based Learning); finally, the researchers should mark these practical activities of the digital game-supported learning for energy knowledge in the aspects of school, teachers, students, and parents.