博碩士論文 965404006 詳細資訊




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姓名 林逸農(Yi-Lung Lin)  查詢紙本館藏   畢業系所 網路學習科技研究所
論文名稱 從人因與互動行為模式的觀點探討數位遊戲式學習輔助能源知識
(Digital Game-Supported Learning for Energy Knowledge: A Human Factor and Interactive Behavioral Pattern Approach)
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摘要(中) 近年來數位遊戲式學習的相關研究有著快速成長的趨勢,研究結果指出數位遊戲式學習對於提升學習動機和學習成效有著顯著的影響。藉著這樣的優勢,許多課程也開始透過遊戲式學習的方式來進行設計,其中能源教育中的知識習得亦是如此。雖然數位遊戲式學習有著不少優勢,但也帶來一些限制;譬如在歷程中過於專注玩遊戲而非學習或是操作複雜度造成學習上的困難。這現象說明著並非所有的學習者都合適透過數位遊戲式學習來進行學習。更確切的說,學習會受到不同習慣、偏好與經驗而有所差異,也就是人因層面會顯著地影響學習結果。
因此本研究著手進行不同人因層面對其數位遊戲輔助能源知識影響的探討,此外也透過遊戲歷程的行為互動分析來瞭解受到人因影響的情況。為此目的,本研究設計兩個實徵研究且開發相關的能源教育遊戲,提供悅趣化的學習環境讓學習者在遊戲中學習到能源知識,並探討人因對於遊戲式能源教育和互動行為模式的影響。
研究一主要是探討控制信念、行為意圖和創新擴散等人因對於學習者在遊戲中學習能源知識的影響。研究結果顯示在透過遊戲輔助後,內控者比外控者在能源知識的學習上表現比較好。在行為意圖上,雖然內控者與外控者在內在行為意圖綠色能源管理與消費上進步相當;但是相較於內控者,遊戲更幫助到外控者在外在行為意圖,包含說服、法律行動和政治行動有更明顯的成長,意旨遊戲或許可以改變了外控者的被動想法成為主動。研究也顯示高行為意圖者他們的學習成效比低行為意圖者有較顯著的成長,且在創新擴散的研究發現,學習者對於數位遊戲式學習有正向的接受度,其中以複雜度是影響學習成效的關鍵,也就是說對於遊戲複雜度有高接受度者他們知識習得的成效較高。
研究二主要是探究學習者在遊戲歷程中其人因影響互動行為的情況。透過認知風格測驗探究學習者的思考策略對於互動行為模式的影響,包含遊戲因子和學習因子。為此目的,在遊戲中收集學習者的歷程記錄,並以「嘗試錯誤」記錄、「提示使用」記錄和「提示交換」記錄做為遊戲層面指標,也透過學習層面的指標,包含「影片重複觀看」記錄、「選項校正」記錄和「答案校對」記錄,進行資料探勘的潛在類別群聚分析。研究可發現三群不同互動行為模式的學習者,而這三群學習者在認知風格上有顯著的差異存在,更精確說認知風格會影響互動行為的模式。其中偏向場獨立的學習者,在知識習得上相對比較好。偏向場依賴這群在遊戲歷程中,有迷失的現象,一直有嘗試錯誤的情況(高頻率的嘗試錯誤記錄),且提示功能使用頻繁(高頻率的提示使用記錄和提示交換記錄),在學習上偏向以猜測答案來完成遊戲任務 (高頻率的選項校正記錄和答案校對記錄),而非願意重新觀看學習內容進行學習(低頻率的影片重複觀看記錄),也因此發現他們的學習成效相對較低。
本研究對於數位遊戲輔助能源知識學習有些貢獻之處。第一,提供人因層面影響性的探討,包含控制信念、行為意圖和創新擴散對於能源知識習得使用教育遊戲的輔助上的影響情況。第二是,在整個遊戲式學習歷程中,認知風格會顯著影響學習者的遊戲和學習的互動行為與其能源知識習得的情況。
對於未來研究,有三個方面可以建議。第一,人因層面與互動行為層面等相關理論的探究,需要更廣且深入的瞭解。其次是遊戲模式的選擇,除了可以考量多人線上遊戲的進行外,也可以採用擴增實境搭配能源教育進行學習,深入探討多人互動學習與虛實整合的學習模式。最後是更需要將數位遊戲輔助能源知識學習的應用落實於學校、教師、學生與家長上。
摘要(英) 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.
關鍵字(中) ★ 行為意圖
★ 認知風格
★ 資料探勘
★ 創新擴散
★ 數位遊戲式學習
★ 能源教育
★ 能源知識
★ 潛在類別分析
★ 控制信念
★ 互動行為模式
★ 人因層面
關鍵字(英) ★ behavioral intention
★ cognitive styles
★ data mining
★ diffusion of innovation
★ digital game-based learning (DGBL)
★ digital game-supported learning
★ energy education
★ energy knowledge
★ latent class analysis (LCA)
★ locus of control
★ interactive behavioral patterns
★ human factors
論文目次 摘要 I
Abstract III
Table of Contents VI
List of Figures IX
List of Tables X
1 Introduction 1
1.1 Research background 1
1.2 Research questions 6
1.3 Aim and objectives 7
1.4 Research process 7
1.5 Significance of the research 8
2 Literature review 9
2.1 Energy knowledge 9
2.1.1 The connotation of energy knowledge 9
2.1.2 The development on learning activities of energy knowledge 10
2.2 Digital game-supported learning 11
2.2.1 Effect of digital games on learning 11
2.2.2 Digital game-supported learning for energy knowledge 12
2.2.3 Exploration of learning process within games 13
2.3 Human factors 15
2.3.1 Effects of Human factors on learning 15
2.3.2 Locus of control 16
2.3.3 Behavioral intention 17
2.3.4 Innovation diffusion 18
2.3.5 Cognitive style 19
2.4 Interactive behavioral pattern 23
2.4.1 Behavioral pattern based on perceived interactivity 23
2.4.2 Patterns modeling technology 25
2.4.3 The applications of interactive behavioral patterns 27
2.4.4 Effects of interactive behavioral patterns on learning performance 29
3 Design and implementation of digital game-supported learning system 30
3.1 Approaches of digital game system designs 30
3.1.1 Instructor approach 30
3.1.2 Learner approach 31
3.1.3 Design of learning contents of the energy knowledge 32
3.2 Design of game based- supported learning system 32
3.2.1 Concept of game system design 32
3.2.2 IPO based game model 34
3.3 The design and development for green life game (GLG) 35
3.3.1 GLG design of game based learning system 35
3.3.2 GLG system framework 36
3.3.3 Design of GLG interaction 42
3.3.4 Features of GLG design 45
3.4 The design and development for green energy game (GEG) 47
3.4.1 GEG design of game-supported learning system 47
3.4.2 Design of GEG interaction 51
3.4.3 Features of GEG design 56
4 Study Ⅰ: The effects of human factor on the digital game-supported learning for energy knowledge: locus of control, behavioral intention and innovation diffusion approach 59
4.1 Research purposes and questions 59
4.2 Methodology 60
4.2.1 Participants 60
4.2.2 Instruments 61
4.2.3 Procedure 64
4.2.4 Data analyses 65
4.3 Results and discussions 66
4.3.1 Part one: locus of control and behavioral intention 66
4.3.2 Part two: learning performance, behavior intention and innovation diffusion 70
4.4 Framework 75
4.5 Summary 78
5 Study Ⅱ: The effects of cognitive style on interactive behavioral patterns and learning performance within the digital game-supported learning for energy knowledge 79
5.1 Research purposes and questions 79
5.2 Methodology 80
5.2.1 Participants 80
5.2.2 Instruments 80
5.2.3 Procedure 80
5.2.4 Data analysis 81
5.3 Results and discussions 84
5.3.1 Cognitive style and clutters 84
5.3.2 The clusters of learning performance 85
5.3.3 Cognitive style and interactive behavioral pattern 86
5.4 Framework 88
5.5 Implications for future Improvement 89
5.6 Summary 90
6 Conclusion 91
6.1 Main conclusion 91
6.2 Limitations and future work 92
References 94
Appendix 111
A. Energy knowledge test 111
B. Locus of control scale 113
C. Behavioral intention scale 114
D. Innovation diffusion questionnaire 116
E. The green energy knowledge test 117
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指導教授 楊接期(Jie-Chi Yang) 審核日期 2016-7-26
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