臺灣博碩士論文加值系統

在全球經濟萎糜的情況下，電玩遊戲產業逆勢成長，成為熱門的「宅經濟」。本研究運用情感工程中所發展出來的情意運算(affection computing)的原理，並以SEM-CPU方法來擬定研究方法和進行實驗設計，以探討使用者於大型螢幕進行電玩遊戲時，使用者經驗和遊戲類型對於使用者進行3D遊戲之情緒反應之研究。
本研究所用之遊戲共九款，三類型，分別為策略模擬、動作冒險與角色扮演；實驗對象為36位具有不同遊戲使用經驗的受測者，並根據其遊戲經驗分為高、中、低三組，實驗過程由兩台攝影機錄影受測者在遊戲操作進行中之情緒和表情反應，情緒和表情實驗數據在FACS(臉部表情編碼)編碼並轉換後，即可決定其情緒向度。實驗結果顯示，(1)在相同遊戲中，使用者經驗對於其情緒反應沒有顯著影響，和(2)在同一使用經驗組中，遊戲類型對其情緒反應則有顯著影響。

Abstract
The video game industry, becoming one of the key elements of “Otaku
Economy”, continues to grow strongly against the overall global economic
downturn. By applying the affective computing theory and the
methodology of SEM-CPU, this study investigates the influence of user
experience and game type on users’ emotional reactions when they are
playing 3D computer games in large display screen.
In the experimental design, nine games divided into three groups of
Strategy, Action, and Role-playing are adopted. The subjects consist of
thirty-six players classified into low-, medium-, and high- experience group
according to their gaming experience. During the experiment, two video
cameras are deployed for recording players’ facial expressions and
emotional reactions. The collected data are then analyzed and evaluated by
Facial Action Coding System (FACS) to determine their emotional effect.
Results show that (1) when playing the same game, user experience has no
significant effect on player’s emotional reaction, and (2) in the same user
experience group, game type has significant effect on player’s emotional
reaction.
keywords: affective computing, large display screen, 3D game, HC

中文摘要 i
Abstract ii
誌 謝 iii
目 錄 iv
圖索引 vi
表索引 vii
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 1
第三節 研究範圍與限制 3
第四節 論文架構 3
第二章 文獻探討 4
第一節 遊戲的意涵 4
第二節 遊戲類型介紹 8
第三節 人機介面中的使用者經驗 10
第四節 情緒之定義 11
第五節 情意運算之定義 15
第三章 研究方法 18
第一節 研究架構 18
第二節 實驗設計 20
第三節 研究工具 26
第四節 研究流程與施測程序 30
第五節 資料分析 32
第四章 資料分析與討論 40
第一節 不同使用者經驗在相同遊戲類群對情緒反應的分析 40
第二節 不同遊戲類型在相同遊戲類群對情緒反應的分析 45
第三節 完成率 52
第四節 結果討論 57
第五章 結論與建議 59
第一節 研究結果之討論 59
第二節 研究限制 59
第三節後續研究建議 60
附錄-FACS 61
附錄-完成率原始資料 66
附錄-PrEmo問卷結果 68
附錄-正負向度原始資料 69
附錄-遊戲畫面 70
參考文獻 72
原文部份 73

圖1-1 論文架構圖 3
圖2-1 魔獸爭霸三 8
圖2-2 最終幻想(Final Fantasy XIII) 9
圖2-3 惡魔獵人4(Devil may cry 4) 10
圖2-4 人類訊息模式 13
圖2-5 情緒反應三層次 13
圖2-6 情緒向度 14
圖3-1 研究架構圖 18
圖3-2 受測者與遊戲類型的搭配方式 22
圖3-3 家庭不同敞域與最適顯示器之選擇 22
圖3-4 實驗環境示意圖 23
圖3-5 根據SEM-CPU所制定之實驗流程圖 24
圖3-6 The Facial Action Coding System 27
圖3-7 PrEmo情緒表情圖形 (Desmet, 2000) 29
圖3-8 研究流程圖 32
圖4-1 完成率平均數 53
圖4-2 第一類群完成率 54
圖4-3 第二類群完成率 55
圖4-4 第三類群完成率 56

表2-1 使用性指標之意義 7
表2-2 介面影響因子之意義 7
表2-3 情緒向度理論分類 14
表3-1 實驗對象分組 20
表3-2 實驗進行遊戲 21
表3-3 FACS之情感分辨 28
表3-4 受測者感受 30
表3-5 開放性譯碼範例 37
表3-6 理論性譯碼範例 37
表3-7 受測者FACS對照圖(正) 38
表3-8 受測者FACS對照圖(負) 38
表4-1 使用者經驗與遊戲類型平均數與標準差 40
表4-2 低、中使用者經驗之組別統計量 41
表4-3 低、中使用者經驗之獨立樣本檢定 42
表4-4 中、高使用者經驗之組別統計量 42
表4-5 中、高使用者經驗之獨立樣本檢定 43
表4-6 低、高使用者經驗之組別統計量 44
表4-7 中、高使用者經驗之獨立樣本檢定 44
表4-8 整體敘述統計 45
表4-9 第一類群敘述統計 46
表4-10 第一類群2-way ANOVA 46
表4-11 第一類群遊戲類別主要效果之敘述統計 47
表4-12 第一類群遊戲類別ANOVA 47
表4-13 第一類群遊戲類別多重比較 47
表4-14 第二類群敘述統計 48
表4-15 第二類群之2-way ANOVA 48
表4-16 第二類群遊戲類別主要效果之敘述統計 49
表4-17 第二類群遊戲類別ANOVA 49
表4-18 第二類群遊戲類別多重比較 50
表4-19 第三類群敘述統計 50
表4-20 第三類群之2-way ANOVA 51
表4-21 第三類群敘述統計 51
表4-22 第三類群遊戲類型之ANOVA 52
表4-23 第三類群多重比較 52
表4-24 整體完成率與平均數 53
表4-25 第一類群的遊戲完成度 54
表4-26 第二類群的遊戲完成度 55
表4-27 第三類群的遊戲完成度 56

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