由於近年情境學習的普及，結合先進技術如虛擬實境（VR）和腦波分析，本研究探討感官刺激、認知契合與情感契合對學習成效的影響。本研究透過虛擬咖啡廳情境的實驗方法和腦波測量，評估感官體驗在線上情境教學策略和學習成果中的作用，強調創建沉浸式學習環境的重要性。利用SmartPLS4.0和EMOTIV進行資料分析，旨在深化我們對如何通過感官刺激和VR技術改善學習過程和結果的理解，為學習者創造更高效、更具吸引力的學習環境，並探索感官刺激如咖啡味對學習過程中的認知和情感反應的影響。

Due to the growing popularity of situated learning in recent years, this study investigates the impact of Sensory Stimulation, cognitive fit, and emotional fit on learning outcomes, integrating advanced technologies like Virtual Reality (VR) and brainwave analysis. Through experimental methods in a virtual café scenario and brainwave measurement, the study evaluates the role of sensory experiences in online situated teaching strategies and learning outcomes, highlighting the importance of creating immersive learning environments. Utilizing SmartPLS 4.0 and EMOTIV for data analysis, the research aims to deepen our understanding of how sensory stimuli and VR technology can enhance the learning process and outcomes, creating more efficient and engaging learning environments for learners, and exploring the impact of sensory stimuli like the scent of coffee on cognitive and emotional responses during the learning process.

目錄
第一章 緒論	1
1.1研究動機與背景	1
1.2研究目的	2
1.3研究流程	2
第二章 文獻探討	4
2.1元宇宙與情境學習理論	4
2.2 EEG腦波與情緒	5
2.3多感官學習(Multisensory Learning)	7
2.4雙決策系統:情感契合(Affective fit)&認知契合(Cognitive fit)	7
2.5學習成效	8
2.5感官刺激((Sensory Stimulation))	9

第三章 研究方法	10
3.1研究模型	10
3.2研究假說	10
3.2.1感官刺激對情感契合與認知契合的影響	10
3.2.2情感契合對壓力與投入度的影響	12
3.2.3認知契合對壓力與投入度的影響	13
3.2.4投入度與壓力對學習成效的影響	14
3.2.5虛擬實境的調節影響	14
3.3實驗流程	15
3.4本研究問卷	17
3.4.1問卷題目	17
3.5實驗對象	21
第四章 資料分析與結果	22
4.1資料分析方法	22
4.2樣本結構分描述	22
4.3模型假說與檢定	24
4.3.1變異數分析	24
4.3.2學習成效比較分析	25
4.3.3量測模型分析	25
4.3.4結構模型分析	27
4.4假說結果驗證	31
第五章 結論與建議	32
5.1 研究結果與討論	32
5.2 理論意涵	33
5.3 實務意涵	34
5.4 研究限制與建議	35
參考文獻	36

圖目錄
圖1-1研究流程	3
圖3-1研究模型	10
圖3-2實驗流程	16
圖4-1樣本結構圖	23
圖4-2主觀結構模型分析	28
圖4-3 客觀結構模型分析	30
表目錄
表 3-1實驗設計	15
表 3-2感官刺激問項	18
表 3-3認知契合度問項	19
表 3-4情感契合度問項	19
表 3-5學習成效問項	20
表 4-1變異數分析	24
表 4-2學習成績基本統計量	25
表 4-3敘述性統計	26
表 4-4 交叉分析	26
表 4-5各構面間之相關係數與AVE的平方根值	27
表 4-6假說結果驗證	31

參考文獻
1.Alwashmi, K., Meyer, G., Rowe, F., & Ward, R. (2023). Enhancing learning outcomes through multisensory integration: A fMRI study of audio-visual training in virtual reality.
2.Baceviciute, S., Terkildsen, T., & Makransky, G. (2021). Remediating learning from non-immersive to immersive media: Using EEG to investigate the effects of environmental embeddedness on reading in Virtual Reality. Computers & Education, 164, Article 104122.
3.Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32-42.
4.Chae, J., Hwang, S., Seo, W., & Kang, Y. (2021). Relationship between rework of engineering drawing tasks and stress level measured from physiological signals. Automation in Construction, 124, 103560.
5.Chang, K.-C., & Cheng, Y.-S. (2023). How sensory perceptions and sensory brand experience influence customer behavioral intentions in the context of cartoon-themed restaurants. International Journal of Hospitality Management, 115, 103604.
6.Chiu, C.-M., Huang, H.-Y., & Yen, C.-H. (2009). Antecedents of trust in online auctions. Electronic Commerce Research and Applications, 8(2), 148-159.
7.Derakhshan, A., Teo, T., & Khazaie, S. (2024). Is game-based language learning general or specific-oriented? Exploring the applicability of mobile virtual realities to medical English education in the Middle East. Computers & Education, 213, Article 105013.
8.Filieri, R., Alguezaui, S., Galati, F., & Raguseo, E. (2023). Customer experience with standard and premium Peer-To-Peer offerings: A mixed-method combining text analytics and qualitative analysis. Journal of Business Research, 167, Article 114128.
9.Flavián, C., Ibáñez-Sánchez, S., & Orús, C. (2019). Integrating virtual reality devices into the body: Effects of technological embodiment on customer engagement and behavioral intentions toward the destination. Journal of Travel & Tourism Marketing, 36(7), 847-863. https://doi.org/10.1080/10548408.2019.1618781
10.Gillespie, B., Muehling, D. D., & Kareklas, I. (2018). Fitting product placements: Affective fit and cognitive fit as determinants of consumer evaluations of placed brands.. Journal of Business Research, 82, 90-102.
11.Iglesias, O., Markovic, S., & Rialp, J. (2019). How does sensory brand experience influence brand equity? Considering the roles of customer satisfaction, customer affective commitment, and employee empathy. Journal of Business Research, 96, 343-354.
12.Jang, H. W., & Lee, S. B. (2019). Applying effective sensory marketing to sustainable coffee shop business management. Sustainability (Switzerland), 11(22), Article 6430.
13.Jebelli, H., Hwang, S., & Lee, S. (2018). EEG-based workers' stress recognition at construction sites. Automation in Construction, 93, 315-324.
14.Jiao, Z., Gao, X., Wang, Y., & Li, J. (2018). Deep Convolutional Neural Networks for mental load classification based on EEG data. Pattern Recognition, 76, 582-595.
15.Journal of Consumer Research , Dec., 1993, Vol. 20, No. 3 (Dec., 1993), pp. 418-430
16.Kopp, T., Riekert, M., & Utz, S. (2018). When cognitive fit outweighs cognitive load: Redundant data labels in charts increase accuracy and speed of information extraction. Computers in Human Behavior, 86, 367-376.
17.Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
18.Li, Y., Gunasekeran, D. V., RaviChandran, N., Tan, T. F., Ong, J. X. L., Thirunavukarasu, A. J., Polascik, B. W., & Habash, R. (2023). The next generation of healthcare ecosystem in the metaverse.
19.Li, Y., He, P., Yuan, X., & Hopfgartner, F. (2024). Developing and evaluating a gamified information retrieval system for Generation Z. Library and Information Science Research, 46, 101270. https://doi.org/10.1016/j.lisr.2023.101270
20.Namkung, Y., & Kim, Y. (2024). Learner engagement in collaborative writing: The effects of synchronous computer-mediated communication (SCMC) mode, interlocutor familiarity, L2 proficiency, and task repetition. System, 121(2024), 103251.
21.Ortiz de Guinea, A., Titah, R., & Léger, P.-M. (forthcoming). Explicit and Implicit Antecedents of Users’Information Systems Behavioral Beliefs: A Neuropsychological Investigation. Journal of Management Information Systems, Special Issue on NeuroIS.
22.Pei, G., Shang, Q., Hua, S., Li, T., & Jin, J. (2024). EEG-based affective computing in virtual reality with a balancing of the computational efficiency and recognition accuracy. Computers in Human Behavior, 152, Article 108085.
23.Rau, P.-L. P., Zheng, J., & Wei, Y. (2020). Distractive effect of multimodal information in multisensory learning. Journal of Educational Psychology, 35(4), 123-145.
24.Raza, A., Wasim, M., & Ishaq, M. I. (2024). Virtual reality-based product displays to inspire consumers’ purchase intentions: An experimental study. Journal of Business Research, 175(2024), 114540.
25.Reinoso-Carvalho, F., Gunn, L., Molina, G., Narumi, T., Spence, C., Suzuki, Y., ... Wagemans, J. (2020). A sprinkle of emotions vs a pinch of crossmodality: Towards globally meaningful sonic seasoning strategies for enhanced multisensory tasting experiences. Journal of Business Research, 117, 389–399.
26.Samuel, J., Nemati, H., & Seyedarabi, F. (2022). Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations.
27.Shiau, W.-L., & Huang, L.-C. (2022). Scale development for analyzing the fit of real and virtual world integration: An example of Pokémon Go. Information Technology & People, 35(2), 789-811.
28.Spangenberger, P., Freytag, S.-C., & Geiger, S. M. (2024). Embodying nature in immersive virtual reality: Are multisensory stimuli vital to affect nature connectedness and pro-environmental behaviour? Computers & Education, 212, 104964.
29.Srivastava, M., & Kaul, D. (2014). Social interaction convenience and customer satisfaction: The mediating effect of customer experience. Journal of Retailing and Consumer Services, 21(2014), 1028-1037.
30.Sun, J. C.-Y., & Yeh, K. P.-C. (2017). The effects of attention monitoring with EEG biofeedback on university students' attention and self-efficacy: The case of anti-phishing instructional materials. Computers & Education, 106, 73-82.
31.Vessey, I. (1991). Cognitive Fit: A Theory-Based Analysis of the Graphs versus Tables Literature. Decision Sciences, 22, 219-240.
32.Yu, H. (2024). Why do people use Metaverse? A uses and gratification theory perspective. Telematics and Informatics, 89, Article 102110.
33.Zhang, Y., Tian, Y., Yao, L., Duan, C., Sun, X., & Niu, G. (2023). Teaching presence promotes learner affective engagement: The roles of cognitive load and need for cognition. Teaching and Teacher Education, 129, 104167.
34.賴思婷. (2020). 從飢餓行銷觀點了解顧客情緒對衝動購買意圖之影響 (Master's thesis). 淡江大學資訊管理學系碩士班.
Ortiz de Guinea, A., Titah, R., & Léger, P.-M. (2014). Explicit and implicit antecedents of users’ IS behavioral beliefs: A neuropsychological investigation. Journal of Management Information Systems, 30(4), 179–209. https://doi.org/10.2753/MIS0742-122230040