| 研究生: |
関詩穎 Kuang, Shih-Ying |
|---|---|
| 論文名稱: |
應用視覺隱喻探討學習者的心流體驗、動機、焦慮及學習效果之影響-以商業模式九宮格為例 Applying Visual Metaphor to Explore the Influence of Learners' Flow Experience, Motivation, Anxiety and Learning Effectiveness-Based on Business Model Canvas |
| 指導教授: |
王維聰
Wang, Wei-Tsong |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 資訊管理研究所 Institute of Information Management |
| 論文出版年: | 2024 |
| 畢業學年度: | 112 |
| 語文別: | 中文 |
| 論文頁數: | 106 |
| 中文關鍵詞: | 視覺隱喻 、學習動機 、心流體驗 、學習焦慮 、學習效果 |
| 外文關鍵詞: | Visual Metaphor, Learning Motivation, Flow Experience, Learning Anxiety, Learning Effectiveness |
| 相關次數: | 點閱:73 下載:0 |
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隨著數位時代的興起,使得線上學習蓬勃發展且在高等教育上被廣泛使用。然而 本研究欲探討線上學習工具要如何吸引學習者,使學習者能投入於教學過程中,進而 降低情緒影響且達到線上學習之目標與效果。目前視覺隱喻被應用於廣告設計、會計 課程教學及個人職涯發展計畫上,但依據過往文獻,將視覺隱喻應用於線上商業分析 課程的教學研究鮮少。因此,本研究將設計一套教學實驗,操弄變項為應用視覺隱喻 於教學過程之品質高低,受試對象為各大專院校學生,並使用問卷進行資料蒐集與驗 證實驗模型。將視覺隱喻應用於教材設計上,並讓學習者依照教學內容之理解,透過 隱喻的方式將學習概念繪製成圖,以此探討透過較高的視覺隱喻品質是否能夠降低學 習焦慮、使學習者進入心流體驗及提升學習動機以及學習者的學習效果是否有正向影 響。
本研究共蒐集 198 份有效問卷,以 t 檢定以及結構方程模式進行驗證性分析,根 據研究結果顯示,實驗組在應用高視覺隱喻品質的教材中學習中,所帶來的效果會負 向影響學習焦慮,且較低的學習焦慮會負向影響學習動機及心流體驗,即提升學習動 機與進入心流體驗,更進而使認知學習效果提升,而對實際學習效果沒有顯著的影響。 然而,實驗組在以案例分析作為實際學習效果的分數平均顯著大於對照組,因此本實 驗方法仍對實際學習效果有所幫助。最後,本研究在學術貢獻上補足了過往較為缺少 的部分,建議在未來仍可以針對研究限制或不同的領域進行深入研究,而實務貢獻上, 研究結果也可作為企業、高等教育的線上教學設計參考。
This study aims to explore how online learning tools can attract learners, engage them in the instructional process, reduce emotional impacts, and achieve the effectiveness of online learning. Currently, visual metaphors are applied in advertising design, accounting course instruction, and personal career development plans. However, according to previous literature, there is limited research on applying visual metaphors to online business analytics courses. Therefore, this study designs a teaching experiment to manipulate the quality of visual metaphors used in the instructional process. The participants are students from various universities, and data collection and model validation are conducted through questionnaires. It aims to investigate whether higher quality visual metaphors can reduce learning anxiety, facilitate flow experience, and enhance learning motivation and effectiveness.
A total of 198 valid questionnaires were collected and analyzed using T-tests and Structural Equation Modeling. The results show that in the experimental group, higher quality visual metaphors negatively affect learning anxiety, which in turn positively affects learning motivation and flow experience, enhancing cognitive learning effectiveness. However, there is no significant impact on actual learning effectiveness. Nonetheless, the experimental group’s average score in case analysis, representing actual learning effectiveness, is significantly higher than the control group, indicating the experimental method's benefit to actual learning effectiveness. In conclusion, this study fills gaps in previous research and suggests future research can further explore limitations or different fields. The practical contributions of this study also provide references for online instructional design in corporate and higher education settings.
Alamer, A., Al Khateeb, A., & Jeno, L. M. (2023). Using WhatsApp increases language students' self‐motivation and achievement, and decreases learning anxiety: A self‐determination theory approach. Journal of Computer Assisted Learning, 39(2), 417-431.
Alyami, H., Sundram, F., Hill, A. G., Alyami, M., & Cheung, G. (2015). Visualizing psychiatric formulation. Australasian Psychiatry, 23(5), 575-580.
Arghashi, V., & Yuksel, C. A. (2022). Interactivity, Inspiration, and Perceived Usefulness! How retailers’ AR-apps improve consumer engagement through flow. Journal of Retailing and Consumer Services, 64, 102756.
Astutik, S., & Prahani, B. K. (2018). The Practicality and Effectiveness of Collaborative Creativity Learning (CCL) Model by Using PhET Simulation to Increase Students' Scientific Creativity. International Journal of Instruction, 11(4), 409-424.
Auld, D. P. (2014). Flow and learning in computer-mediated learning environments: a meta-analytic review. Unpublished doctoral dissertation, Fordham University, New York, NY.
Brom, C., Buchtová, M., Šisler, V., Děchtěrenko, F., Palme, R., & Glenk, L. M. (2014). Flow, social interaction anxiety and salivary cortisol responses in serious games: A quasi-experimental study. Computers & Education, 79, 69-100.
Buil, I., Catalán, S., & Martínez, E. (2019). The influence of flow on learning outcomes: An empirical study on the use of clickers. British Journal of Educational Technology, 50(1), 428–439.
Chai, C. S., Wong, L. H., & King, R. B. (2016). Surveying and modeling students’ motivation and learning strategies for mobile-assisted seamless Chinese language learning. Educational Technology & Society, 19(3), 170–180.
Chang, C. C., Liang, C., Chou, P. N., & Lin, G. Y. (2017). Is game-based learning better in flow experience and various types of cognitive load than non-game-based learning? Perspective from multimedia and media richness. Computers in Human Behavior, 71, 218-227.
Chang, H. Y., Binali, T., Liang, J. C., Chiou, G. L., Cheng, K. H., Lee, S. W. Y., & Tsai, C. C. (2022). Ten years of augmented reality in education: A meta-analysis of (quasi-) experimental studies to investigate the impact. Computers & Education, 191, 104641.
Chen, H., Wigand, R. T., & Nilan, M. (2000). Exploring web users' optimal flow experiences. Information Technology and People, 13, 263–281.
Chen, Y. C. (2019). Effect of mobile augmented reality on learning performance, motivation, and math anxiety in a math course. Journal of Educational Computing Research, 57(7), 1695-1722.
Cheng, K. H., & Tsai, C. C. (2020). Students’ motivational beliefs and strategies, perceived immersion and attitudes towards science learning with immersive virtual reality: A partial least squares analysis. British Journal of Educational Technology, 51(6), 2140-2159.
Cheng, Y. M. (2021). Investigating medical professionals' continuance intention of the cloud-based e-learning system: an extension of expectation–confirmation model with flow theory. Journal of Enterprise Information Management, 34(4), 1169-1202.
Chien, S. Y., Hwang, G. J., & Jong, M. S. Y. (2020). Effects of peer assessment within the context of spherical video-based virtual reality on EFL students’ English-Speaking performance and learning perceptions. Computers & Education, 146, 103751.
Choi, D. H., Kim, J., & Kim, S. H. (2007). ERP training with a web-based electronic learning system: The flow theory perspective. International Journal of Human-Computer Studies, 65(3), 223–243.
Christakou, A., Zervas, Y., Psychountaki, M., & Stavrou, N. A. (2012). Development and validation of the attention questionnaire of rehabilitated athletes returning to competition. Psychology, Health & Medicine, 17(4), 499-510.
Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11), 4715-4729
Clarke, J., & Holt, R. (2017). Imagery of ad-venture: Understanding entrepreneurial identity through metaphor and drawing. Journal of Business Venturing, 32(5), 476-497.
Credé, M., & Phillips, L. A. (2011). A meta-analytic review of the Motivated Strategies for Learning Questionnaire. Learning and Individual Differences, 21(4), 337-346.
Csikszentmihalyi, M. (1975). Play and intrinsic rewards. Journal of Humanistic Psychology. 15 (3), 41-63
Csikszentmihalyi, M. (1988). The flow experience and its significance for human psychology. Optimal experience: Psychological Studies of Flow in Consciousness, 2, 15-35.
Csikszentmihalyi, M. (1997). Finding Flow: the Psychology of Engagement with Everyday Life, Basic Books, New York, NY.
Csikszentmihalyi, M., & Csikzentmihaly, M. (1990). Flow: The psychology of optimal experience (Vol. 1990): Harper & Row, New York, NY.
Csikszentmihalyi, M., & Csikszentmihalyi, M. (2014). Toward a psychology of optimal experience. In Mihaly Csikszentmihalyi (Ed.), Flow and the foundations of positive psychology (pp. 209-226). Dordrecht, NL: Springer.
Cuevas, L., Lyu, J., & Lim, H. (2021). Flow matters: antecedents and outcomes of flow experience in social search on Instagram. Journal of Research in Interactive Marketing, 15(1), 49-67.
Diefenbach, M. A., Weinstein, N. D., & O'reilly, J. (1993). Scales for assessing perceptions of health hazard susceptibility. Health Education Research, 8(2), 181-192.
Ebadi, S., & Ashrafabadi, F. (2022). An exploration into the impact of augmented reality on EFL learners’ Reading comprehension. Education and Information Technologies, 27(7), 9745-9765.
Eppler, M. J. (2006). A comparison between concept maps, mind maps, conceptual diagrams, and visual metaphors as complementary tools for knowledge construction and sharing. Information Visualization, 5(3), 202-210.
Esteban-Millat, I., Martinez-Lopez, F. J., Huertas-Garcia, R., Meseguer-Artola, A., & Rodriguez-Ardura, I. (2014). Modeling students’ flow experiences in an online learning environment. Computers & Education, 71, 111–123.
Foss, S. K. (2005). Theory of visual rhetoric. In Smith,K. L., Moriarty S., Kenney K., Barbatsis G.(Eds.), Handbook of visual communication (pp. 141-152). New York, NY: Routledge
García-Gutiérrez, I., & Martínez-Borreguero, F.J. (2016). The innovation pivot framework: fostering business model innovation in startups, Research-Technology Management, 59(5), 48-56.
García-Madariaga, J., Moya, I., Recuero, N., & Blasco, M. F. (2020). Revealing unconscious consumer reactions to advertisements that include visual metaphors. A neurophysiological experiment. Frontiers in Psychology, 11, 760.
Gibelli, J., Aubin-Horth, N., & Dubois, F. (2019). Individual differences in anxiety are related to differences in learning performance and cognitive style. Animal Behaviour, 157, 121-128.
Giesbers, B., Rienties, B., Tempelaar, D., & Gijselaers, W. (2013). Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing. Computers in Human Behavior, 29(1), 285-292.
Gilal, F. G., Zhang, J., Paul, J., & Gilal, N. G. (2019). The role of self-determination theory in marketing science: An integrative review and agenda for research. European Management Journal, 37(1), 29-44.
Goh, T. T., & Yang, B. (2021). The role of e-engagement and flow on the continuance with a learning management system in a blended learning environment. International Journal of Educational Technology in Higher Education, 18, 1-23.
Guay, F., Chanal, J., Ratelle, C. F., Marsh, H. W., Larose, S., & Boivin, M. (2010). Intrinsic, identified, and controlled types of motivation for school subjects in young elementary school children. British Journal of Educational Psychology, 80(4), 711–735.
Guo, Z., Xiao, L., van Toorn, C., Lai, Y., & Seo, C. (2016). Promoting online learners’ continuance intention: An integrated flow framework. Information & Management, 53, 279–295.
Guyon, A. J., Hildebrandt, H., Güsewell, A., Horsch, A., Nater, U. M., & Gomez, P. (2022). How audience and general music performance anxiety affect classical music students’ flow experience: a close look at its dimensions. Frontiers in Psychology, 13, 959190.
Ha, Y., & Im, H. (2020). The Role of an Interactive Visual Learning Tool and its Personalizability in Online Learning: Flow Experience. Online Learning, 24(1), 205-226.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM)(3rd ed.). Thousand Oaks: Sage.
Heckel, C., & Ringeisen, T. (2019). Pride and anxiety in online learning environments: Achievement emotions as mediators between learners' characteristics and learning outcomes. Journal of Computer Assisted Learning, 35(5), 667-677.
Hinton, P., McMurray, I., & Brownlow, C. (2014). SPSS Explained. London, UK: Routledge Press.
Ho, L. A., & Kuo, T. H. (2010). How can one amplify the effect of e-learning? An examination of high-tech employees’ computer attitude and flow experience. Computers in Human Behavior, 26(1), 23-31.
Hong, J. C., Tsai, C. R., Hsiao, H. S., Chen, P. H., Chu, K. C., Gu, J., & Sitthiworachart, J. (2019). The effect of the “Prediction-observation-quiz-explanation” inquiry-based e-learning model on flow experience in green energy learning. Computers & Education, 133, 127-138.
Hong, J. C., Tai, K. H., & Ye, J. H. (2019). Playing a Chinese remote‐associated game: The correlation among flow, self‐efficacy, collective self‐esteem and competitive anxiety. British Journal of Educational Technology, 50(5), 2720-2735.
Hsieh, Y. H., Lin, Y. C., & Hou, H. T. (2016). Exploring the role of flow experience, learning performance and potential behavior clusters in elementary students' game-based learning. Interactive Learning Environments, 24(1), 178-193.
Huang, C. F., Nien, W. P., & Yeh, Y. S. (2015). Learning effectiveness of applying automated music composition software in the high grades of elementary school. Computers & Education, 83, 74–89.
Huang, W.-H., Huang, W.-Y., & Tschopp, J. (2010). Sustaining iterative game playing processes in DGBL: The relationship between motivational processing and outcome processing. Computers and Education, 55(2), 789–797.
Hwang, G. J., Hsu, T. C., Lai, C. L., & Hsueh, C. J. (2017). Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns. Computers & Education, 106, 26-42.
Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The Flow State Scale. Journal of Sport and Exercise Psychology, 18(1), 17-35.
Jansson, B., & Najström, M. (2009). Is preattentive bias predictive of autonomic reactivity in response to a stressor?. Journal of Anxiety Disorders, 23(3), 374-380.
Jebur, G., Al-Samarraie, H., & Alzahrani, A. I. (2022). An adaptive Metalearner-based flow: a tool for reducing anxiety and increasing self-regulation. User Modeling and User-Adapted Interaction, 32(3), 469-501.
Jeong, S. H. (2008). Visual metaphor in advertising: Is the persuasive effect attributable to visual argumentation or metaphorical rhetoric?. Journal of Marketing Communications, 14(1), 59-73.
Jin, S. H. (2017). Using visualization to motivate student participation in collaborative online learning environments. Journal of Educational Technology & Society, 20(2), 51-62.
Joo, Y. J., Lim, K. Y., & Kim, J. (2013). Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university context. Computers & Education, 62, 149–158.
Joo, Y. J., Joung, S., & Kim, J. (2014). Structural relationships among self-regulated learning, learning flow, satisfaction, and learning persistence in cyber universities. Interactive Learning Environments, 22(6), 752-770.
Joo, Y. J., Oh, E., & Kim, S. M. (2015). Motivation, instructional design, flow, and academic achievement at a Korean online university: a structural equation modeling study. Journal of Computing in Higher Education, 27(1), 28–46.
Kajanus, M., Iire, A., Eskelinen, T., Heinonen, M., & Hansen, E. (2014). Business model design: new tools for business systems innovation. Scandinavian Journal of Forest Research, 29(6), 603-614.
Kashdan, T. B., Rose, P., & Fincham, F. D. (2004). Curiosity and exploration: Facilitating positive subjective experiences and personal growth opportunities. Journal of Personality Assessment, 82(3), 291-305.
Kelly, M. M., Tyrka, A. R., Anderson, G. M., Price, L. H., & Carpenter, L. L. (2008). Sex differences in emotional and physiological responses to the Trier Social Stress Test. Journal of Behavior Therapy and Experimental Psychiatry, 39(1), 87-98.
Kirkpatrick, D. L. (1975). Evaluating training programs: Tata McGraw-Hill Education.
Kline, R. B. (2015). Principles and practice of structural equation modeling(4th ed.). New York: The Guilford Press.
Kubey, R. W., & Csikszentmihalyi, M. (1990). Television as escape: Subjective experience before an evening of heavy viewing. Communication Reports, 3(2), 92-100.
Lakoff, G.(1993). The contemporary theory of metaphor. In Andrew Ortony (Ed.), Metaphor and thought (pp.202–251). New York, NY: Cambridge University Press.
Lakoff, G., & Johnson, M. (2008). Metaphors we live by. University of Chicago press.
Lee, C. H., & Wu, J. J. (2017). Consumer online flow experience: The relationship between utilitarian and hedonic value, satisfaction and unplanned purchase. Industrial Management & Data Systems, 117(10), 2452-2467.
Li, R., Meng, Z., Tian, M., Zhang, Z., & Xiao, W. (2021). Modelling Chinese EFL learners’ flow experiences in digital game-based vocabulary learning: The roles of learner and contextual factors. Computer Assisted Language Learning, 34(4), 483–505.
Lin-Stephens, S., Manuguerra, M., & Bulbert, M. W. (2022). Seeing is relieving: effects of serious storytelling with images on interview performance anxiety. Multimedia Tools and Applications, 81(16), 23399-23420.
Lin, T. C., Liang, J. C., & Tsai, C. C. (2015). Conceptions of memorizing and understanding in learning, and self-efficacy held by university biology majors. International Journal of Science Education, 37(3), 446-468.
Lin, T. J., & Tsai, C. C. (2013). A Multi-dimensional instrument for evaluating taiwanese high school students’science learning self-efficacy in relation to their approaches to learning science. International Journal of Science and Mathematics Education, 11(6), 1275-1301.
Makransky, G., Borre‐Gude, S., & Mayer, R. E. (2019). Motivational and cognitive benefits of training in immersive virtual reality based on multiple assessments. Journal of Computer Assisted Learning, 35(6), 691-707.
Mannell, R. C., Zuzanek, J., & Larson, R. (1988). Leisure states and “flow” experiences: Testing perceived freedom and intrinsic motivation hypotheses. Journal of Leisure Research, 20(4), 289-304.
Molero, D., Schez-Sobrino, S., Vallejo, D., Glez-Morcillo, C., & Albusac, J. (2021). A novel approach to learning music and piano based on mixed reality and gamification. Multimedia Tools and Applications, 80, 165-186.
Mueller, F. A., & Wulf, T. (2022). Blended learning environments and learning outcomes: The mediating role of flow experience. International Journal of Management Education, 20(3), 100694.
Norris, R. L., Bailey, R. L., Bolls, P. D., & Wise, K. R. (2012). Effects of emotional tone and visual complexity on processing health information in prescription drug advertising. Health Communication, 27(1), 42-48.
Novak, T. P., Hoffman, D. L., & Yung, Y.-F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22-42.
Osgerby, J., Marriott, P., & Gee, M. (2018). Accounting students perceptions of using visual metaphor as part of personal development planning: an exploratory case study. Accounting Education, 27(6), 570-589.
Osterwalder A. (2004) The business model ontology: A proposition in a design science approach. Doctoral dissertation. University of Lausanne
Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, game changers, and challengers. Hoboken, NJ: Wiley & Sons.
Özhan, Ş. Ç., & Kocadere, S. A. (2020). The effects of flow, emotional engagement, and motivation on success in a gamified online learning environment. Journal of Educational Computing Research, 57(8), 2006-2031.
Parker, P. C., Perry, R. P., Hamm, J. M., Chipperfield, J. G., Pekrun, R., Dryden, R. P., Daniel, L. M., & Tze, V. M. (2021). A motivation perspective on achievement appraisals, emotions, and performance in an online learning environment. International Journal of Educational Research, 108, 101772.
Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students' self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91-105.
Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-Based Virtual Learning Environments: A Research Framework and a Preliminary Assessment of Effectiveness in Basic IT Skills Training. MIS Quarterly, 25(4), 401–426.
Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor MI: The University of Michigan, Technical Report no. 91-B-004.
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95, 667–686.
Rodriguez-Ardura, I., & Meseguer-Artola, A. (2016). E-learning continuance: The impact of interactivity and the mediating role of imagery, presence, and flow. Information & Management, 53, 504–516.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.
Russell, V. (2020). Language anxiety and the online learner. Foreign Language Annals, 53(2), 338-352.
Sajjacholapunt, P., & Ball, L. J. (2014). The influence of banner advertisements on attention and memory: Human faces with averted gaze can enhance advertising effectiveness. Frontiers in Psychology, 5, 166.
Sarason, I. G. (1984). Stress, anxiety, and cognitive interference: reactions to tests. Journal of Personality and Social Psychology, 46(4), 929-938.
Seipp, B. (1991). Anxiety and academic performance. A meta-analysis of findings. Anxiety Research, 4(1), 27-41.
Shin, N. (2006). Online learner’s ‘flow’experience: an empirical study. British Journal of Educational Technology, 37(5), 705-720.
Siemens, J. C., Smith, S., Fisher, D., Thyroff, A., & Killian, G. (2015). Level up! The role of progress feedback type for encouraging intrinsic motivation and positive brand attitudes in public versus private gaming contexts. Journal of Interactive Marketing, 32, 1–12.
Snow, R. E., & Swanson, J. (1992). Instructional psychology: Aptitude, adaptation, and assessment. Annual Review of Psychology, 43, 583-626.
Sparviero, S. (2019). The case for a socially oriented business model canvas: The social enterprise model canvas. Journal of Social Entrepreneurship, 10(2), 232-251.
Stewart, J. A., Wood, L., Wiener, J., Kennedy, G. D., Chu, D. I., Lancaster, J. R., & Morris, M. S. (2021). Visual teaching aids improve patient understanding and reduce anxiety prior to a colectomy. The American Journal of Surgery, 222(4), 780-785.
Stipek, D. J. (2002). Motivation to learn: Integrating theory and practice(4th ed.). Boston: Allyn & Bacon.
Su, C. H. (2016). The effects of students' motivation, cognitive load and learning anxiety in gamification software engineering education: a structural equation modeling study. Multimedia Tools and Applications, 75, 10013-10036.
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296.
Tayie, S. (2005). Research Methods and Writing Research Proposals. Center for Advancement of Postgraduate Studies and Research in Engineering Sciences, Faculty of Engineering-Cario University(CAPSCU): Pathways to Higher Education.
Taylor, M., Marrone, M., Tayar, M., & Mueller, B. (2018). Digital storytelling and visual metaphor in lectures: a study of student engagement. Accounting Education, 27(6), 552-569.
Tsai, C. A., Song, M. Y. W., Lo, Y. F., & Lo, C. C. (2023). Design thinking with constructivist learning increases the learning motivation and wicked problem-solving capability—An empirical research in Taiwan. Thinking Skills and Creativity, 50, 101385.
Wang, C. Y., Zhang, Y. Y., & Chen, S. C. (2021). The empirical study of college students’ E-learning effectiveness and its antecedents toward the COVID-19 epidemic environment. Frontiers in Psychology, 12, 573590.
Wang, W. T., Lin, Y. L., & Lu, H. E. (2023). Exploring the effect of improved learning performance: A mobile augmented reality learning system. Education and Information Technologies, 28(6), 7509-7541.
Warr, P. B., & Bunce, D. J. (1995). Trainee characteristics and the outcomes of open learning. Personnel Psychology, 48, 347-375.
Webster, J., & Martocchio, J. J. (1992). Microcomputer playfulness: Development of a measure with workplace implications. MIS Quarterly, 16(2), 201-226.
Wigfield, A., & Wentzel, K. (2007). Introduction to motivation at school: Interventions that work[Special Issue: Promoting motivation at school: Interventions that work]. Educational Psychologist, 42(4), 191–196.
Wijsman, L. A., Warrens, M. J., Saab, N., Van Driel, J. H., & Westenberg, P. M. (2016). Declining trends in student performance in lower secondary education. European Journal of Psychology of Education, 31(4), 595-612.
Wolf, S., Brölz, E., Keune, P. M., Wesa, B., Hautzinger, M., Birbaumer, N., & Strehl, U. (2015). Motor skill failure or flow-experience? Functional brain asymmetry and brain connectivity in elite and amateur table tennis players. Biological psychology, 105, 95-105.
Yang, T.-C., Chen, M. C., & Chen, S. Y. (2018). The influences of self-regulated learning support and prior knowledge on improving learning performance. Computers & Education, 126, 37-52.
Yen, W. C., & Lin, H. H. (2020). Investigating the effect of flow experience on learning performance and entrepreneurial self-efficacy in a business simulation systems context. Interactive Learning Environments, 1-16.
Yoo, C. W., Sanders, G. L., & Cerveny, R. P. (2018). Exploring the influence of flow and psychological ownership on security education, training and awareness effectiveness and security compliance. Decision Support Systems, 108, 107-118.
Younas, M., Noor, U., Zhou, X., Menhas, R., & Qingyu, X. (2022). COVID-19, students satisfaction about e-learning and academic achievement: Mediating analysis of online influencing factors. Frontiers in Psychology, 13, 948061.
Zhao, H., & Khan, A. (2022). The students’ flow experience with the continuous intention of using online english platforms. Frontiers in Psychology, 12, 807084.
Zhao, Y., Wang, A., & Sun, Y. (2020). Technological environment, virtual experience, and MOOC continuance: A stimulus–organism–response perspective. Computers & Education, 144, 103721.
校內:2029-06-27公開