自2014年，內容農場進入台灣百大網站的榜單中開始，各大社群平台、通訊軟體中，便開始被各種「農場文」佔據，這些農場文幾乎都來自於網路中的眾多匿名寫手。由於內容農場的主要目的在於衝流量與曝光度，因而各種標題聳動、品質低劣、來源不明、真偽混雜的農場文開始在網路中大量發散，內容農場也成為各種假訊息的發源地。近年來，假訊息的氾濫已經開始對社會產生危害，也開始成為被社會各界關注的議題。

媒體素養教育困難、民眾的媒體素養認知不足，是假訊息橫行無阻的主因，雖然媒體素養教育已經漸漸的被重視，但是在教育體制中能被分配到的資源依舊與主流科目有相當的差距，如此情況下，想加強媒體素養教育，就只能夠用一些輔助課程內容的方式，例如在課程中加入實際的訊息查證操作，讓學生藉由情境體驗，利用經驗學習以及反思方式，盡可能加強短期課程的效果。但是傳統的人工查證方式過於耗時，難以融入本就時數不足的媒體素養課程。

現今已經有許多人工智慧的訊息辨識系統被開發出來，不但具有相當的辨識準確率，相較於傳統的人力查證方式，人工智慧輔助辨識系統的操作方式簡單、檢驗時間迅速，更加適合加入到媒體素養課程之中。

本研究將利用自行開發的人工智慧輔助訊息可信度辨識系統，配合經驗學習與反思，以及科技採用行為的相關理論，建立一個研究模型，以使用後進行問卷調查的方式來進行研究，探討人工智慧輔助訊息可信度辨識系統對於媒體素養的反思效果以及使用者在使用過後的認知態度。

Since 2014, major social platforms and communication software have begun to be occupied by "farm texts." These farm texts have sensational titles, low quality, unknown sources, mixed authenticity, and a large number of dissemination on the Internet. Content farms have also become The birthplace of all kinds of fake news.

Insufficient media literacy is the main cause of the proliferation of fake news. To strengthen media literacy education, some methods can be used to supplement the content of the curriculum, such as adding actual information verification operations to the curriculum, allowing students to use experience learning to promote reflection and strengthen the curriculum effect.

The artificial intelligence-assisted identification system has simple operation methods and quick inspection time, which is very suitable for adding to the media literacy course.

This study aims to Analyze the self-reflective on media literacy and cognition attitude after using artificial intelligence news source credibility identification system

中文文獻
台灣媒體觀察教育基金會（2019）2019台灣新聞媒體可信度研究。取自https://www.mediawatch.org.tw/news/9911
白亦方＆楊雅惠(2003)。跨越學門的鴻溝-社會領域結合音樂題材之統整教學。 花蓮師院學報 (教育類)， (16)，211-231。
朱其慧(2001)。媒體素養社會行銷研究----以台灣主要推行媒體素養非營利組織為例。國立台灣師範大學大眾傳播研究所碩士論文，未出版，台北。
朱則剛, & 吳翠珍. (1994). 我國國小學生電視識讀能力研究. 國科會專題研究計畫成果報告 (報告編號: NSC 81-0301-H-032-504). 台北市: 國科會. 連結.
朱則剛. (2005). 加拿大媒體素養教育探討. 圖書資訊學刊, 3(1&2), 1-13.
自由時報（2018）假新聞傳播 比真相快6倍。取自https://news.ltn.com.tw/news/focus/paper/1182676
何吉森(2018)。假新聞之監理與治理探討。傳播研究與實踐，8(2)，1-41。
吳木崑(2009)。杜威經驗哲學對課程與教學之啟示。臺北市立教育大學學報。 教育類, 40(1), 35-54.
吳佩陵(2007)。國民小學校長反思與校長專業能力發展之研究。政治大學教育研究所學位論文，1-213。
吳知賢. (1998). 兒童與電視. 桂冠.
吳翠珍 (2004)。台灣媒體教育的實驗與反思. 台灣教育, (629), 28-39.
吳翠珍（2003）。媒體素養教育教什麼？師友月刊，436。
李常井（1985）。Dewey經驗概念剖析。中央研究院三民主義研究所專題選刊，66，1-26。
李嘉峰(2004)。親子共視，解讀黑盒子。 教師之友, 45(5), 64-69.
周慧美.（1999）."國小學童電視識讀能力之探討及電視識讀教學成效分析.
林秀珍(2007)。經驗與教育探微: 杜威 (J. Dewey) 教育哲學之詮釋。師大師苑有限公司。
林羿妏＆陳昭秀(2012)。大專院校學生的 Facebook 使用特性，批判思考與資訊驗證行為的關係 (Doctoral dissertation)。
林逢祺(2003)。由思維歷程透視教學原理: 杜威《思維術》方法論之衍釋。 教育研究集刊，(49：1)，1-29。
金車文教基金會（2017）自媒體時代來臨 青少年自認媒體素養高。取自https://kingcar.org.tw/survey/500070
金車文教基金會（2019）《青少年媒體素養》七成多青少年自認媒體素養不足。取自 https://kingcar.org.tw/survey/500773
胡林辳（2019）。植基於深度學習假新聞人工智慧偵測：台灣與美國真實資料實作（未出版碩士論文），國立台北大學，台北市。
國家發展委員會（2019）108年個人家戶數位機會調查報告。徐宗林 & 周愚文(1997) 教育史。五南圖書出版股份有限公司。
教育部（2002）。媒體素養教育政策白皮書。台北：教育部。
許佳琪(2011)。杜威的教育哲學對於終身學習之啟示。刊於《育達科大學報》 (29)，163-176。
陳世敏. (2005). 媒介素養的基本概念. 媒介素養概論》(頁 3–22). 台北:台灣五南圖書出版股份有限公司.
陳炳宏(2017)。假新聞與媒體素養教育。取自 https://tw.appledaily.com/forum/daily/20170221/37558056
創市際雙周刊（2020）新聞篇與新聞資訊類網站使用概況。取自https://www.ixresearch.com/wp-content/uploads/2020/03/InsightXplorer-Biweekly-Report_20200316.pdf
單文經（譯）（2015）。經驗與教育（原作者：John Dewey）。臺北市：聯經。（原著出版年：1938）
報導者（2019）直擊鬼島狂新聞、全球華人聯盟背後的內容農場帝國LINE群組的假訊息從哪來？跨國調查，追出內容農場「直銷」產業鏈。取自https://www.twreporter.org/a/information-warfare-business-disinformation-fake-news-behind-line-groups
曾文志 (2004)。真實與虛擬的拔河—如何和孩子談新聞報導。師友月刊,(447), 68-74.
新興科技媒體中心（2020）【2020年科學媒體素養民調記者會】民眾願主動查證可強化科學媒體識讀能力。取自 https://smctw.tw/7559/
楊洲松(2004)。解放與賦權－媒體素養教育的理念與實踐。台灣教育，629，2-8。
葉玉珠（1999）。批判思考意向量表，未發表之量表。
數位時代（2015）[解讀Web 100] 內容農場遍地開花。取自 https://www.bnext.com.tw/article/35528/BN-ARTICLE-35528
蕭玉品(2018)。台灣內容農場寫手八成是馬來西亞華人。遠見雜誌，384，185。
戴廷芳（2016）臉書開源旗下AI函式庫FastText，10分鐘訓練機器學習模型超過10億個單詞。取自 https://www.ithome.com.tw/news/107845
羅世宏(2018)。關於 [假新聞] 的批判思考：老問題，新挑戰與可能的多重解方。資訊社會研究，(35)，51-86。
饒淑梅（1996）。國民中學實施電視素養課程之研究。台北市：國立台灣師範大學公民訓育研究所碩士論文（未出版）

 
英文文獻
Afroz, S., Brennan, M., & Greenstadt, R. (2012, May). Detecting hoaxes, frauds, and deception in writing style online. In 2012 IEEE Symposium on Security and Privacy (pp. 461-475). IEEE.
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies?. Decision sciences, 30(2), 361-391.
Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological bulletin, 82(2), 261.
Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-36.
Alzahrani, A. I., Mahmud, I., Ramayah, T., Alfarraj, O., & Alalwan, N. (2019). Modelling digital library success using the DeLone and McLean information system success model. Journal of Librarianship and Information Science, 51(2), 291-306.Alpaydin, E. (2016). Machine learning: the new AI. MIT press.
Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing science, 12(2), 125-143.
Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer satisfaction, market share, and profitability: Findings from Sweden. Journal of marketing, 58(3), 53-66.
Bahuleyan, H., & Vechtomova, O. (2017, August). UWaterloo at SemEval-2017 Task 8: Detecting stance towards rumours with topic independent features. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) (pp. 461-464).
Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management science, 29(5), 530-545.
Bard, R. (2014). Focus on Learning: Reflective Learners & Feedback. TESL-EJ, 18(3), 1-18.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
Bontchev, B., Vassileva, D., Aleksieva-Petrova, A., & Petrov, M. (2018). Playing styles based on experiential learning theory. Computers in Human Behavior, 85, 319-328.
Boonsiritomachai, W., & Pitchayadejanant, K. (2017). Determinants affecting mobile banking adoption by generation Y based on the Unified Theory of Acceptance and Use of Technology Model modified by the Technology Acceptance Model concept. Kasetsart Journal of Social Sciences.
Bourgonje, P., Schneider, J. M., & Rehm, G. (2017, September). From clickbait to fake news detection: an approach based on detecting the stance of headlines to articles. In Proceedings of the 2017 EMNLP workshop: natural language processing meets journalism (pp. 84-89).
Breakstone, J., Smith, M., & Wineburg, S. (2019). Students'civic online reasoning. A National Portrait. Available online at: https://stacks. stanford. edu/file/gf151tb4868/Civic% 20Online% 20Reasoning% 20National% 20Portrait. pdf (accessed May 16, 2020).
Brown, A. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. Metacognition, motivation, and understanding.
Brown, C., Willett, J., Goldfine, R., & Goldfine, B. (2018). Sport management internships: Recommendations for improving upon experiential learning. Journal of Hospitality, Leisure, Sport & Tourism Education, 22, 75-81.
Burkov, A. (2019). The hundred-page machine learning book (Vol. 1, pp. 3-5). Canada: Andriy Burkov.
Canniford, L. J., & Fox-Young, S. (2015). Learning and assessing competence in reflective practice: Student evaluation of the relative value of aspects of an integrated, interactive reflective practice syllabus. Collegian, 22(3), 291-297.
Carrie, S. (2019). High school students are unprepared to judge the credibility of information on the internet, according to Stanford researchers. Retrieved from https://news.stanford.edu/2019/11/18/high-school-students-unequipped-spot-fake-news/
Carter, L., & Bélanger, F. (2005). The utilization of e‐government services: citizen trust, innovation and acceptance factors. Information systems journal, 15(1), 5-25.
Chaudhry, A. K., Baker, D., & Thun-Hohenstein, P. (2017). Stance detection for the fake news challenge: identifying textual relationships with deep neural nets. CS224n: Natural Language Processing with Deep Learning.
Chen, Y., Conroy, N. J., & Rubin, V. L. (2015). Misleading online content: recognizing clickbait as" false news". In Proceedings of the 2015 ACM on workshop on multimodal deception detection (pp. 15-19).
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Chiu, C. M., Hsu, M. H., Sun, S. Y., Lin, T. C., & Sun, P. C. (2005). Usability, quality, value and e-learning continuance decisions. Computers & education, 45(4), 399-416.
Choudhary, A., & Arora, A. (2021). Linguistic feature based learning model for fake news detection and classification. Expert Systems with Applications, 169, 114171.
Costa, A. L., & Kallick, B. (2000). Getting into the Habit of Reflection. Educational leadership, 57(7), 60-62.
Cui, Y., Mou, J., Cohen, J., & Liu, Y. (2019). Understanding information system success model and valence framework in sellers’ acceptance of cross-border e-commerce: a sequential multi-method approach. Electronic Commerce Research, 19(4), 885-914.
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems. Cambridge, MA.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information systems research, 3(1), 60-95.
Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of B2C channel satisfaction and preference: validating e-commerce metrics. Information systems research, 13(3), 316-333.
Dewey, J. (1906). Experience and objective idealism. The Philosophical Review, 15(5), 465-481.
Dewey, J. (1910). How we think. Boston, MA：D. C. Health
Dewey, J. (1916). Democracy and education. New York, NY：Macmilan
Dewey, J. (1929). Experience and nature. London, England：George Allen＆Unwin.
Dewey, J. (1934). Art as experience. New York, NY：G. P. Putnam’s Sons.
Dewey, J. (1938). Experience and education. New York, NY：Macmilan
Ein-Dor, P., & Segev, E. (1978). Organizational context and the success of management information systems. Management Science, 24(10), 1064-1077.
Esmaeilzadeh, S., Peh, G. X., & Xu, A. (2019). Neural abstractive text summarization and fake news detection. arXiv preprint arXiv:1904.00788.
Estriegana, R., Medina-Merodio, J. A., & Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model. Computers & Education, 135, 1-14.
Falloon, G. (2019). Using simulations to teach young students science concepts: An Experiential Learning theoretical analysis. Computers & Education, 135, 138-159.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American customer satisfaction index: nature, purpose, and findings. Journal of marketing, 60(4), 7-18.
Fürnkranz, J. (1998). A study using n-gram features for text categorization. Austrian Research Institute for Artifical Intelligence, 3(1998), 1-10.
Giac, C. C., Gai, T. T., & Hoi, P. T. T. (2017). Organizing the experiential learning activities in teaching science for general education in Vietnam. World Journal of Chemical Education, 5(5), 180-184.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT press.
Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European business review.
Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. saGe publications.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Prentice-Hall.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
Hee, O. C. (2014). Validity and reliability of the big five personality traits scale in Malaysia. International Journal of Innovation and Applied Studies, 5(4), 309.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., ... & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational research methods, 17(2), 182-209.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited.
Horne, B., & Adali, S. (2017). This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 11, No. 1).
Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of management information systems, 16(2), 91-112.
Iivari, J., & Koskela, E. (1987). The PIOCO model for information systems design. MIS quarterly, 401-419.
Jaafreh, A. B. (2017). Evaluation information system success: applied DeLone and McLean information system success model in context banking system in KSA. International review of management and business research, 6(2), 829-845.
Joo, Y. J., Park, S., & Shin, E. K. (2017). Students' expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behavior, 69, 83-90.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
Juliansyah, M. R. (2018). Pengaruh Image Product Terhadap Loyalitas Pengguna E-Commerce di Indonesia dengan Menggunakan Model Expectation Confirmation Theory (ECT) dan Theory Reasoned Action (TRA) (Bachelor's thesis, Jakarta: Fakultas Sains Dan Teknologi UIN Syarif Hidayatullah).
Kannan, A., Kurach, K., Ravi, S., Kaufmann, T., Tomkins, A., Miklos, B., ... & Ramavajjala, V. (2016, August). Smart reply: Automated response suggestion for email. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 955-964).
Kelleher, J. D. (2019). Deep learning. MIT press.
Kember, D., Leung, D. Y., Jones, A., Loke, A. Y., McKay, J., Sinclair, K., ... & Yeung, E. (2000). Development of a questionnaire to measure the level of reflective thinking. Assessment & evaluation in higher education, 25(4), 381-395.
Klaus Greff, Rupesh Kumar Srivastava, Jan Koutink, Bas R. Steunebrink, Jurgen Schmidhuber（2017）, A Search Space Odyssey, Transactions on Neural Networks and Learning Systems,IEEE,2017,pp2222-2232.
Ko, H. H., & Aung, P. E. P. The Study of Reflective Thinking of University Students.
Kochkina, E., Liakata, M., & Augenstein, I. (2017). Turing at semeval-2017 task 8: Sequential approach to rumour stance classification with branch-lstm. arXiv preprint arXiv:1704.07221.
Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ：Prentice Hall.
Kshemkalyani, A. D., & Singhal, M. (2011). Distributed computing: principles, algorithms, and systems. Cambridge University Press.
Kumar, R., Sachan, A., & Mukherjee, A. (2018). Direct vs indirect e-government adoption: an exploratory study. Digital policy, regulation and governance.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Lee, C. S., & Ma, L. (2012). News sharing in social media: The effect of gratifications and prior experience. Computers in human behavior, 28(2), 331-339.
Lee, Y. J. (2020). An Empirical Study on the Factors Affecting Continuance Intention to Use of Online Distance Learning of Airline Department Collegue Students: Focused on Post-Acceptance Model. Journal of Information Technology Services, 19(5), 107-124.
Li, F. H., Huang, M., Yang, Y., & Zhu, X. (2011, June). Learning to identify review spam. In Twenty-second international joint conference on artificial intelligence.
Lim, S. H., Kim, D. J., Hur, Y., & Park, K. (2019). An empirical study of the impacts of perceived security and knowledge on continuous intention to use mobile fintech payment services. International Journal of Human–Computer Interaction, 35(10), 886-898.
Limayem, M., & Cheung, C. M. (2008). Understanding information systems continuance: The case of Internet-based learning technologies. Information & management, 45(4), 227-232.
Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & management, 42(5), 683-693.
McKinney, V., Yoon, K., & Zahedi, F. M. (2002). The measurement of web-customer satisfaction: An expectation and disconfirmation approach. Information systems research, 13(3), 296-315.
Mezirow, J. (1978). Perspective transformation. Adult education, 28(2), 100-110.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546.
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. MIT press.
Moon, J. (2004). Using reflective learning to improve the impact of short courses and workshops. Journal of continuing education in the health professions, 24(1), 4-11.
Morris, T. H. (2020). Experiential learning–a systematic review and revision of Kolb’s model. Interactive Learning Environments, 28(8), 1064-1077.
Mukherjee, A., Liu, B., & Glance, N. (2012, April). Spotting fake reviewer groups in consumer reviews. In Proceedings of the 21st international conference on World Wide Web (pp. 191-200).
Nasir, J. A., Khan, O. S., & Varlamis, I. (2021). Fake news detection: A hybrid CNN-RNN based deep learning approach. International Journal of Information Management Data Insights, 1(1), 100007.
Nick, N. (2018). Journalism, Media and Technology Trends and Predictions 2018. Retrieved from https://reutersinstitute.politics.ox.ac.uk/our-research/journalism-media-and-technology-trends-and-predictions-2018
Oh, S., Ahn, J., & Kim, B. (2003). Adoption of broadband Internet in Korea: the role of experience in building attitudes. Journal of Information Technology, 18(4), 267-280.
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, 17(4), 460-469.
Oliver, R. L., & Bearden, W. O. (1985). Disconfirmation processes and consumer evaluations in product usage. Journal of business research, 13(3), 235-246.
Oliver, R. L., & DeSarbo, W. S. (1988). Response determinants in satisfaction judgments. Journal of consumer research, 14(4), 495-507.
Olsen, S. O. (2002). Comparative evaluation and the relationship between quality, satisfaction, and repurchase loyalty. Journal of the academy of marketing science, 30(3), 240-249.
Parthasarathy, M., & Bhattacherjee, A. (1998). Understanding post-adoption behavior in the context of online services. Information systems research, 9(4), 362-379.
Potter, W. J. (1998). Media literacy. London: Sage.
Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., & Stein, B. (2017). A stylometric inquiry into hyperpartisan and fake news. arXiv preprint arXiv:1702.05638.
Rahi, S., & Ghani, M. A. (2019). Integration of expectation confirmation theory and self-determination theory in internet banking continuance intention. Journal of Science and Technology Policy Management.
Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information systems research, 13(1), 50-69.
Riemenschneider, C. K., & Hardgrave, B. C. (2001). Explaining software development tool use with the technology acceptance model. Journal of Computer Information Systems, 41(4), 1-8.
Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696.
Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696.
Rodgers, C. (2002). Defining reflection: Another look at John Dewey and reflective thinking. Teachers college record, 104(4), 842-866.
Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.
Rogers, E. M., & Williams, D. (1983). Diffusion of Innovations (Glencoe, IL: The Free Press, 1962).
Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445-128462.
Scharle, A., & Szabo, A. (2007). Learner autonomy: A guide to developing learner responsibility. Ernst Klett Sprachen.
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35.
Seddon, P., & Kiew, M. Y. (1996). A partial test and development of DeLone and McLean's model of IS success. Australasian Journal of Information Systems, 4(1).
Seddon, P., & Kiew, M. Y. (1996). A partial test and development of DeLone and McLean's model of IS success. Australasian Journal of Information Systems, 4(1).
Sejnowski, T. J. (2018). The deep learning revolution. Mit Press.
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2017). Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics, 22(5), 1589-1604.
Shih, W. C. (2019, July). Integrating Computational Thinking into the Process of Learning Artificial Intelligence. In Proceedings of the 2019 3rd International Conference on Education and Multimedia Technology (pp. 364-368).
Shuib, L., Yadegaridehkordi, E., Ainin, S., & Feng, G. C. (2019). Malaysian urban poor adoption of e-government applications and their satisfaction. Cogent Social Sciences, 5(1), 1565293.
Spreng, R. A., & Chiou, J. S. (2002). A cross‐cultural assessment of the satisfaction formation process. European journal of marketing.
Sugerman, D. A., Doherty, K. L., & Garvey, D. E. (2000). Reflective learning: Theory and practice. Kendall Hunt.
SUMARMİ, S., BACHRİ, S., IRAWAN, L. Y., PUTRA, D. B. P., RİSNANİ, R., & ALİMAN, M. (2020). The Effect of Experiential Learning Models on High School Students Learning Scores and Disaster Countermeasures Education Abilities. Journal for the Education of Gifted Young Scientists, 8(1), 61-85.
Tallim, J. (2010). What is media literacy? Retrieved Octorber 30, 2010, Retrieved from http://www.mediaawareness.ca/english/teachers/media_literacy/what_is_media_literacy. cfm
Tan, C. C. (2019). Intercepting Stimulus-Organism-Response Model, Theory of Planned Behavior and Theory of Expectancy Confirmation in the Study of Smartphone Consumer Behavior: A Thai University Student Perspective. Asia Pacific Journal of Religions and Cultures, 3(2), 27-48.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.
Thoman, E. (1999). Skills and strategies for media education. Educational Leadership, 56, 50-54.
Thong, J. Y., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of human-computer studies, 64(9), 799-810.
Tong, D. H., Loc, N. P., Uyen, B. P., & Cuong, P. H. (2020). Applying Experiential Learning to Teaching the Equation of a Circle: A Case Study. European Journal of Educational Research, 9(1), 239-255.
Trendmicro（2017）Fake News and Cyber Propaganda: The Use and Abuse of Social Medi. Retrieved from https://www.trendmicro.com/vinfo/pl/security/news/cybercrime-and-digital-threats/fake-news-cyber-propaganda-the-abuse-of-social-media
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Information & management, 41(6), 747-762.
Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015). Show and tell: A neural image caption generator. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3156-3164).
Wang, S., Yang, D. M., Rong, R., Zhan, X., & Xiao, G. (2019). Pathology image analysis using segmentation deep learning algorithms. The American journal of pathology, 189(9), 1686-1698.
Wardle, C., & Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe report, 27, 1-107.
Wineburg, S., & McGrew, S. (2016). Evaluating information: The cornerstone of civic online reasoning.
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., ... & Dean, J. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
Zhang, X. D. (2020). Machine learning. In A Matrix Algebra Approach to Artificial Intelligence (pp. 223-440). Springer, Singapore.
Zhigang, W., Lei, Z., & Xintao, L. (2020). Consumer Response to Corporate Hypocrisy From the Perspective of Expectation Confirmation Theory. Frontiers in Psychology, 11.
Zhiyong Cui, Ruimin Ke, Ziyuan Pu, and Yinhai Wang（2018）,Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction, arXiv：1801.02143