研究生: |
曾絲宜 Tzeng, Sy-Yi |
---|---|
論文名稱: |
兩岸新科技學習行為之比較研究-以數位出版專業領域為例 A Comparison of New-Technology Learning Behavior in Digital Publish Domain between Mainland China and Taiwan |
指導教授: |
徐昊杲
Hsu, How-Gao |
學位類別: |
博士 Doctor |
系所名稱: |
工業教育學系 Department of Industrial Education |
論文出版年: | 2016 |
畢業學年度: | 104 |
語文別: | 中文 |
論文頁數: | 333 |
中文關鍵詞: | 數位出版專業領域 、新科技學習行為 、科技學科教學知識理論框架 |
英文關鍵詞: | digital publish domain, new-technology learning behavior, TPACK |
DOI URL: | https://doi.org/10.6345/NTNU202205115 |
論文種類: | 學術論文 |
相關次數: | 點閱:238 下載:30 |
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本研究比較兩岸數位出版專業領域學生的新科技學習行為,研究方法採用問卷調查法及專家訪談法,問卷調查對象為大陸及臺灣數位出版專業領域各363及285位大學生進行施測,並安排5位大陸授課教師及3位臺灣授課教師進行專家訪談。問卷調查工具方面,問卷設計以Fishbein、Ajzen(2010)的計畫行為理論(theory of plan behavior)為基礎,結合科技接受模式及社會認知理論觀點,同步發展適合兩岸文化用語的數位出版專業領域新科技學習行為問卷,經過專家修訂後進行施測。專家訪談工具方面,以Mishra、Koehler(2006)的科技學科教學知識理論框架(technological pedagogical and content knowledge framework)為理論基礎,研擬訪談大綱,訪談後編碼覆校分析研究結果。統計分析方法使用描述性統計、相關、驗證性因素分析及結構方程模式的多群組比較分析等方法,進行資料統計分析。研究結論歸納以下3點:1. 兩岸數位出版專業領域新科技學習模式的行為意向對學習行為皆無顯著中介影響效果,未能完全符合理論模式,可能原因是兩岸大學生的學習行為受到自我評價的影響,或受限於授課教師課程活動的安排;2. 兩岸數位出版專業領域新科技學習模式有差異,且變項間因果關係不同,研究認為是受到兩岸師生軟體應用多元性與自主性差異所造成的影響。3. 兩岸各自具有不同的教育及資訊文化,研究結果認為兩岸可以各自發展有利的大學生學習及教師教學型式,大陸教師應適當提升學生新科技使用的多元性,臺灣教師應適當透過新科技增加與學生之間的互動,同時,兩岸教師皆應努力營造學生良好的新科技學習經驗,建立學生正向的專業自我評價。
This research compared the new-technology learning behaviors of students from both sides of the straits. The study employed both the questionnaire method and the expert interview method. The questionnaire targeted 363 university students from the mainland and 285 university students from Taiwan. All of them specialize in the digital publishing field. Five mainland lecturers and three Taiwanese lecturers were invited to conduct the expert interviews. In regard to questionnaire tools, the design of the questionnaire was based on the theory of plan behavior by Fishbein & Ajzen (2010). The questionnaire adopted the Technology Acceptance Model (TAM) and took into account social cognitive views. The terminologies of the digital publishing domain were also localized to reconcile any cultural discrepancies between each place. The final version of the questionnaire underwent expert revision. As to the expert interview tools, the outline of the interviews was designed based on the technological, pedagogical, and content knowledge framework proposed by Mishra and Koehler (2006). The data collected were then reviewed using code analysis. The data collected were analyzed using such analysis methods as descriptive statistics, correlation analysis, and confirmatory factor analysis, as well as the multi-group structural equation modeling technique. The study reached three conclusions. First, there was no remarkable mediation effect difference between the behavioral intention and actural learning behavior displayed by the digital publishing students from each place; these results were not fully consistent with theory, a probable cause for which was that the learning behaviors of the university students from both sides were influenced by self-evaluation or were limited by the curriculum arrangements made by the lecturers. Second, there were differences between the modes of learning in the digital publishing domain in each place and the causal relationships between variables were not consistent; the researchers believe that the study was influenced by the diversity of software applications used by the teachers and students in both places and the variation in the degree of autonomy in both places. Third, both places enjoy educational and information cultures of their own; the research results showed that both places were capable of developing their own favorable ways of learning and teaching. While teachers in the mainland should enhance the diversity of new technologies students come in touch with, teachers in Taiwan should increase their degree of interaction with students with the help of new technologies. Meanwhile, teachers from both sides should do their best to create a favorable learning experience about new technologies for students and enable them to conduct self-evaluation in a professional manner.
壹、中文文獻
中國文化大學資訊傳播學系所(2015)。資傳系所介紹,歷史沿革。2015年9月23號,取自http://www.gcd.pccu.edu.tw/02about/history2.asp
江文雄(2000)。職業類科教材教法。臺北市:師大書苑。
北京印刷學院藝術設計學院(2015)。數字媒體藝術教學宗旨與理念。2015年10月23號,取自:http://art.bigc.edu.cn/rcpy/51508.htm
史梅岑(1986)。中國印刷發展史。臺北市:商務印書館。
西安理工科技大學印刷包裝與數字媒體學院(2012)。印刷包裝與數字媒體學院學院概況。2015年10月23號,取自:http://ybxy.xaut.edu.cn/School_profile.asp
成舍我(1956)。世新大學創校理念。世新大學,校史與沿革。2015年9月23號,取自:https://www.shu.edu.tw/about/about_5.htm
巫靜宜(2000)。比較網路教學與傳統教學對學習效果之研究-以Word2000之教學為例(未出版之碩士論文),淡江大學,新北市。
宋曜廷、張國恩(2012)。數位學習研究方法。臺北市:高等教育。
李金銓(2012)。報人報國:中國新聞史的另一種讀法。香港:中文大學出版社。
杭州電子科技大學數字媒體與藝術設計學院(2015)。數字媒體與藝術設計學院簡介。2015年10月23號,取自:http://syxy.hdu.edu.cn/index.php?c=content&a=list&catid=11
徐明珠(2006)。文化創意產業與圖文傳播人才培育。國政研究報告,教文(研)095-008號。2015年8月23號,取自:http://old.npf.org.tw/PUBLICATION/EC/095/EC-R-095-008.htm
郝振省(2005)。2004年數字出版產業發展報告。2005首屆中國數位出版博覽會,高峰論壇嘉賓演講。2015年8月15號,取自:http://digi.it.sohu.com/20050708/n240153675.shtml
郝振省、魏玉山、張立、王颩(2012)。2011-2012中國數字出版產業年度報告。北京:中國書籍出版社。
國立臺灣藝術大學(2011)。1955-2011 年校史大事記。「珍貴檔案與校史風華的再現」計畫結案報告。2015年10月23號,取自:http://hc.ntua.edu.tw/ftp/20110922011547.pdf
國立臺灣藝術大學(2014)。大觀臺藝數位校園期刊。2015年10月23號,取自:https://itunes.apple.com/tw/app/da-guan-tai-yi/id705774286?l=zh&mt=8
國立臺灣藝術大學圖文傳播藝術學系(2014)。圖文傳播藝術學系四大發展領域。2015年10月23號,取自:http://gca.ntua.edu.tw/main/about-gca.html
國立臺灣師範大學圖文傳播學系(2010)。圖傳簡介。2015年10月23號,取自:http://www.gac.ntnu.edu.tw/intro/super_pages.php?ID=intro4
曾絲宜(2012)。圖文傳播課程導入環境教育之研究(未出版之碩士論文),國立臺灣藝術大學,新北市。
曾絲宜、林宣瑋(2015年5月)。專業分工與多元融合-使用脈絡分析法探討兩岸圖文傳播教育歷史脈絡及其對臺灣的啟示。郝宗瑜(主持人),圖文傳播管理與教育。兩岸數位媒體與圖文傳播學術研討會,新北市國立臺灣藝術大學。
曾絲宜、韓豐年、徐昊杲(2013)。臺法藝術學院教育影響環境行為之比較。藝術教育研究,26,37-76。
景文科技大學視覺傳達設計系(2014)。景文科技大學視覺傳達設計系電子書與APP課程作品精選集。2015年10月23號,取自:https://itunes.apple.com/tw/app/jing-wen-shi-chuan/id898675483?l=zh&mt=8
黃國禎、蘇俊銘、陳年興(2012)。數位學習導論與實務。新北市:博碩文化。
張立、王颩(2015)。2014-2015中國數字出版產業年度報告。北京:中國書籍出版社。
張其昀(1989)。張其昀先生文集。臺北市:中國文化大學出版部。
張樹棟、龐多益、鄭如斯(2005)。中華印刷通史。臺北市:印刷傳播基金會。
劉怡甫(2013a)。與全球十萬人作同學:談MOOC現況及其發展。評鑑雙月刊,42。2015年5月15號,取自: http://epaper.heeact.edu.tw/archive/2013/03/01/5945.aspx
劉怡甫(2013b)。2013地平線報告─高教篇報導。2015年5月15號,取自:http://ocw.fju.edu.tw/elearning/index.php?option=com_content&view=article&id=154:2013&catid=28:current-users&Itemid=55
劉怡甫(2013c)。以Coursera為例 談MOOC教學設計了些什麼?。評鑑雙月刊,45。2015年5月15號,取自: http://epaper.heeact.edu.tw/archive/2013/09/01/6053.aspx
蔡易伶(2014)。以ARCS理論檢視行動學習融入國中視覺藝術課程之影響:以龍山寺文化之旅為例(未出版之碩士論文),國立臺灣藝術大學,新北市。
蔡政宏(2011)。科技內容教學知識(TPACK)理論架構對教師專業發展之啟示。新竹縣教育研究發展暨網路中心,計畫編號99補201。2015年11月16號,取自:http://www.nc.hcc.edu.tw/ezfiles/119/1119/img/805/99017.pdf
財團法人資訊工業策進會(2012)。台灣數位內容產業年鑑。臺北市:經濟部工業局。
財團法人資訊工業策進會(2013)。2013~2015數位內容產業專業人才供需調查。臺北市:經濟部工業局。
薛良凱(2004)。中華民國出版年鑑。臺北市:行政院新聞局。
貳、英文文獻
Abaidoo, N., & Arkorful, V. (2014). Adoption and effective integration of ICT in teaching and learning in higher institutions in Ghana. International Journal of Education and Research, 2(12), 411-422.
Agudo-Peregrina, A. F., Hernandez-Garcia, A., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314.
Ajzen, I. (1985). From intention to actions: A theory of planned behavior. In J. Kuhl & J. Bechmann (Eds.), Action-control: From cognition to behavior (pp. 11-39). Heidelberg, DE: Springer.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Akman, I., & Turhan, C. (2014). Investigating gender diversity in adopting social media for education: theory of planned behavior. Proceedings of the International Conference on Economics, 234-236. http://dx.doi.org/10.15242/ICEHM.ED1214033
Angeli, C., & Valanides, N. (2009). Epistemological and methodological issues for the conceptualization, development, and assessment of ICT-TPCK: Advances in technological pedagogical content knowledge (TPCK). Computers &Education, 52(1), 154-168.
Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
Bandura, A. (1991). Self-regulation of motivation through anticipatory and self-reactive mechanisms. In R. A. Dienstbier (Ed.), Nebraska Symposium on Motivation, 1990(Vol. 38, 69-164). Lincoln, NE: University of Nebraska Press.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.
Battelle, J. L. (2010). Toward a new undersranding of publishing. Retrived from: http://battellemedia.com/archives/2010/03/toward_a_new_understanding_of_publishing_part_1.php
Benbasat, I., & Barki, H. (2007). Quo vadis, TAM? Journal ofthe Associationfor Information Systems, 8(4), 211-218.
Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods and Research, 21, 205-229.
Brewer, W. F. (1974). There is no convincing evidence for operant or classical conditioning in adult humans. In W. B. Weimer & D. S. Palermo (Eds.), Cognition and the symbolic processes (pp. 1-42). Hillsdale, NJ: Erlbaum.
Broadhurst, P. L. (1957). Emotionality and the Yerkes-Dodson Law. Journal of Experimental Psychology, 54, 345-352.
Brown, J. S., & Burton, R. R. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155-191.
Chao, Y. L. (2012). Predicting people’s environmental behavior: Theory of planned behavior and model of responsible environmental behavior. Environmental Education Research, 18(4), 437-461.
Carbonell, J. R., & Collins, A. (1973). Natural semantics in artificial intelligence. Proceedings of the third international joint conference on artificial intelligence (pp. 344-351). Stanford, CA: Stanford University.
Chang, C. S., Chen, T. S., & Hsu, W. H. (2011). The study on integrating WebQuest mobile learning for environmrntal education. Computers & Education, 57(1), 1228-1239.
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054-1064.
Chen, C. P., Lai, H. M., & Ho, C. Y. (2015). Why do teachers continue to use teaching blogs? The roles of perceived voluntariness and habit. Computers & Education, 82, 236-249.
Chen, H. J. (2010). Linking enployees’ e-learning system use to their overall job outcomes: An empirical study based on the IS success model. Computers & Education, 55(4), 1628-1639.
Chen I. Y. L., Chen, N. S., Kinshuk (2009). Examining the factors influencing participants’ knoeledge sharing behavior in virtual learning communities. Educational Technology & Society, 12(1), 134-148.
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175.
Cho, C., Yu, S., Chen, C., & Wu, H. C. (2009). Tool, Toy, Telephone, Territory, or Treasure of Information: Elementary school students’ attitudes toward the internet. Computers & Education, 53(2), 308-316.
Cho, V., Cheng, T. C. E., & Lai, W. M. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53(2), 216-227.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
Compeau, D. R., Higgins, C. A. & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: a longitudinal study. MIS Quarterly, 23(2), 145-158.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology a comparison of two theoretical models. Management Science, 35(8), 928-1003.
Dede, C., & Grimson, E. (2013). New technology-based models for postsecondary learning: Conceptual frameworks and research agendas. In C. Dede & E. Grimson (Eds.), Report of a National Science Foundation-Sponsored Computing Research Association Workshop (pp. 1-47). MA: Cambridge.
Dweck, C. S. (1991, January). Self-theories and goals: Their role in motivation, personality, and development. In Nebraska symposium on motivation (Vol. 38, No. 3, pp. 199-235). Lincoln, NE: University of Nebraska Press.
Eccles, J. S. (1983). Expectancies, values, and academic behaviors, J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75-146). San Francisco: Freeman.
Eccles, J. S., & Wigfield, A. (1985). Teacher expectations and student motivation. In J. B. Dusek (Ed.), Teacher expectancies (pp. 185-226). Hillsdale, NJ: Erlbaum.
Eisenhardt, S. M., Birlin, L., Toolin, R., & Pintauro, S. J. (2015). Online college energy balance course improves determinants of behavior and student knowledge. Journal of Dietetics Research and Nutrition, 1(1), 1-9.
Elliott, E. S., & Dweck, C. S. (1988). Goals: An approach to motivation and achievement. Journal of Personality and Social Psychology, 54, 5-12.
Elsenhart, D. M. (1994). Publishing in the information age: a new management framework for the digital era. Westport, Conn.: Quorum Books.
Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. New York: Psychology Press (Taylor & Francis).
Graham, C. R. (2011). Theoretical considerations for understanding technological pedagogical content knowledge (TPACK). Computers & Education, 57(3), 1953-1960.
Graham, S., & Williams, C. (2009). An attributional approach to motivation in school. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 11-33). New York: Routledge.
Griffin, P., McGaw, B., & Care, E. (2012). Assessment and teaching of 21st Century skills. New York, NY: Springer
Heider, F. (1946). Attitudes and cognitive organization. Journal of Psychology, 21, 107-112.
Hull, C. L. (1943). Principles of behavior: An introduction to behavior theory. New York: Appleton-Century-Crofts.
Hung, M. C., Chang, I. C., & Hwang, H. G. (2011). Exploring academic teachers’ continuance toward the web-based learning system: The role of causal attributions. Computers & Education, 57(2), 1530-1543.
Karaali, D., Gumussoy, C. A., & Calisir, F. (2011). Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Computers in Human Behavior, 27(1), 343-354.
Kern, T., & Rubin, A. (2012). Innovating toward new learning model: Insights from a national convening on blended-personalized learning models. Retrieved from http://net.educause.edu/ir/library/pdf/CSD6148.pdf
Lai, H. M. & Chen, C. P. (2011). Factors influencing secondary school teachers’ adoption of teaching blogs. Computers & Education, 56(4), 948-960.
Lai, T. L., & Lin, H. F. (2015). Exploring mathematics teachers’ perception of technological pedagogical content knowledge. Journal of Educational Media & Library Sciences, 52(1), 59-82.
Lee, B. C., Yoon, J. O., Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320-1329.
Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, 54(2), 506-516.
Lee, Y. C. (2008). The role of perceived resources in online learning adoption. Computers & Education, 50(4), 1423-1438.
Lee, Y. H., Hsieh, Y. C., & Ma, C. Y. (2011). A model of organizational employees’ e-learning systems acceptance. Knowledge-based systems, 24(3), 355-366.
Liaw, S. S. (2008) Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873.
Liew, S. S., Huang, H. M., & Chen, G. D. (2006). Surveying instructor and learner attitudes toward e-learning. Computers & Education, 49, 1066-1080.
Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes towards toward e-learning. Computers & Education, 49(4), 1066-1080.
Lin, K. M. (2011). E-learning continuance intention: Moderating effects of user e-learning experience. Computers & Education, 56(2), 515-526.
Lin, W. S. (2012). Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives. International journal of human-computer studies, 70(7), 498-507.
Lin, W. S., & Wang, C. H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & Education, 58(1), 88-99.
Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010a). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers & Education, 54(2), 600-610.
Liu, Y., Li, H., & Carlsson, C. (2010b). Factors driving the adoption of m-learning: An empirical study. Computers & Education, 55(3), 1211-1219.
Maina, M. K., & Nzuki, D. M. (2015). Adoption determinants of e-learning management system in institutions of higher learning in Kenya: a case of eelected universities in Nairobi Metropolitan. International Journal of Business and Social Science, 6(2), 233-248.
Maslow, A. H. (1970). Motivation and personality (2nd ed.). New York: Harper & Row.
McGill, T. J., & Klobas, J. E. (2009). A task-technology fit view of learning management system impact. Computers & Education, 52(2), 496-508.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054.
Mitra, S. (2003). Minimally invasive education: a progress report on the hole-in-thewall”experiments. British Journal of Educational Technology, 34(3), 367-371.
Mitra, S. (2006). The hole in the wall : self-organising systems in education. New Delhi: New York Tata-McGraw-Hill Pub. Co. Ltd.
Mitra, S. (2009). Remote Presence: Technologies for ‘Beaming’ Teachers: Where they cannot go. Journal of Emerging Technologies in Web Intelligence, 1(1), 55-59.
Mitra, S., Dangwal, R. (2010). Limits to self-organising systems of learning-the Kalikuppam experiment. British Journal of Educational Technology, 41(5), 672-688.
Mitra, S., Dangwal, R., Chatterjee, S., Jha, S., Bisht, R. S. and Kapur, P. (2005). Acquisition of computing literacy on shared public computers: Children and the "hole in the wall". Australasian Journal of Educational Technology, 21(3), 407-426.
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374.
Motaghian, H., Hassanzadeh, A., & Moghadam, D. K. (2013). Factors affecting university instructors’ adoption of web-based learning systems: Case study of Iran. Computers & Education, 61, 158-167.
Niess, M. L. (2005). Preparing teachers to teach science and mathematics with teachnology: Developing a technology pedagogical content knowledge. Teaching and Teacher Education, 21, 509-523.
Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250-267.
Padilla-Melendez, A. Garrido-Moreno, A. & Aguila-Obra, A. R. D. (2008). Factors affecting e-collaboration technology use among management students. Computers & Education, 51(2), 609-623.
Pavlov, I. P. (1927). Conditioned reflexes (G. V. Anrep, Trans.). London: Oxford University Press.
Pavlov, I. P. (1928). Lectures on conditioned reflexes (W. H. Gantt, Trans.). New York: International Publishers.
Pavlov, I. P. (1932a). Neuroses in man and animals. Journal of the American Medical Association, 99, 1012-1013.
Pavlov, I. P. (1932b). The reply of a physiologist to psychologists. Psychological Review, 39, 91-127.
Pavlov, I. P. (1934). An attempt at a physiological interpretation of obsessional neurosis and paranoia. Journal of Mental Science, 80, 187-197.
Pellegrino, J. W., & Hilton, M. L. (2012). Education for life and work: Developing transferable knowledge and skills in the 21st century. Washington: The national academic press.
Petri, H. L. (1986). Motivation: Theory and research (2nd ed.). Belmont, CA: Wadsworth.
Pitch. K. A., & Lee, Y. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244.
Pynoo, B., Devolder, P., Tondeur, J., Van Braak, J., Duyck, W., & Duyck, P. (2011). Predicting secondary school teachers’ acceptance and use of a digital learning environment: A cross-sectional study. Computers in Human Behavior, 27(1), 568-575.
Pynoo, B., & Van Braak, J. (2014). Predicting teachers’ generative and receptive use of an educational pertal by intention, attitude and self-reported use. Computers in Human Behavior, 34, 315-322.
Robinson, K., & Aronica, L. (2009). The element: how finding your passion changes everything. NY: Penguin.
Roca, J. C., & Gagne, M. (2008). Undersanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585-1604.
Rogers, C. R. (1969). Freedom to learn. Columbus, OH: Merrill.
Rogers, C. R. & Friberg, H. J. (1994). Freedom to learn (3rd ed.). Columbus, OH: Merrill/Prentice Hall.
Ross, S. M., McDonald, A. J., Alberg, M., & McSparrin-Gallagher, B. (2007). Achievement and Climate Outcomes for the Knowledge Is Power Program in an Inner-City Middle School. Journal of Education for Students Placed at Risk, 12(2), 137-165.
Sanchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers & Education, 26(6), 1632-1640.
Sanchez-Franco, M. J. (2010). WebCT – The quasimoderating effect of perceived affective quality on an extending Technology Acceptance Model. Computers & Education, 54(1), 37-46.
Sanchez-Franco, M. J., Martinez-Lopez, F. J. & Martin-Velicia, F. A. (2009). Exp;oring the impact of individualism and uncertainty avoidance in Web-based electronic learning: An empirical analysis in European higher educaton. Computers & Education, 52(3), 588-598.
Schunk, D. H. (2012). Learning theory: An educational perspective. MA: Pearson.
Schunk, D. H., & Pajares, F. (2002). The development of academic self-efficacy. In A. Wigfield & J. S. Eccles (Eds.), Development of academic motivation (pp. 15-31). San Diego: Academic Press.
Schunk, D. H., Pintrich, P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, applications (3rd ed.). Upper Saddle Rever, NJ: Pearson Education.
Shin, H. P. (2008). Using a cognition-motivation-control view to assess the adoption intention for Web-based learning. Computers & Education, 50(1), 327-337.
Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4-14.
Shultz, T. R., & Lepper, M. R. (1996). Cognitive dissonance reduction as constraint satisfaction. Psychological Review, 103, 219-240.
Skinner, B. F. (1953). Science and human behavior. New York: Free Press.
Sorebo, O., Halvari, H., Gulli, V. F., & Kristiansen, R. (2009). The role of self-determination theory in explaining teachers’ motivation to vontinue to use e-learning technology. Computers & Education, 53(4), 1177-1187.
Tao, Y. H., Cheng, C. J., & Sun, S. Y. (2009). What influences college students to continue using business simulation games? The Taiwan experience. Computers & Education, 53(3), 929-939.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144-176.
Teo, T., & Lee, C. B. (2010). Explaining the intention to use technology among student teachers. Campus-Wide Information Systems, 27(2), 60-67.
Teo, T., Lee, C. B., Chai, C. B., & Wong, S. L. (2009). Assessing the intention to use technology among pre-servive teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM). Computers & Education, 53(3), 1000-1009.
Thompson, C. (2011). How Khan Academy is changing the rules of education? Wired Digital. Retrieved from: http://resources.rosettastone.com/CDN/us/pdfs/K-12/Wired_KhanAcademy.pdf
Toral, S. L., Barrero, F., & Martinez-Torres, M. R. (2007). Analysis of utility and use of a web-based tool for digital signal processing teaching be means of a technological acceptance model. Computers & Education, 49(4), 957-975.
Tzeng, S. Y., & Nieh, H. M. (2015, October). How self-concept and self-efficacy relate to achievement outcomes: new technology-based learning models for science and technology universities students. In A. Toth (Chair), 2015 International Conference on Interactive Collaborative Learning (ICL). Symposium conducted at the meeting of the universita degli studi firenze, Florence, FLR. Retrieved from: http://www.weef2015.eu/Proceedings_WEEF2015/proceedings/papers/Contribution1265.pdf
Valente, T. W., & Rogers, E. M. (1995). The origins and development of the diffusion of innovations paradigm as an example of scientific growth. Science Communication, 16(3), 242-273.
Van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838-852.
Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 769-774.
Weiner, B. (1992). Human motivation: Metaphors, theories, and research. Newbury Park, CA: SAGE Publications.
Wheeler, L., & Suls, J. (2005). Social comparison and self-evaluations of competence. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 566-578). New York: Guilford Press.
Wigfield, A. (1994). The role of children’s achievement values in the self-regulation of their learning outcomes. In D. H. Schunk & B. J. Zimmerman (eds.), Self-regulation of learning and performance: Issues and educational applications (pp. 101-124). Hillsdale, NJ: Erlbaum.
Woodworth, R. S. (1918). Dynamic psychology. New York: Columbia University Press.
Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, 18, 459-482.
Zhang, Y., Fang, Y., Wei, K. K., & Wang, Z. (2012). Promoting the intention of students to continue their participation in e-learning systems. Information Technology & People, 25(4), 356-375.