自我效能和性別差異對於科技接受度之影響：以大規模開放式線上學習環境為例

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林祐綸(Yu-lun Lin) 查詢紙本館藏

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資訊工程學系

論文名稱

自我效能和性別差異對於科技接受度之影響：以大規模開放式線上學習環境為例
(The impact of self-efficacy and gender differences in technology acceptance model: An example of MOOCs.)

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摘要(中)

大規模開放式線上課程(MOOCs)，為最近國外大學興起的大型開放式線上課程，因為平台其不分年齡、不分學歷、不限地點、不限時間的學習特色，也成為了國內近年來力推的線上學習平台。先前的學者認為如此新穎的新興學習科技平台，對學生或教師而言，其科技接受度(TAM)就顯得格外重要，而也有學者提出了自我效能對於TAM模型和其使用意圖有所影響，再者，先前研究指出性別差異也會影響科技接受度，且科技系統的使用有一定的性別差異，因此，本研究加入自我效能以及性別差異這兩個外在變因，提出一個新的研究模型來重新測量學生對於MOOCs的科技接受度，利用迴歸分析觀察自我效能及性別差異對於科技接受度的影響程度。研究結果顯示本研究所提出之八項假說都是支持的，這八項假說分別為自我效能和感知易用性會對感知有用性有顯著的影響，自我效能對感知易用性有顯著的影響，自我效能、感知有用性和感知易用性會對使用態度有顯著的影響，使用態度會對使用意圖有顯著影響，最後自我效能會對使用意圖有顯著的影響，分析結果為自我效能對於MOOCs的科技接受度有一定的影響力，且自我效能和使用態度為影響使用意圖的因素，而性別差異對於採用MOOCs做為學習平台與否則是沒有太大的影響。

摘要(英)

MOOCs is an open online course which is rising of foreign universities recently. It becomes a famous online learning platform because people can learn in all age, educational background, location and time. Previous scholars believe that new emerging learning technology platform, of which technology acceptance (TAM) is particularly important for students or teachers. While scholars have suggested that the self-efficacy have an impact on intention to use and TAM model. Moreover, previous studies indicate that gender difference also influence technology acceptance and using IT systems have certain gender difference. Therefore, this study added self-efficacy and gender difference to TAM model, and proposing a new research model to remeasure the students’ technology acceptance on MOOCs platform. Then, we use regression analysis to observe the influence of self-efficacy and gender difference on TAM model. The results showed that eight hypotheses proposed in this study are supported, and these eight hypotheses were self-efficacy and perceived ease of use will have a significant impact on perceived usefulness, self-efficacy will have a significant impact on perceived ease of use, self-efficacy, perceived usefulness and perceived ease of use will have a significant impact on the attitude, attitude will have a significant impact on the intended use, and self-efficacy will have a significant impact on the intention to use. The results shows that self-efficacy have certain influence on technology acceptance of MOOCs, and self-efficacy and attitude are the two factors that affecting the intention to use. And it is found that no gender difference on adopting the NCUx platform or not.

關鍵字(中)

★ 大規模開放式線上課程
★ 科技接受度模型
★ 自我效能
★ 性別差異

關鍵字(英)

★ MOOCs
★ Technology Acceptance Model
★ Self-efficacy
★ Gender difference

論文目次

摘要.....................................i
ABSTRACT.....................................ii
目錄.....................................iii
圖目錄.....................................v
表目錄.....................................vi
第一章緒論.....................................1
第二章文獻探討.....................................3
2.1 MOOCs.....................................3
2.1.1 自主學習(self-paced learning).....................................5
2.1.2 自我規範學習(self-regulated learning).....................................6
2.1.3 精熟學習(mastery learning).....................................7
2.2 科技接受度(TAM).....................................8
2.2.1動機行為理論(TRA : Theory of Reasoned Action).....................................8
2.2.2科技接受度模型(TAM : Technology Acceptance Model).....................................8
2.3 自我效能(Self-efficacy).....................................11
2.4 性別與科技接受度模型(Gender and TAM).....................................14
2.5 研究模型.....................................15
第三章系統說明.....................................16
3.1 系統架構.....................................16
3.2 功能說明.....................................17
第四章研究方法.....................................24
4.1 研究對象.....................................24
4.2 資料蒐集.....................................24
4.3 分析工具.....................................25
第五章分析結果.....................................27
5.1描敘性統計.....................................27
5.2效度、信度和維度的測量尺度分析.....................................30
5.3 皮爾森積差相關分析(Pearson’s correlation matrix).....................................32
5.4 迴歸分析.....................................33
5.5結果與討論.....................................42
第六章結論與建議.....................................47
6.1 結論.....................................47
6.2 未來研究建議.....................................48
參考文獻.....................................49

參考文獻

Allison, E. K. (1976). Three years of self-paced teaching in introductory economics at Harvard. American Economic Review, 66(2), 222-28.
Bandura, A. (1986). Social foundations of thought and action (pp. 5-107). Prentice Hall.: Englewood Cliffs, NJ.
Bandura, A. (1997). Self-efficacy: The exercise of control. Macmillan.
Barclay, C., & Logan, D. (2013). Towards an Understanding of the Implementation & Adoption of Massive Online Open Courses (MOOCs) in a Developing Economy Context.
Billings, D. B. (1974). PSI versus the lecture course in the principles of economics: a quasi-controlled experiment. In Personalized Instruction in Higher Education: Proceedings of the First National Conference held by the Center for Personalized Instruction (pp. 30-37).
Bloom, B.S. (1968). Learning for mastery.
Bloom, B. S. (1981). All our children learning: A primer for parents, teachers, and other educators. New York: McGraw-Hill.
Bretz, R., & Johnson, L. (2000). An innovative pedagogy for teaching and evaluating computer literacy. Information Technology and management, 1(4), 283-292.
Burgess, L. A. (2003). WebCT as an e-learning tool: A study of technology students’ perceptions. Journal of Technology Education, 15(1), 6–15.
Burkhardt, M. E., & Brass, D. J. (1990). Changing patterns or patterns of change: The effects of a change in technology on social network structure and power. Administrative Science Quarterly, 35(1), 104-127.
Carroll, J.B. (1989). The Carroll model: A 25 year retrospective and prospective view. Educational Researcher, 18(1), 26-31.
Chatzoglou, P. D., Sarigiannidis, L., Vraimaki, E., & Diamantidis, A. (2009). Investigating Greek employees’ intention to use web-based training. Computers & Education, 53(3), 877-889.
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.
Chou, C., Wu, H. C., & Chen, C. H. (2011). Re-visiting college students’ attitudes toward the Internet-based on a 6-T model: Gender and grade level difference. Computers & Education, 56(4), 939-947.
Compeau, D.R., Higgins, C.A., (1995). Computer self-efficacy: development of a measure and initial test. MIS Quarterly 19, 189–211.
Cyr, D., Hassanein, K., Head, M., & Ivanov, A. (2007). The role of social presence in establishing loyalty in e-Service environments. Interacting with Computers, 19(1), 43-56.
Daniel, J. (2012). Making Sense of MOOCs: Musings in a Maze of Myth, Paradox and Possibility. Journal of Interactive Media in Education.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Escobar-Rodriguez, T., & Monge-Lozano, P. (2012). The acceptance of Moodle technology by business administration students. Computers & Education, 58(4), 1085–1093.
Gist, M.E., Schwoerer, C., Rosen, B., 1989. Effects of alternative training methods on self-efficacy and performance in computer software training. Journal of Applied Psychology,74(6), 884.
Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365–374.
González-Gómez, F., Guardiola, J., Martín-Rodríguez, O., & Montero-Alonso, M. A. (2012). Gender differences in e-learning satisfaction. Computers & Education, 58(1), 283–290.
Gordon, S. C., Dembo, M. H., & Hocevar, D. (2007). Do teachers’ own learning behaviors influence their classroom goal orientation and control ideology? Teaching and Teacher Education, 23(1), 36–46.
Heikkilä, A., & Lonkab, K. (2006). Studying in higher education: students’ approaches to learning, self-regulation, and cognitive strategies. Studies in Higher Education, 31(1), 99–117.
Imhof, M., Vollmeyer, R., & Beierlein, C. (2007). Computer use and the gender gap: The issue of access, use, motivation, and performance. Computers in Human Behavior, 23, 2823-2837.
Junco, R., & Mastrodicasa, J. (2007). Connecting to the net. generation: What higher education professionals need to know about today′s students. NASPA, Student Affairs Administrators in Higher Education.
Kauffman, D. F. (2004). Self-regulated learning in web-based environments: instructional tools designed to facilitate cognitive strategy use, metacognitive processing, and motivational beliefs. Journal of Educational Computing Research, 30(1&2), 139–161.
Law, Y. K., Chan, C. K., & Sachs, J. (2008). Beliefs about learning, self-regulated strategies and text comprehension among Chinese children. British Journal of Educational Psychology, 78(1), 51–73.
Lai, C., Wang, Q., & Lei, J. (2012). What factors predict undergraduate students′ use of technology for learning? A case from Hong Kong. Computers & Education, 59(2), 569-579.
Liaw, S. S., & Huang, H. M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24.
Limayem, M., & Cheung, C. M. K. (2011). Predicting the continued use of Internet-based learning technologies: the role of habit. Behavior and Information Technology, 31(1), 91–99.
Martín-Blas, T., & Serrano-Fernández, A. (2009). The role of new technologies in the learning process: Moodle as a teaching tool in physics. Computers & Education, 52(1), 35–44.
Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies.
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.
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.
Ocorr, K., & Osgood M. P. (2003). Self or Help? A comparison of a personalized system of instruction biochemistry class to a standard lecture-based biochemistry class. Biochemistry and Molecular Biology Education, 31(5), 308-312.
Ong, C. S., & Lai, J. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816-829.
Papastergiou, M., & Solomonidou, C. (2005). Gender issues in internet access and favourite internet activities among Greek high school pupils inside and outside school. Computers & Education, 44(4), 377–393.
Pajares, F. (1996). Current directions in self-efficacy research. Advances in motivation and achievement, 10(149).
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–686.
Rodriguez, C. O. (2012). MOOCs and the AI-Stanford Like Courses: Two Successful and Distinct Course Formats for Massive Open Online Courses.European Journal of Open, Distance and E-Learning.
Sánchez-Franco, M. J. (2006). Exploring the influence of gender on the web usage via partial least squares. Behaviour & Information Technology, 25(1), 19–36.
Sánchez-Franco, M. J. (2010). WebCT–the quasimoderating effect of perceived affective quality on an extending technology acceptance model. Computers & Education, 54(1),37–46.
Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in human behavior, 26(6), 1632-1640.
Schunk, D. H. (1991). Self-efficacy and academic motivation. Educational psychologist, 26(3-4), 207-231.
Schunk, D. H. (2005a). Commentary on self-regulation in school contexts. Learning and Instruction, 15(2), 173–177.
Schunk, D. H. (2005b). Self-regulated learning: the educational legacy of Paul R. Pintrich. Educational Psychologist, 40(2), 85–94.
Soper J. C., & Thornton R. M. (1976). Self-paced economics instructions: A large-scale disaggregated evaluation. Journal of Economic Education, 81-91.
Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. Prcentice Hall, New Jersey. Titman, S. and R. Wessels, 1(1988), 1-19.
Taylor, S., Todd, P.A., 1995. Understanding information technology usage: a test of competing models. Information Systems Research , 6(2), 144–176.
Teo, T. S., & Lim, V. K. (1996). Factors influencing personal computer usage: the gender gap. Women in Management Review, 11(8), 18-26.
Terzis, V., & Economides, A. A. (2011). Computer based assessment: gender differences in perceptions and acceptance. Computers in Human Behavior, 27(6), 2108–2122.
Tsai, C. C. (2004) Adolescents’ perceptions toward the Internet: A 4-T framework, CyberPsychology & Behavior, 7(4), 458–463.
Tsai, C.C. & Lin, C.C. (2004) Taiwanese adolescents’ perceptions and attitudes regarding the Internet: exploring gender differences, Adolescence, 39, 725–734.
Twigg, C. A. (2003). Quality, cost and access: The case for redesign. The wired tower: Perspectives on the impact of the internet on higher education, 111-143.
Venkatesh, V., (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research ,11(4), 342–365.
Venkatesh, V., Davis, F.D. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences ,27(3), 451–481.
Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761-774.
Webster, J., Martocchio, J.J. (1992). Microcomputer playfulness: development of a measure with workplace implications. MIS Quarterly , 201–226.
Whitley, B. E. (1997). Gender differences in computer-related attitudes and behavior: a meta-analysis. Computers in Human Behavior, 13(1), 1–22.
Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International journal of human-computer studies, 59(4), 431-449.
Yi, Y., Wu, Z., & Tung, L. L. (2006). How individual differences influence technology usage behavior? Towards an integrated framework. Journal of Computer Information Systems, 46(2), 52-63.
Yuan, L., & Powell, S. (2013). MOOCs and Open Education: Implications for Higher Education. Cetis White Paper.
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of educational psychology, 81(3), 329-339.

指導教授

楊鎮華(Jhen-Hua Yang)

審核日期

2014-7-8

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