臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：影響國中自然科教師使用資訊科技融入教學之意向模式研究
作者：吳為聖
作者(外文)：Weishen Wu
校院名稱：國立彰化師範大學
系所名稱：科學教育研究所
指導教授：張惠博
學位類別：博士
出版日期：2007
主題關鍵詞：資訊科技融入教學；融入意向；影響因素；integrating IT into teaching；intention to infusion；influencing factors
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(1) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:39
當學校努力增設資訊設備之際，許多的研究報告卻顯示僅有少數的中學教師真正實際使用資訊科技上課。過去，探討影響教師採用資訊科技教學的因素已經累積豐碩的實徵研究，然而，較少有研究進行相關理論的驗證。本研究目的旨在建立影響國中自然科教師實施資訊科技融入教學意向的模型，藉以實徵影響因素對融入意向的相關性及其影響路徑，同時檢驗不同背景教師對融入意向及其影響因素的認知是否有顯著差異。
本研究參考社會認知理論、創新擴散理論和任務科技適配理論的相關構念，以解構的計畫行為理論結合修訂的科技接受模式為基本模型，加入影響融入意向的社會文化因素以及有用性知覺和易用性知覺的前置因素作為研究架構。經分層抽樣全國461所國中共1,835名自然科（生物、理化、地球科學）教師作為研究對象，透過網路問卷方式收集457份有效樣本。
利用探索性及驗證性因素分析確認量表的因素結構，結果顯示問卷的建構效度和內部一致性信度皆良好。以結構方程模式分析模型的契合度和路徑屬性，經測量模型考驗模型的契合度，結果顯示樣本資料不否定研究模型，研究模型的外在品質在可接受的範圍。結構模式的分析結果顯示，除了易用性知覺並未顯著影響有用性知覺，其餘的假設皆成立。研究模型對融入意向可解釋61%的變異量，其中，以資源可得性對融入意向的影響效果最強，其餘依序為有用性知覺、易用性知覺、適配性知覺、考試壓力、自我效能、個人創新性，而主觀規範和形象則較弱。分析影響融入意向的路徑共有13條，由內生變數影響融入意向的路徑有3條，影響效果最強的是適配性知覺→有用性知覺→融入意向（0.27），其次是自我效能→易用性知覺→融入意向（0.18），以及個人創新性→易用性知覺→融入意向（0.11）；由外生變數影響融入意向有10條路徑，但影響效果較內生變數的路徑弱。基本模型增加資源可得性後大幅提高科技接受模式的解釋力(R2=0.59)，然而，增加其他的變數並沒有增加太多的解釋變異量。
本研究發現自然科教師使用資訊科技教學的意向受到個人認知與特質、學校脈絡和社會文化等層面共九個因素所影響，影響因素與融入意向的關係如下：
(一)資源可得性、有用性知覺、易用性知覺正向直接影響融入意向，升學壓力直接反向影響融入意向。
(二)主觀規範經由有用性知覺和形象的中介間接正向影響融入意向。
(三)易用性知覺直接正向影響融入意向。
(四)主觀規範、形象、適配性知覺正向影響有用性知覺，這三個前置因素經由有用性知覺影響融入意向。
(五)自我效能和個人創新性正向影響易用性知覺，這兩個前置因素經由有用性知覺影響融入意向；個人創新性也經由主觀規範影響易用性知覺。
利用變異數分析考驗不同背景教師對融入意向及其影響因素的差異性，結果顯示：
(一)碩士教師的融入意向較強，學士教師居次，博士教師較弱。
(二)男性教師對有用性知覺、易用性知覺、形象、自我效能、和個人創新性的認知程度大於女性教師。
(三)在易用性知覺、主觀規範、資源可得性、適配性知覺、自我效能和個人創新性等因素上，碩士教師比學士和博士教師的認知程度強。
(四)北部地區教師對有用性知覺和個人創新性的因素認知程度上大於東部及離島地區的教師。
本研究結果確認社會心理觀點的行為理論對教師使用資訊科技教學的適用性，研究結果增進對教師使用科技教學的瞭解。同時驗證整合計畫行為理論和科技接受模式在教育情境的適用性，增加變數的模型提高原始理論之價值。實務上，驗證所得的因素可作為解釋或預測自然科教師實施資訊科技融入教學的指標，研究發現可作為規劃相關研習活動的參考依據。
While schools are striving for instating the use of information technology (IT), many researches reported only a few secondary teachers have used IT in their teaching practices. Previous researches have explored the factors influencing teachers’ adoption of IT; however, there has been relatively few works on the theoretical verifications. This study aims to establish a research model by which to examine the factors influencing science teachers’ intentions to infuse IT into teaching, and to investigate the discrepancies of individual differences toward intentions of infusion and the influencing factors.
Combining the revised Technology Acceptance Model (TAM) with the decomposed Theory of Planning Behavior, this study added new socio-cultural factors and external variables derived from the Social Cognitive Theory, the Theory of Innovation Diffusion, and the Task-technology Fit Theory as the research framework. An online questionnaire was administrated to 1,835 science teachers in 461 middle schools based on a stratified random sampling method. A total of 457 effective samples from 461 middle schools were obtained.
Results of exploratory and confirmatory factor analyses showed that the reliability and validity of the instrument were at acceptable levels. The model fitness and path attributes were examined by a structural equation modeling approach. The model fitness indices showed the samples had a good fit with the research model. Nine factors were revealed, including the resource availability (RA), perceived usefulness (PU), perceived ease-of-use (PE), entrance stress (ES), subjective norm (SN), image, (IM), perceived fit (PF), personal innovativeness (PI) and self-efficacy (SE). Results showed all hypotheses were supported, except for the relationship between PE and PU. The research model explained 61 percent of variance of intention to infusion (ITI). The stronger effects to ITI included the RA, PU, PE, PF, ES, SE and PI while the weaker effects to ITI included the SN and IM. There were thirteen paths to ITI, including three paths from endogenous variables and ten paths from exogenous variables. Of all paths to ITI, the strongest path was the PFPUITI. With the RA, the baseline model dramatically increased the total variance explained (R2=0.59); however, the full model only slightly increased the total variance explained.
Results indicated that science teachers’ intentions to infusion were influenced by nine factors within the personal cognitions and characteristics, school contests, and socio-cultural dimensions. Their relationships are summarized as follows:
(1)The RA, PU and PE positively influenced ITI while ES negatively influenced ITI;
(2)The SN positively influenced ITI by ways of PU and IM;
(3)The PE positively influenced ITI;
(4)As the antecedents of PU, SN, IM and PF positively influenced ITI via PU;
(5)As the antecedents of PE, SE and PI positively influenced ITI via PE; PI significantly determined SN.
The discrepancies of individual differences between intention and infusion, as well as the influencing factors, were tested by the one-way ANOVA. Results were summarized as the follows:
(1)The Science teachers with a Master’s degree held the strongest intentions of infusion, followed by those with Bachelor’s degrees and those with Ph.D.’s;
(2)The Science teachers with Master’s degrees had stronger cognitions on PE, SN, RA, PF, SE and PI than those with Bachelor’s degrees and Ph. D’s;
(3)Male Science teachers had stronger cognitions on PU, PE, IM, SE and PI than female science teachers;
(4)Science teachers in the northern districts had stronger cognitions on PU and PI than science teachers in the eastern districts and off-shore islands.
The present study confirms the applicability of the research model on explaining or predicting teachers’ adoption of IT in their teaching practices. It supports application of TAM in the educational setting and enhances understanding of teacher’s IT usage. The findings provide useful information for designing teacher growth programs in practices.
1. Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227-247.
2. Agarwal, R. (2000). Individual acceptance of information technologies. In R. W. Zmud (Ed.), Framing the domains of IT management: Projecting the future through the past (pp.85-104), Cincinnati, Ohio: Pinnaflex Educational Resources Inc.
3. Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.
4. Ajzen, I (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action control: from cognition to behavior (pp.11-39). New York: Springer-Verlag.
5. Ajzen, I. (1988). Attitudes, personality, and behavior. Chicago: Open University Press.
6. Ajzen, I., & Fishbein, M. (1980). Understanding attitude and predicting social behavior. Englewood Cliffs, New Jersey: Prentice Hall.
7. Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22, 453-74.
8. Al-Gahtani, S. S., & King, M. (1999). Attitudes, satisfaction and usage: Factors contributing to each in the acceptance of information technology. Behavior and Information Technology, 18(4), 277-297.
9. Anderson, R. E., Hansen, T. P., Johnson, D. C., & Klassen, D.L. (1979). Minnesota Computer literacy awareness assessment (Technical Report). St. Paul MS: Minnesota Educational Computing Consortium.
10. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation modes. Journal of the Academy of Marketing Science, 16, 74-94.
11. Bailey, J. E., & Person, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530-545.
12. Bandura, A. (1977). Social learning theory. New Jersey: Prentice-Hall.
13. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, New Jersey: Prentice-Hall.
14. Bandura, A. (1997). Self-efficacy: The exercise of control. NewYork: W. H. Freeman and Company.
15. Barron, A. E., Kemker, K., Harmes, C., & Kalaydjian, K. (2003). Large-scale research study on technology in K-12 schools: Technology integration as it relates to the national technology standards. Journal of Research on Technology in Education, 35(4), 489-507.
16. Baylor, A., & Ritchie, D. R. (2002). What factors facilitate teacher skill, teacher morale, and perceived student learning in technology-using classrooms? Computers and Education, 39(4), 395-414.
17. Beach, L. R., & Mitchel, T. R. (1998). The basic of Image theory. In L. R. Beach, (Ed.), Image theory: Theoretical and empirical foundations (pp. 3-18). Mahwah, New Jersey: Lawrence Erlbaum Associates.
18. Beach, L. R., & Mitchell, T. R. (1978). A contingency model for the selection of decision strategies. Academic Management Review, 3, 439-449.
19. Becker, H. J. (1991). How computers are used in United States schools: Basic data from the 1989 IEA computers in education survey. Journal of Educational Computing Research, 7(4), 385-406.
20. Becker, H. J. (1994). Analysis and trends of school use of new information technologies. Report for the Office of Technology Assessment, US Congress. Irvine: Department of Education, University of California.
21. Becker, H. J. (1994). How exemplary computing-using teachers different from other teachers: Implications for realizing the potential of computers in school. Journal of Research on Computing in Education, 26 (3), 291-321.
22. Becker, H. J. (2000). Access to classroom computers. Communications of the ACM, 43(6), 24-25.
23. Berger, C. F., Lu, C. R., Belzer, S. J., & Voss, B. E. (1995). Research on the uses of technology in science education. In D. L. Gabel (Ed.), Handbook of research on science teaching and learning (pp. 466-490). New York: Macmillan.
24. Berthon, P., Pitt, L., Ewing, M., & Carr, C. L. (2002). Potential research space in MIS: A framework for envisioning and evaluating research replication, extension, and generation. Information Systems Research, 13(4), 416-427.
25. Blumenfeld, P., Fishman, B. J., Krajcik, J., Marx, R. W., & Soloway, E. (2000). Creating usable innovations in systemic reform: Scaling-up technology-embedded project-based science in urban schools. Educational Psychologist, 35(3), 149-164.
26. Bruner, I., Buchsbaum, H. Hill, M., & Orlando, L. (1992). School reform: Why you need technology to get there? Electronic Learning, 11, 22-28.
27. Bybee, R. W., & DeBoer, G. (1994). Research on goals for the science curriculum. In D. L. Gabel (Ed.) Handbook of research on science teaching and learning (pp. 357-387). New York: Macmillan.
28. Casey, H., & Rakes, G. C. (2002). An analysis of the influence of technology training on teacher stages of concern regarding the use of instructional technology in schools. Journal of Computing in Teacher Education, 18(4), 124-132.
29. Chan, L.-J., Hong, J.-C., Horng, J.-S., Chang, S.-H., & Chu, H.-C. (2006). Factors influencing technology integration in teaching: A Taiwanese perspective. Innovations in Education and Teaching International, 43(1), 57–69.
30. Chau, P. Y. K. (2001). Influence of computer attitude and self-efficacy on IT usage behavior. Journal of End User Computing, 13(1), 26-33.
31. Chau, P. Y. K., & Hu, P. J. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Science, 32(4), 699-719.
32. Cheney, P. H., Mann, R. I., & Amoroso, D. L. (1986). Organizational factors affecting the success of end-user computing. Journal of Management Information Systems, 3(1), 65-79.
33. Chiero, R. T. (1997). Teachers’ perspectives on factors that affect computer use. Journal of Research on Computing in Education, 30(2), 133-145.
34. Clark, K. F. (2000). Urban middle school teachers' use of instructional technology. Journal of research of computing education. 33(2), 1-18.
35. Compeau D., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.new window
36. Cox, M. (2000). Information and communications technologies: Their role and value for science education. In M. Mork & J. Osborne (Ed.), Good practice in science teaching: What research has to say (pp. 190-207). Buckingham, England: Open University Press.
37. Cradler, J., Freeman, M., Cradler, R., & McNabb, M. (2002). Research implications for preparing teachers to use technology. Learning & Leading with Technology, 30(1), 50-57.
38. Crawley, F. E., & Koballa, T. R. (1992). Attitude/behavior change in science education: Part I-models and methods. Paper presented at the annual meeting of the National Association for Research in Science Teaching, Boston, MA.
39. Crawley, F.E. & Black, C.B. (1992). Causal Modeling of Secondary Science Students Intentions to enroll in Physics. Journal of Research in Science Teaching, 29, 585–599.
40. Davidson-Shrives, G., Morris, S., & Sriwongkol, T. (2001). Gender and online discussions: Similarities or differences? World Conference on Educational Multimedia, Hypermedia, and Telecommunications, 1, 361-366.
41. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Sloan School of Management, 11-34.
42. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, September, 319-340.
43. Davis, F. D., Bagozzi, R., & Warshaw, P. R. (1989). User acceptance of computer technology: A Comparison of two theoretical models. Management Science, 35(8), 982-1003.
44. Demetriadis, S., Barbas, A., Molohides, A., Palaigeorgiou, G., Psillos, D., Vlahavas, I., Tsoukalas, I., & Pombortsis, A. (2003). Cultures in negotiation: Teachers’ acceptance/resistance attitudes considering the infusion of technology into schools. Computers and Education, 41, 19-37.
45. DeSouza, S. (1994). Do science teachers intend to engage in collaborative reflective practice? Paper presented at the annual meeting of the National Association for Research in Science Teaching, Anaheim, CA.
46. DeVellis, R. F. (1991). Scale development: Theory and applications. Newbury Park, California: Sage Publications, Inc.
47. Dexter, S. L., Anderson, R., & Becker, H. J. (1999). Teachers’ view of computers as catalysts for changes in their teaching practice. Journal of Research on Computing in Education, 31, 221-238.
48. Dimitrov, D. M., McGee, S., & Howard, B. (2002). Changes in student’s science ability produced by multimedia learning environment: Applications of linear logisitic model for change. School Science and Mathematics, 102(1), 15-24.
49. Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information and Management, 36, 9-21.
50. Dooley, L., Metcalf, & Martinez, A. (1999). A study of the adoption of computer technology by teachers. Educational Technology and Society, 2(4), 1436-4522.
51. Dori, Y. J., Tal, R., & Peled, Y. (2002). Characteristics of science teachers who incorporate web-based teaching. Research in Science Education, 32, 511-547.
52. Drenoyianni, H, & Selwood, I. D. (1998). Conceptions or misconceptions? Primary teachers' perceptions and use of computers in the classroom. Education and Information Technologies, 3, 87-99.
53. Dusick, D. M. (1998). What social cognitive factors influence faculty members’ use of computers for teaching? A literature review, Journal of Research on Computing in Education, 31(2), 123-137.
54. Enochs, L., Riggs, I. M. & Ellis, J. D. (1993). The development and partial validation of microcomputer utilization in teaching efficacy beliefs instrument in a science setting. School Science & Mathematics, 93(5), 257-263.
55. Ertmer, P. A. (1999). Addressing first- and second-order barriers to changes: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47-61.
56. Fann, G. L., Lynch, D.H., & Murranka, C. (1989). Integrating technology: Attitudes as a determinant of the use of microcomputers. Journal of Education Technology Systems, 17(4), 307-317.
57. Fishman, B. J., & Marx, R. (2001). Design research on professional development in a systemic reform context. Seattle, Washington: American Educational Research Association.
58. Fishman, B. J., Gomez, L. M., & Soloway, E. (2001). New technologies and the challenge for school leadership. A white paper for the Joyce Foundation Wingspread Meeting: “Technology’s Role in Urban School Reform: Achieving Equity and Quality”.
59. Forgasz, H. (2006). Factors that encourage or inhibit computer use for secondary mathematics teaching. Journal of Computers in Mathematics and Science Teaching, 25(1), 77-93.
60. Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equations with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18, 39-50.
61. Frese, M., Kring, W., Soose, A., & Zempel, J. (1996). Personal Initiative at work: Differences between East and West Germany. Academy of Management Journal, 39(1), 37-63.
62. Fullan, M. (1982). Research into educational innovation. In H. Gray (Ed.), Management of educational institutions (pp. 245-261). New York: McGraw-Hill.
63. Gallini, J. K., & Barron, D. (2001). Participants’ perceptions of web-infused environments: A survey of teaching beliefs, learning approaches, and communication. Journal of Research on Technology in Education, 34(2), 139-56.
64. Goodhue, D. L. (1995). Understanding user evaluations of information systems. Management Science, 41(12), 1827-1844.
65. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236.
66. Gorsuch, R. L. (1983). Factor analysis. Hillsdale, New Jersey: Lawrence Erlbaum.
67. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998), Multivariate Data Analysis, New Jersey: Prentice Hall.
68. Haney, J. J., Czerniak, C. M., & Lumpe, A. T. (1996). Teacher beliefs and intentions regarding the implementation of science education reform strands. Journal of Research in Science Teaching, 33(9), 971-993.
69. Haney, J. J., Czerniak, C. M., & Lumpe, A. T. (2003). Constructivist beliefs about the science classroom learning environment: Perspectives from teachers, administrators, parents, community members, and students. School Science and Mathematics, 103(8), 366-377.
70. Hasan, B. (2006). Delineating the effects of general and system-specific computer self-efficacy beliefs on IS acceptance. Information and Management, 43(5), 565-571.
71. Hennessy, S., Ruthven, K., & Brindley, S. (2005). Teacher perspectives on integrating ICT into subject teaching: commitment, constraints, caution and change. Journal of Curriculum Studies, 37 (2), 25-63.
72. Herbig, P. A. (1994) The innovation matrix: Culture and structure prerequisites to innovation. Westport, CT. Quorum Books.
73. Holalhan, P. J., Aronson, Z. H, Jurkat, M. P., & Schoorman, F. D. (2004). Implementing computer technology: A multiorganizational test of Klein and Sorra’s model. Journal of Engineering and Technology Management, 21, 31-50.new window
74. Holloway, R. E. (1996). Diffusion and Adoption of Educational Technology: A Critique of Research Design. In D.H. Jonassen (Ed.), Handbook of Research for Educational Communications and Technology (pp. 622-633), New York: Macmillan.
75. Hong, W., Thong, J. Y. L., Wong, W., & Tam, K. (2002). Determinants of user acceptance of digital libraries: An empirical examination of individual differences and system characteristics. Journal of Management Information Systems, 18(3), 97-124.
76. Hsi, S. (1998). A teacher's reflection on the virtual high school cooperative. Learning Technology Review, 4, 4-16.
77. Hu, P. J., Clark, T. H. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: A longitudinal study. Information, & Management, 41, 227-241.
78. Igbaria, M., & Iiravi, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 166-175.
79. Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, September, 279-302.
80. Ives, B., Olson, M. H., & Baroudi, J. J. (1983). The measurement of user information satisfaction. Communications of the ACM, 26(10), 785-793.
81. Jaber, W. E., & Moore, D. M. (1999). A survey of factors which influence teachers’ use of computer-based technology. International Journal of Instructional Media, 26, 253-266.
82. Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system, Decision Science, 28(2), 357-389.
83. Jonassen, D. H., & Reeves, T. C. (1996). Learning with technology: Using computers as cognitive tools. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 693-719). New York: Macmillan.
84. Jöreskog, K. G. (1993). Testing structural equation models. In K. A. Bollen & J. S. Lang (Eds.), Testing structural equation models (pp. 294-316). Newbury Park, California: Sage.
85. Kagan, D. M. (1998). Learning how to program or use computers: A review of six applied suties, Educational Technology, 28(3), 49-51.
86. Khalili, A., & Shashaani, L. (1994). The effectiveness of computer applications: A meta-analysis. Journal of Research on Computing in Education, 27(1), 48-61.
87. Klopfer, E., Yoon, S., & Um, T. (2005). Teaching complex dynamic systems to young students with StarLogo. Journal of Computers in Mathematics and Science Teaching. 24(2), 157-178.
88. Kozma, R. (Ed.) (2003). Technology, innovation and educational change: A global perspective. Eugene, OR: International Society for Technology in Education.
89. Kozma, R. B. (2003). Technology and classroom practices: An international study. Journal of Research on Technology in Education, 36(1), 1-13.
90. Krajcik, J., & Layman, J. (1993). Microcomputer-based laboratories in science classroom. Research Matters to Science Teachers, 31,
91. Kukafka, R., Johnson, S. B., Linfante, A., & Allegrante, J. P. (2003). Grounding a new information technology implementation framework in behavioral science: A systematic analysis of the literature on IT use. Journal of Biomedical Informatics, 36, 218-227.
92. Kulik, C-L. C., & Kulik, J. A. (1986). Effectiveness of computer-based education in colleges. AEDS Journal, 19, 81-108.
93. Kwajewski, K. (1997). Technology as a core value, Learning and Leading with Technology, 24(5), 54-56.
94. Lajoie, S. P. (1993). Computer environments as cognitive tools for enhancing learning. In S. D. Lajoie & S. J. Derry (Eds.), Computers as cognitive tools, (pp. 261-288), Hillsdale, New Jersey: Lawrence Erlbaum Associates.
95. Lee, Y., Lee, J., & Lee, Z. (2006). Social Influence on Technology Acceptance Behavior: Self-Identity Theory Perspective. The Data Base for Advances in Information Systems, 37(2-3), 60-75.
96. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management, 40, 191-204.
97. Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Quarterly, 27(4), 657–679.
98. Linn, M. C., & Hsi, S. (2000). Computers, peers, teacher: Science learning partners. New Jersey: Lawrence Erlbaum Associates.
99. Litt, M. D., & Turk, D. C. (1985). Source of stress and dissatisfaction in experienced high schools. Journal of Educational Research, 78(3), 178-185.
100. Lou, H., Lou, W., & Strong, D. (2000). Perceived critical mass effect on groupware acceptance. European Journal of Information System, 9, 91-103.
101. Lovallo, W. R. (1997). Stress and health: Biological and psychological interactions. Thousand Oaks, CA: Sage Publications.
102. Lu, J., Yao, J, E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. Strategic Information Systen, 14, 245-268.
103. Lumpe. A. T., & Chambers, E. (2001). Assessing teachers’ context beliefs about technology use. Journal of Research on Technology in Education, 34(1), 93-107.
104. Ma, Q., & Liu, L. (2004). The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16(1), 59-72.
105. Marakas, G. M., Yi, M. Y., & Johnson, R. D. (1998). The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research. Information System Research, 9(2), 126-163.
106. Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391-410.
107. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information System Research, 2, 173-191.
108. Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: The influence of perceived user resources. The Data Base for Advances in Information Systems, 32(3), 86-112.
109. McGinnis, J. R., Simmons, R., Atwater, M. M., Hatfield, L., Olive, J., & Hunt, A. (1996). Beliefs and perceived needs of rural K-12 teachers of science toward the uses of computing technologies. Journal of Science Education and Technology, 5(2), 111-120.
110. Melone, N. P. (1990). A theoretical assessment of the user-satisfaction construct in information system research. Management Science, 36(1), 76-91.
111. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222
112. Murphy, K. R., & Davidshofer, C. O. (2001). Psychological testing: Principles andapplications. (5th. Ed.). Upper Saddle River, New Jersey: Prentice-Hall.
113. National Center for Education Statistics (2001). Teachers’ tools for the 21st century: A report on teachers’ use of technology. Washington, DC: Author.
114. National Science Council (1996). National science education standards. Washington, DC: National Academy Press.
115. Norris, C., Sullivan, T., Poirot, J., & Soloway, E. (2003). No access no use, no impact: Snapshot surveys of educational technology in K-12. Journal of Research on Technology in Education, 36(1), 15-27.
116. Norton, S., McRobbie, C. J., & Cooper, T. J. (2000). Exploring secondary mathematics teachers’ reasons for not using computers in their teaching: Five case studies. Journal of Research on Computing in Education, 33(1), 87-109.
117. Nunnally, J. C. (1978). Psychometric theory (2nd. Ed.). New York: McGraw-Hill.
118. Office of Technology Assessment (1995). Teachers and technology: Making the connection. (OTA-HER-616) Washington, DC: US. Government Printing Office.
119. Ong, C. S., Lai, J. Y., & Wang, Y. S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information and Management, 41, 795-804.
120. Pajares, M. F. (1992). Teacher's beliefs and educational research: Cleaning up a messy construct. Review of Educational Research, 62, 307-322.
121. Peck, K & Dorricott, D. (1994). Why use technology? Educational Leadership, 51, 11-14.
122. Pedersen, J. E., & Yerrick, R. K. (2000). Technology in science teacher education: Survey of current uses and desired knowledge among science educators. Journal of Science Teacher Education, 11(2), 131-153.
123. Pelgrum, W. J. (2001). Obstacles to the integration of ICT in education: Results from a worldwide educational assessment. Computers and Education, 37, 163-178.
124. Pelgrum, W. J., & Plomp, T. (1993). The use of computers in education in 18 countries. Studies in Eduational Evaluation, 19, 101-125.
125. Pierson, M. E. (2001). Technology integration practice as a function of pedagogical expertise. Journal of Research on Computing in Education, 33(4), 413-430.
126. Plomp, T., Nieveen, N., & Pegrum, W. J. (1996). Curricular aspect of computers in education. Cross National Policies and Practices on Computers in Education, 9-26.
127. Plomp, T., Pegrum, W. J., & Steerneman, A. (1990). Influence of computer use on schools’ curriculum: Limited integration. Computers in Education, 14, 159-171.
128. Riemenschneider, C. K., Harrison, D. A., & Mykytyn Jr., P. P. (2003). Understanding IT adoption decision in small business: Integrating current theories. Information and Management, 40(4), 269-287.
129. Roblyer, M. D., & Edwards, J. (2000). Integrating educational technology into teaching. (2nd. Ed.), New Jersey: Prentice-Hall.
130. Roblyer, M. D., & Knezek, G. A. (2003). New millennium research: A call for a national research agenda. Journal of Research on Technology in Education, 36(1), 60-71.
131. Roblyer, M. D., Castine, W. H., & King, F. J. (1988). Assessing the impact of computer-based instruction: A review of recent research. Binghamton, New York: Haworth.
132. Rocaa, J. C., Chiub, C., & Martı´nez, 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.
133. Rogers, E.M. (1983). Diffusion of innovations, (3rd. Ed.). New York: Free Press.
134. Rosen, L. D., & Weil, M. M. (1995). Computer availability, computer experience and technophobia among public school teachers. Computers in Human Behavior, 11(1), 9-31.
135. Seels, B., & Richey, R. (1994). Instructional technology: The definition and domains of the field. Washington DC: Association for Educational Communications & Technology (AECT).
136. Shackel, B. (1991). Usability: Context, framework, definition, design and evaluation. In B. Shackel and S. Richardson (Eds.), Human factors for informatics usability (pp. 21-37). Cambridge: Cambridge University Press.
137. Shroyer, M. G., & Borchers, C. A. (1996). Factors that support change to enhance the use of microcomputers in rural schools. School Science and Mathematics, 96(8), 419-431.
138. Smylie, M. (1988). The enhancement function of staff development: Organizational and psychological antecedents to individual teacher change. American Educational Research Journal, 25(1), 1-30.
139. Soh, C., Kien, S. S., & Tay-Yap, J. (2000). Cultural fits and misfits: Is ERP a universal solution? Communications of the ACM, 43(4), 47-51.
140. Songer, N. B. (1996). Exploring learning opportunities in coordinated network-enhanced classroom: A case of kids as global scientists. The Journal of the Learning Science, 5, 297-327.
141. Songer, N. B., Lee, H.-S., & Kam, R. (2002). Technology-rich inquiry science in urban classrooms: What are the barriers to inquiry pedagogy? Journal of Research in Science Teaching, 39(2), 128-50.
142. Staples, D. S., & Seddon, P. (2004). Testing the technology-to-performance chain model. Journal of Organizational and End User Computing, 16(4), 17-36.
143. Sugar, W., Crawley, F., & Fine, B. (2004). Examining teachers’ decisions to adopt new technology. Educational Technology and Society, 7(4), 201-213.
144. Summer, M., & Hostetler, D. (1999). Factors influencing the adoption of technology in teaching. Journal of Computer Information Systems, Fall, 81-87.
145. Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85-92.
146. Taylor, S., & Todd, P. (1995a). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.new window
147. Taylor, S., & Todd, P. (1995b). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561-570.
148. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991).Personal computing: Toward a conceptual model of utilization. MIS Quarterly, March, 125-143.
149. Tornatzky, L. G., & Klein, J. K. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28-45.
150. Tschannen-Moran, M., Woolfolk Hoy, A., & Hoy, W. K. (1998). Teacher efficacy: Its meaning and measure. Review of Educational Research, 68(2), 202-248.
151. ven Braak, J. (2001). Factors influencing the use of computer mediated communication by teachers in secondary schools, Computers and Education, 36, 41-57.
152. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
153. Venkatesh, V., & Morris, M. G. (2000). Why do men not ever stop to ask for direction? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115-139.
154. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
155. Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Science, 33(2), 297-316.
156. Vroom, V. H. (1964). Work and Motivation. New York, NY: Wiley.
157. Wang, L., Ertmer, P. A., & Newby, T. J. (2004). Increasing pre-service teachers’ self-efficacy beliefs for technology integration. Journal of Research on Technology in Education, 36(3), 231-250.
158. Wang, P., & San, C. P. (1995). Advantages, disadvantages, facilitators, and inhibitors of computer-aided instruction in Singapore's secondary schools. Computers and Education, 25(3), 151-162.
159. Wang, Y. S., Lin, H. H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information Systems Journal, 16, 157-179.
160. Watson, D. M. (2001). Pedagogy before technology: Rethinking the relationship between ICT and teaching. Education and Information Technologies, 6(4), 251-266.
161. Wiburg, K. (1996). Changing teaching with technology. Learning and Leading With Technology, 23(4), 46-48.
162. Wiesenmayer, R. L., & Koul, R. (1998). Integrating Internet resources into the science classroom : Teachers’ perspectives. Journal of Science Education and Technology, 7(3), 271-277.
163. Windschitl, M., & Sahl, K. (2002). Tracing teachers’ use of technology in a laptop computer school: The interplay of teacher beliefs, social dynamics, and institutional culture. American Educational Research Journal, 39(1), 165-205.
164. Wu, H.K., Krajcik, J.S., & Soloway, E. (2001). Promoting understanding of chemical representations: Students’ use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38(7), 821-842.
165. Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information and Management, 43, 350-363.
166. Yuen, A. H. K., & Ma. W. W. K. (2002). Gender difference in teacher computer acceptance. Journal of Technology and Teacher Education, 10(3), 365-382.
167. Zacharia, Z. (2003). Beliefs, attitudes, and intentions of science teachers regarding the educational use of computer simulations and inquiry-based experiments in physics, Journal of Research in Science Teaching, 40(80), 792-823.
168. Zammit, S. (1992). Factors facilitating or hindering the use of computers in schools. Educational Research, 34(1), 57-66.
169. Zhao, Y. (1998). Design for adoption: the development of an integrated web-based education environment. Journal of Research on Computing in Education, 30(3), 307-308.
170. Zhao, Y., Byers, J., Mishra, P., Topper, A., Chen, H., Enfield, M., Ferdig, R., Frank, K. A., Pugh, K., & Tan, H. (2001). What do they know? A comprehensive portrait of exemplary technology-using teachers. Journal of Computing in Teacher Education, Winter, 25-37.
171. Zint, M. (2002). Comparing three attitude-behavior theories for predicting science teachers’ intentions. Journal of Research in Science Teaching, 39(9), 819-844.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：1018（臺灣：1014，外國：4） ／ 本年度總使用人次：2111296 ／ 訪客人次（自102年09月）：67909289
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上