Abstract
The purpose of this study was to: a) investigate student teachers’ and experienced classroom teachers’ computer usage beliefs, intentions, and self-reported computer usage in the classroom using a mixed methodology approach (i.e., quantitative and qualitative), and b) examine the efficacy of the technology acceptance model (TAM) and the decomposed theory of planned behavior (DTPB) for predicting computer usage intentions. This study consisted of a sample of 160 student teachers and 158 experienced teachers from classes within a large urban university. All participants completed a Computer Usage Intention Survey. This survey was developed using a theoretical framework of the technology acceptance model (TAM) (Davis, 1989, 1993; Davis, Bagozzi, & Warshaw, 1989). The survey determined participants’ beliefs, future intentions usage (for the coming 6 months) and self-reported usage (for the past three months) of integrating computer applications (e.g. Word Processing, Spreadsheets, Database, Multimedia, Internet, Games, Drill and Practice, Simulations, Tutorials, Problem Solving, and educational subject-specific software) into subjectspecific lessons. After completion of the Computer Usage Intentions Survey, a purposeful sample of the study’s participants was selected for semi-structured interviews. This sample consisted of a total of 19 participants, 10 student teachers and 9 experienced classroom teachers. The interview questionnaire was developed using a theoretical framework of the decomposed theory of planned behavior (Taylor & Todd, 1995). Although the TAM was a good predictor of intentions, the DTPB emerged as the most important model for predicting teachers’ intentions. Similarities as well as significant differences were found between student teachers’ and experienced teachers’ computer usage.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
REFERENCES
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11–39). New York: Springer-Verlag.
Anfara, V. A., Jr., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage: Making the research process more public. Educational Researcher, 31(7), 28–38.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice-Hall.
Becker, H. J. (1994). How exemplary computer-using teachers differ from other teachers: Implications for realizing the potential of computers in schools. Journal of Research on Computing in Education, 26(3), 291–321.
Becker, H. J. (1998). Running to catch a moving train: Schools and information technologies. Theory Into Practice, 37(1), 20–30.
Benson, L. F., Farnsworth, B. J., Bahr, D. L., Lewis, V. K., & Shaha, S. H. (2004). The impact of training in technology assisted instruction on skills and attitudes of pre-service teachers. Education, 124(4), 649–663.
Bliss, T. J., & Bliss, L. L. (2003). Attitudinal responses to teacher professional development for the effective integration of educational technology. Journal of In-Service Education, 29(1), 81–99.
Brent, R., Brawner, C. E., & Van Dyk, P. (2002). Factors influencing student teachers’ use of technology. Journal of Computing in Teacher Education, 19(2), 61–68.
Brzycki, D., & Dudt, K. (2005). Overcoming barriers to technology use in teacher preparation programs. Journal of Technology and Teacher Education, 13(4), 619–641.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211.
Constas, M. A. (1992). Qualitative analysis as a public event: The documentation of category development procedures. American Educational Research Journal, 29(2), 253–266.
Cuban, L. (2001). Oversold and underused: Computers in the classrooms. Cambridge, MA: Harvard University Press.
Culp, K. M., Honey, M., & Mandinach, E. (2005). A retrospective on twenty years of education technology policy. Journal of Educational Computing Research, 32(3), 279–307.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38, 475–487.
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), 982–1003.
Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Man-Machine Studies, 45, 19–45.
Dawson, C., & Rakes, G. C. (2003). The influence of principals’ technology training on the integration of technology into schools. Journal of Research on Technology in Education, 36(1), 29–49.
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36, 9–21.
Doering, A., Hughes, J., & Huffman, D. (2003). Preservice teachers: Are we thinking with technology? Journal of Research on Technology in Education, 35(3), 342–361.
Doll, W. J., Hendrickson, A., & Deng, X. (1998). Using Davis’s perceived usefulness and ease-of-use instruments for decision making: A confirmatory and multigroup invariance analysis. Decision Sciences, 29(4), 839–869.
Drennan, J., Kennedy, J., & Pisarski, A. (2005). Factors affecting student attitudes toward flexible online learning in management education. The Journal of Educational Research, 98(6), 331–338.
Driscoll, M. (2001). Computers for what? Examining the roles of technology in teaching and learning. Educational Research and Evaluation, 7(2–3), 335–349.
Edwards, V. B., Chronister, G., & Hendrie, C. (2006). The information edge. Education Week Technology Counts, The Information Edge: Using Data to Accelerate Achievement, 8–9.
Ertmer, R. A., Addison, P., Lane, M., Ross, E., & Woods, D. (1999). Examining teachers’ beliefs about the role of technology in the elementary classroom. Journal of Research on Computing in Education, 32(1), 54–73.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior. Reading, MA: Addison-Wesley Publishing Company, Inc.
Gao, Y. (2005). Applying the technology acceptance model (TAM) to educational hypermedia: A field study. Journal of Educational Multimedia and Hypermedia, 14(3), 237–247.
Gibson, S., & Nocente, N. (1998). Addressing instructional technology needs in faculties of education. The Alberta Journal of Educational Research, 64(3), 320–331.
Granger, C. A., Morbey, M. L., Lotherington, H., Owston, R. D., & Wideman, H. H. (2002). Factors contributing to teachers’ successful implementation of IT. Journal of Computer Assisted Learning, 18, 480–488.
Hadley, M., & Sheingold, K. (1993). Commonalities and distinctive patterns in teachers’ integration of computers. American Journal of Education, 101, 261–315.
Hall, L. (2006). Modeling technology integration for preservice teachers: A PT3 case study. Contemporary Issues in Technology and Teacher Education, 6(4), 436–455.
Hendrickson, A. R., & Collins, M. R. (1996). An assessment of structure and causation of IS usage. The DATA BASE for Advances in Information Systems, 27(2), 61–67.
Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87–114.
Igbaria, M., Schiffman, S. J., & Wieckowski, T. J. (1994). The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology. Behaviour & Information Technology, 13(6), 349–361.
Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-ofuse. Information & Management, 35, 237–250.
Kay, R. (1993). An exploration of theoretical and practical foundations for assessing attitudes toward computers: The computer attitude measure (CAM). Computers in Human Behavior, 9, 371–386.
Kay, R. H. (2006). Evaluating strategies used to incorporate technology into preservice education: A review of the literature. Journal of Research on Technology in Education, 38(4), 383–408.
Kelleher, T., & O’Malley, M. (2006). Applying the technology acceptance model to assess outcomes in a globally linked strategic communication project. Journalism & Mass Communication Educator, 60(4), 402–414.
Lanahan, L. (2002). Beyond school-level Internet access: Support for instructional use of technology (NCES 2002–029). Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement, Office of Educational Research and Improvement, National Center for Education Statistics.
Lee, M. J. W., & Chan, A. (2005). Exploring the potential of podcasting to deliver mobile ubiquitous learning in higher education. Journal of Computing in Higher Education, 18(1), 94–115.
Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the world wide web. Decision Support Systems, 29, 269–282.
Ma, W. W. K., Anderson, R., & Streith, K. O. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer Assisted Learning, 21, 387–395.
Marcinkiewicz, H. R. (1994). Computers and teachers: Factors influencing computer use in the classroom. Journal of Research on Computing in Education, 26(2), 220–237.
Mathieson, K. (1991). Predicting use intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191.
Merriam, S. B. (1998). Qualitative research and case study applications in education. San Francisco: Jossey-Bass Inc.
Mims, C., Polly, D., Shepherd, C., & Inan, F. (2006). Examining PT3 projects designed to improve preservice education. TechTrends, 50(3), 16–24.
Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world wide web context. Information & Management, 38, 217–230.
Motiwalla, L. F. (2007). Mobile learning: A framework and evaluation. Computers & Education, 49, 581–596.
Moursund, D., & Bielefeldt, T. (1999). Will new teachers be prepared to teach in a digital age? Retrieved October 25, 2009, from http://www.mff.org/publications/publications.taf?page=154
Mouza, C. (2003). Learning to teach with new technology: Implications for professional development. Journal of Research on Technology in Education, 35(2), 2772–2289.
Mowrer-Popiel, E., Pollard, C., & Pollard, R. (1994). An analysis of the perceptions of preservice teachers toward technology and its use in the classroom. Journal of Instructional Psychology, 21(2), 131–138.
Ong, C. S., & Lai, J. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22, 816–829.
Ngai, E. W. T., Poon, J. K. L., & Chan,Y. H. C. (2007). Empirical examination of the adoption on WebCT using TAM. Computers & Education, 48, 250–267.
Pan, C. C., Sivo, S., Gunter, G., & Cornell, R. (2005). Students’ perceived ease of use of an e-learning management system: An exogenous or endogenous variable? Journal of Educational Computing Research, 33(3), 285–307.
Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47, 222–244.
Raaij, E. M. V., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50, 838–852.
Schnackenberg, H. L., Luik, K., Nisan, Y. C., & Servant, C. (2001). A case study of needs assessment in teacher in-service development. Educational Research and Evaluation, 7(2–3), 137–160.
Selim, H. M. (2003). An empirical investigation of student acceptance of course websites. Computers & Education, 40, 343–360.
Smarkola, C. (2007). Technology acceptance predictors among student teachers and classroom teachers. Journal of Educational Computing Research, 37(1), 65–82.
Smarkola, C. (2008a). Efficacy of a planned behavior model: Beliefs that contribute to computer usage intentions of student teachers and experienced teachers. Computers in Human Behavior, 24(3),1196–1215.
Smarkola, C. (2008b). Developmentally responsive technology use in education: Are teachers helping students meet gradelevel national technology standards? Journal of Educational Computing Research, 38(4), 387–409.
Smerdon, B., Cronen, S., Lanahan, L., Anderson, J., Iannotti, N., & Angeles, J. (2000). Teachers’ tools for the 21st century: A report on teachers’ use of technology (NCES Publication No. 2000–102).
Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement, National Center for Statistics.
Swanson, C. B. (2006). Tracking U.S. trends. Education Week Technology Counts, The Information Edge: Using Data to Accelerate Achievement, 50–55.
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85–92.
Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches. Thousand Oaks, CA: Sage Publishers.
Taylor, S., & Todd, P. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.
Triandis, H. C. (1971). Attitude and attitude change. New York: John Wiley & Sons, Inc.
Trotter, A. (2006). National effort. Education Week Technology Counts, The Information Edge: Using Data to Accelerate Achievement, 48–49.
U.S. Congress. (2000). Goals 2000 educational act. Retrieved October 26, 2009, from http://www.ed.gov/legislation/GOALS2000/TheAct
U.S. Congress Office of Technology Assessment. (1995). Teachers and technology: Making the connection (OTA-EHR-616). Washington, DC: U.S. Government Printing Office.
U.S. Department of Education. (2002). No child left behind act of 2001. Retrieved October 26, 2009, from http://www.ed.gov/nclb/overview/intro/progsum/progsum.pdf
U.S. Department of Education. (2004). Toward a new golden age in American education-How the Internet, the law and today’s students are revolutionizing expectations: National education technology
plan 2004. Office of Educational Technology. Retrieved October 26, 2009, from http://www.ed.gov/about/offices/list/os/technology/plan/2004/plan_pg3.html#execsum.
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.
Wedman, J., & Diggs, L. (2001). Identifying barriers to technology-enhanced learning environments in teacher education. Computers in Human Behavior, 17(4), 421–430.
Willis, E. M., & Sujo de Montes, L. (2002). Does requiring a technology course in preservice teacher education affect student teacher’s technology use in the classroom? Journal of Computing in Teacher Education, 18(3), 76–80.
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.
Yuen, A. H. K., & Ma, W. W. K. (2002). Gender differences in teacher computer acceptance. Journal of Technology and Teacher Education, 10(3), 365–382.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Sense Publishers
About this chapter
Cite this chapter
Smarkola, C. (2011). A Mixed-Methodological Technology Adoption Study. In: Teo, T. (eds) Technology Acceptance in Education. SensePublishers. https://doi.org/10.1007/978-94-6091-487-4_2
Download citation
DOI: https://doi.org/10.1007/978-94-6091-487-4_2
Publisher Name: SensePublishers
Online ISBN: 978-94-6091-487-4
eBook Packages: Humanities, Social Sciences and LawEducation (R0)