Abstract
Instructional technology plays a key role in many teaching reform efforts at the postsecondary level, yet evidence suggests that faculty adopt these technology-based innovations in a slow and inconsistent fashion. A key to improving these efforts is to understand local practice and use these insights to design more locally attuned interventions. This exploratory study draws on systems-of-practice theory from distributed cognition research to provide a framework for producing comprehensive accounts of technology use. This account includes three components: (a) awareness of the local resource base for instructional technology, (b) decision-making processes regarding tool use, and (c) actual classroom use of technology. Interviews and classroom observations of 40 faculty in math, physics, and biology departments at three research universities in the U.S. were analyzed using thematic and causal network analysis. Results indicate that faculty have both a shared and discipline-specific resource base for instructional technology. The adoption, adaptation, or rejection of technology-based innovations is influenced by the alignment among pre-existing beliefs and goals, prior experiences, perceived affordances of particular tools, and cultural conventions of the disciplines. Classroom use of technology varied across disciplinary groups, with mathematicians and biologists exhibiting relatively limited repertoires of tool use while physicists used a larger variety of tools. Additionally, different tools were associated with different teaching methods and types of student cognitive engagement. Policymakers and instructional designers can use these insights to inform the design and implementation of technology-based initiatives, especially in ensuring that innovations resonate with existing belief systems and practices.
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Notes
By faculty, we mean all people, including graduate students, who hold undergraduate teaching positions (excluding TA’s)—whether full- or part-time, tenured or untenured—in postsecondary institutions, except for emeritus instructors and postdoctoral researchers.
This means that, at least initially, each instructor has multiple rows of data, one for each 5-min interval that was observed.
A typical 50-min class would have ten 5-min intervals worth of data per respondent.
References
Becher, T., & Trowler, P. R. (2002). Academic tribes and territories. Buckingham: Open University Press.
Blumenfeld, P. C., Kempler, T. M., & Krajcik, J. S. (2006). Motivation and cognitive engagement in learning environments. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 475–488). New York, NY: Cambridge University Press.
Bonk, C. J., & Graham, C. R. (Eds.). (2005). Handbook of blended learning: Global perspectives, local designs. San Francisco, CA: Pfeiffer Publishing.
Brown, G., & Bakhtar, M. (1987). Styles of lecturing: A study and its implications. Research Papers in Education, 3(2), 131–153.
Brown, A. L., & Campione, J. C. (1994). Guided discovery in a community of learners. In K. McGilly (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 229–270). Cambridge, MA: MIT Press/Bradford Books.
Chi, M. (1997). Quantifying qualitative analyses of verbal data: A practical guide. Journal of the Learning Sciences, 6(3), 271.
Clark, R. (2009). Translating research into new instructional technologies for higher education: The active ingredient process. Journal of Computing in Higher Education, 21(1), 4–18.
Cobb, P., Zhao, Q., & Dean, C. (2009). Conducting design experiments to support teachers’ learning: A reflection from the field. Journal of the Learning Sciences, 18(2), 165–199.
Coburn, C. E. (2001). Collective sensemaking about reading: How teachers mediate reading policy in their professional communities. Educational Evaluation and Policy Analysis, 23, 145–170.
Cohen, D. K., & Ball, D. L. (1999). Instruction, capacity, and improvement. Consortium for Policy Research in Education Rep. No. RR-43. Philadelphia: University of Pennsylvania, Graduate School of Education.
Collins, A., & Halverson, R. (2009). Rethinking education in the age of technology: The digital revolution and schooling in America. New York, NY: Teachers College Press.
Crandall, B., Klein, G., & Hoffman, R. R. (2006). Working minds: A practitioner’s guide to cognitive task analysis. Cambridge, MA: The MIT Press.
Cuban, L. (2001). Oversold and underused: Computers in the classroom. Cambridge, MA: Harvard University Press.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration. Educational Technology Research and Development, 53(4), 25–39.
Fairweather, J. (2008). Linking evidence and promising practices in science, technology, engineering, and mathematics (STEM) undergraduate education: A status report. Commissioned Paper for the Board of Science Education Workshop, Evidence on Promising Practices in Undergraduate Science, Technology, Engineering, and Mathematics (STEM) Education.
Fishman, B. (2005). Adapting innovations to particular contexts of use: A collaborative framework. In C. Dede, J. Honan, & L. Peters (Eds.), Scaling up success: Lessons learned from technology-based educational innovation (pp. 48–66). New York, NY: Jossey-Bass.
Garrison, D., & Akyol, Z. (2009). Role of instructional technology in the transformation of higher education. Journal of Computing in Higher Education, 21(1), 19–30.
Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7, 95–105.
Gee, J. P. (2007). What video games have to teach us about learning and literacy. New York: Palgrave MacMillan.
Gibson, J. J. (1977). The theory of affordances. In R. E. Shaw & J. Bransford (Eds.), Perceiving, acting and knowing. Hillsdale, NJ: Erlbaum.
Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies of qualitative research. London: Wledenfeld and Nicholson.
Greeno, J. G. (1994). Gibson’s affordances. Psychological Review, 101(2), 236–342.
Halverson, R. (2003). Systems of practice: How leaders use artifacts to create professional community in schools. Educational Policy Analysis Archives, 11(37), 1–35.
Halverson, R. R., & Clifford, M. A. (2006). Evaluation in the wild: A distributed cognition perspective on teacher assessment. Educational Administration Quarterly, 42(4), 578–619.
Hativa, N. (1995). What is taught in an undergraduate lecture? Differences between a matched pair of pure and applied disciplines. New Directions for Teaching and Learning, 64, 19–27.
Hativa, N., & Goodyear, P. (Eds.). (2002). Teacher thinking, beliefs, and knowledge in higher education. Norwell, MA: Kluwer Academic Publishers.
Henderson, C., & Dancy, M. (2008). Physics faculty and educational researchers: Divergent expectations as barriers to the diffusion of innovations. American Journal of Physics (Physics Education Research Section), 76(1), 79–91.
Hora, M. T. (2012). Organizational factors and instructional decision-making: A cognitive perspective. The Review of Higher Education, 35(2), 207–235.
Kezar, A., & Eckel, P. (2002). The effect of institutional culture on change strategies in higher education. Journal of Higher Education, 73(4), 435–460.
Klein, G. (2008). Naturalistic decision making. Human Factors, 50(3), 456–460.
Lane, C. A., & Lyle, H. F. (2011). Obstacles and supports related to the use of educational technologies: The role of technological expertise, gender, and age. Journal of Computing in Higher Education, 23(1), 38–59.
Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life. Cambridge, UK: Cambridge University Press.
Lazerson, M., Wagener, U., & Shumanis, N. (2000). What makes a revolution? Teaching and learning in higher education, 1980–2000. Change, 32(3), 12–19.
Leinhardt, G., & Greeno, J. G. (1986). The cognitive skill of teaching. Journal of Educational Psychology, 78(2), 75–95.
Martinko, M. J., Henry, J. W., & Zmud, R. W. (1996). An attributional explanation of individual resistance to the introduction of information technologies in the workplace. Behaviour & Information Technology, 15(5), 313–330.
Marrs, K. A., & Novak, G. (2004). Just-in-time teaching in biology: Creating an active learner classroom using the Internet. Cell Biology Education, 3, 49–61.
Mazur, E. (1997). Peer instruction: A user’s manual. New Jersey: Prentice Hall.
Mazur, E. (2009). Farewell, lecture? Science, 323, 50–51.
Meltzer, D. E., & Manivannan, K. (2002). Transforming the lecture-hall environment: The fully interactive physics lecture. American Journal of Physics, 70(6), 639–654.
Miles, M., & Huberman, A. M. (1994). Qualitative data analysis. Thousand Oaks: Sage Publications.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A new framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.
Molenda, M., & Bichelmeyer, B. (2006). Issues and trends in instructional technology: Slow growth as economy recovers. In M. Orey, J. McClendon, & R. M. Branch (Eds.), Educational media and technology yearbook (Vol. 31, pp. 3–32). Englewood, CO: Libraries Unlimited.
Nakamura, J., & Csikszentmihalyi, M. (2005). Engagement in a profession: The case of undergraduate teaching. Daedalus, 134(3), 60–67.
National Research Council. (2000). How people learn: Brain, mind, experience and school. Washington, D.C.: National Academy Press.
National Research Council. (2010). Rising above the gathering storm, revisited: Rapidly approaching category 5. Washington, D.C.: National Academy Press.
Neumann, R., Parry, S., & Becher, T. (2002). Teaching and learning in their disciplinary contexts: A conceptual analysis. Studies in Higher Education, 27(4), 405–417.
Norman, D. (1998). The design of everyday things. New York, NY: Doubleday.
Osthoff, E., Clune, W., Ferrare, J., Kretchmar, K., & White, P. (2009). Implementing immersion: Design, professional development, classroom enactment and learning effects of an extended science inquiry unit in an urban district. Madison, WI: University of Wisconsin-Madison: Wisconsin Center for Educational Research.
Piderit, S. K. (2000). Rethinking resistance and recognizing ambivalence: A multidimensional view of attitudes toward an organizational change. Academy of Management Review, 25(4), 783–794.
Porter, A. C. (2002). Measuring the content of instruction: Uses in research and practice. Educational Researcher, 31(7), 3–14.
President’s Council of Advisors on Science and Technology. (2010). Prepare and inspire: K-12 education in science, technology, engineering, and math (STEM) for American’s future. Washington, DC: White House Office of Science and Technology Policy.
Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York, NY: Simon & Schuster, Inc.
Ryan, G. W., & Bernard, H. R. (2003). Techniques to identify themes. Field Methods, 15(1), 85–109.
Schoenfeld, A. H. (2000). Models of the teaching process. The Journal of Mathematical Behavior, 18(3), 243–261.
Smart, J. C., & Ethington, C. A. (1995). Disciplinary and institutional differences in undergraduate education goals. New Directions for Teaching and Learning, 64, 49–57.
Smeby, J. C. (1996). Disciplinary differences in university teaching. Studies in Higher Education, 21(1), 69–79.
Spillane, J. P. (2006). Distributed leadership. San Francisco, CA: Jossey-Bass.
Spillane, J., Halverson, R., & Diamond, J. (2001). Investigating school leadership practice: A distributed perspective. Educational Researcher, 30(3), 23–28.
Spillane, J. P., Reiser, B. J., & Reimer, T. (2002). Policy implementation and cognition: Reframing and refocusing implementation research. Review of Educational Research, 72(3), 387–431.
Spotts, T. H., Bowman, M. A., & Mertz, C. (1997). Gender and use of instructional technologies: A study of university faculty. Higher Education, 34(4), 421–436.
Stark, J. S. (2000). Planning introductory college courses: Content, context and form. Instructional Science, 28, 413–438.
Turpen, C., & Finkelstein, N. (2009). Not all interactive engagement is the same: Variations in physics professors’ implementation of “peer instruction”. Physical Review Special Topics: Physics Education Research, 5(2), 020101-1–020101-18.
Umbach, P. D. (2007). Faculty cultures and college teaching. In R. P. Perry & J. C. Smart (Eds.), The Scholarship of Teaching and Learning in Higher Education: An Evidence-Based Perspective. New York, NY: Springer.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
Weick, K. E. (1995). Sensemaking in organizations. Thousand Oaks, CA: Sage.
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.
Yin, R. (2008). Case study research: Design and methods (4th ed.). Thousand Oaks, CA: Sage Publications, Inc.
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Hora, M.T., Holden, J. Exploring the role of instructional technology in course planning and classroom teaching: implications for pedagogical reform. J Comput High Educ 25, 68–92 (2013). https://doi.org/10.1007/s12528-013-9068-4
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DOI: https://doi.org/10.1007/s12528-013-9068-4