As faculty transition to teaching in Team-Based Learning classrooms, they must make decisions not only about their pedagogy, but also about how they will situate their instruction within a team-based learning environment using a variety of technology tools. However studies have largely ignored the perceived technology barriers and adoption factors faculty experience in these classrooms (Walker, Brooks, & Baepler, 2011). This study investigates a group of faculty members converting a traditional lecture course to a team-based learning class for the first time providing an opportunity to investigate the perceived technology barriers and adoption factors in team-based learning classrooms.

The Fellowship for Innovative Teaching program was implemented at a research-intensive, public university to support faculty to redesign their courses. The program focused on helping faculty to realign their course pedagogy with four basic Team-Based Learning elements: forming/designing the teams for collaborative learning, managing teams to enhance inter and intra learning communities, designing and implementing meaningful team assignments to foster deeper learning, and evaluating team and individual performance to assure team as well as individual accountability (Shih & Han, 2013).

Using the combined theoretical framework of Rogers’ (2003) diffusion of innovation and Ajzen’s (1985) theory of planned behavior, the author piloted a survey instrument and interviewed a cohort of faculty members transitioning their traditional lecture course to a team-based learning class for the first time through the Fellowship for Innovative Teaching program.

Given the non-traditional nature of team-based learning classroom environments, the engaged student-centered nature of the pedagogy, and the plethora of technology available to provide active learning experiences, there are unique perceived technology barriers and adoption factors in these spaces. However studies have largely ignored the barriers and adoption factors faculty experience in these classrooms (Walker, Brooks, & Baepler, 2011). This study provides insights for faculty and faculty development practitioners with regards to aligning team-based learning pedagogy with technology integration.

Faculty Adoption of Technologies in Team-Based Learning Classrooms Bradford D. Wheeler, Mei-Yau Shih, Gabriela C. Weaver

University of Massachusetts, Amherst, MA College of Education, Department of Math, Science, and Learning Technologies & Center for Teaching and Faculty Development

Introduction Methods

Abstract

Review of Literature

Constructivist Learning Spaces• SCALE-UP Classrooms• TILE (Transform, Interact

Learn, Engage) Classrooms• TEAL (Technology Enabled

Active Learning) Classrooms• TBL (Team-Based Learning)

Classrooms

Faculty Development in Technology

Adoption and Barriers Literature• First-order (lack of time,

training, support, etc.)• Second-order (compatibility

with beliefs, philosophy, etc.)

Data Analysis Results

Based on diffusion of innovations and theory of planned behavior, questionnaire results provided the researcher with an overview of tools and platforms used by instructors and general indications of why certain information technology tools are used in the TBL curriculum. This information was used to inform subsequent qualitative data collection and analysis.

Qualitative data analysis is being conducted through open coding which helps uncover themes and subthemes in qualitative data (Ryan & Bernard, 2003). Braun and Clarke’s thematic analysis guide (2006) will be utilized as a framework to analyze the qualitative interview data.

ReferencesAjzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior. Springer.

Beichner, R. J. et al. (2007). The Student-Centered Activities for Large Enrollment Undergraduate Programs (SCALE-UP) Project. Research-Based Reform of University Physics, 1(1), 2–39.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. doi:10.1191/1478088706qp063oa

Cenzon, C. G. (2009). Examining the Role of various Factors and Experiences in Technology Integration: A Description of a Professional Model. George Mason University.

Dahlstrom, E., & Brooks, D. C. (2014). Study of Faculty and Information Technology, 2014. Louisville, CO.

Ertmer, P. A. (1999). Addressing first- and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development. doi:10.1007/BF02299597

Reilly, C. A. (2014). Information and Communication Technology Use in the College Classroom: Adjunct Faculty Perspectives Dissertation Submitted to Northcentral University Graduate Faculty of the School of Education in Partial Fulfillment of the Requirements for the Degree of. Northcentral University.

Rogers, E. M. (2003). Diffusion of Innovations. New York: Free Press.

Ryan, G. W., & Bernard, H. R. (2003). Techniques to identify themes. Field Methods, 15(1), 85–109.

Shih, M.-Y., & Han, S. (2013). Promoting Student-Centered Learning: Team-Based Learning in a Technology-Rich Classroom. In New England Faculty Development Consortium.

Van Horne, S., Murniati, C. T., Saichaie, K., Jesse, M., Florman, J. C., & Ingram, B. F. (2014). Using Qualitative Research to Assess Teaching and Learning in Technology-Infused TILE Classrooms. New Directions for Teaching and Learning, 2014(137), 17–26. doi:10.1002/tl.20082

Walker, J. D., Brooks, D. C., & Baepler, P. (2011). Pedagogy and Space: Empirical Research on New Learning Environments. EDUCAUSE Quarterly, 34(4).

Weigel, F. K., Hazen, B. T., Cegielski, C. G., & Hall, D. J. (2014). Diffusion of Innovations and the Theory of Planned Behavior in Information Systems Research: A Metaanalysis. Communications of the Association of Information Systems, 34(31), 619–636.

Background

Literature Review Rationale

Research Question Data Collection Data Analysis

FeedbackNext Steps!

Theoretical Framework

Rogers (2003) Diffusion of Innovations

• Adoption of an innovation “a new [to the individual] idea, practice, or object”

Ajzen (1985) Theory of Planned Behavior

• Variables that impact behavior

Combining both models should provide an opportunity to better

understand the decision to adopt an innovation” (Weigel et al.,

2014).

Data Collection (Pilot Study)Quantitative Survey Instrument

• Adopted from Cenzon, 2009, Reilly, 2014 and Van Horne et al., 2014

Qualitative Instrument• Semi-structured interviews with participants

Data Collection (Pilot Study)Sample: 15 surveys sent, 12 completedInterviews: 11 interviews including reflection on survey submission

Analysis Framework (Braun and Clarke, 2006)

Emergent Findings

Data analysis is at Phase I. Preliminary and early analysis indicates the following about faculty:

• Lack of time to prepare lessons• Lack of technology to meet all

disciplines (for example, audio equipment is not sufficient for faculty in fields that use sound, e.g. music, phonetics)

• Mobile technology including iPads are difficult for faculty to employ into the curriculum due to lack of apps and training

• Unreliable equipment and hardware failures are cited frequently, faculty often avoided such tools afterwards.

• Access to support services is not always clear or accessible

• Converting a course pedagogically to active learning is very demanding, the technology required to support this shift is also administratively challenging