Abstract
Recommendations to improve university Science, Technology, Engineering, and Mathematics (STEM) education often emphasize the role of classroom assessment in supporting student learning. Despite extensive efforts to support instructors in reforming their practices, many continue to teach didactically with limited classroom assessment. Instructors’ decisions to adopt new practices have previously been characterized with the innovation decision model, and the spread of such practices can be explored through an innovation diffusion lens. Innovation diffusion of high-impact assessment practices requires interaction between experienced assessment users and less-experienced users. Within a university STEM department, instructors’ assessment experiences were documented through the Faculty Self-Reported Assessment Survey (FRAS), and interactions between instructors were characterized with social network analysis. Results show that instructors with higher self-reported assessment experience had more teaching-specific peer interactions within the department, and that instructors of all assessment experience levels interacted more often with more experienced instructors. This study demonstrates and characterizes the conditions for innovation diffusion in a university STEM department and highlights the potential role of peer interactions for supporting the spread of innovative teaching ideas.
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AAAS. (2015). Vision and change: chronicling change, inspiring the future. Washington, DC: AAAS.
Andrews, T. C., & Lemons, P. P. (2015). It’s personal: biology instructors prioritize personal evidence over empirical evidence in teaching decisions. CBE Life Sciences Education, 14(1), ar7. https://doi.org/10.1187/cbe.14-05-0084.
Andrews, T. C., Conaway, E. P., Zhao, J., & Dolan, E. L. (2016). Colleagues as change agents: how department networks and opinion leaders influence teaching at a single research university. CBE Life Sciences Education, 15(2), ar15. https://doi.org/10.1187/cbe.15-08-0170.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B: Methodological, 57(1), 289–300.
Brewer, C. A., & Smith, D. (2011). Vision and change in undergraduate biology education: a call to action. Washington, D.C.: AAAS.
Brownell, S. E., & Tanner, K. D. (2012). Barriers to faculty pedagogical change: lack of training, time, incentives, and…tensions with professional identity? CBE Life Sciences Education, 11(4), 339–346. https://doi.org/10.1187/cbe.12-09-0163.
Bush, S. D., Rudd, J. A., Stevens, M. T., Tanner, K. D., & Williams, K. S. (2016). Fostering change from within: influencing teaching practices of departmental colleagues by science faculty with education specialties. PLoS One, 11(3), e0150914. https://doi.org/10.1371/journal.pone.0150914.
Christakis, N. A., & Fowler, J. H. (2013). Social contagion theory: examining dynamic social networks and human behavior. Statistics in Medicine, 32(4), 556–577. https://doi.org/10.1002/sim.5408.
Committee on STEM Education of the National Science & Technology Council. (2018). Charting a course for success: America’s Strategy for STEM Education. Washington, D.C.: Executive Office of the President of the United States.
Cooper, M. M., Caballero, M. D., Ebert-May, D., Fata-Hartley, C. L., Jardeleza, S. E., Krajcik, J. S., … Underwood, S. M. (2015). Challenge faculty to transform STEM learning. Science, 350(6258), 281–282. https://doi.org/10.1126/science.aab0933
Core Team, R. (2017). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing https://www.R-project.org/.
Cox, M. D. (2004). Introduction to faculty learning communities. New Directions for Teaching and Learning, 2004(97), 5–23. https://doi.org/10.1002/tl.129.
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5). http://igraph.org
Dancy, M., & Henderson, C. (2010). Pedagogical practices and instructional change of physics faculty. American Journal of Physics, 78(10), 1056–1063. https://doi.org/10.1119/1.3446763.
Dancy, M., Henderson, C., & Turpen, C. (2016). How faculty learn about and implement research-based instructional strategies: the case of peer instruction. Physical Review Physics Education Research, 12(1). https://doi.org/10.1103/PhysRevPhysEducRes.12.010110.
Durham, M. F., Knight, J. K., & Couch, B. A. (2017). Measurement instrument for scientific teaching (MIST): a tool to measure the frequencies of research-based teaching practices in undergraduate science courses. CBE Life Sciences Education, 16(4), ar67. https://doi.org/10.1187/cbe.17-02-0033.
Ebert-May, D., Derting, T. L., Hodder, J., Momsen, J. L., Long, T. M., & Jardeleza, S. E. (2011). What we say is not what we do: effective evaluation of faculty professional development programs. BioScience, 61(7), 550–558. https://doi.org/10.1525/bio.2011.61.7.9.
Eddy, S. L., & Hogan, K. A. (2014). Getting under the hood: how and for whom does increasing course structure work? CBE Life Sciences Education, 13(3), 453–468. https://doi.org/10.1187/cbe.14-03-0050.
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111.
Gess-Newsome, J., Southerland, S. A., Johnston, A., & Woodbury, S. (2003). Educational reform, personal practical theories, and dissatisfaction: the anatomy of change in college science teaching. American Educational Research Journal, 40(3), 731–767.
Grosjean, P., & Ibanez, F. (2018). pastecs: Package for analysis of space-time ecological series. R package version 1.3.21. https://CRAN.R-project.org/package=pastecs
Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014). Understanding classrooms through social network analysis: a primer for social network analysis in education research. CBE Life Sciences Education, 13(2), 167–178. https://doi.org/10.1187/cbe.13-08-0162.
Haak, D. C., HilleRisLambers, J., Pitre, E., & Freeman, S. (2011). Increased structure and active learning reduce the achievement gap in introductory biology. Science, 332(6034), 1213–1216. https://doi.org/10.1126/science.1204820.
Hanauer, D. I., & Bauerle, C. (2015). The Faculty Self-Reported Assessment Survey (FRAS): differentiating faculty knowledge and experience in assessment. CBE Life Sciences Education, 14(2), ar17. https://doi.org/10.1187/cbe.14-10-0169.
Handelsman, J., Miller, S., & Pfund, C. (2007). Scientific teaching. Macmillan.
Henderson, C. (2005). The challenges of instructional change under the best of circumstances: a case study of one college physics instructor. American Journal of Physics, 73(8), 778–786.
Henderson, C., Beach, A., & Finkelstein, N. (2011). Facilitating change in undergraduate STEM instructional practices: an analytic review of the literature. Journal of Research in Science Teaching, 48(8), 952–984. https://doi.org/10.1002/tea.20439.
Henderson, C., Dancy, M., & Niewiadomska-Bugaj, M. (2012). Use of research-based instructional strategies in introductory physics: where do faculty leave the innovation-decision process? Physical Review Special Topics - Physics Education Research, 8(2), 020104.
Judson, E., & Lawson, A. E. (2007). What is the role of constructivist teachers within faculty communication networks? Journal of Research in Science Teaching, 44(3), 490–505. https://doi.org/10.1002/tea.20117.
Kezar, A. (2014). Higher education change and social networks: a review of research. The Journal of Higher Education, 85(1), 91–125. https://doi.org/10.1080/00221546.2014.11777320.
Knaub, A. V., Henderson, C., & Fisher, K. Q. (2018). Finding the leaders: an examination of social network analysis and leadership identification in STEM education change. International Journal of STEM Education, 5(1). https://doi.org/10.1186/s40594-018-0124-5
Lane, A. K., Skvoretz, J., Ziker, J. P., Couch, B. A., Earl, B., Lewis, J. E., … Stains, M. (2019). Investigating how faculty social networks and peer influence relate to knowledge and use of evidence-based teaching practices. International Journal of STEM Education, 6(1), 28. https://doi.org/10.1186/s40594-019-0182-3
Lund, T. J., & Stains, M. (2015). The importance of context: an exploration of factors influencing the adoption of student-centered teaching among chemistry, biology, and physics faculty. International Journal of STEM Education, 2(1). https://doi.org/10.1186/s40594-015-0026-8
Ma, S., Herman, G. L., Tomkin, J. H., Mestre, J. P., & West, M. (2018). Spreading teaching innovations in social networks: the bridging role of mentors. Journal for STEM Education Research, 1(1-2), 60–84.
McAlpine, L., Weston, C., Beauchamp, J., Wiseman, C., & Beauchamp, C. (1999). Building a metacognitive model of reflection. Higher Education, 37(2), 105–131.
Middleton, J. A., Krause, S., Beeley, K., Judson, E., Ernzen, J., & Culbertson, R. (2015). Examining the relationship between faculty teaching practice and interconnectivity in a social network. In 2015 IEEE Frontiers in Education Conference (FIE) (pp. 1-7). IEEE.
Offerdahl, E. G., McConnell, M., & Boyer, J. (2018). Can I have your recipe? Using a Fidelity of Implementation (FOI) Framework to identify the key ingredients of formative assessment for learning. CBE Life Sciences Education, 17(4), es16. https://doi.org/10.1187/cbe.18-02-0029.
Otero, V., Pollock, S., & Finkelstein, N. (2010). A physics department’s role in preparing physics teachers: the Colorado Learning Assistant Model. American Journal of Physics, 78(11), 1218–1224. https://doi.org/10.1119/1.3471291.
Owens, M. T., Trujillo, G., Seidel, S. B., Harrison, C. D., Farrar, K. M., Benton, H. P., et al. (2018). Collectively Improving our teaching: attempting biology department–wide professional development in scientific teaching. CBE Life Sciences Education, 17(1), ar2. https://doi.org/10.1187/cbe.17-06-0106.
Piovesana, A., & Senior, G. (2018). How small is big: sample size and skewness. Assessment, 25(6), 793–800.
Quardokus, K., & Henderson, C. (2015). Promoting instructional change: using social network analysis to understand the informal structure of academic departments. Higher Education, 70(3), 315–335. https://doi.org/10.1007/s10734-014-9831-0.
Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press.
Roxå, T., & Mårtensson, K. (2015). Microcultures and informal learning: a heuristic guiding analysis of conditions for informal learning in local higher education workplaces. International Journal for Academic Development, 20(2), 193–205.
RStudio Team. (2016). RStudio: integrated development for R. Boston: RStudio, Inc..
Siciliano, M. D. (2016). It’s the quality not the quantity of ties that matters: social networks and self-efficacy beliefs. American Educational Research Journal, 53(2), 227–262. https://doi.org/10.3102/0002831216629207.
Smolla, M., & Akçay, E. (2019). Cultural selection shapes network structure. Science Advances, 5(8), eaaw0609.
Stains, M., Harshman, J., Barker, M. K., Chasteen, S. V., Cole, R., DeChenne-Peters, S. E., … Young, A. M. (2018). Anatomy of STEM teaching in North American universities. Science, 359(6383), 1468–1470. https://doi.org/10.1126/science.aap8892
Sturtevant, H., & Wheeler, L. (2019). The STEM Faculty Instructional Barriers and Identity Survey (FIBIS): development and exploratory results. International Journal of STEM Education, 6(1), 35.
Sun, M., Wilhelm, A. G., Larson, C. J., & Frank, K. A. (2014). Exploring colleagues’ professional influence on mathematics teachers’ learning. Teachers College Record, 116(6), 1–30.
Thiele, L., Sauer, N. C., & Kauffeld, S. (2018). Why extraversion is not enough: the mediating role of initial peer network centrality linking personality to long-term academic performance. Higher Education, 76(5), 789–805. https://doi.org/10.1007/s10734-018-0242-5.
Thomson, K. E., & Trigwell, K. R. (2018). The role of informal conversations in developing university teaching? Studies in Higher Education, 43(9), 1536–1547. https://doi.org/10.1080/03075079.2016.1265498.
Valente, T. W. (2012). Network interventions. Science, 337(6090), 49–53. https://doi.org/10.1126/science.1217330.
Van Waes, S., Van den Bossche, P., Moolenaar, N. M., De Maeyer, S., & Van Petegem, P. (2015). Know-who? linking faculty’s networks to stages of instructional development. Higher Education, 70(5), 807–826. https://doi.org/10.1007/s10734-015-9868-8.
Wickham, H. (2016). ggplot2: elegant graphics for data analysis. New York: Springer-Verlag.
Wu, Q., & Jessop, T. (2018). Formative assessment: missing in action in both research-intensive and teaching focused universities? Assessment & Evaluation in Higher Education, 43(7), 1019–1031. https://doi.org/10.1080/02602938.2018.1426097.
Zwolak, J. P., Dou, R., Williams, E. A., & Brewe, E. (2017). Students’ network integration as a predictor of persistence in introductory physics courses. Physical Review Physics Education Research, 13(1). https://doi.org/10.1103/PhysRevPhysEducRes.13.010113.
Acknowledgments
We thank the instructors within the department studied for their participation in the surveys. We also thank Caitlin Anderson, Jeffrey Boyer, Brian Farlow, Jennifer Momsen, Tammi Neville Rebecca Reichenbach, Rachel Salter, Tara Slominski, and Kurt Williams for thoughtful conversations and helpful comments on earlier versions of the article, and two anonymous reviewers for their comments that substantially improved the manuscript.
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This work was supported in part by a research fellowship awarded by the North Dakota State University Graduate School for the STEM Education graduate program.
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Supplemental Figure 1.
Practices scores of participants over time. The black lines represent the five instructors (two highs, one mid, and two lows) discussed within the text who demonstrated relatively clear, sustained change in Practices score over time, while the grey lines in the background represent the other participants. (JPG 157 kb)
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McConnell, M., Montplaisir, L. & Offerdahl, E. Meeting the Conditions for Diffusion of Teaching Innovations in a University STEM Department. Journal for STEM Educ Res 3, 43–68 (2020). https://doi.org/10.1007/s41979-019-00023-w
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DOI: https://doi.org/10.1007/s41979-019-00023-w