Investigating how faculty social networks and peer influence relate to knowledge and use of evidence-based teaching practices

Authors

A. K. Lane, University of Nebraska - LincolnFollow
J. Skvoretz, University of South FloridaFollow
J. P. Ziker, Boise State UniversityFollow
B. A. Couch, University of Nebraska - LincolnFollow
B. Earl, Boise State UniversityFollow
J. E. Lewis, University of South FloridaFollow
J. D. McAlpin, University of South Florida
L. B. Prevost, University of South FloridaFollow
S. E. Shadle, Boise State UniversityFollow
M. Stains, University of VirginiaFollow

ORCID IDs

J. Skvoretz

Document Type

Article

Date of this Version

2019

Citation

International Journal of STEM Education (2019) 6:28

Comments

© The Author(s).

Open access

https://doi.org/10.1186/s40594-019-0182-3

Abstract

Background: Calls for science education reform have been made for decades in the USA. The recent call to produce one million new science, technology, engineering, and math (STEM) graduates over 10 years highlights the need to employ evidence-based instructional practices (EBIPs) in undergraduate STEM classes to create engaging and effective learning environments. EBIPs are teaching strategies that have been empirically demonstrated to positively impact student learning, attitudes, and achievement in STEM disciplines. However, the mechanisms and processes by which faculty learn about and choose to implement EBIPs remain unclear. To explore this problem area, we used social network analysis to examine how an instructor’s knowledge and use of EBIPs may be influenced by their peers within a STEM department. We investigated teaching discussion networks in biology and chemistry departments at three public universities.

Results: We report that tie strength and tie diversity vary between departments, but that mean indegree is not correlated with organizational rank or tenure status. We also describe that teaching discussion ties can often be characterized as strong ties based on two measures of tie strength. Further, we compare peer influence models and find consistent evidence that peer influence in these departments follows a network disturbances model.

Conclusions: Our findings with respect to tie strength and tie diversity indicate that the social network structures in these departments vary in how conducive they might be to change. The correlation in teaching practice between discussion partner and peer influence models suggest that change agents should consider local social network characteristics when developing change strategies. In particular, change agents can expect that faculty may serve as opinion leaders regardless of their academic rank and that faculty can increase their use of EBIPs even if those they speak to about teaching use EBIPs comparatively less.