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

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.

A model of peer effects on instructor innovation adoption

“…Instructors interacting within an academic unit form a social network through which information and opinions can be shared (Andrews et al 2016;Burt 2000;Grunspan et al 2018;Kezar 2014;McConnell et al 2019;Wasserman and Faust 1994). An individual's social network is known to be an influential factor in human behavior; the decisions an individual makes are heavily influenced by relationships and interactions (Dancy et al 2016;Lane et al 2019;Van Waes et al 2015). Academic peer interactions facilitate the exchange of information, ideas, and awareness of teaching innovations, and provide encouragement and/or discouragement that can ultimately support or inhibit instructional change (Andrews and Lemons 2015;Dancy et al 2016;Henderson 2005;Lund and Stains 2015;Rogers 2003).…”

“…The nature and frequency of interactions are influenced by the departmental and institutional context. Further, peer interactions also shape perceptions of context through communication of beliefs and practices (Grunspan et al 2018;Pataraia et al 2015;Roxå and Mårtensson 2009;Thomson and Trigwell 2018), thereby affecting perceptions of departmental supports and barriers and ultimately the likelihood of adopting teaching innovations (Bathgate et al 2019;Kezar 2014;Lane et al 2019;Shadle et al 2017).…”

“…The model presented here is unique in that it accounts explicitly for potential mechanisms by which peer interactions influence knowledge and affective state in particular. We assert that peers act on an instructor's innovation-decision process in three specific ways: (1) they provide information to shape an instructor's knowledge (e.g., Dancy et al 2016;Henderson et al 2011;Lane et al 2019;Pataraia et al 2015), (2) they reinforce or change an instructor's affective state (e.g., Andrews and Lemons 2015;Marbach-Ad and Hunt Rietschel 2016;Roxå and Mårtensson 2009;Sturtevant and Wheeler 2019;Wieman et al 2013), and (3) they shape and communicate the teaching climate of the department, which can indirectly impact an instructor's affective state (e.g., Grunspan et al 2018;Kezar 2014;Landrum et al 2017;Lund and Stains 2015;Shadle et al 2017). All downstream effects of peers (i.e., decisions by an instructor to implement or reject an innovation or seek more knowledge) are the result of one or more changes in either knowledge or affective state, or both.…”

Innovative teaching knowledge stays with users

“…Social network analyses in science departments reveal a connection between a person's teaching discussion partners and their EBIP use (7). However, it remains unclear who regular EBIP users talk to about teaching and if they speak to colleagues with less EBIP experience, as necessary for diffusion to occur.…”

Using SenseMaker® to Examine Student Experiences in Engineering: A Discussion of the Affordances and Limitations of this Novel Research Approach

“…Some exceptions are the practical application of complex systems thinking to interdisciplinary faculty collaborations [47] and faculty development/ institutional change efforts [48,49]. Another exception is the recent surge in interest in using social network analysis (SNA) to investigate the diffusion of evidence-based teaching practices in STEM higher education settings [50][51][52][53]. It is worthwhile noting, however, that while mixed methods SNA studies are increasingly recognized for their value [54], these approaches are not yet capable of linking quantitative and qualitative data in parallel.…”