Colleagues as Change Agents: How Department Networks and Opinion Leaders Influence Teaching at a Single Research University

Andrews, Tessa C.; Conaway, E. P.; Zhao, Junsan; Dolan, Erin L.

doi:10.1187/cbe.15-08-0170

Cited by 50 publications

(39 citation statements)

References 52 publications

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“…This requires different indicators. For instance, research on social networks can be useful because it helps identify opinion leaders and tracks relationships across a social system, such as a department (e.g., Andrews, Conaway, Zhao, & Dolan, 2016;Grunspan, Wiggins, & Goodreau, 2014;Hayward & Laursen, 2018;Quardokus & Henderson, 2015). Examples of social network indicators may be increasing the number of opinion leaders, increasing the density of a network, or increasing the strength of relationships related to undergraduate teaching in a department.…”

Section: Indicatorsmentioning

confidence: 99%

Change theory and theory of change: what’s the difference anyway?

2020

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This commentary focuses on the difference between a theory of change and change theory, as it relates to systemic change projects in STEM higher education. A theory of change is project-specific and related to evaluation. It makes the underlying rationale of a project explicit, which supports planning, implementation, and assessment of the project. In addition, a theory of change is often required by funding agencies as part of grant proposals. In contrast, change theories represent theoretical and empirically grounded knowledge about how change occurs that goes beyond any one project. Ideally, a theory of change is informed by change theories. This essay describes the connections between a theory of change and change theory and provides examples of how change theory can inform a project's theory of change. Grounding projects in change theory allows change agents to draw on existing knowledge and to better contribute to our collective knowledge about how to achieve meaningful change in STEM higher education.

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Section: Indicatorsmentioning

confidence: 99%

Change theory and theory of change: what’s the difference anyway?

2020

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“…Auerbach and colleagues [54] used Poisson regression to model the number of times an instructional practice, such as promoting metacognition, was noticed by experts compared to novices as they analyzed videos of active-learning classrooms. Andrews and colleagues [55] by counting the number of times a faculty member was reported in a survey as the cause of a change, thus necessitating a Poisson model of their count data.…”

Section: F Poisson Regression 1 When To Use Poisson Regressionmentioning

confidence: 99%

Beyond linear regression: A reference for analyzing common data types in discipline based education research

Theobald

Aikens

Eddy

et al. 2019

Phys. Rev. Phys. Educ. Res.

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This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] A common goal in discipline-based education research (DBER) is to determine how to improve student outcomes. Linear regression is a common technique used to test hypotheses about the effects of interventions on continuous outcomes (such as exam score) as well as control for student nonequivalence in quasirandom experimental designs. (In quasirandom designs, subjects are not randomly assigned to treatments. For example, when treatment is assigned by classroom, and observations are made on students, the design is quasirandom because treatment is assigned to classroom, not subject (students).) However, many types of outcome data cannot be appropriately analyzed with linear regression. In these instances, researchers must move beyond linear regression and implement alternative regression techniques. For example, student outcomes can be measured on binary scales (e.g., pass or fail), tightly bound scales (e.g., strongly agree to strongly disagree), or nominal scales (i.e., different discrete choices for example multiple tracks within a physics major), each necessitating alternative regression techniques. Here, we review extensions of linear modeling-generalized linear models (glms)-and specifically compare five glms that are useful for analyzing DBER data: logistic, binomial, proportional odds (also called ordinal; including censored regression), multinomial, and Poisson (including negative binomial, hurdle, and zero-inflated) regression. We introduce a diagnostic tool to facilitate a researcher's identification of the most appropriate glm for their own data. For each model type, we explain when, why, and how to implement the regression approach. When: we provide examples of the types of research questions and outcome data that would motivate this regression approach, including citations to articles in the DBER literature. Why: we name which linear regression assumption is violated by the data type. How: we detail implementation and interpretation of this modeling approach in R, including R syntax and code, and how to discuss the regression output in research papers. Code accompanying each analysis can be found in the online github repository that is associated with this paper (https://github.com/ejtheobald/BeyondLinearRegression). This paper is not an exhaustive review of regression techniques, nor does it review nonregression-based analyses. Rather, it aims to compile and summarize regression techniques useful for the most common types of DBER data and provide examples, citations, and heavily annotated R code so that researchers can easily implement the technique in their work.

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“…Many studies have relied on co-authorship or citation networks to study faculty social networks, but these networks may not appropriate for studying faculty teaching social networks (Kezar 2014a;Xian and Madhavan 2014). Indeed, Quardokus and Henderson (2015) argued that networks such as departmental affiliation are poor proxies for faculty teaching networks and Andrews et al (2016) show that discipline-based education researchers maintain teaching networks that transcend their individual departments. Although, few studies have examined how the teaching social networks of faculty influence their teaching practices, we describe the few that we know of.…”

Section: Social Network Analysis and Faculty Teaching Practicesmentioning

confidence: 99%

“…Both of these studies suggest that most faculty teaching networks are too sparse to benefit from either strong or bridging network ties. Andrews et al (2016) found that faculty change their teaching practices in response to colleague-colleague interactions, especially with colleagues deemed as opinion leaders. Opinion leaders were identified as faculty who were seen as excellent teachers or had unique teaching expertise because they conducted discipline-based education research.…”

Section: Social Network Analysis and Faculty Teaching Practicesmentioning

confidence: 99%

Spreading Teaching Innovations in Social Networks: the Bridging Role of Mentors

Herman

Tomkin

et al. 2018

Journal for STEM Educ Res

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Although Evidence-Based Instructional Practices (EBIP) generally improve students' performance in STEM, traditional lecturing remains the most common instructional practice in postsecondary settings. This study examines an institutional change program that organized STEM faculty into communities of practice (CoPs) to facilitate the adoption and spread of EBIP in postsecondary classrooms. In this program, CoPs were mentored by faculty members who have a track record as advocates for high-quality teaching. In order for practices to spread, knowledge about those practices needs an avenue to spread. We hypothesized that CoP mentors provide these avenues by creating bridging ties between the disparate CoPs, thus spanning structural holes. To test the hypothesis, a sociometric survey was administered to document 100 faculty members' social interactions concerning teaching. A Monte Carlo permutation test revealed that the mentors significantly increased the density, connectedness, and centralization of the institutional change program's teaching social network more than any other random selection of faculty members. We also found that CoP mentors were the most likely individuals to connect otherwise unconnected CoP participants. These findings suggest that the CoP mentors played an important role in providing the bridges that can facilitate the spread of knowledge about teaching innovations across the network.

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Colleagues as Change Agents: How Department Networks and Opinion Leaders Influence Teaching at a Single Research University

Cited by 50 publications

References 52 publications

Change theory and theory of change: what’s the difference anyway?

Change theory and theory of change: what’s the difference anyway?

Beyond linear regression: A reference for analyzing common data types in discipline based education research

Spreading Teaching Innovations in Social Networks: the Bridging Role of Mentors

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