From Conversation to Collaboration: How the Quality of Teacher Work Group Meetings Influences Social Networks

• In Event: 54.072 - The Role of Teachers' Social Networks in the Development of Ambitious Mathematics Instruction

Abstract

Objective. We examine the extent to which the quality of mathematics teacher workgroup conversations influences advice-seeking patterns.
Theoretical Framework. Teacher workgroups are a common strategy for supporting instructional improvement and student achievement. Workgroup meetings are promising sites for teacher learning, as they provide opportunities to discuss problems of practice. Teachers’ school-based communities can support ambitious instruction (Lampert et al., 2011), but simply requiring that teachers meet is insufficient to ensure such improvement (Little, 1990).
We extend the work of Horn and colleagues (Horn et al., under review), applying their taxonomy of learning opportunities in workgroup meetings to a social network analysis. We operationalize professional learning opportunities along two dimensions: pedagogical concepts developed in conversation and preparation for future work. In lower-quality meetings, teachers prepare for future instruction without delving into pedagogical concepts. In higher-quality meetings, teachers collectively investigate problems of practice; such conversations provide richer learning opportunities by positioning teachers to engage in new practices. We investigate how participation in high-quality meetings fosters collaboration as teachers develop larger advice-seeking networks.
Data Sources. From a larger study, we selected eight mathematics teacher workgroups each year for four years, purposively selecting successful workgroups (Yin, 2014). We videotaped 4-6 meetings from each group during each school year (111 meetings). Meetings were coded for conversational quality (Horn et al., under review).
Each year, we collected advice-seeking data by asking teachers with whom they discuss mathematics instruction; teachers (egos) nominated up to 10 individuals (alters). We also collected measures of expertise, including instructional quality (IQA; Boston & Wolf, 2005), vision of high-quality mathematics instruction (Munter, 2014), vision of students’ mathematical capabilities (Jackson & Gibbons, 2014), and mathematical knowledge for teaching (Hill et al., 2005).
Methods. Using three-level hierarchical generalized linear models, we estimated the probability of new advice-seeking ties. As the outcome of interest is a binary variable (assumed to follow a Bernoulli distribution), we used logistic regression with geometrically weighted degree count, controlling for alter's in-degree and ego's out-degree distribution (Snijders et al., 2006). We controlled for relative attributes between ego and alter at the first level, characteristics of ego at the second level, and school characteristics at the third level.
Findings. We found that the odds that a teacher seeks another for advice are 2.78 times greater if they attended high-quality meetings together (e 1.021=2.78). There is no similar effect for attending low-quality meetings together. Furthermore, teachers are more likely to seek advice from others with greater instructional expertise (IQA). For a one-unit increase in the alter’s IQA score, we estimate a 77% increase in the odds of new advice-seeking behavior.
Significance. Many schools invest in teacher collaboration time for instructional improvement efforts. These findings underscore that the quality of teacher workgroup discussions matters for meeting the goal of increased collaboration. Higher-quality meetings bring teachers together, influencing their advice-seeking behaviors outside of regularly-scheduled meetings. In addition to allocating time for workgroup meetings, instructional leaders should identify leaders with instructional expertise while promoting deeper conversations that strengthen teacher collaboration and teacher learning.

Authors

• I-Chien Chen, Michigan State University

• Brette Garner, Vanderbilt University - Peabody College

• Kenneth A. Frank, Michigan State University

• Ilana S. Horn, Vanderbilt University