Identifying High-Quality Principal Evaluation Feedback Using Automated Text Classification

• In Event: Sunday Roundtable 9:45 am
  In Roundtable Session: Hiring, Compensating, and Evaluating Principals: The Role of Gender, Quality Feedback, and Performance-Based Measures (Table 29)

Abstract

The past decade has seen a renewed focus on principal evaluation in education policy reforms, with a focus on evaluation feedback. Most states now require observations and feedback in their principal evaluation policies. However, we know very little about the feedback that principals receive from their formal, mandated evaluations. In this study, we use statewide data including microdata on principals’ written feedback text and employ machine learning techniques to construct measures of effective feedback derived from the literature, namely the extent to which feedback is specific, actionable, evidence-based, and in a supportive tone (praise). We examine the extent to which principals’ evaluation feedback meets these standards, finding significant variation in the quality of feedback that principals receive.

Authors

• Karin Gegenheimer, Research for Action

• Ellen B. Goldring, Vanderbilt University