The Science Student Electronic Exit Ticket (SEET) System

Visualizations to Help Teachers Notice and Reflect on Classroom Inequalities

Authors

DOI:

https://doi.org/10.18608/jla.2024.8199

Keywords:

learning analytic dashboards, equity visualizations, studies of teacher adoption and use, learning sciences, science education, research paper

Abstract

This study examined the ways in which an equity analytics tool — the SEET system — supported middle school science teachers’ reflections on the experiences of diverse students in their classrooms. The tool provides teachers with “equity visualizations” — disaggregated classroom data by gender and race/ethnicity — designed to support teachers to notice and reflect on inequitable patterns in student participation in classroom knowledge-building activities, as well as “whole class visualizations” that enable teachers to look at participation patterns. The visualizations were based on survey data collected from students reflecting on the day’s lessons, responding to questions aligned with three theoretical constructs indicative of equitable participation in science classrooms: coherence, relevance, and contribution. The study involved 42 teachers, divided into two cohorts, participating in a two-month professional learning series. Diary studies and semi-structured interviews were used to probe teachers’ perceptions of the visualizations’ usability, usefulness, and utility for supporting their reflections on student experiences and instructional practices. A key result is that only the “equity visualizations” prompted teacher reflections on diverse student experiences. However, despite the support equity visualizations provided for this core task, the teachers consistently ranked the whole class visualizations as more usable and useful.

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Published

2024-03-06

How to Cite

Raza, A., Sumner, T., & Penuel, W. R. (2024). The Science Student Electronic Exit Ticket (SEET) System: Visualizations to Help Teachers Notice and Reflect on Classroom Inequalities. Journal of Learning Analytics, 11(1), 87-100. https://doi.org/10.18608/jla.2024.8199