Disaggregate participation data intersectionally by race and gender to surface disparities hidden in aggregate analyses¶

Strategy · All strategies
Evidence · no claims cited

Description¶

This strategy operationalizes EQUIP data for equity analysis: record low-inference participation indicators (turns, talk type, talk length, solicitation, evaluation) during whole-class discussion, cross-tabulate them with student demographic categories such as gender and race, and additionally disaggregate by the intersection of categories (race x gender) to surface disparities hidden in aggregate analyses. Statistical tests such as chi-squared identify significant participation differences that can inform equitable discussion practices. The article's finding that aggregate gender analyses showed no differences while intersectional analysis exposed lower Why-level talk by Latin@ males illustrates why the intersectional step matters.

Design Implications¶

Context¶

Requirements¶

• Participation indicators and student demographic categories must be recorded so they can be cross-tabulated and disaggregated.

Constraints¶

• The article demonstrates the strategy in one elementary mathematics classroom over two weeks and frames quantitative analysis as complementing qualitative methodologies.

Target Learners¶

• elementary mathematics students

Target Learning Goals¶

• equitable participation in whole-class discussion

Related Strategies¶

• Empowering Teachers with EQUIP
• Equitable Classroom Observations
• Equity Audits
• Examine literacy screening data for intersectional differences not apparent from simple disaggregation and consider student and school characteristics together when targeting supports

Examples¶

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Key Sources¶

• Shah, N., Reinholz, D., Guzman, L. M., Bradfield, A., Beaudine, W., & Low, J. (2016). EQUIP: Equity Quantified in Participation. Proceedings of PME-NA 38. https://www.pmena.org