Initially focused on reducing gender bias in STEM education, we quickly noticed a common trend in our research.
It is impossible to talk about bias math classes without talking about implicit bias in teaching overall.
So, we dug into the evidence.
Implicit bias impacts teacher-student relationships at multiple levels in the classroom. Research has shown that girls are graded more harshly than boys in math classes, black students are often held to lower expectations than their white peers, and students who are a different race from their teachers are more likely to be perceived as difficult or disruptive. This has ripple effects beyond the classroom, adversely impacting academic and labor market outcomes in the long-term.
We then talked to teachers.
Based on early survey and interview responses, we learned a few key insights:
1. Teachers believe implicit bias can influence instruction
2. Teachers don't feel like they have the tools to understand and combat implicit biases
3. Existing technology is either too expensive, too difficult to integrate, or overloaded with data and lacking in direction
That's where we come in.
Our hypothesis is that we can help teachers create equitable classrooms by streamlining student data they already collect, analyzing trends, and providing actionable steps to help reduce bias in instruction.
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