Improvement Science Meets Neuroscience
Confirmation bias and implicit bias pack a double punch in shaping how we use data in our professional learning communities and classrooms to serve our kids. We can analyze student data as much as we want, but if we are looking at the wrong data or interpreting it via implicit blinders, the science of our data-driven process isn’t going to save us from false assumptions.
In the previous blog post, I wrote an example of how teachers can interpret a moment of student “struggle” in different ways. Now let’s look at the neuroscience behind that story, and the ways our brains can lead us to detours when we observe students with the goal of refining our teaching.
Our Brains Deceive Us
These two types of unconscious bias shape the data we gather about our students, and how we interpret data to take actions in our teaching or leadership.
- Confirmation Bias leads humans to naturally observe, interpret, and remember data that confirms our pre-existing beliefs. The effect of confirmation bias is strongest with deeply-help beliefs, such as political perspectives. (For example, do your family members with different deeply-held political views ever change their minds upon the introduction of new data? Do you?) Applied to education, confirmation bias can make it our default to keep doing what we perceive is the best approach—even when there is evidence that this approach is failing our kids.
- Implicit Bias refers to stereotypes and attitudes that affect our understandings, decisions, and actions in an unconscious way. Read more in my post Get Explicit about Implicit Bias. Implicit racial bias, class bias, language bias, and cultural bias are relevant to the story above, regardless of the good intentions and explicit belief systems of the people involved, and even with many different identities (racial, cultural, linguistic) of the teachers on the team. If we are not intentional in disrupting inherited defaults, they can shape what we see, don’t see, and our interpretations of classroom data to the detriment of our kids.
What Is the Problem to Solve?
In our teaching and leadership, unconscious biases shape the problems we identify to solve, and how we solve them. They shape what we see and don’t see in student data. They shape how we interpret what students say and do in our classrooms, and the conclusions we draw when students struggle.
When we collaborate to improve outcomes with multilingual learners—MLs—our implicit biases about race, class, language, and more shape the way we frame the problem to solve. When data reveals that MLs are “underperforming,” for example, what’s the next step?
Is it always to provide more supports, scaffolds, and specialists? Or is it to ask more questions about what the data tells us and doesn’t tell us? Is it to ask critical questions about how our perceptions, practices, policies, and Tier I teaching under-serve MLs?
It’s hard to see beyond our own biases alone. Collaborating via a culture of courageous learning and shared leadership is important. Collaborating with colleagues and stakeholders who have diverse backgrounds, lived experiences and perspectives is important. Using effective protocols, such as Observation Inquiry, with an equity lens helps teacher teams get clarity about what they see together and make intentional decisions to disrupt inequities and create new possibilities together.
Subtle Shifts Matter
The 4th grade team discussed in my previous post illuminates how a very subtle shift in what a team observes changes how the team interprets the data and plans next steps for instruction. The added insight of one colleague helped the team shift from gathering data about what students did wrong, to gathering data about what they did right and, more specifically, about how students approached the task. This shift means everything in using data to improve equitable outcomes in our schools.
It is the shift from blaming to reframing.
Such a shift is possible when we are aware that our ways of knowing and doing things are not the only way, and even more possible when we collaborate across different roles and perspectives with a shared culture of humble inquiry to seek data and divergent points of view that challenge us to evolve.
- How do confirmation bias and implicit biases impact data analysis in your setting?
- Are your PLC collaboration protocols helping people go deeper than first assumptions? Do they help teams identify student assets, ways of thinking and learning?
- How are you building the capacity of teacher teams to use formative data to co-reflect and adapt teaching?