02 January 2016

Counting on You

Even though the winter season is less than two weeks old, I view the return to work following winter break as the "spring" semester. It feels more hopeful and serves as a reminder that summer is coming...someday...even if the view outside my window suggests otherwise. I am officially done with my admin credential program, which means that when I return to work on Monday, I can "just" do my job.

I have another blog, which focuses on data use in the educational setting. And recently, I've been wrestling over there with how to accurately represent and communicate about populations of students. And while readers here might not care so much about the nuts and bolts of some of this work, I do think you're perfectly poised to comment on some of the ethics and expectations associated with it. So, let me share a little background...some ideas...and then see what you can add.

I work in a district that has mostly white students from middle-class backgrounds. When I represent the achievement gap at a grade level, it looks something like this:

In some ways, this doesn't look all that different from what you might expect. White or Asian female students who do not receive special services or participate in the federal free or reduced lunch program perform better than the district average on the state assessment. (More information on this chart is over here, if you're so inclined.)

But I want to talk about the second line of data (representing race) and talk about those groups for a moment. I can't show you the actual numbers of students in each category without violating FERPA rules around student privacy. At a grade level, for example, we might only have two black students. If one meets the standards on the assessment and the other doesn't...it looks bleak. Ditto if they both don't meet the standards. However, if both of them do, they show up at 100% on the graphic above and this may also result in some ennui.

This is where I struggle with how best to represent the data. Yes, we need to consider each and every child. Every student as an individual is important and worthy of our attention and support as educators. However, small population sizes difficult to interpret for adults...and somewhat unfair to students.

For example, let's say that one of our high schools has six Native American students. When do we become concerned about disproportionality? Is it fair to assume that a "proportional" amount of those six students will take every AP course and participate in every sport or activity? Probably not, especially since those six students are spread out across four grade levels. But it is also not okay if they not represented at all. So what would make sense? Show a rolling average across 3 to 5 years?

Recently, I tried something else.

This shows student performance on the most recent state assessment in English Language Arts for one grade at one school. Every student's score is represented by a circle on the chart (n = 69). Grey circles are for scores from white students (n = 54)...pink circles are for scores by students of color (n = 15). Ordinarily, I stay away from lumping various racial groups into one category. I have really struggled with the decision here. However, instead of trying to navigate the discussion about one Native American student, what we see here is that one-third of our students of color scored in the lowest category of performance...and overall, two-thirds of white students met the standards compared to only one-third of students of color. It's a pattern that's more difficult to ignore for adults, but I still want to keep individual students in mind. (More information on the graph above is here.)

So what do you think? When there are ultra-small numbers of students in a given population, how do we accurately represent them while having enough integrity with our data to feel confident in decisions we make from them? How do we best serve our students in this area? I'm counting on you for some new ideas.

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