Why measuring growth matters

Last week’s CSAP results were coupled with the Colorado Growth Model in a way that began to peel the onion back on school and district performance.  However, one major piece was, to my mind, still missing. The growth model does not differentiate between the performances of schools or districts with vastly different percentages of Free and Reduced Lunch (FRL) students.  For me, one of (if not the) primary goals of education reform is to lower and eliminate the achievement gap that persists between low-income students and their affluent peers.  Unless you measure these students compared to their peers, it’s hard to see who you are helping.

As an example, EdNews helpfully provided a list of some of the larger Colorado school districts with both their status and growth scores.  But missing was any sense of which districts serve the most (or least) FRL kids.

Well, here is the regression graph on FRL & status scores by school district (based on the EdNews data):

status1

This is pretty much the same graph that has haunted public education in the US for the last 50 years.

The paradigm is this: As the percentage of FRL students goes up, the percent of proficient students goes down (I took the EdNews total proficiency and divided by the three subjects).  Note that this is a very consistent line –  a few outliers deviate from the trend, but the pattern is stark and clear.  Income, one might say, is destiny (or said another way – districts cannot improve achievement without solving poverty).

But look what happens to the data when you do a regression with FRL and Growth:

growth1

All of a sudden, the data scatters: the pattern between income and growth is deeply inconsistent, and shows some real differences by school district.  In particular, Aurora, Denver and Mapleton are doing significantly better than the averages (well above the line) while a number of districts (who are below the line) do not look nearly as good.

And look at the differences even on the same horizontal or vertical line.  There are roughly five school districts with FRL populations between 60-70%, but their growth scores vary from the high 30s to the mid 50s.  There is something very different going on in Aurora and Denver than in Pueblo and Westminster.

Likewise look at schools with similar growth scores — particularly as the growth scores are clustered around a median of 50%, there were 11 school districts with growth scores between 50 and 55. But look at the difference in FRL numbers even at the similar growth scores: FRL percentages vary from below 10% to over 65%.  How is Denver — with a FRL population of 67% able to get a growth percentile just one point less than districts with FRL percentages of 8 and 9%? Perhaps income is not destiny.

The correlation in the growth graph is much looser (I did not calculate the R2).   Low-income districts (the unweighted average is 37% FRL) are still a minority of the high growth percentile districts, but there are some considerable anomalies.

A few caveats: this quick analysis does not count as statistical research (it was done, in its entirely, in a middle seat in a crowded plane and with a small data set).  Better statisticians than I can crunch the numbers. And particularly with the growth numbers there is the problem of measuring the tallest pygmy — Denver and Aurora (and others) are starting from a very low base, so even this growth (as the Post tellingly reported) essentially dooms generations of kids to a life of functional illiteracy. Plus, there are bound to be even bigger discrepancies at the individual school level within districts (which would be more interesting).

But even if directionally correct, the growth data tells a story that the status data never revealed. There are big differences between the growth numbers of Colorado school districts that cannot be explained by differences in income alone.

Hopefully the second graph is a paradigm slowly starting to shift.

This entry was posted in District Performance, Poverty, Student Achievement. Bookmark the permalink.

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