Yesterday, I described the relationship between low-income and poor performance in English and math in Massachusetts (see the post for methodological details). Well, I’ve saved the worst for last–science education:
Just to remind everyone, the horizontal axis is the percentage of children in a school who qualify for free lunch, and the vertical axis is the percentage of children who, according to their MCAS scores, are either classified as “Need Improvement” or “Warning/Failing” in science.
The R2–how much of the school to school variation is accounted for by variation in school lunch eligibility–is 0.69, which is a stronger relationship than seen with either the English or math scores. Worse, the slope of the line is 0.88, which means that a one percent increase in school lunch eligibility means the expected percentage of poorly performing students in math increases 0.88%, which is also much higher than either English or math.
A note about the difficulty of the science portion of the MCAS. It is more difficult in that the y-intercept of the science education regression is significantly higher (this is how high the line is ‘pushed up’ the graph; if you remember your algebra, this is the “b” in “y = mx + b”). However, this should not be reflected in the steeper increase in poor performance due to low-income. It’s also interesting that the variance around the estimate, unlike either the English or math scores doesn’t increase with increasing poverty. I’m not sure what to make of that though.
Just to remind you, statewide, the top quartile (the 75% highest) is 38.1%, while the lowest (25% ‘highest’) is 7.8%. Thus, the expected tenth grade science poor performance rates are 49.5% and 22.9%, respectively. If we compare the poorest tenth percentile (66.1%) to the wealthiest tenth (3.8%), the expected difference in poor performance rates is even starker: 19.4% versus 74.1%. As I noted yesterday, some schools will perform better, but some also do worse than expected.
Poverty is something that’s never mentioned in improving K-12 science education, yet it’s critical to performance. It seems to me we’re dancing around a key issue. If we want to improve K-12 scientific literacy, the biggest variable is income inequality. The more I look at these data–and the MCAS database has a lot of other data–poverty keeps rearing its head. I realize that correlation does not equal causality (yes, I too can read cookie fortunes), but the signal is so strong and stark that it is overwhelming other demographic factors (English as a second language, for instance, is a rather weak predictor of performance).
Keep in mind these data are from Massachusetts, which not only has high educational performance, but performs better than ‘demographic destiny’ would predict. Yet low-income schools are still getting whacked, especially in science. I suspect that this is partly due to No Child Left Behind along with other educational ‘reforms’ which have emphasized English and math at the expense of science, but that’s just speculation on my part.
But blaming teachers is so much easier. And it allows us to worship the ‘metrics.’ So we’ll probably be pursuing marginal educational ‘reforms’ instead of tackling a huge impediment to science education: poverty.