Can an algorithm ID high-school dropouts in first grade?

Early warning systems to detect high-school dropouts are all the rage in education data circles. See this post on a new early warning system in Wisconsin. Like the Wisconsin example, most data systems focus on identifying middle-school students. But what if researchers could use grades, attendance and behavior data to identify at-risk students as soon as possible — as early as first grade? That would really give counselors more time to try to motivate these kids and keep them in school!

Thomas C. “Chris” West at the Montgomery Country Public School district is probably the first person in the country to build a first-grade early warning system. He presented it on Friday, July 19, 2013 at the STATS-DC 2013 Data Conference in Washington, D.C., sponsored by the National Center for Education Statistics. Montgomery County is a great place for data geeks. It’s a wealthy suburb of Washington, D.C. that’s been keeping excellent data records for more than a decade. And that’s what lured West, who worked with Johns Hopkins’s Robert Balfanz and other trailblazers in the field of detecting dropouts, to mine the data there.

West studied the county’s senior class of 2011, in which 833 (or 7.4 percent) of the 11,241 students dropped out of high school. When he traced these students back to their first-grade report cards and attendance records, he found that 75 percent of the dropouts could be identified at the the tender age of six or so. In other words, three-quarters of the students who would eventually drop out showed warning signs, such as missing school for more than three days each quarter or performing below grade-level in math or reading.”It’s depressing to hear, but it’s also an opportunity to work with these students,” said West.

(Interesting aside: A little less than one fourth of Montgomery County students qualify for free or reduced-price lunch. But less than 40 percent of the county’s dropouts come from this bottom quartile. Over 60 percent of the dropouts aren’t poor).

West found that the most important marker was academic performance. Behavior issues and attendance were less important, partly because Montgomery Country rarely uses recorded punishments, such as suspensions, and partly because first-graders don’t play hooky. “The message in Montgomery County is that the kids are there in school, but they’re not doing well,” said West.

The big problem with West’s model is that it not only  identifies eventual dropouts, but it overidentifies almost half the students in the first grade as being at risk for dropping out. He identifies 48.6 percent of the student body to find the 7.4 percent that will drop out. But teachers and counselors have no idea which of the 48.6 percent to focus on. Some of them might have had some medical issues that kept them out of school. Others might have been slower to learn to read. They don’t all need the same kind of interventions.

West also found that as these first-graders progressed through their education, they would go in and out of the warning zone. For example, 20 percent of the first-graders who had a dropout indicator no longer did in sixth grade. And 14 percent of first-graders who didn’t have an indicator later developed an indicator by sixth grade. Only a quarter of the first-grade class had a warning indicator in both grades.

This first-grade dropout model is still a data-crunching experiment. Montgomery County has not implemented this model for identifying which current first-graders are at risk of dropping out.


POSTED BY Jill Barshay ON July 23, 2013

Comments & Trackbacks (5) | Post a Comment

ceolaf

But what is the false positive rate for this detection method.

I have a method that would detect all of the drop outs, but it identifies 12.5 students incorrectly for every one it gets right.

Jill Barshay

@ceolaf — the false positive rate is very high. the detection method identifies 48.6 percent of all first graders, but only 7.4 percent of first-graders eventually dropped out. So 40.8 percent of the first grade class is a false positive. Huge! It identifies 5.5 students incorrectly for every one it gets right. (Hope I did my math correctly here: 40.8%/7.4%)

Dr. Jackie

To what end is this data used? Is it helpful to prevent high school dropout? Why do you want to identify children who have drop-out indicators? Would it not be more productive to provide a variety of educational opportunities that will enhance outcomes of every student? The same funds for this data, could actually be used to enhance the curriculum, and develop it to reflect truths from a real human rights perspective.

[...] Can an algorithm ID high school drop outs in first grade? Hechinger: Early warning systems to detect high-school drop outs are all the rage in education data circles. See this post on a new early warning system in Wisconsin. Like the Wisconsin example, most data systems focus on identifying middle school students. But what if researchers could use grades, attendance and behavior data to identify at-risk students as soon as possible — as early as first grade? That would really give counselors more time to try to motivate these kids and keep them in school! [...]

carol

thanks

Your email is never published nor shared.

Required
Required