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This data story was last update in December 2013. This story is scheduled for update in 2021. Unfortunately, the visuals are not interactive until the update. Thank you for your patience.
Which schools have high numbers of students at risk of involvement with the juvenile justice system, and how can data help these schools and others manage the risks?
A profile of at risk youth in schools can help us help them.
The DataHUB allows policymakers and program planners to access data gathered from across state agencies in order to target scarce public resources. Data at the item level are linked behind firewalls, protecting confidentiality. Individual records are then aggregated according to various characteristics, such as poverty or academic achievement, to help users see the larger landscape of children's well-being and conditions.
We know that school dropouts can become the responsibility of DCYF, eventually. Our goal is to design an engaging interface that helps agencies and programs interact with large amounts of linked data in order to identify and respond to various risk factors early on in a child's life to reduce the likelihood of DCYF involvement.
Robert Balfanz, Ph.D., a leading dropout-prevention researcher at Johns Hopkins University, has identified 4 school-related early risk factors for dropping out:
Using the data of students who have enrolled in DCYF schools, we created a profile of students who are not yet involved with DCYF schools but who we believe are at risk. Our factors include the Balfanz indicators as well as several others. They are:
Using our risk factors, this chart compares the 737 students whom we know enrolled in DCYF schools in 2008-2009 (based on RIDE enrollment data), to those who did not.
By changing the indicator on the bar chart, we can see that 73% of the DCYF enrollees were chronically absent in the year prior to their enrollment in a DCYF school, compared with 21% of the non-enrollees.
Similarly, 28% of the DCYF enrollees attended 3 or more schools within a single school year (excessively mobile), compared with just under 2% of the non-enrollees.The two groups were found to be significantly different (p<.01) for each of the risk factors.
Now, we review risk-factor prevalence among students at individual high schools. This chart is sorted by the percentage of students in each high school whom we have labeled "at risk."
Note that the map shows us that the schools with high levels of at-risk kids are concentrated in the urban districts.
If we change the vertical axis to consider prior-year chronic absenteeism, we find that some schools need to consider why such a high percentage of their students are not coming to school. Chronic absenteeism puts a student at high risk for dropping out and DCYF-schools involvement.
This chart shows the same chronic-absenteeism data as we saw on the previous chart. But on this scatterplot, the horizontal axis sorts schools by reading proficiency. The colors, blue to brown, indicate the percentage of students at-risk from low risk (blue) to high risk (brown).
If we change the vertical axis to our Maternal Health Risks indicator (data provided by RIDOH), we see that students living in neighborhoods with high concentrations of maternal risk factors also tend to go to high-poverty schools.
Middle schools students have fewer risk factors in general, but the risks rise steeply in schools that have high concentrations of students in poverty.
If we change the vertical axis to % Ever Retained in Grade, we see a tall bar at the right representing UCAP, a special program specifically for students who are at least 1 or more grades below where they should be.
Higher rates of chronically-absent students (and therefore higher rates of at-risk students) are associated with lower levels of reading proficiency in Middle School students.
If we change the vertical axis to our Disaffection Indicator, (# students per 100 suspended for disaffection - skipping class, insubordination, disrespect, among others), we see schools that could develop more welcoming school climates. Research shows that suspensions tend to further alienate students from school and do not have the effect of teaching necessary social emotional skills. Disengagement from school is another Balfanz indicator of a future drop-out.
With additional item-level data, the DataHUB can help agencies and programs target public resources more strategically and effectively.
They could, for example: