When IBM PR folks sent me their press release on their recent efforts with the Mobile County Public Schools in Alabama, I was moderately impressed. They were harnessing a lot of data for educators but it hardly seemed revolutionary in this era of data-driven instruction.
Then I spoke with Mobile County’s Executive Manager of IT services, David Akridge, and realized that not only was this the culmination of 4 years of intensive work, but it would provide the district with a “Business Intelligence” approach to improving instruction and helping at-risk kids.
BI is hardly a new buzzword. SAS has been using the term business intelligence to sell their wares for quite some time and corporations rely on data analysis at many levels to determine risk, make decisions, and modify practices. No Child Left Behind, despite its many problems, has driven schools to begin using data to modify instructional practices, as well, but the rigorous business approach to data has largely not been applied to schools.
According to the IBM release,
IBM (NYSE:IBM) announced today that Mobile County (Alabama) Public Schools has selected IBM analytics technology to more effectively measure student performance, immediately identify students “at risk”, and adjust academic programs in real-time in order to best deliver smarter education services that prepare students with 21st century skills.
The real beauty of the system, as Mr. Akridge explained, is that the district, which serves almost 64,000 students and employs about 4500 staff, can program business rules that automatically flag students when they reach specific thresholds for interventions. With a dropout rate approaching 48%, the schools are finally able to analyze data from a variety of disparate sources longitudinally in a single data warehouse.
Although the district previously had a student information system, performance monitoring systems, human resource systems, etc., administrators had never had the means to look at the ways in which all of these data interact. Similarly, teachers had not been able to access the wealth of data directly; now, the primary focus of the data usage model is with classroom teachers who can access data easily and from anywhere through a web-based dashboard.
More importantly, kids who might ordinarily have slipped through the cracks can now receive early intervention from guidance counselors and administrators. Imagine a student who didn’t make trouble in class, received only slightly below-average grades, and basically “slid by.” What if that student started missing more school? Began missing more assignments? In a large district (and even in smaller schools), it would be all too easy for that student to just disappear and become another dropout.
However, business rules have already been established to look at aggregated data and identify increasing absences, changes in grades, etc., and automatically flag the students for counselor followup. This is only a very simple example of extensive business rules that have been built into the system. These same business rules have allowed the school system to partner with the Mobile County District Attorney’s office to identify and intervene with at-risk students.
Teachers, as well, can now be identified who would benefit from professional development or lateral moves into areas of greater expertise. While Mr. Akridge was careful to note that the system would not be used punitively for any teachers whose classes may have lower performance, he also noted that it was a real opportunity for the district to help teachers improve.
This is what NCLB was supposed to be. The technology is here to collect extraordinary amounts of data on our kids. It’s not meant to be Orwellian; rather, it’s meant to help us improve their performance and give heterogeneous populations as many chances to succeed as possible (and identify struggling subgroups and individuals). Other schools, districts, and states are undertaking similar efforts and I applaud the move to bring BI and near-real-time data analysis to schools where it really can make a difference.