Monitoring What Matters
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STATE legislatures across the country are struggling with how best to "monitor what matters." Although federal guidelines to help standardize the collection of data on school dropouts came out several years ago with the goal of improving the consistency in how districts and schools gather numbers, the guidelines did nothing to help establish the information systems necessary to simplify the process and ensure more accurate numbers. Now, with nearly every state requiring districts to produce "report cards," policy makers are realizing that the data being reported leave much to be desired. For example, when a student leaves a high school, the next school he or she attends may or may not request a transfer of the student's records. But the student might not attend another school. Or the student might prefer to "start fresh" and attempt to prevent contact with the previous school, perhaps by using a middle name. And counselors at the first school don't usually have the time to determine where the student enrolled next or whether he or she enrolled anywhere. In some large schools, it's even possible for students to drop out and be gone for several months before the record keeping jells enough for someone in the office to discover that he or she has not been attending. Addressing this problem requires a way to make it easy to learn whether a student has enrolled elsewhere - an identification number, for example, that can be easily tracked across districts. Few states have the infrastructure to support this type of data collection. Moreover, a number of legislative proposals would ban the use of identification numbers, such as social security numbers, from being used to track student data. Georgia navigated this politically touchy area by passing a state law that permits student data to be tracked as long as doing so doesn't associate that data with individual students and maintains student and family privacy. The Georgia law stipulates that student identification numbers must be encoded to prevent unauthorized use. Statewide Models Ohio is one of several states that have taken the lead in tracking student-level data. In the same omnibus bill that established a strong accountability system, the state legislature created the Education Information Management System, an infrastructure to house the type of data necessary to determine how well schools and districts are doing. In 1999, the Massachusetts legislature required each school district to adopt and maintain a reliable data collection system. Part of the system requires each district and charter school to have a "unique, permanent, and unduplicated ID" for each student. The Texas legislature established a similar system in 1995. For the accountability report cards published in Texas, data are disaggregated by student groups: African American, Hispanic, white, and economically disadvantaged. The Outside Perspective In another aspect of using data to "monitor what matters," the New Ohio Institute published a report this spring titled "Smart Schools: Does Ohio Put Its Money Where It Matters?" (www.newohio.org). In it, the New Ohio Institute concludes that, while the state had set higher student achievement as the primary goal of elementary and secondary education, it had failed to align its resources fully behind that goal. The Institute points to a lack of strategic planning, charging that "the state spent $4.8 billion a year on public education in programs that were largely devoid of a structure that would make the attainment of high academic achievement a priority." Setting aside such noninstructional activities as school nutrition services and pupil transportation, the Institute examined such instructionally targeted programs as gifted education, teacher recruitment, special education, proficiency testing, public preschool, school improvement models, vocational education, and reading improvement initiatives. …
[1] J. LoGerfo. Explorations in Quality Assessment and Monitoring. Volume I: The Definitions of Quality and Approaches to its Assessment , 1981 .
[2] C. Sherbourne,et al. The MOS 36-Item Short-Form Health Survey (SF-36) , 1992 .
[3] A. Hutchinson,et al. Research methods used in developing and applying quality indicators in primary care , 2002, BMJ : British Medical Journal.