Sunday, February 05, 2006

Data Quality Campaign and Data System Goals

Fundamental in designing a longitudinal data system

It is clear that these elements are necessary but not sufficient for a robust longitudinal data system. Listed below are other fundamental issues to address when designing a longitudinal data system:
  • Privacy Protection: One of the critical concepts that should underscore the development of any longitudinal data system is preserving student privacy. An important distinction needs to be made between applying a "unique student identifier" and making "personally identifiable information" available. It is possible to share data that are unique to individual students but that do not allow for the identification of that student.
    • These practices are well understood outside of education. This is probably the easiest barrier to overcome.
  • Data Architecture: Data architecture defines how data are coded, stored, managed, and used. Good data architecture is essential for an effective data system.
    • It would be tempting to simply adopt dictionary standards being developed by the SIF group, NCES, or other standards groups. What this misses is the unique accounability and political organization of each state. One size will not fit all states.
  • Data Warehousing: Policymakers and educators need a data system that not only links student records over time and across databases but also makes it easy for users to query those databases and produce standard or customized reports. A data warehouse is, at the least, a repository of data concerning students in the public education system; ideally, it also would include information about educational facilities and curriculum and staff involved in instructional activities, as well as district and school finances.
    • This is still a relatively new area of work in IT. Many of the turn-key "warehouse" systems are still driven by a compliance reporting mindset that only integrates data for reporting up - not for cross program analysis and improvement.
  • Interoperability: Data interoperability entails the ability of different software systems from different vendors to share information without the need for customized programming or data manipulation by the end use. Interoperability reduces reporting burden, redundancy of data collection, and staff time and resources.
    • SIF is the leading effort in this area. Again, the reduction of burden is very likely the best argument to use for leveraging bureaucratic resistance. The long term payoff will be the ability to study program effectiveness. The increases in student learning, professional development alignment, and feedback to teacher education instiutions will likely be far greater.
  • Portability: Data portability is the ability to exchange student transcript information electronically across districts and between PreK-12 and postsecondary institutions within a state and across states. Portability has at least three advantages: it makes valuable diagnostic information from the academic records of students who move to a new state available to their teachers in a timely manner; it reduces the time and cost of transferring students' high school course transcripts; and it increases the ability of states to distinguish students who transfer to a school in a new state from dropouts.
    • This is a long term goal. Most districts in Wisconsin still have no consistent transcript data online.
  • Professional Development around Data Processes and Use: Building a longitudinal data system requires not only the adoption of key elements but also the ongoing professional development of the people charged with collecting, storing, analyzing and using the data produced through the new data system.
    • This will be the largest area of cost. The resources need to train SEA, school and district staff dwarf the costs of developing the systems.
  • Researcher Access: Research using longitudinal student data can be an invaluable guide for improving schools and helping educators learn what works. These data are essential to determining the value-added of schools, programs and specific interventions.
    • We only now entering an era in which research about what works in schools can be done at scale. This work has the potential to transform education and education research.


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