The No Child Left Behind program instituted by the Federal Department of Education is a fine example of data quality challenges. The intent is admirable: to make sure that all children in America receive a basic education. The challenges come into play with implementation and execution. In my opinion, the biggest challenges revolve around data quality and management! Here is a closer look at the program from a data perspective.
Some states have a history of being progressive with their own standards and accountability. These state education agencies and school districts have already been through the rough learning curve of collecting and validating data and thus were in a much better position to make decisions to implement No Child Left Behind program. However, for many school districts, the challenges of data quality are just beginning to emerge.
A fundamental principle to ensure high data quality is to include validation checks at the point of entry. Make corrections at the original source when possible. Much of the data is entered into student software packages by people who are not data professionals but a combination of administrative office staff and professional educators. What type of computer and, specifically, data training is provided to these people? Sometimes data is collected from bubble sheets completed by students themselves. Some districts have the financial and computer resources to pre-print labels with data known to the district to minimize errors.
Even if data is entered accurately and collected properly, there needs to be a clear consistent set of standards that all schools must follow. No Child Left Behind allows for a great deal of flexibility where the individual states set the definition and criteria for key data points. While this is intended to assist each state to implement an accountability system that will best serve students, it can cause serious data problems. For example, the definition of full-year academic year, racial/ethnic groups to be tracked and the minimum group size to measure sub-group performance are set by each state. Then, within each state, individual school districts interpret the definitions and guidelines. This makes it difficult to compare results between different states and draw meaningful conclusions.
To perform reliable data quality checks, the state education agencies must collect and manage individual student-level data. To protect student privacy, some states have developed a statewide student identifier. This ID is attached to all of the student data, but specific attributes that would allow identification of that individual child (name, address or social security number) are not captured at the state level. This provides the foundation of an environment to begin to identify data quality issues. Student-level data supports the ability to identify ever-changing populations of students, especially in urban and suburban schools. Checking statistics to determine the percentage of students who were evaluated requires that testing data and up-to-date enrollment data are used together.
Often, initiatives to improve data quality are only funded when serious problems have surfaced. If we have a hard time convincing large systems-savvy enterprises to devote resources to improving data quality, imagine the challenges facing the education system.
Additionally, there are significant challenges in working toward enterprise integration when all of the employees ultimately report to the president/CEO - consider the magnitude of the problem for this program. At the heart of the American education system is local control - each district elects its own school board and hires staff. Additionally, more than half of funds used for education are provided from local sources, not from the state or federal governments.
We may have a false sense of security about how our schools are performing because, in fact, the No Child Left Behind results may be more of a measure of how well a school district is able to understand, manage and report data than school performance. There is a need for reliable and consistent data in an environment to support reporting and analysis. Education professionals and researchers could have access to data that will allow them to dig down to understand patterns and anomalies. Then, strategies and plans could be developed to address specific problems that have been identified.
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