Mastering Data Management

More in Business Process Management

• New ebook: Understanding Big Data
• 37 Definitions of “Employee ID”
• Defining Terms: Agree, or Doom your Project
• Establishing a Valid Business “Need to Know” for Sensitive Data

More in Government

• More Mobile, More Competitive Government
• We Are Our Own Columbos
• The End of the Social Security Number?
• Five Rules of Successful Identity and Social Network Analysis

More in MDM

• Best Practices in Test Data Management: Discovering and Subsetting
• TDWI Chicago Preview: Data Warehousing, Analytics & More
• Who’s Afraid of the Big Bad Data?
• MDM for Chief Privacy Officers: A healthcare provider example

More from Aeastman

• Assessing the Risk: MDM for compliance and risk management
• Not Your Grandmother’s MDM: Introducing MDM Application Toolkit
• What to Look for in a Master Student Index
• From K to 12 and Beyond: Tracking Student Progress

Share this Post

Recent Comments

• ICMCC News Page » Where East Meets West: ehealth in China and the US on Where East Meets West: ehealth in China and the US
• Kim’s Training Podcast on Training as a Safety Net: Kim May
• TDWI Chicago Preview: Data Warehousing, Analytics & More | Mastering Data Management on New ebook: Understanding Big Data

Can you use educational data to predict graduation rates?

Are children that attend Head Start programs more likely to graduate from college? Are students in accelerated learning programs more likely to succeed in the workforce? Are there early warning signs that predict whether a child is likely to drop out before graduating high school?

Questions like these are surprisingly difficult to answer. The data required to answer these questions are spread across multiple systems owned by different organizations. Individual schools, school districts, and postsecondary institutions all have their own data systems. Other challenges contribute to the difficulty of analyzing student progress across a student’s educational career:

• Students don’t always live in the same place for their entire educational career. They move within a school district, across school districts to other areas of the same state, or even across states. Some students move out of state for a time, only to return years later. This means that data for a student will wind up in different systems across a state or even across the country.
• A student’s name and information could differ across systems. Alex Eastman living at 11705 Oak Lane in one system could be Alexander Eastman living at 11705 Oak Ave in another. People use nicknames, names get misspelled, typographical errors occur, and as mentioned, students’ addresses change.

The Educational Technical Assistance Act of 2002 encouraged states to establish longitudinal data systems to address some of these challenges. Longitudinal data systems are intended to manage, analyze, and use data such as student records, to make data-driven decisions to increase student achievement and close achievement gaps.

Many states have started longitudinal data systems by collecting data from Kindergarten through 12^th grade (K-12). Unfortunately, these longitudinal data systems still lack the breadth and depth of data they need to answer questions like those posed above.

Expanding Longitudinal Data Systems

The Race to the Top program announced in 2009 encourages states to expand their use of longitudinal data systems to incorporate additional programs such as early childhood programs, English language learner programs, at-risk and dropout prevention programs, and postsecondary education.

As states look to expand their longitudinal data systems they face additional challenges.

First, states must collaborate with not just public, but also private educational institutions (preschools and universities). This means that states will have less direct control over how these other institutions cooperate to achieve public goals. Solutions that are invasive or require a lot of effort and expense from these third parties will hamper cooperation.

Second, states are facing austere budget constraints so they must solve the problem as much as possible with existing systems and investments.

In the next post, we’ll look at a couple of options for expanding longitudinal data systems with these challenges in mind.