See how record linkage software helped school districts effect changes in grades, reduce truancy, address disciplinary problems, and significantly increase overall student performance.
Understanding Data-Driven Decision Making
Education does not stop and start at the classroom door. With over 55 million students in grades K-12 this year, how do administrators make sense of what really impacts student achievement?
With information gathered from statewide P-20 longitudinal data systems (SLDS), educators and administrators will soon have these answers. The introduction of the Common Core State Standards (CSSS) has brought educational leaders and policymakers together with the need to implement an integrated and comprehensive system that will drive student performance and achievement.
Big data is driving this eﬀort forward.
Data-Driven Decision Making (DDDM) uses student assessment data and relevant supporting information to make decisions related to planning and implementing strategies at the district, school, classroom, and individual student levels.
Understanding the value of data quality and record linkage tools is fundamental in evaluating a data quality program for schools. With these tools, states can monitor their reforms and make any changes needed.
Data mining and data analytics software can help educators understand various patterns that might predict outcomes in student achievement, as well as identify teachers who are succeeding or need improvement.
Special Needs for the Education Industry
There are numerous benefits to using data tools to help improve performance within the education sector. Using record linkage tools, schools have developed dashboards that allow them to monitor learning, performance, and behavioral issues at both the student and school level.
Several states have already begun implementing these dashboards, including:
Evidence of the benefits of data-driven decision making has also been seen throughout the business world. Research done by several economists at the Massachusetts Institute of Technology studied 179 large companies. Those that adopted a data-driven decision-making process achieved productivity that was 5-6 percent higher than could be explained by other factors.
Data-driven technology enables learning through both predictive and diagnostic assessments. Through analyzing data, educators can not only predict how students will perform on standardized tests but also determine which instructional techniques work best on individual students.
Record linkage tools can bring together disparate data such as:
Student demographic information
Teacher demographic data, including licensing & preparation
Immediate vs. delayed entry into post-secondary education
Data mining techniques can also help identify problem areas for at-risk students. These may include:
Changes in Grades
Changes in Performance
Tale of Two Cities
Understanding Best Practices
In one state with a record linkage program in place, a sample was done evaluating the number of students in one year who attended post-secondary education in a specific city. With the old existing program, the sample found that 22 percent of the 5,344 students in that city had gone on to higher education. After using our record linkage software, that number went up to nearly 41 percent, nearly double the first figure!
The chart in front compares this sample to a city of comparable numbers (5,025 students) with no new program in place.
Average Data Linkage Rate
Data Ladder has seen several examples of programs that had preexisting data quality and fuzzy logic solutions in place but saw significant improvements after implementing our enterprise-grade data matching and cleansing solutions. Our work at the state level exceeds the industry standard; while the normal data linkage rate is 90 percent, our average linkage is at about 97 percent.
Understanding Best Practices
Imagine the effects that improved match accuracy can have when comparing schools in two cities. Just a small improvement can make the difference in everything – from property values to policy-making – in those areas.
It is important for administrators of an SLDS or P-20 program to understand some best practices when evaluating a match accuracy improvement program:
Locate an expert with proven results
Implement a good workflow
Strong reporting tools
Configure a system that can Identify correct settings to be used
Ability to review matches and non-matches quickly and efficiently