With student information systems, learning management systems and data warehouses, education officials can deliver personalized education, improve school efficiency and promote innovation.
Data-driven education can improve student and school performance as well as reduce educational disparities between rich and poor, according to a new report. “Building a Data-Driven Education System in the United States,” produced by the technology think tank Center for Data Innovation, discusses how a data-driven system can help educators meet today’s goals:
- Personalization – tailoring lesson plans, educational materials and assessments to each student.
- Evidence-based learning – basing classroom and school management decisions on data.
- School efficiency – studying the relationships among student achievement, teacher performance and administrative decisions to more effectively allocate resources and increase transparency.
- Continuous innovation – using data to create new products and services that improve the education system.
Achieving these goals requires three building blocks: student information systems, learning management systems and data warehouses, according to the report’s author, Joshua New.
“Though many schools already utilize at least some of these technologies, they do so in only rudimentary capacities, such as to simply store data more easily,” New said. “Equally importantly, many of these systems are siloed and not linked to national data analytics systems.”
New defines these building blocks as follows:
Student information systems. Digital tools can collect, store, analyze and report comprehensive student records in a structured format. Information stored in these systems includes attendance, grades, disciplinary actions, extracurricular activities and health records. Today these tools are used to simplify or automate routine administrative procedures.
New technologies, however, can allow for a wider range of data collection. Radio frequency identification chips in student identification cards can record attendance, monitor when and where students board and get off school buses and keep track of students in the event of an emergency, for example. Advanced testing software can assess noncognitive skill development, and predictive analytics systems can flag students at risk of failing or dropping out based on factors such as declining performance and regular absenteeism.
Learning management systems. These systems help educators improve their instructional content and analyze student performance. LMS systems include educational software, online educational content and digital assessments. LMS assessment software can be used for curriculum development by establishing an incoming class’s baseline knowledge at the beginning of the school year. Assessment tools can also capture data in real time as students take a test, more accurately determining a student’s ability than traditional testing and generating specific action learning plans for that student. Adaptive learning technology, when capturing real-time information, can adjust content based on a student’s answers.
Online homework applications can track how long students spend on different assignments, the information from which can be used to determine if particular students are struggling or if the class needs additional review of certain content. Adaptive learning technology can adjust how content is delivered based on its analysis of student performance.
Data warehouses. Aggregating multiple data sources allows policymakers, administrators and researchers to link and analyze data across the education system. The most comprehensive of these in use today are the Statewide Longitudinal Data Systems, which track a student’s educational record from earliest entry into the school system through high school to the workforce. This information, known as P-20w data, can, for example, link pre-K education to starting salary, analyze the effectiveness of a particular textbook and curriculum on standardized testing averages or monitor the effectiveness of after school programs. Nationally tracking and linking this information allows researchers to perform large scale analysis and more easily exchange student data when students move between schools and states.
To build a data-driven education system, policymakers should:
- Establish best practices for using, sharing and storing data.
- Require data system interoperability among all stakeholders.
- Allow students and parents to easily access and export their data
- Provide educators with tools, training and incentives to use the educational data.
- Launch pilot programs to demonstrate data’s potential.
- Use the data to promote educational equality.
A data-driven education system would require large-scale data collection, sharing and analysis, the report said, but “failure to transform the U.S. education system by leveraging data will have considerable consequences not just for individual students and taxpayers, but for U.S. productivity growth and competitiveness.”
Read the full report here.