Data Analytics

What is the issue and why is it important?

Data analytics is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.

Big data describes data sets so large and complex that they become awkward to work with using standard statistical software. Schools, districts and college and universities collect and store vast amounts of education data about students, instructors, courses, assessments, and facilities . The real value of the data is in analyzing and making use of it, not in gathering and storing it. But few use the information effectively to make informed decisions that could enhance teaching and learning, and improve student success and financial efficiencies. After all, what good is collecting the data if it’s not used effectively?

States need four key elements for good data analysis: hardware, software, people, and data that can be shared among systems. States have made significant progress in hardware and software — essentially, creating data warehouses and longitudinal systems. What lies ahead is ensuring that staff are properly trained to report on and analyze the data. States need to create an environment of data-driven decision-making. They should begin by identifying the questions they need answers to — essentially, a research agenda — and identifying the data elements required to answer those questions. They will need highly competent people along the “data continuum,” who collect high-quality data, manage longitudinal data systems, formulate research and policy questions, thoughtfully and carefully analyze data, communicate the findings to stakeholders, and can act on the results.

The intent of analyzing data is to understand better what works in education and what does not — to identify students’ stumbling blocks and help make corrections in learning paths in order to improve retention and graduation rates. Effective data analysis turns data into meaningful information. It guides educators to give personalized attention based on individual student needs. It enables administrators to develop effective plans for their institutions and to evaluate and develop their instructional staff and curriculum. With state longitudinal data systems, agencies within that state have the ability to combine data from sectors of levels of education to better analyze, for example, how events in high school (like taking a calculus class) affect retention and graduation in college.

Effective data analysis turns data into meaningful information. Done well, it allows educators to develop curricula and tailor instruction to meet individual student needs. It also enables administrators to identify potential weaknesses in the curriculum or instruction and create professional development opportunities for educators to address such weaknesses. Being able to review the results of the data analysis from different sources and points in the education pipeline means administrators and educators can use the information to make long- and short-term decisions to improve their institutions. It reveals what works in low-performing institutions so education leaders can know what is most effective to help them. All of these benefits rely on having well-trained analysts and well-developed data analysis software.

What if SREB states do not made adequate progress on this issue?

Without effective use, data lose much of their potential worth. At the individual student level, failure to identify patterns in data will lead to missed opportunities to create personalized learning programs, or to intervene with at-risk students to ensure they complete high school or a college degree. On a larger scale, it will result in misguided policies that invest precious resources in ineffective programs.

We will be answering these questions:

• What is the status in SREB states?
• What measurements can SREB states use to assess progress?
• What are some next steps SREB states can take?
• Where can I learn more?