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Data-Driven decision making in education is an essential form of data application. The fourth domain referenced in our white paper, Supercharging District Success, data application can enhance every decision. The best application depends on executing four data-driven routines:
Data-Driven Connections
Data in isolation is too random to inform effective decisions. So, the best way to drive decisions with data is to plan ahead and get the data you need. As Ron Redden, who leads data quality for the Brazosport (Texas) school district, advises us, “Know your questions to be answered ahead of time! Teams should always decide in advance which questions they need to answer. That decision will determine the data subjects, the collection instrument, and the form of the data. Data that is pre-connected by design is always superior to data connected after the fact.
Quantitative Data:
Some applications need precise numerical data. Therefore, leaders who analyze trends, levels, and comparisons should plan to use scales and ratings. Likert scales, ratings from 1-10, and ranking a list of options are all ways to collect reliable data. Other data-driven decisions like educational programming, examining assessment scores, or other academic data can build informative connections.
Dr. Shala Flowers, Assistant Superintendent of School Improvement for A+ Charter Schools, embeds numerical and narrative data for formative and summative assessment. “We have an embedded data-driven instruction process where data is reviewed, and instructional decisions are made based upon data. Formative assessment is an inherent part of our process.”
Qualitative Data:
Some data-driven tasks depend on sentiment and perspective. For those types of topics, narrative data like comments and word selection are more revealing. Often, conversational data methods like focus groups and interviews effectively gather connected data for qualitative purposes.
Data-Driven Monitoring
“We do monthly progress monitoring and data analysis meetings. We then target students who perform below level, and we move students who are performing well to another level.” This description, from principal Steve Guglich, reflects how leaders at Missouri Ridge K-8 School in North Dakota employ two types of data-driven monitoring to support students.
Active Monitoring:
Teams that practice data-driven decisions monitor data at all times. Purposeful monitoring of data sources ensures that problems are identified and corrected early on. If data measures academic performance, then careful monitoring permits classroom adjustments. If data measures organizational performance, monitoring helps leaders set priorities and correct practices.
Ongoing Monitoring:
Continuous monitoring is an important skill for teams and leaders to develop. Data-driven decision making in education does not always depend on a single point of consideration. For some decisions, cumulative data or trends over time are more important than a snapshot at a point in time. Ongoing observation of data provides insights about in-progress developments.
Data-Driven Collaboration
Data sets create infinite interactions. The best way to deal with all that complexity is through collaboration. Teams that help decide which questions to answer are more likely to select and monitor connected data. Examining data as a team creates multiple perspectives and practical synthesis. Instead of depending on one leader as a single point of analysis, collaboration fosters more robust examination.
The other advantage of collaborating in teams is building shared ownership for decisions. People who understand the logic behind a decision become advocates and champions. Creating enthusiasm for shared action is one of the hallmarks of excellent data-driven decision making in education.
Stacy Egar-Converse is Assistant Superintendent for Instruction at Watertown City School District in New York. She describes data-driven collaboration to address academic gaps. “We take the student data and break it down into skills, which then allows us to create instructional materials or activities that help us fill the gaps of the low-performing students.”
Data-Driven Visualization
We use “color-codes, charts, sticky-notes, etc., to make data use as easy and manageable as possible. Data walls with sticky-notes have allowed us to manage ever-changing data with student progress.” – Vicki Wilson, 2020 National Distinguished Principal from Homer Elementary in Oklahoma.
Ms. Wilson exemplifies a best practice for data-driven decision making. For many decisions, pure numerical or textual data is incomplete. Adding a visual to show relative relationships (a word cloud) or levels and trends (a chart) can add resolution to the data-driven process.
Conclusion
For every choice, there is a data-driven application that applies and illuminates the decision. Following the four steps of connecting, monitoring, collaborating, and visualizing will supercharge your data intelligence and lead to better leadership and service.
For more how-to details and insights, join us on March 30th for our free webinar on Supercharging District Success With Data-driven Decision Making, featuring ten data leaders from schools across the country.
