Data-Driven Decision Making

Determining purpose

Determining the purpose requires educators to identify evaluation questions that can lead to meaningful change. These questions might relate to systems, curricula, instruction, or individual students.

Collecting data

Educators must then identify existing data sources or collect data to answer their driving questions. Many common data sources are readily available to educators, including graduation and dropout data, attendance, state assessment scores, progress reports, transcripts, and discipline data. Less common sources of data might include post–school outcomes data, student reflective writing samples, and parent feedback.

The data collection process includes discerning which data will be most useful for answering the driving questions. Preferably, multiple sources of data are used for decision making. This helps ensure that incorrect decisions aren’t made. For instance, one poor test grade may mean that a student was tired rather than meaning that the student needs intervention to master the content. Conversely, a poor test grade, incomplete assignments, and a rating of emerging on a performance-based assessment might indicate that an intervention is needed.

It’s important to recognize that educators don’t always have complete or accurate data. For instance, when collecting post–school outcomes data, it is sometimes difficult to reach all students. This can distort the data and limit interpretation. Educators may need to strategize ways to obtain accurate and reliable data.

Analyzing data

Once collected, raw data still have to be organized in a way that yields useful information. Information doesn’t become actionable knowledge until individuals or teams synthesize the data, apply judgments to prioritize it, and consider the relative merits of possible actions. Relaying information to the people who need it, in a format that they can use it, is critical. Visual displays can guide data interpretation. Graphics that present data over time can identify trends or patterns and assist individuals in making connections between various data. For instance, looking at displays of least restrictive environment data next to academic achievement data can illustrate how inclusion relates to learning for students with and without disabilities.

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