<span style="font-weight: 400; font-family: montserrat_lightregular; font-size: 16px;">Start the data-driven decision-making process by asking yourself these questions. </span>
<span style="font-weight: 400;">What Question(s) Are You Trying to Answer?</span>
<p><span style="font-weight: 400; font-family: montserrat_lightregular; font-size: 16px;">The first step to using data to guide decision-making is to identify what questions are going to be answered by collecting data. This allows educators to be intentional about what type of data to collect. </span></p>
<p><span style="font-weight: 400; font-family: montserrat_lightregular; font-size: 16px;">Data is more than just standardized test scores. Educators can collect data on student attendance, scores on formative assessments, and even qualitative observations. Why not encourage your students to collect their own data? This will help them see value in data collection and creates greater transparency. It’s also an opportunity for them to set goals, understand where they are currently in relation to the goal, and take immediate action toward making significant gains. </span></p>
<span style="font-weight: 400;">What Is Good Data? </span>
<span style="font-weight: 400; font-family: montserrat_lightregular; font-size: 16px;">Good, or high-quality, data is data that people can use, make sense of, and leverage to make changes in their environment. It is information that is collected and stored safely. This goes back to the first point above — what question are you trying to answer? Decide what to measure and how to measure it. If the data is useless or provides extraneous information, it’s no good to the decision-maker. When collecting data, ask yourself, “Does this data provide an answer toward the overarching question?” Make it as clean and easy to understand as possible. </span>
<span style="font-weight: 400;">What Happens to the Data After It’s Collected? </span>
<p><span style="font-weight: 400; font-family: montserrat_lightregular; font-size: 16px;">Use it! Analyzing and interpreting certain data is a job unto itself. A fun activity for students may be to try displaying the data in different ways (e.g., scatter plot, pie chart, bar graph, etc.). Not only is this a way for them to practice math skills, but they also learn which figures make sense for the type of data collected (and for what audience). </span></p>
<p><span style="font-weight: 400; font-family: montserrat_lightregular; font-size: 16px;">Remember that data can never prove a hypothesis (or a hunch). Rather, you can only fail to reject the hypothesis. No matter how much data you collect, chance could always interfere with your results. However, you can still use the data collected to drive your next steps. </span></p>
<p><span style="font-weight: 400; font-family: montserrat_lightregular; font-size: 16px;">As you continue to practice data collection and data-driven decision-making, take time to reflect on the process itself. </span></p>
