Idea and Concept by Katie Patton
Similar to engaging with a new piece of technology, coaches engage with data in a variety of ways. Which type of data person are you when it comes to engaging with your coaching data? Do you spend hours reviewing each piece of data? Or is your focus on asking “why” repeatedly? While there isn’t a wrong way to look at data, it is important to recognize your style and if there are other ways to engage with your coaching data. Let’s look at six different data engagement styles:
A data collector can never get enough. Once you have one set of data you start gathering other data points to find correlations, themes, and trends. If a teacher asks you to help them sort their student’s reading mid-year assessment data on fluency, you are quick to ask the teacher for comprehension scores, phonics data, qualitative data on informal reading evaluations, and anything else that you can get your hands on to help the teachers find connections between student fluency and other aspects of the essential components of reading. A collector tends to have multiple spreadsheets open at once.
A data sender likes to involve others. You find value in sharing key and relevant data pieces out to team members, leadership, and other key stakeholders. When you notice a trend across your school’s math data, you are quick to highlight this trend and share it out with team members to either celebrate or look for strategies to support growth. A sender is someone who finds value in including others and collaborating with teammates to better understand key data points.
A data reader spends a lot of time looking at each individual data piece. You spend more time than most dissecting the data in front of you and you don’t move on, share out, or bring others into the conversation until you feel confident in your understanding of the data you are reading. If you are a data reader, you prefer to get your hands on data prior to meeting with a team. It is important to you that you can evaluate each number in front of you before having a conversation or beginning to plan out action steps. A reader internalizes data prior to having a discussion.
A data learner asks all of the questions. You scan the data and begin asking “why” and “what” questions. There is value in asking questions and learning from those who were working directly with the students, teachers, or team involved in collecting the data. Whether it is an end of year exam or unit pre-assessment, you want to know what barriers students were facing, why students performed the way they did, what the purpose of the assessment was, what the expected outcomes were… and so on. It is important to you that you ask questions and learn from others to help you interpret the data in front of you.
A data responder is action oriented. Your focus immediately turns to strategizing and prioritizing best uses for data outcomes. It is important to you that data is collected with purpose and that action comes from the data. Teachers and teammates often come to you with sets of data when they are struggling to come up with their next steps or when they need help prioritizing student needs. A piece of data is a beginning point to each action plan that you create. A responder likes graphic organizers and plugging in ideas, strategies, and actions into their organizer while looking at sets of data.
A data researcher looks for deeper understanding. You explore a variety of outlets to leverage data to the next level of success. It is important to you to do research beyond receiving and interpreting a set of data. Researchers can often be found reading books, inquiring with online communities (Google Groups, Social Media groups), scouring social media and online education articles, observing classrooms, and asking questions of stakeholders. Through multiple perspectives and learning about new concepts and ideas, researchers can find new interpretations of data. When a researcher shares out data they reference others in their findings and strategies.
Do any of these resonate with you? Do you gravitate toward one style or do you dabble in a few areas? Is there a new data approach that you are willing to try the next time you sit down with your coaching data?