Becoming a Data Champion – Part 2
March is here, and you know what that means? If you’re thinking Spring Break, you’re probably not alone. However, when I think of March I think of Spring Training! In March, Major League Baseball players flock to the southern states to practice for their upcoming season. Spring training in baseball is much like the few weeks before school starts – a time where players (teachers) are able to develop new skills, meet new players (teachers), and for some to get used to a new team (school/district). Let’s take some time so you can get acclimated to your team’s situation in regard to its use of data.
Similar to when you are planning a trip to an exotic location, it helps to familiarize yourself with the lay of the land and the culture of the people – data is no different. Using data effectively is dependent upon the type of data culture that has been established in your workplace. If you and your coworkers take time to collect, analyze, discuss, and act upon the data that has been collected on your students (or clients), then odds are you are in an environment where data is emphasized and embraced. Others may be in environments where the collection and analysis of data is not a priority when making decisions which may hinder their practice and ultimately the success of their team or organization. One factor that may play into workplace culture related to using data is a common language.
Becoming a Data Champion Tip #2.
Ensure there is a common language regarding data
In education, there are tons of acronyms and jargon especially related to assessments and data. Terms such as percentile, standard deviations, scale scores, and percentage growth may be confusing or have different definitions in different contexts or situations. The absence of a common data language could lead to misunderstandings and inaccurate decision-making at the classroom or system level. To put this to an example, you would probably be confused if you ordered a margarita by the pool and your server came back with a piña colada. The same thing can occur in the world of data if you asked for your school’s average GPA (Grade Point Average), but instead received your school’s Graduation Percentage Annual.
When data is being collected and analyzed, it is important that all of those involved are speaking the same language and that there is a shared understanding regarding the data. Bringing this back to an earlier example, baseball is an international game played throughout the world, and thus the language barrier can become an issue for coaches of players coming from different racial/ethnic backgrounds. As a result of this (and preventing one team from stealing another team’s game plan) coaches implement a set of common signals in order to guide their players throughout the game. As a coach, you would not want to give a sign for a batter to take a pitch, but have the baserunner on third think that sign means to steal home. Similar to the baseball example, you would not want to have one teacher assign an “A” for 75% or above and another teacher assign an “A” for 90% or above.
Ensuring that there is a common language and understanding regarding the Data Meaning (S.1.D) is integral to using data effectively. For example, School A in a district may classify absences as missing an entire day of school, whereas School B may classify absences as missing at least half of the school day. When analyzing student attendance rates across the schools in the district, the superintendent may interpret the higher absentee rates in School B as a need for extending the length of the school year or school day, when the absences are actually due to tardiness or an ineffective transportation schedule.
Along with ensuring there is a common language regarding data, it is also important to ensure that there is consistency in the collection of data. If teachers are expected to report all negative behavior interactions of students to the office, but a teacher only reports incidents involving physical altercations between students, the administrators may be unaware of a more problematic issue of bullying in the school due to inconsistent reporting of bullying incidents.
As the old saying goes “A team is only as strong as its weakest link.” If there are individuals slacking in the consistent collection of data, the data your team collects may suffer. Take a page out of baseball’s playbook and schedule some time (a spring training if you will) before and periodically throughout the year to ensure that everyone is up to speed on what data is being collected, how, and for what reason. At the end of the year you can celebrate because you know your team has what it takes to be data champions. And remember – always data responsibly.