Becoming a Data Champion – Part 1
February is often regarded as the month of love, and I’m here to share with you some pleasantries about one of my true loves – data. I recognize that for many people their relationship with data could be regarded as “it’s complicated” – they love it when it makes sense and they hate it when it doesn’t. For others, this relationship may be non-existent or even a long-term separation. Although, I do not expect everyone to love data as much as I do, it is important to note that data is used almost everywhere and when used correctly can help you make a real impression. Whatever your relationship status with data is, I’m here to be your matchmaker.
I grew up playing baseball and was an all-around sport fanatic. I used to be regularly exposed to a myriad of data and statistics for my favorite players and teams. Going through my high school and college years, I became very acquainted with the title “student-athlete.” At the time, it seemed like education and athletics were about as far apart on the spectrum as home plate and the outfield wall. The more time I spent increasing my knowledge by hitting the books, the less time I was able to devote to honing my talents for hitting home runs. What I did not realize back then is that these two seemingly opposite worlds shared a very common bond – their collection and use of data.
Fast forward to the present and here I am – a Data Coordinator for a regional education association. When I tell people my job title I tend to get a lot of blank stares and prods for more information. Upon further review (and oftentimes more blank stares), people typically respond with “Well, I’m glad you like doing it, because I definitely couldn’t.” To my dismay, I simply laugh it off, and explain to them that similar to playing a sport, it just takes practice. Over the next few months I will be sharing tips with you about collecting and using data effectively that I have picked up throughout my career as I have traded my score book for a spreadsheet, dugout for a cubicle, and batting average for grade point averages, so that you too can be a data champion.
Choose the right data
One of the fundamental skills for becoming a data champion is choosing the right data to analyze. In baseball, there are tons of data collected during each game – so much so that it has emerged into its own field called Sabermetrics. Essentially, baseball managers are constantly collecting and analyzing data on every pitch and using this data to ensure that their team has the greatest opportunity to win each game (playing the percentages if you will). The important part of playing the percentages is that you are using the right data to make your decisions.
For example, a baseball manager would want to look at data such as a player’s batting average, not the number of errors they make in the field, when determining where they should bat in the batting order. Similar to the baseball analogy, it is important when trying to make decisions using data in education that the data is appropriate. It would be inappropriate to assign a student’s grade in science based on their scores on a spelling test in English. Instead it would be more appropriate to assign the student’s grade based on their score on a test of their knowledge of the periodic table of elements. Similarly, when deciding whether or not a student may need remedial work in calculus, it would be inappropriate to base it on their attendance in physical education.
In my role as Data Coordinator, it is one of my primary responsibilities to determine whether the professional learning opportunities and programs we offer are making an impact or are achieving the results we expect to occur.
I cannot make an accurate assessment of our professional learning and programs if we are not using the right data to analyze them.
If there is only one thing you take home from this article, it is the sentence you just read. Prior to the beginning of any project, program, or intervention, I meet with the content expert in charge to identify the Goals and Questions (S.1.A)* of the project and how the project will be conducted. In order to complete my job, I need to have a solid grasp of the different Types of Data (K.1.C)* available and the Data Context (K.2.D)*. Once they have identified and discussed the goals or expected outcomes for the project, I work with them to make these goals SMART (Specific, Measurable, Attainable/Achievable, Relevant, and Timely).
Specifically, I follow up with questions such as “How would we know if this goal has been achieved? What data would tell us this? What data would tell us if this goal had not been achieved? Is this a realistic goal/objective given the expectations of the project/program?” Their responses to each of these questions help us to pinpoint just what data is needed to make an accurate assessment or evaluation of the program of interest.
- Data use standard within the SLDS Data Use Standards.
School X implemented a supplemental reading program for all students who scored below proficiency in Reading on the state assessment during the fall. It is expected (the goal) that this supplemental reading program will help increase students’ reading scores on the state assessment (type of data to collect).
In the spring, the state assessment in Reading was administered again to all students. An analysis of all of the students’ scores in the spring indicates that the percentage of students meeting proficiency on the state Reading assessment in School X increased non-significantly from 80% to 82%. Due to the lack of a significant increase in students’ reading scores, the school board elected to stop funding and eliminate this supplemental reading program citing “there is just not enough bang for the buck.”
Did the school board for School X make a good decision? No!
As a result of not using the right data, the school board made a poor decision based on a misrepresentation of data. Had they looked more closely into the data they may have realized that the percentage of students who were below proficient in reading during the fall significantly decreased by 30% in the spring as a result of this program. Due to this groups’ small representation in the entire student body, this program only led to an increase in the total percentage of students meeting proficiency in the spring by 2%. Had the school board been more aware of the Data Context (K.2.D.), they may have realized that this program was actually quite successful.
Just like the old saying “Garbage in, garbage out” if you are not using the right data to assess for a specific outcome, then you are liable for an inaccurate analysis of the results which may lead to misinformed decision-making. So before you find yourself making a decision that will be detrimental to your team, be sure to have a meeting with your data coach to talk things over and create a game-plan.