-by Pamela Dean, M.Ed.
According to a review of Data Literacy of Educators: Making it Count in Teacher Preparation and Practice in the Harvard Educational Review, data literacy in education is the ability to create actionable instruction based on the collection and examination of student data such as attendance, grades, test scores, behavior, and motivation.
Sounds easy enough: Use the data we have about our students, our classes, and our schools to create effective instruction to improve some aspect of teaching and learning. However, in my experience, there are many nuances that affect how we interpret data. To be effective, we must have accurate baselines and clear ideas about what we want to measure and why.
Grading practices, especially equitable grading practices, are one of the things I have spent a lot of time researching and discussing with colleagues. This stems from an experience that my daughter had when she was in 9th grade. If you had known my daughter as a teenager, all she did was type from sun up until sundown as she wanted to be an author. In two years she wrote over 650,000 words! All ninth graders at her school were required to take a business class where the first quarter was spent on typing. Her diagnostic showed her typing speed as 112 wpm and she ended the quarter with a speed of 120 wpm.
She got an F because she did not show appropriate growth.
We contacted the teacher who told us that the grade was automatically generated by the district using an algorithm. Since she only had 7% growth, it was an F. She looked at the teacher for a minute, then said, “So [John] could type 10 words per minute and now he can type 20 and he got 100% in the course.” The teacher said yes, because [John] had shown 100% growth. My daughter then said, “So I was already typing twice as fast as professionals do and I learned to type even faster. Did you really expect me to be able to type 224 wpm?” The teacher looked at us thoughtfully, then said he was going to contact the district to discuss the issues with their algorithm.
In the end, the district changed the way that it graded that class. Instead of assigning a grade based on a percentage increase, they developed a scoring rubric showing how many wpm it would take to earn each letter grade. This is a far more equitable grading practice especially since the rubric correlated with business standards.
Another area where data interpretation can have a tremendous impact on students and their futures is in special education. I used to teach students with profound special needs. At one training meeting, we were examining data sets to consider student placement in classrooms for higher levels of need. One student’s data appeared to show that the student was violent – hitting, pinching, biting, kicking. The consensus seemed to be that the student needed higher levels of support than her current classroom was able to provide. The next level of placement would have put her on a trajectory to move to a group home for violent young adults once she turned 21.
As this group of educators discussed this student, it became apparent that the student only exhibited these behaviors with one staff member. Further questioning showed that everyone else who worked with the student during classtime sat across the table from her, while the support worker in question sat next to the student. What the data actually showed, then, was that the student felt that this support person was invading her personal space. Since she had no words to express that because of her disability, her only resort was to push the support person away. The support person said she would sit across from the student, the student stayed in her current placement, and her trajectory remained to live in assisted living and attend workshops for adults with special needs. The reexamination and reinterpretation of the data literally changed this student’s adult life.
These may seem like two extreme cases, but it is indicative of the need for true data literacy in education.
How we approach data can tell a lot about our educational philosophy. If we collect data without a clear vision of why and how we should use the data, it can have serious and inequitable impacts on our students’ success in many different areas. But if we commit to become truly data literate, if we use data in equitable ways to find the root of student issues and to promote student achievement, we can gain valuable insights that will help us prepare them for successful futures at school and in life.
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