Teaching with incomplete student data is like drawing a picture from memory. Sure, you know your students. You know what they look like, what they act like in class, if they do their homework, etc. But do you know enough detail to draw an accurate portrait of them as a scholar? How much school have they missed so far this year? How did they perform on last year’s state assessment? What is their current reading level? Are these results improving over time? The more vivid a picture that you have about your students, the more prepared you are to effectively teach them and change the trajectory of their life. Remember, great teaching is transformative.
Limited knowledge provides limited student academic profiles. Good teachers commits out-of-school time to gather additional data about their students beyond grades and classroom observation. They may, for instance, explore the available data on the last interim assessment, looking beyond the overall scores. Who demonstrated growth? What were the most common misconceptions? How should I create small group instruction cohorts?
These are terrifically helpful questions to answer. Unfortunately, the students’ academic portrait is still woefully incomplete. What it is missing is context and it is context that brings such data into keen focus. How do these results compare to prior interim assessments or other similar assessments? How do results compare to key achievement indicators such as reading level or attendance rate? This deep dive cannot happen until all of this data is pulled from the various websites and sources, in a timely manner, and centralized in easy to read data visualizations. Is this task the role of the teacher, the school, or the district?
It is truly impressive what skilled and dedicated classroom teachers can accomplish on their own. They have a special connection of trust with their students that allow students to venture outside of their individual comfort zones. However, an effective teacher’s major limitation to professional growth is his ready access to data. This is why I prefer the term “data-enhanced instruction”. One cannot work optimally without the other. You cannot paint a student’s academic portrait with only a limited palette. Data fills in the missing colors and students will suddenly view a portrait of themselves that they have never seen before.