Many education leaders find they are working with exponentially more data today than in years back. And with the increase in data comes a higher need for concrete approaches to understand and make effective decisions using that data.
Take for instance a common school goal to raise their PISA scores by 10 per cent. The school leader would likely say they were on track to meet the goal if they were at +7 per cent in growth on practice tests at mid-year. But what if the school chose to look deeper?
Behind every percentage is a whole lot of detail that does not always get its share of consideration. For instance, how did boys perform compared to girls? How did new students do when compared to those who had been in the school for the previous two years? Or how did PISA scores compare to local assessments given by teachers? In other words, did a student earning an A in English fare well in PISA reading section, and the other way around.
Becoming a data lover is a relentless task requiring spreadsheets and numbers galore. But in academics, the payoff is a deeper understanding of student performance and how to change it for the better. Consider this point: when we deeply understand the performance of boys vs. girls, new students vs. veteran students, and the alignment (or lack) with localised grades, we can better determine how to intervene and help each student.
In my own previous work, I created a school level achievement gaps Excel tool that looked at annual assessments and compared student performance by gender, ethnicity, special population (English learner, SEN, economic hardships) to help teachers understand who to help.
The truth is that every student deserves our best so that they can reach excellence. And we can only help students give their level best when we have a robust understanding of data and how to use it to streamline the school’s supports.