By Jill Barshay, founding editor and writer of Education By the Numbers
DataBlogPikSmallThe fetishization of data has hit both education and journalism. And that’s why I’m starting this datablog. My aims are many. I plan to list and summarize which data sets and studies are available on certain education topics as a resource for journalists and other lay people. I’d like to write about interesting people who are crunching education data. And I will write about new data studies or stories about the use of data. At times, I will try my own hand at some data analysis and graphs.
Most of all, my aim is to become educated enough about education data to develop ideas for data projects. I’m convinced the way to become a new-fangled data journalist is to start getting messy with numbers.
I’m new at this. I’ve been an old-fashioned print and radio reporter for 20 years. I have a few graduate courses in statistics under my belt. From that experience, it seems to me that data analysis is a group exercise. At a minimum, it’s helpful to have a partner to check your work and find your mistakes. So this will be an experiment in flying solo. I’m hoping readers will comment and criticize like peer reviewers. I will keep refining and editing as we go along.
I’m fascinated with what data can tell us about which education programs work and which don’t. I’m openly curious about so-called “adaptive learning” — the idea that we can mine data to understand how each human brain learns and design personalized curriculum for each student.
For me, it all began with a data analysis course at Columbia Teachers College taught by Prof. Doug Ready. Our seminar class spent a year playing around with a large data set of kindergarten students as a vehicle for learning statistical techniques and SPSS statistical software. I loved asking the data questions. Does music instruction improve math ability? Do the children of Tiger Mothers score higher on tests? The answers often depended on socio-economic status. One answer for rich kids. The opposite answer for poor kids.
As I begin this, I’m not sure how much I trust the answers that the data spit out. I’ve learned that small changes in how you crunch the numbers can generate different answers. Sometimes the data you have isn’t really the right data to answer your question. Often the correlations you find are just coincidental and you can’t conclude that doing x will produce y result.
At its core, data analysis is unsettling. Until now, we’ve relied on the experience, wisdom and hunches of veteran educators to say how to teach our children. Many now hope the data know better and will somehow save education.
I’m curious to see what the data say and factor it in.