Data is a hot topic in education. In 2009, under Race to the Top, all 50 states committed to creating data systems with 12 key elements. These included tracking each student by a unique statewide identifier, and seeing what happens when they leave school: do they graduate? Enroll in college? Do they need remedial classes in college? In 2013, 41 states reported in a national survey that they had dedicated state funding to building these data systems, and that they trained principals and teachers in how to use them.
The Data Quality Campaign bills itself as a “nonprofit, nonpartisan advocacy organization committed to realizing an education system in which all stakeholders—from parents to policymakers—are empowered with high-quality data from the early childhood, K–12, postsecondary, and workforce systems.” They have released a policy brief with a set of recommendations and are holding an event in DC this afternoon to promote their agenda and specifically, the importance of data literacy to improve instruction.
Data-driven decisionmaking currently has a political image problem. It is often associated strongly with the use of scores on standardized tests, and with accountability and teacher evaluation systems that make much heavier use of the stick than the carrot. Many of the major figures in the Data Quality Campaign come from the accountability world, but this Campaign is clearly striving to put forth a more empowering image in which teachers use data to make better decisions, instead of data being used to make decisions about teachers.
In this vision, the campaign emphasizes, state test scores are a drop in the bucket. There’s a kaleidoscope of data that teachers might find relevant: demographics (English learners, free and reduced lunch, ethnic and minority status, homelessness), records of attendance, behavior, involvement with law enforcement, medical or mental health diagnoses, course grades and patterns, interventions, growth, teacher observations. All of this can bring crucial context to the student-teacher interaction, both in the moment and longitudinally.
On top of all that, students are increasingly spending time with learning software that generates a “digital ocean” of data, tracking mouse movements at tenth-of-a-second intervals. Some computer systems have the ability to log breathing, heart rate, facial expressions, even brainwaves.
The Data Quality Campaign cites several studies that show student achievement can improve when teachers are trained to use data and given the time to do it. But drawing useful insights from this chaos of dashboards, not to mention complying with all the reporting requirements imposed by states, can be a panic point for teachers. Many are overwhelmed as it is with the day-to-day work of teaching, and see the ever-shifting availability of different types of data as more of an imposition on their time than something helping them do their jobs better.
The Campaign’s recommendation to this end is likely to spawn its own controversy. They would like to see states use licensure exams to require teachers to demonstrate “data literacy,” as 19 states already do. And they would like to see data literacy covered in performance evaluations for teachers on the job.
Broadening data use beyond test scores, and giving teachers and administrators the time, training, and reinforcement they need to make effective use of data, seem like good moves. But five-0dd years into the data revolution, it’s a bit hasty to make data literacy part of the definition of quality teaching.
- These data systems are very new, and we need more research to see what’s truly relevant for teaching and learning.
- A large number of schools and districts are using a hodgepodge of antiquated legacy software and lightweight apps. Crafting a literacy policy before upgrading the infrastructure seems like putting the cart before the horse.
- In smaller schools, where teachers get to know students well over time, they have less need to look at a printout to see how students are progressing, their strengths and special challenges. Data literacy requirements need to be crafted carefully to respect the wide range of contexts in which teachers do their jobs.