If your career choice is teaching, are you data literate enough?

Pieter Smith

Ellen Mandinach is an educational psychologist and senior research scientist at WestEd, a education research institution in the United States. She specialises in data-driven decision making, focusing on understanding how educators are using data to inform practice.

In a recent article, she and her co-author argue that data use has become an emphasis in education but that few educators have received sufficient training or preparation pertaining to data literacy skills. They lay out the framework, identifying the specific knowledge, skills, and dispositions teachers need to use data effectively and responsibly. The research was funded by grants from the Bill and Melinda Gates Foundation, the Michael and Susan Dell Foundation, and the Spencer Foundation.

In a nutshell, the authors conceptualise the following framework for data literacy for teachers, which they admit is a complex domain:

  1. Identify problems and frame questions
  2. Use data (includes e.g. identification of data sources, generation of sources, data quality, data manipulation)
  3. Transform data into information (includes e.g. statistics, probe for causality, summarise and explain data)
  4. Transform information into a decision
  5. Evaluate outcomes of the decision making process

The authors conclude with a call to schools of education and teacher preparation programs to begin to integrate data literacy into curricula and practical experiences.

Born to Learn Training perspective: The need for data literacy is likely to increase for many careers that have traditionally only required basic record keeping and reporting skills. Students who have some basic skills in database design, SQL programming, working with data, Microsoft Excel, and statistics, and that are able to use data visualisation software, may find that they are better able to cope in a world increasingly flooded with data and in desperate need for being able to handle this deluge. However, there is also a risk. An important consideration is to remember that data, depending on how you define it, has its limitations. As William Bruce Cameron wrote in 1963: "Not everything that counts can be counted, and not everything that can be counted counts." Any data literacy course should also touch on the value of stepping back and out of the data stream, and consider how a simple narrative or story can sometimes contribute equally, or even more, to a decision when compared to a statistical analysis based on massive amounts of data. Both approaches have value and can be extremely powerful when combined.

References:

What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions (Mandinach and Gummer, Teaching and Teacher Education, Volume 60, November 2016, Pages 366–376). Available here.

Wake Me Up When the Data Is Over: How Organizations Use Stories to Drive Results, Lori Silverman (editor), published by Jossey-Bass, 2006, 320 pages.