CIRCL perspectives offer a window into the different worlds of various stakeholders in the cyberlearning community — what drives their work, what they need to be successful, and what they think the community should be doing. Share your perspective.

William Finzer is a Senior Scientist at the Concord Consortium, and helped organize the 2017 Data Science Education Technology Conference that featured the Concord Consortium’s Common Online Data Analysis Platform (CODAP) project.

How does your work on data and data science relate to cyberlearning?

Working with data is a technologically mediated process. Data are ubiquitous in the workplace and research labs and need to become ubiquitous in classrooms as well. Data science is a partial union of math and statistics, computational thinking, and subject matter expertise. We have entered an age when a great deal of the learning that we do is mediated by those three things. When using technology, the learning students do generates data, whether it is online through logged data, or through use of sensors in the learning environment. How can we harness and use these data? Learning theories help us understand the data generated by learners. It’s not useful to know students click this button twice as much as they clicked that button; we need to understand what type of learning the pattern of button clicks represents. We can use data generated by students in real classrooms to validate our learning theories and to generate feedback to improve curriculum materials.

What drives your work?

The idea that I can help get more young people excited about working with data is what really drives me. This began for me in the early 1980’s when I started creating microworlds and games based on simulation of random processes and use of what we now call informal inference.

In the 90’s, statistics education emerged as an important area of curriculum development and research. . By the turn of the century, the data revolution was in full swing and data were permeating every walk of life. Except classrooms, and that’s still true today. Learning with data should be part of every subject area. The data experiences that students have in classrooms are too often superficial. We need to create experiences in which students gather rich, relevant data, immerse themselves in that data, and use that data tomake discoveries. There is so much unexplored data that students have opportunities to make discoveries that no one else has made before.

All of the problems that face our civilization require people who are expert with data to be part of the solution. There is already a severe shortage of such people. As students in K-12 schools learn with data they will develop the intuitions, skills, and data habits of mind that will turn them into 21st century problem solvers.

What are you working on right now?

Right now I am working on CODAP, a Common Online Data Analysis Platform, funded by the NSF. The idea is that if data are to pervade learning experiences, curriculum developers need to be able to provide tools for students to use with that data. CODAP software is open source, free, runs in browsers, and can be easily integrated into online materials. If CODAP is successful one significant bottleneck to getting data into the classroom will be opened up.

What are you struggling with now?

My colleagues and I would like to broaden the view of what it means to learn with data. This is a struggle for a number of reasons. First, we don’t know very much about learners’ conceptions of data; data science education research is just beginning to happen! Second, most of today’s educators grew up before the data revolution got going. How are people who are not themselves “data natives” supposed to develop data-rich learning experiences? Third, the fierce pace of technological change forces us to look beneath the technology for the lasting truths—no easy task! I feel lucky to have these as my struggles and to be working in a field—cyberlearning—that attracts so many talented and committed people.