Making data science “count”: Data science and learning, design, and technology research

Abstract

Analyzing and interpreting data is essential to the practice of scientists and is also an essential science and engineering practice for science teaching and learning. Although working with data has benefits in terms of student learning, it is also challenging, particularly with respect to aspects of work with data that are not yet very common, such as developing quantitative models, understanding variation in data, and using larger, complex data sources. In this article, we aim to describe tools for engaging students in work with data in your class as well as three general strategies, from understanding how the data were collected to how to include the messier parts of preparing a data set for analysis and then modeling the data in order to answer a driving question. We show how these strategies can be employed using the freely-available, browser-based tools.

Publication
Research Methods in Learning Design & Technology
Joshua M. Rosenberg
Assistant Professor, STEM Education

I am an Assistant Professor of STEM Education at the University of Tennessee, Knoxville.