Using R to Visualize and Make Claims About Data

In this project, we developed an 8-session introductory data science course for middle grades (ages 11 - 13), and observed how participants learned data science practices and processes through the combination of nonprogramming activities and programming activities using the language R. We designed activities that were learner-centered and allowed students to create “objects-to-think-with” such as R code or visual data representations. The curriculum was designed as an interdisciplinary approach between statistics and computer programming.

The first half of the course was an introduction to data science that aimed to enhance statistical thinking and help students understand how data can be used to gain information. The last half of the course was dedicated to a final project in which students identified existing, publicly available data sets and analyzed their data using R. Students then presented their projects in an expo-style format on the last day in which their parents and family were invited to attend.

Thinking About Algorithm Bias

Beyond learning to program in R, the course encouraged students to engage in discussions around algorithm bias. Issues were discussed around racist, sexist, and discriminatory algorithms, the consequences of biased algorithms on marginalized populations, and ethical obligations of stakeholders involved including tech companies.

Related Publications

Thompson, J. & Arastoopour Irgens, G. (2022). "Data Detectives: A Data Science Program for Middle Grade Learners." Journal of Statistics and Data Science Education.

Arastoopour Irgens G. & Thompson, J. (2020). “Would You Rather Have it be Accurate or Diverse?” How Male Middle-School Students Make Sense of Algorithm Bias and Racial/Gender Discrimination. In M. Gresalfi and I.S. Horn (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 2. (pp. 751 - 752). Nashville, Tennessee: International Society of the Learning Sciences.