Students' approaches to exploring relationships between categorical variables

Abstract

In the context of an afterschool program in which students explore relatively large authentic datasets, we investigated how 11-to 14-year old students worked with categorical variables. During the program, students learned to use the Common Online Data Analysis Platform (CODAP), a statistical analysis platform specifically designed for middle and high school students, to create and interpret graphs. Following the program, we conducted individual clinical interviews, during which students used CODAP to answer questions about relationships between variables.Here, we describe how students engaged in exploratory data analysis that involved looking at relationships between two categorical variables. Students worked from data in table form and created "contingency graphs," a variant of contingency tables, which they used to analyze and draw insights from the data. Our research identified four strategies that students used to examine the data in order to explore patterns, make comparisons, and answer questions with the data.

Introduction to rethinking learners' reasoning with nontraditional data

“…Higgins et al [14] consider how middle and secondary students use CODAP to visualize and consider relationships between categorical variables in large data sets. While traditional statistics education approaches have typically meant that students do not spend time examining relationships in categorical data until secondary school, Higgins et al [14] suggest that younger students can engage in these explorations with dynamic visualizations. The use of CODAP provides students with the tools to construct dynamic visualizations that allow younger students to examine relationships in complex categorical variables.…”

“…The articles in this issue focus on new approaches to the teaching and learning of data practices related to messy, complex, or nontraditional data from the youngest learners [8,19] to secondary learners [14,17], undergraduate students [15,16], graduate students, teachers, and researchers [2,5,9,17]. There are two overarching themes in the articles in this special issue: new ways to consider data visualizations in the classroom [2,5,14,17,19] and new approaches or elements that need to be considered in the teaching and learning of data science practices [8,9,15,16].…”