What goes before the CART? Introducing classification trees with Arbor and CODAP

Abstract

This volume is largely about nontraditional data; this paper is about a nontraditional visualization: classification trees. Using trees with data will be new to many students, so rather than beginning with a computer algorithm that produces optimal trees, we suggest that students first construct their own trees, one node at a time, to explore how they work, and how well. This buildit-yourself process is more transparent than using algorithms such as CART;we believe it will help students not only understand the fundamentals of trees, but also better understand tree-building algorithms when they do encounter them. And because classification is an important task in machine learning, a good foundation in trees can prepare students to better understand that emerging and important field. We also describe a free online tool-Arbor-that students can use to do this, and note some implications for instruction.

Introduction to rethinking learners' reasoning with nontraditional data

“…Erickson and Engel [5] utilize fairly traditional data; however, they explore these data through nontraditional forms of data visualization. Their work focuses on the use of classification trees in CODAP using a plug‐in called Arbor.…”

“…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].…”