Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks. (25th June 2021)

• Record Type: Journal Article
• Authors: Biehler, Rolf; Fleischer, Yannik
• Is Part Of: Teaching statistics. Volume 43(2021)Supplement 1
• Page Start: S133
• Page End: S142
• Publication Date: 2021-06-25
• Subjects: teaching -- data science -- data science education -- decision trees -- Jupyter Notebooks -- machine learning; Statistics -- Study and teaching (Elementary) -- Periodicals; Statistics -- Study and teaching (Secondary) -- Periodicals; 373
• DOI: 10.1111/test.12279
• Languages: English
• ISSNs: 0141-982X

Abstract

Abstract: This paper reports on progress in the development of a teaching module on machine learning with decision trees for secondary‐school students, in which students use survey data about media use to predict who plays online games frequently. This context is familiar to students and provides a link between school and everyday experience. In this module, they use CODAP's "Arbor" plug‐in to manually build decision trees and understand how to systematically build trees based on data. Further on, the students use a menu‐based environment in a Jupyter Notebook to apply an algorithm that automatically generates decision trees and to evaluate and optimize the performance of these. Students acquire technical and conceptual skills but also reflect on personal and social aspects of the uses of algorithms from machine learning.