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Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks
Author(s) -
Biehler Rolf,
Fleischer Yannik
Publication year - 2021
Publication title -
teaching statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.425
H-Index - 13
eISSN - 1467-9639
pISSN - 0141-982X
DOI - 10.1111/test.12279
Subject(s) - decision tree , computer science , context (archaeology) , machine learning , artificial intelligence , plug in , mathematics education , psychology , paleontology , biology , programming language
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.