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Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks
Teaching Statistics Pub Date : 2021-06-25 , DOI: 10.1111/test.12279
Rolf Biehler 1 , Yannik Fleischer 1
Affiliation  

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.

中文翻译:

向学生介绍使用 CODAP 和 Jupyter Notebooks 的决策树机器学习

本文报告了为中学生开发具有决策树的机器学习教学模块的进展,其中学生使用有关媒体使用的调查数据来预测谁经常玩网络游戏。这种背景对学生来说是熟悉的,并提供了学校和日常经验之间的联系。在这个模块中,他们使用CODAP的“Arbor”插件手动构建决策树,了解如何基于数据系统地构建决策树。此外,学生们在 Jupyter Notebook 中使用基于菜单的环境来应用自动生成决策树的算法,并评估和优化决策树的性能。学生获得技术和概念技能,但也会反思机器学习算法使用的个人和社会方面。
更新日期:2021-06-28
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