使用数据卡构建基于数据的决策树的教学和学习，作为初中机器学习的第一课

Q3 Social Sciences

Statistics Education Research Journal Pub Date : 2024-08-10 DOI:10.52041/serj.v23i1.450

Yannik Fleischer, Susanne Podworny, Rolf Biehler

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引用次数: 0

摘要

本研究探讨了 11 至 12 岁的学生如何利用数据卡构建基于数据的决策树，以达到分类的目的。我们研究了学生在这一过程中的启发式方法和推理。研究基于一个为期八周的教学单元，在此期间，学生们标注数据、构建决策树，并使用测试数据对其进行评估。他们学会了手动构建决策树，将食品分类为可推荐或不可推荐。他们利用带有启发式的数据卡，这种启发式是机器学习算法的简化形式。我们报告了可向中学生教授这一主题的证据，以及改进我们的教学方法的见解和在学校层面教授机器学习的更广泛意义。

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TEACHING AND LEARNING TO CONSTRUCT DATA-BASED DECISION TREES USING DATA CARDS AS THE FIRST INTRODUCTION TO MACHINE LEARNING IN MIDDLE SCHOOL

This study investigates how 11- to 12-year-old students construct data-based decision trees using data cards for classification purposes. We examine the students' heuristics and reasoning during this process. The research is based on an eight-week teaching unit during which students labeled data, built decision trees, and assessed them using test data. They learned to manually construct decision trees to classify food items as recommendable or not. They utilized data cards with a heuristic that is a simplified form of a machine learning algorithm. We report on evidence that this topic is teachable to middle school students, along with insights for refining our teaching approach and broader implications for teaching machine learning at the school level.

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来源期刊

Statistics Education Research Journal Social Sciences-Education

CiteScore

1.30

自引率

0.00%

发文量

期刊介绍： SERJ is a peer-reviewed electronic journal of the International Association for Statistical Education (IASE) and the International Statistical Institute (ISI). SERJ is published twice a year and is free. SERJ aims to advance research-based knowledge that can help to improve the teaching, learning, and understanding of statistics or probability at all educational levels and in both formal (classroom-based) and informal (out-of-classroom) contexts. Such research may examine, for example, cognitive, motivational, attitudinal, curricular, teaching-related, technology-related, organizational, or societal factors and processes that are related to the development and understanding of stochastic knowledge. In addition, research may focus on how people use or apply statistical and probabilistic information and ideas, broadly viewed. The Journal encourages the submission of quality papers related to the above goals, such as reports of original research (both quantitative and qualitative), integrative and critical reviews of research literature, analyses of research-based theoretical and methodological models, and other types of papers described in full in the Guidelines for Authors. All papers are reviewed internally by an Associate Editor or Editor, and are blind-reviewed by at least two external referees. Contributions in English are recommended. Contributions in French and Spanish will also be considered. A submitted paper must not have been published before or be under consideration for publication elsewhere.