MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH

Q3 Social Sciences

Statistics Education Research Journal Pub Date : 2022-07-04 DOI:10.52041/serj.v21i2.45

Koby Mike, O. Hazzan

引用次数: 0

Abstract

Data science is a new field of research, with growing interest in recent years, that focuses on extracting knowledge and value from data. New data science education programs, which are being launched at a growing rate, are designed for multiple levels, beginning with elementary school pupils. Machine learning is an important element of data science that requires an extensive background in mathematics. While it is possible to teach the principles of machine learning as a black box, it might be difficult to improve algorithm performance without a white box understanding of the underlaying learning algorithms. In this paper, we suggest pedagogical methods to support white box understanding of machine learning algorithms for learners who lack the needed graduate level of mathematics, particularly high school computer science pupils.

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非专业机器学习:白盒方法

数据科学是一个新的研究领域，近年来人们对它的兴趣越来越大，它专注于从数据中提取知识和价值。新的数据科学教育项目正在以越来越快的速度启动，从小学生开始，针对多个层次设计。机器学习是数据科学的一个重要组成部分，需要广泛的数学背景。虽然可以将机器学习的原理作为黑盒来教授，但如果没有对底层学习算法的白盒理解，可能很难提高算法性能。在本文中，我们建议了一些教学方法，以支持缺乏所需数学研究生水平的学习者，特别是高中计算机科学学生，对机器学习算法的白盒理解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.