向低社会经济地位背景的初高中学生教授机器学习

IF 2.1 Q1 EDUCATION & EDUCATIONAL RESEARCH Informatics in Education Pub Date : 2023-11-14 DOI:10.15388/infedu.2024.13
Ramon Mayor Martins, Christiane Gresse Von Wangenheim, Marcelo Fernando Rauber, Jean Carlo Rossa Hauck, Melissa Figueiredo Silvestre
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引用次数: 0

摘要

关于机器学习的知识正变得越来越重要,但它仍然是一种有限的特权,可能无法获得来自低社会经济地位背景的学生。因此,为了提供平等的机会,我们向来自巴西低社会经济背景的158名初高中学生教授ML概念和应用。结果表明,这些学生能够理解机器学习的工作原理,并执行以人为中心的图像分类模型开发过程的主要步骤。在班级时间、教育阶段和出生性别方面没有观察到实质性的差异。这门课程被认为是有趣和激励的,尤其是对女孩来说。尽管在这方面存在局限性,但结果表明它们是可以克服的。缓解方案包括社会机构和大学之间的伙伴关系、适应的教学方法以及增加一对一的援助。这些发现可用于指导在社会经济地位背景较低的贫困学生的背景下教授ML的课程设计,从而有助于将这些学生纳入其中。
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Teaching Machine Learning to Middle and High School Students from a Low Socio-Economic Status Background
Knowledge about Machine Learning is becoming essential, yet it remains a restricted privilege that may not be available to students from a low socio-economic status background. Thus, in order to provide equal opportunities, we taught ML concepts and applications to 158 middle and high school students from a low socio-economic background in Brazil. Results show that these students can understand how ML works and execute the main steps of a human-centered process for developing an image classification model. No substantial differences regarding class periods, educational stage, and sex assigned at birth were observed. The course was perceived as fun and motivating, especially to girls. Despite the limitations in this context, the results show that they can be overcome. Mitigating solutions involve partnerships between social institutions and university, an adapted pedagogical approach as well as increased on-by-one assistance. These findings can be used to guide course designs for teaching ML in the context of underprivileged students from a low socio-economic status background and thus contribute to the inclusion of these students.
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来源期刊
Informatics in Education
Informatics in Education EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
6.10
自引率
3.70%
发文量
20
审稿时长
20 weeks
期刊介绍: INFORMATICS IN EDUCATION publishes original articles about theoretical, experimental and methodological studies in the fields of informatics (computer science) education and educational applications of information technology, ranging from primary to tertiary education. Multidisciplinary research studies that enhance our understanding of how theoretical and technological innovations translate into educational practice are most welcome. We are particularly interested in work at boundaries, both the boundaries of informatics and of education. The topics covered by INFORMATICS IN EDUCATION will range across diverse aspects of informatics (computer science) education research including: empirical studies, including composing different approaches to teach various subjects, studying availability of various concepts at a given age, measuring knowledge transfer and skills developed, addressing gender issues, etc. statistical research on big data related to informatics (computer science) activities including e.g. research on assessment, online teaching, competitions, etc. educational engineering focusing mainly on developing high quality original teaching sequences of different informatics (computer science) topics that offer new, successful ways for knowledge transfer and development of computational thinking machine learning of student''s behavior including the use of information technology to observe students in the learning process and discovering clusters of their working design and evaluation of educational tools that apply information technology in novel ways.
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