高中机器学习教学的发现:十年系统文献综述

IF 2.1 Q1 EDUCATION & EDUCATIONAL RESEARCH Informatics in Education Pub Date : 2022-09-09 DOI:10.15388/infedu.2023.18
Ramon Mayor Martins, Christiane Gresse von Wangenheim
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引用次数: 18

摘要

机器学习(ML)越来越多地出现在我们的生活中。因此,在高中引入机器学习是很重要的,使年轻人成为智能解决方案的有意识的用户和创造者。然而,由于ML通常只在高等教育中教授,因此仍然缺乏如何正确教授年轻学生的知识。因此,在这篇系统的文献综述中,我们分析了高中机器学习教学在内容、教学策略和技术方面的发现。结果表明,高中生能够理解和应用基本的机器学习概念、算法和任务。注重积极的基于问题/项目的实践方法的教学策略成功地吸引了学生,并展示了积极的学习效果。可视化和基于文本的编程环境支持学生以有效的方式构建机器学习模型。然而,该评论还指出,需要对如何教授机器学习进行更严格的评估。
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Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review
Machine Learning (ML) is becoming increasingly present in our lives. Thus, it is important to introduce ML already in High School, enabling young people to become conscious users and creators of intelligent solutions. Yet, as typically ML is taught only in higher education, there is still a lack of knowledge on how to properly teach younger students. Therefore, in this systematic literature review, we analyze findings on teaching ML in High School with regard to content, pedagogical strategy, and technology. Results show that High School students were able to understand and apply basic ML concepts, algorithms and tasks. Pedagogical strategies focusing on active problem/project-based hands-on approaches were successful in engaging students and demonstrated positive learning effects. Visual as well as text-based programming environments supported students to build ML models in an effective way. Yet, the review also identified the need for more rigorous evaluations on how to teach ML.
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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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