以职业教育与培训为重点的数据科学与机器学习教学实践

IF 2.1 Q1 EDUCATION & EDUCATIONAL RESEARCH
G. Nadzinski, B. Gerazov, Stefan Zlatinov, Tomislav Kartalov, Marija Markovska Dimitrovska, H. Gjoreski, Risto Chavdarov, Z. Kokolanski, Igor Atanasov, Jelena Horstmann, Uros Sterle, M. Gams
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引用次数: 0

摘要

随着技术的发展,数据科学和机器学习在我们的日常生活中迅速扩张,全球就业市场正在形成一个巨大的缺口,对这些领域合格工人的需求无法得到适当满足。这一令人担忧的趋势要求在教育方面立即采取行动,必须以有效和最新的方式向各级学生传授这些技能。本文概述了全球数据科学和机器学习教育的现状,包括高中和大学水平,同时概述了一些说明性和积极的例子。特别关注的是职业教育和培训(VET),这些技能的教学才刚刚开始。此外,还介绍和分析了斯洛文尼亚、塞尔维亚和北马其顿的VET学生的调查结果,以及他们在数据科学和机器学习方面的知识、兴趣和先决条件。这些结果证实有必要在职业学校就这些科目编制有效和容易获得的课程和课程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Science and Machine Learning Teaching Practices with Focus on Vocational Education and Training
With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools.
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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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