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Data Science and Machine Learning Teaching Practices with Focus on Vocational Education and Training.
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- المؤلفون: NADZINSKI, Gorjan; GERAZOV, Branislav; ZLATINOV, Stefan; KARTALOV, Tomislav; MARKOVSKA DIMITROVSKA, Marija; GJORESKI, Hristijan; CHAVDAROV, Risto; KOKOLANSKI, Zivko; ATANASOV, Igor; HORSTMANN, Jelena; STERLE, Uros; GAMS, Matjaz
- المصدر:
Informatics in Education; Dec2023, Vol. 22 Issue 4, p671-690, 20p
- الموضوع:
- معلومة اضافية
- الموضوع:
- نبذة مختصرة :
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. [ABSTRACT FROM AUTHOR]
- نبذة مختصرة :
Copyright of Informatics in Education is the property of Informatics in Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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