КЛАСТЕРИ НАВЧАЛЬНОГО МАТЕРІАЛУ З ОСНОВ МАШИНОЗНАВСТВА

The article justifies the need for clustering of machine learning knowledge of future teachers of labor training and technologies. It is established that their training in the basics of machine learning should be based on the study of three clusters of educational material on technical phenomena of the drive of a technological machine. Clusters of educational material on technical phenomena of the drive of a technological machine will contribute to the organization of the educational process using methods of activating and stimulating the educational and cognitive activity of higher education students. The cluster approach will allow forming the machine learning literacy of future teachers of labor training and technologies relevant to the content of their future professional activities.

Authors

Publication Details

Published
2026-03-23
DOI
https://doi.org/10.31652/3041-1017-saae-2025.1.10
Primary Topic
Innovative Educational Technologies
Type
article
Field-Weighted Citation Impact
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article

КЛАСТЕРИ НАВЧАЛЬНОГО МАТЕРІАЛУ З ОСНОВ МАШИНОЗНАВСТВА

Р.О. Головін, А.В. Іванчук, Д.О. Радомський
Innovative Educational Technologies
article

КЛАСТЕРИ НАВЧАЛЬНОГО МАТЕРІАЛУ З ОСНОВ МАШИНОЗНАВСТВА

Р.О. Головін, А.В. Іванчук, Д.О. Радомський
article en

Abstract

The article justifies the need for clustering of machine learning knowledge of future teachers of labor training and technologies. It is established that their training in the basics of machine learning should be based on the study of three clusters of educational material on technical phenomena of the drive of a technological machine. Clusters of educational material on technical phenomena of the drive of a technological machine will contribute to the organization of the educational process using methods of activating and stimulating the educational and cognitive activity of higher education students. The cluster approach will allow forming the machine learning literacy of future teachers of labor training and technologies relevant to the content of their future professional activities.

(1)
Quality Education
Openalex Percentile: Top 7%
Innovative Educational Technologies
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