METACOGNITIVE COMPETENCE OF FUTURE COMPUTER SCIENCE TEACHERS IN THE CONTEXT OF NEURAL NETWORK ADVANCEMENT
DOI:
https://doi.org/10.18372/2411-264X.28.21589Keywords:
metacognitive competence, future Computer Science teacher, digital competence, digital learning environment, human-computer interaction, neural networksAbstract
The article substantiates the importance of metacognitive competence for the professional training of future Computer Science teachers under the active influence of neural network advancements.
The interrelationship between the cognitive and metacognitive levels of future educators' activity is revealed. Metacognitive competence is conceptualized as an integration of knowledge-based, operational-activity, motivational-value, and behavioral-experiential components, the functioning of which is sustained by processes such as awareness, self-regulation, and reflection.
Research Purpose: to theoretically substantiate the essence and functional purpose of future Computer Science teachers' metacognitive competence in the context of neural network advancement in education.
Research Methods: analysis and synthesis of scientific propositions, comparison, classification, systematization, and theoretical generalization.
Research Procedure: involved the analysis and synthesis of scientific principles, comparison of cognitive, digital, and metacognitive competencies, classification of their structural and functional characteristics, systematization of requirements for Computer Science teachers' professional training, and theoretical generalization of the obtained results.
Research Results indicated that metacognitive competence extends beyond digital literacy, performing regulatory and supervisory functions regarding the application of previously acquired knowledge, skills, and abilities. Its role in data processing, programming, solving routine and non-routine problems, as well as organizing human-computer and human-neural network interaction has been identified. The advantages and cognitive risks of intensive digital technology use have been highlighted, including information overload, attention dispersion, superficial analysis, dependence on search engine and neural network systems, and the illusion of competence. It has been proven that metacognitive monitoring, self-evaluation, and self-control are essential conditions for the responsible application of digital tools and the construction of effective professional learning strategies for future Computer Science teachers.
Conclusions: effective training of future Computer Science teachers must integrate subject-technological, adaptive, ethical, and metacognitive dimensions. Systematic monitoring, self-assessment, and correction of one's own cognitive activities ensure the responsible use of digital technologies and foster the development of impactful professional learning strategies
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