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Neural Networks in Identifying Teachers’ Self-Perceived Competencies in Digital Gamification

Year: 2024
Type: conference-paper
Study design: Observational Study
Abstract: In the last ten years, the integration of information and communication technology (ICT) into education has become more and more prevalent, as ICT has become an indispensable part of everyday educational life. With the development of ICT, the number of teaching strategies involving the use of ICT has also increased. One of the latest strategies for the use of ICT in education is digital gamification. ICT can serve as a technological basis for the creation and further development of gamification experiences. The development of new technologies and teaching strategies implies the development of teacher competencies for the application of teaching methods based on the use of ICT, including digital gamification. The application of gamification in education brings numerous benefits and is used for various purposes, from influencing student motivation and engagement, improving educational outcomes and creating interactive learning opportunities, to the application of gamification in student evaluation. With the aim of better understanding teachers’ self-assessed competencies in the use of digital gamification, this paper focuses on the association between these assessments and their overall ICT use and gamification in student evaluation with other demographic variables. The aim was to develop a successful neural network model using these variables that is able to identify the teachers with a high level of competence. The best neural network model obtained had an overall classification accuracy of 75% and was able to identify all teachers with a high self-assessed level of competence in the use of digital gamification in the education of students.
DOI: 10.22364/htqe.2024.10
Language: English
Field: Psychology
Open Access status: bronze
OpenAlex ID: W4404134315
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