Retrieved from Vol. 28, No. 1, 2025
Pages 54 -65
Received 12.01.2025
Revised 04.05.2025
Accepted 29.05.2025
Retrieved from Vol. 28, No. 1, 2025
Pages 54 -65
Abstract
The problem of the emergence and overcoming of educational losses of future geography teachers in higher education institutions of Ukraine in the context of modern challenges is relevant: the consequences of the COVID-19 pandemic, military actions and martial law. The aim of the study was to provide a theoretical justification for the methodological principles of using diagnostic competence-oriented tasks as a means of minimising educational losses among students, based on empirical research. The study used methods of analysis, synthesis and systematisation in processing the source base. To identify the causes of educational losses and determine their scope and content, testing, pedagogical observation and interviews were used. The terms “educational losses”, “learning gaps” and “learning gaps” were systematised as pedagogical categories; the causes of educational losses, the scale and duration of their impact, and ways to overcome them were analysed. A step-by-step algorithm for correcting educational losses and the need to use digital teaching aids were substantiated, and the positive and negative consequences of using artificial intelligence to overcome educational losses among applicants were characterised. The paper defines the essence of diagnostic competence-oriented tasks, conducts research on the scope and content of educational losses based on their application, provides examples of the author’s diagnostic tasks in the discipline ‘Methods of Teaching Geography,’ and a graphical diagram of the process of awareness and correction of educational losses using these tasks. The result of the authors’ research was the development of a methodological model for assessing and overcoming educational losses, which has practical significance and can be used by teachers in educational institutions
Keywords:
higher education seekers; learning gaps; learning gaps; artificial intelligence; methodological model