ACADEMIC TEXTS REPOSITORY

Summary

This paper substantiates micro-decomposition of learning content as a strategic didactic approach for preparing students for ZNO and NMT examinations in mathematics, specifically addressing the systemic educational losses caused by the pandemic and the war. Based on a comprehensive analysis of national and international research (2022–2026), it identifies trends in declining proficiency and highlights the challenges of traditional methods that lead to cognitive overload due to excessive theoretical volume. The article advocates for adaptive learning to facilitate personalized environments and the effective recovery of structural gaps in knowledge. The theoretical foundations integrate cognitive load theory, microlearning, and mastery learning concepts. The proposed 'Chunk-Objective' model partitions complex topics into elementary, measurable 'micro-skills,' enabling students to internalize material without exceeding their cognitive limits. This strategy is particularly effective for mathematics, which demands higher working memory resources for processing abstract symbols and logical transitions. The model integrates diagnostic screening, adaptive instruction, and formative evaluation into a unified five-stage didactic system: diagnostics, decomposition, micro-learning, correction, and integration. Practical implementation is detailed through its successful testing in the in-service teacher training system at the Mykolaiv Regional Institute and various student initiatives such as 'Express Mathematics.' The results demonstrate that forming automated cognitive units enhances student performance in high-stakes testing by fostering motivation, autonomy, and the ability to solve multi-component problems. This approach effectively localizes specific learning deficits and constructs resilient competencies required for further success in higher education.