Forming an Individual Approach in Teaching Algorithmic Languages and Programming through Adaptive Learning Technologies

Abstract

This article explores the application of adaptive learning technologies in teaching the subject “Algorithmic Languages and Programming” in higher education institutions. The study presents the theoretical foundations of adaptive learning, highlighting its alignment with pedagogical frameworks such as constructivism and the Zone of Proximal Development. A practical adaptive instructional model is proposed, incorporating diagnostics, level-based topic delivery, personalized task assignment, and gamified engagement. An experimental study comparing adaptive and traditional instruction methods demonstrates significant improvements in student engagement, performance, and satisfaction. The paper concludes with recommendations for integrating adaptive systems into programming education and outlines prospects for AI-driven personalization and hybrid learning models.