DEVELOPING A PERSONALIZED EDUCATIONAL TRAJECTORY FOR TEACHING SPECIALIZED ECONOMICS SUBJECTS: A METHODOLOGICAL FRAMEWORK

Abstract

The increasing demand for specialized knowledge in economics necessitates the development of personalized educational trajectories tailored to individual learners. This research proposes a methodological framework for designing and implementing personalized learning paths in teaching specialized economics subjects. The study combines insights from educational psychology, learning analytics, and pedagogy to create a learner-centered approach that enhances engagement and outcomes. The proposed framework integrates adaptive learning technologies, competency-based assessment, and modular content delivery to align with students' diverse learning needs, prior knowledge, and career aspirations. It employs machine learning algorithms to analyze learners' progress and preferences, providing real-time feedback and adjusting educational content accordingly. Additionally, the framework emphasizes the role of experiential learning, enabling students to apply theoretical knowledge to practical economic challenges through simulations and case studies. The study also addresses the critical role of instructor support and collaborative learning environments in fostering deeper understanding and motivation. The framework is evaluated through pilot studies in university settings, demonstrating its potential to enhance learner performance, reduce dropout rates, and prepare students for dynamic economic landscapes. This paper contributes to the growing discourse on personalized education by offering a scalable, evidence-based model tailored to the complexities of specialized economics education.