LMS tools for identifying and analyzing motivational indicators of higher education students

Abstract

The article explores the potential of Learning Management Systems (LMS) for identifying and analyzing motivational indicators of higher education students. It outlines the theoretical foundations of academic motivation, its structure, and behavioral manifestations in digital learning environments. The paper examines LMS tools such as activity logs, analytics dashboards, tracking systems, embedded surveys, and adaptive learning mechanisms that provide quantitative and qualitative data on students’ intrinsic and extrinsic motivation. The significance of educational analytics and machine learning algorithms for interpreting motivational indicators, predicting risks of motivational decline, and enabling timely pedagogical intervention is emphasized. Methodological principles for using LMS as a management instrument to create personalized learning pathways are proposed.