Abstract
The features of student learning analytics technology are presented. The changes in the landscape of modern educational technologies are demonstrated. Existing analytics methods are described. A definition of student learning analytics is given, and its meaning is revealed. Possible processes and environments of student learning analytics are shown. The stages of formative and summative assessment of student activities are disclosed. Existing methodologies for evaluating students' academic performance are detailed. Adaptive learning environments have been developed, representing educational-methodical tools that allow students to study more independently, taking into account their individual needs. The role of artificial intelligence in enhancing the effectiveness of student learning is explained, which can help personalize their learning in several ways. It is established that the most well-known form of learning analytics for teaching staff today is information dashboards: visual displays that provide information about students' activities and their progress. The implementation of learning analytics contributes to a deeper understanding of the educational process and the identification of problem areas at early stages. Personalization of learning through AI opens new opportunities to increase students' motivation and success. In the future, the development of analytics technologies will play a key role in shaping effective educational systems and methodologies.