ANALYSIS OF EXISTING TECHNOLOGIES FOR ACCOUNTING AND PROCESSING STUDENTS ' DATA

Abstract

Education is currently undergoing a revolution full of information technology. They have not only changed the ways of learning, but also enriched the learning process with new data that can be used to improve learning outcomes and personalize the approach to each student. The analysis of existing technologies and methods of accounting and processing student data is becoming the main tool for the aspirations of educational institutions and teachers to effective learning. One of the most important technologies is Learning Management Systems, learning management systems that allow students to collect data such as time spent on the platform, past modules, test scores and tasks. Learning Management Systems allows you to analyze this data to evaluate the effectiveness of training programs, identify weaknesses and adjust courses. Another important tool is adaptive educational platforms that use machine learning algorithms to personalize learning. They analyze student data such as preferences, educational level, and learning rate, and provide personalized materials and assignments based on this data. Data analysis technologies play a crucial role in processing the collected data of students. They allow for various analyses, such as clustering students by academic performance or determining general trends in the learning process. These analytical tools help to identify successful learning methods and predict learning outcomes.