Designing an Algorithm for Automated Analysis of Completed Tasks in a Mobile Learning System

Abstract

Purpose: this abstract discusses an urgent problem in the modern education system associated with the need to adapt to rapid changes in social spheres and the economy. The main purpose of the study is to develop an adaptive mobile learning environment for higher education institutions, with an emphasis on the use of electronic (mobile) learning and information and analytical support for the educational process.

Methods: to achieve this goal, an analysis of existing research and publications in the field of educational technologies and the effectiveness of e-learning was carried out. The objectives of adaptive mobile learning technologies are formulated, including the creation of a virtual learning environment and the development of effective methods of text processing in distance learning.

Results: in the course of the study, an algorithm for an adaptive mobile learning environment system based on an autonomous analysis of completed tasks using neural networks was proposed. To ensure the reliability of the system, it is proposed to implement a task analysis service as a background process. A scheme for transferring information from mobile applications to a background analytical service has been developed, taking into account the use of the RabbitMQ queue for organizing communication between services. An algorithm for detailed analysis of the text of completed assignments of students has also been developed.

Conclusions: the study highlights the need to adapt educational systems to modern challenges and the possibility of using adaptive mobile learning technologies. The developed algorithm and system provide an effective analysis of assignments and text responses of students, which makes them relevant in the context of modern information technologies and education.