Automated formation of discipline sequences for higher ecological and agricultural education using mivar expert systems

Abstract

Training qualified specialists for agriculture, ecology and industry is becoming increasingly important in today’s rapidly changing world. The constant development of science and technology leads to an expansion of the required knowledge, which creates difficulties for students in assimilating huge amounts of information in a limited time. This discrepancy requires constant updating and improvement of educational programs, ensuring the inclusion of relevant courses and workshops, and maintaining a logical sequence to prevent knowledge gaps. At Bauman Moscow State Technical University (BMSTU), about 25,000 students study in more than 600 programs, including ecology and forestry, using the Electronic University system for automated management of educational processes. Logical AI helps in planning individual educational trajectories, improving decision-making and quality control, especially in the field of agriculture and ecology. The development of mivar networks for educational programs further optimizes management, an example of which is the construction of mivar networks for specific courses. This approach solves the problem of managing large unstructured volumes of data, providing a model for transforming input knowledge into competencies. The integration of mivar expert systems offers a structured method for sequencing courses, ultimately improving the educational structure at Bauman Moscow State Technical University.