Logic Artificial Intelligence Application for the Students Individual Trajectories Introduction

Abstract

The individual trajectories and other student learning individualization forms introduction in engineering education are becoming an important competitive university advantage. However, you should be mindful of the choices of learning paths within the framework of requirements of Federal State Educational Standards (FSES): to receive a diploma the student must fulfill all requirements of the FSES. Individualization cannot be arbitrary and must fit within the established framework of the curriculum. Students can study more than the established requirements of the FSES on an individual program. On the other hand, within the established restrictions of the FSES, there are enough alternatives for individualized training to choose the specialization of a certain professional area. For example, for students studying information technology, this specialization can be a choice between different economy sectors: banks, telecommunications, industrial production, logistics, aircraft and rocket engineering, car industry, Internet companies, social networks, etc. If we take developing computer technologies as a basis, then individualization can consist in a more detailed study of one area in IT: databases; expert systems; data security; distributed registries; artificial intelligence (AI); machine learning and image recognition; understanding of natural language; automated systems management and technological processes; robotics, etc. As we can see, opportunities for individualization of training for students exist even within the strict framework of training standards. The paper provides examples of such individualization of training with BMSTU students. Practical work has shown that individualization complicates the work and increases the time spent by university staff on managing trajectories in student learning. The achievements of mivar technologies of logical artificial intelligence allow automating routine operations for managing students’ individual trajectories. In general, artificial intelligence can help in almost all tasks of engineering education in the transition to continuous people training “through life”.

Student Performance Prediction Approach Based on Educational Data Mining

“…Numerous studies related to student development have been published within the field of education. Numerous studies cover various aspects, including student dropout rates [2], student profiling [3]- [5], monitoring student academic performance [6]- [9], and predicting at-risk students [10].…”