Application of self-organizing neural networks to the process of forming an individual learning path

The problem of dynamic classification of students within the framework of supporting the process of forming an individual trajectory of the user of an electronic learning system is considered. The training model is designed for a mixed form of educational activities with partial independent work and periodic control events in the form of distance tests, partial full-time work with offline tests, and offset tests. The purpose of the classification is to determine the category of the student based on the results of the next control event. The semantics of the categories suggests the possibility of an individual task of different levels of task complexity at the next step of learning. The direction of improving the existing methods of classification is the rejection of the accumulation and use of statistics from previous (other) groups of students. The absence of samples of correct classification justified the use of self-organizing neural networks. For the solution, Kohonen's maps were used, the standard version of which is adapted to the existing learning model and to the task of taking into account the subjective evaluation policy of the teacher. Three variants of the self-learning algorithm are described. Experimental research was carried out, its results are illustrated.