Personalized training model for organizing blended and lifelong distance learning courses and its efectiveness in Higher Education

Abstract

The main goal of this research is to improve the personifcation of learning in higher education. The proposed fexible model for organizing blended and distance learning in higher education involves the creation of an individual learning path through testing students before the start of training. Based on the learning outcomes, the student is credited to the learning path. The training path consists of mandatory and additional modules for training; additional modules can be skipped after successfully passing the test, without studying these modules. The paper examines the composition of intelligent learning systems: student model, learning model and interface model. A student model is described, which contains the level of their knowledge, skills and abilities, the ability to learn, the ability to complete tasks (whether they know how to use the information received or not), personal characteristics (type, orientation) and other factors. The student’s model is described by a mathematical formula. Thus, being described using logical rules, which have formed the basis for the software implementation of mixed and distance learning rules for lifelong learning courses. There is an interface model presented in the paper, and the results of the course of the proposed fexible model for the organization of mixed and distance learning “Digital Skills of a Modern Teacher in the Context of Distance Learning”, as well as the face-to-face course “Digital Learning for Everyone” before the start of the pandemic which is close in its content to the course under study. Based on the results of the analysis, we introduced criteria for the efectiveness of the training course, proposed the weighting coefcients for evaluating the training course, carried out the assessment and drew conclusions.