This paper discusses the factors that determine the customisation of e-learning programmes. The process of customisation depends on many parameters, such as the objectives of the programme, the quantity and order of the learning materials, the personality and abilities of the student, and the resources within the learning system. Curriculum developers are able to put together these parameters in varying combinations, reflecting differing educational strategies. Because of this possibility it has become important to study how one can determine an appropriate strategy or learning path for any individual student. This is becoming particularly relevant because curriculum developers have to consider large numbers of already developed learning courses, modules, and technologies. One of the approaches to addressing this problem is the classification, or taxonomy, of customisation parameters. This paper reviews published material from highly-rated journals dealing with customisation of learning. As a result of this review the groups of customisation parameters are identified and a generalised scheme of grouped parameters, and their sequence, corresponding to the inner logic of the learning process are developed. This taxonomy allows the educational activities to be arranged so that learners can achieve their learning goals more efficiently.

The article presents the practical implementation of the module for calculating the individual schedule for mastering the materials of an academic discipline by students. The aim of the study was to calculate the complexity of the learner’s assimilation of the concepts of the studied subject area, correlate the calculated data with the actual ones and establish the applicability of the proposed methodology for calculating an individual schedule for mastering educational materials for planning the educational process. The generation of the sum of concepts for mastering was carried out on the basis of the concept tree of the subject area. The labor intensity of mastering an academic discipline is considered as the sum of the complexity of each concept. The calculation of the labour intensity of the concept was carried out taking into account the time of perception of information by students, the complexity of the concept and taking into account the type of activity. As a result, a program implementation of the algorithm for forming an individual schedule for mastering the materials of an academic discipline was made, the labor intensity of mastering each concept was calculated and the total labor intensity of mastering an academic discipline was obtained. When comparing the estimated labor intensity with the actual one, it was established that the difference is not more than 10 %. The generated sequence of concepts for study is sufficient to achieve the goals of training students in the direction of “Information systems and technologies”. The presented results are of direct practical importance and can be used to plan a training course and to build a dynamically changing individual schedule for mastering the materials of an academic discipline, taking into account the cognitive and physical characteristics of students, as well as taking into account the peculiarities of the organization of the learning process.

The article is devoted to the automation of the educational process in terms of measuring the achievements of student in electronic training systems (EOS) to solve the problems of creating an individual training schedule. Psychological and pedagogical concepts used for evaluation of learning material acquisition are considered. It has been found that the approaches used to measure the achievements of students are not suitable for the electronic environment. In order to build an individual schedule for studying materials in the educational discipline in the EOS, it is proposed to measure the amount of work done by the student. To achieve this goal, a scale developed on the basis of the structure of its activity of the trainee in the process of achieving the objectives of the training is proposed. Five steps are highlighted in the achievement of the learning goal: 1) mastering the conceptual framework, 2) assimilation of knowledge of a way of actions, 3) formation of ability, 4) formation of skill, 5) formation of ability to solve non-standard problems. Considering the obtained stages of formation of ability to carry out studied activities as levels of mastery of educational material, a scale of levels of mastery is obtained. The types of tasks in LMS Moodle are given, which can be used to monitor the achievement of the selected levels of learning material. This scale can be used to calculate the labour intensity of the student’s work during the mastering of educational material, to plan the curriculum, to form an individual training schedule.

The article presents some methods for creating knowledge graphs – hierarchical structures used in the educational environment for course development. It is found that the educational environment requires subject-oriented knowledge graphs, for the creation of which the methods of creating general and open graphs are not suitable. The methods of constructing the system of notions of educational discipline on the basis of: analysis of educational texts; human activity in the studied subject area; analysis of the structure of the main sections of knowledge are considered. The central aspect of the study was the possibility of using the resulting tree of concepts to form the content of the training course and to build an individual educational trajectory. The results of the work demonstrate the possibility of applying the developed hierarchical structures to personalize learning. The practical significance of the obtained results lies in the fact that the proposed solutions are focused on computer implementation and are relevant for the management of the learner’s learning activities in the electronic environment. The proposed methodology involves the participation of professional and scientific communities, teachers and students in the development of the knowledge graph. This makes it possible to develop subsequently, on the basis of these graphs, training programs, taking into account the demands of the labor market, the capabilities of the training organization, as well as the goals of trainees. One important indicator of the quality of knowledge graphs is their relevance to a dynamically changing environment. The ability to build up knowledge graphs allows to maintain the relevance of training courses and individual educational trajectories created on their basis.