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Development of the architecture of a recommendation system for choosing online courses

The article provides a rationale for the relevance of developing a recommender system in the field of e-learning. The main approaches to building a recommender system are analyzed: collaborative, content and hybrid filtering. The main objects of the recommender system for choosing online courses are presented: the student, training modules (online courses), elements of knowledge that the user can receive at the end of the training. In algorithmic support, methods for creating recommender systems, such as machine learning, neural networks, genetic algorithms, are considered. Problems in the methods of building recommender systems have been identified: sparseness; cold start; scalability; searching for elements that are most likely to be preferred by the user from a common set of elements. The main problem of recommender systems is to obtain an accurate and high-quality recommendation for the selection of educational objects in accordance with user preferences. It is concluded that it is necessary to build an architecture of a recommender system, including a model of an individual learning trajectory. Filtration of educational objects occurs with the help of a genetic algorithm. The expediency of using a microservice approach to create a web application is determined. The functional tasks of the developed system are highlighted, such as data collection, analysis of user requests, the formation of educational objects using an individual learning trajectory and the issuance of recommendations for choosing online courses. An algorithm for the functioning of the recommender system, a scheme for the operation of the recommender system, as well as information support for the operation of this system have been developed. A general approach to the development of a universal recommender system that can be integrated into the client's service is proposed. The purpose of developing a recommender system for choosing online courses is to provide students with the most appropriate learning objects (sequence of objects) to study in accordance with the characteristics of the student and fragments of knowledge (competencies). Read more...

Formation of an individual trajectory of online learning on the basis of cluster analysis

Justification for the relevance of developing an individual learning path in the field of online learning. The problems of forming an individual learning trajectory are analyzed. The main problem of personalization of learning from the point of view of the student is highlighted – the difficulty in finding the most appropriate sequence of studying educational objects that best suit their skills and preferences. It is concluded that the existing practices and methods of organizing a personalized educational process of courses in online learning are focused on the statistical characteristics of students that do not change during the study of an online course. Therefore, there is a need to develop a methodology for the formation of an individual learning path. The proposed approach allows us to consider the formation of recommendations as a dynamic process. An algorithm for the formation of an individual learning trajectory has been developed, which consists of a multi-criteria choice of a sequence of online courses at each moment of decision-making according to a given set of criteria and sequential mastering of skills. The choice of online courses is carried out using the cluster analysis method – k-means. Groups of clusters that meet the criteria of online courses have been identified. Each cluster consists of the closest objects – online courses. Based on these results, a sequential selection of online courses is made, using the available information about the user»s requirements and the skills that the learner needs to acquire. The purpose of developing for the formation of an individual learning trajectory is to provide students with the most appropriate sequence of learning objects in accordance with their skills and preferences. Read more...