Results of developing the recommendation system for electronic educational resource selection

Abstract

This paper proposes developing a recommendation system based on fuzzy logic methods for expert evaluation of electronic educational resources (EERs) and decision-making regarding selecting the most effective resources for educational processes. The concepts of recommendation systems for selecting optimal EERs are examined and analyzed. Scientific publications on expert evaluation and recommendation system utilization are reviewed. The overall structure of the recommendation system is presented, along with descriptions of its subsystems. Fuzzy logic methodologies are used for the assessment of EERs, with a welldefined procedural framework and explicit algorithmic representation. Expert analysis results in a compilation of recommended EER alternatives that align with specified criteria. The EER selection recommendation system is further elucidated through the generation of UML diagrams delineating use cases, sequences, and activities. The initial phases of user engagement with the recommendation system are described in depth, facilitating the selection of electronic learning resources. The recommendation system is introduced with a three-tier architecture consisting of presentation, application, and data administration layers. EER collection, expert criterion-based evaluation input, recommendation rating computation, recommended resource list formation, data visualization, authorization implementation, access provisioning, and creation of the administrator interface are the main technological elements that are described in detail. For each of these elements, implementation strategies and tools are explained. Programming code for the development of each of the key recommendation system stages is provided. The most significant elements of the web application interface are demonstrated, including the recommendation system's criterion selection page, administrator panel, category modification and addition panel, and the recommendation system's output.