Technologies and Algorithms for the Implementation of the Recommendation System for Creating an Individual Study Plan for a Higher Education Student

EasyChair Preprint 15240

Abstract

The functionality of recommendation systems is implemented in a lot of popular services and marketing platforms, as they provide an opportunity to analyze and filter information in terms of choosing the most suitable products, content, or services for the user according to his preferences and capabilities. However,in education, such functionality is also in great demand, especially when it comes to choosing educational content according to preferences and learning styles in order to create an educational trajectory based on previous experience and academic performance. Lack of understanding of their capabilities and preferences or inability to build a structured learning vector is inherent for applicants and first-year students. That is why this article analyzes the main problems in the implementation of a recommendation system for building an individual study plan for a higher education student, identifies the main ways to implement such a system, and builds a basic algorithm for a our recommendation system that can provide top disciplines for a student to study based on his or her previous learning outcomes, the learning outcomes of students of the same specialty for a certain period, and the factor of similarity of subjects and the choice of his or her specialization.

Keyphrases: C#, Individual Study Plan, Microsoft.ML, Recommendation System, collaborative filtering

@booklet{EasyChair:15240,
  author    = {Olena Hlazunova and Yaroslav Ponzel},
  title     = {Technologies and Algorithms for the Implementation of the Recommendation System for Creating an Individual Study Plan for a Higher Education Student},
  howpublished = {EasyChair Preprint 15240},
  year      = {EasyChair, 2024}}