Development and Research of Algorithms for the Formation of an Optimal Individual Educational Path for Online Courses

DOI

10.2991/aebmr.k.200502.161How to use a DOI?

Keywords

genetic algorithm, greedy algorithm, discrete optimization, individual educational path, coverage problem

Abstract

An important condition for making an effective decision in the field of e-learning is the analysis of data from participants in the educational process at its various stages. Currently, the volume of data circulating in a digital educational environment that supports working with online courses is growing exponentially. This is facilitated by the rapidly growing demand for open education. In such an environment, for the effective study of materials, it is necessary to carry out a personified process control of the flow of knowledge. One way is the formation of individual educational paths. In the framework of this study, a model for the formation of the individual educational path of the student based on the solution of the problem of minimal coverage of the graph is proposed. As the basic parameters of the model, a competency-based approach is applied, superimposed on the structural description of the relationships of the individual components of the online course. In the framework of the study, genetic and greedy algorithms were used to form an educational path, as well as a comparative analysis of the approaches presented.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - D.I. Parfenov
AU  - L. Zabrodina
AU  - V. Zaporozhko
PY  - 2020
DA  - 2020/05/05
TI  - Development and Research of Algorithms for the Formation of an Optimal Individual Educational Path for Online Courses
BT  - Proceedings of the 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth” (MTDE 2020)
PB  - Atlantis Press
SP  - 978
EP  - 983
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.200502.161
DO  - 10.2991/aebmr.k.200502.161
ID  - Parfenov2020
ER  -