Systems and Means of Informatics
2023, Volume 33, Issue 1, pp 68-77
METHODS OF CLASSIFYING THE DISTANCE LEARNING SYSTEM USERS IN THE MODEL OF CONSTRUCTING THEIR PERS ONALIZED LEARNING STRATEGIES
- Ya. G. Martyushova
- T. A. Mineyeva
- A. V. Naumov
Abstract
The article examines the problem of adaptation of the distance learning system to the contingent of users by constructing personalized learning strategies using their previous tests results. The main part of the suggested model is classifying users by various academic progress criteria. Comparative analysis of results of applying different classifiers for this purpose is presented.
The following types of classifiers were used: Bayes classifier, logistic regression, k-nearest neighbors algorithm, decision tree, random forest, boosting, and bootstrap aggregating classifier that uses a majority vote as the voting scheme.
The article presents the results of a numerical experiment using the data on the work of MAI distance learning system CLASS.NET.
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[+] About this article
Title
METHODS OF CLASSIFYING THE DISTANCE LEARNING SYSTEM USERS IN THE MODEL OF CONSTRUCTING THEIR PERS ONALIZED LEARNING STRATEGIES
Journal
Systems and Means of Informatics
Volume 33, Issue 1, pp 68-77
Cover Date
2023-05-11
DOI
10.14357/08696527230107
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
finite classification task; cause-and-effect relationships; machine learning in distortions conditions
Authors
Ya. G. Martyushova , T. A. Mineyeva , and A. V. Naumov
Author Affiliations
Moscow State Aviation Institute (National Research University), 4 Volokolamskoe Shosse, Moscow 125933, Russian Federation
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