Machine learning to identify key success indicators

This article explores the application of machine learning techniques in the context of identifying and analyzing key indicators of learner success. In particular, the paper focuses on the application of machine learning techniques such as decision trees, Kohonen maps and neural networks. Decision tr...

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Bibliographic Details
Published inE3S web of conferences Vol. 431; p. 5014
Main Authors Nelyub, Vladimir, Glinscaya, Anna, Kukartsev, Vladislav, Borodulin, Alexey, Evsyukov, Dmitry
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 01.01.2023
Subjects
Data structures
Decision trees
Indicators
Learning algorithms
Machine learning
Neural networks
Optimization
Regression analysis
Teaching methods
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Summary:This article explores the application of machine learning techniques in the context of identifying and analyzing key indicators of learner success. In particular, the paper focuses on the application of machine learning techniques such as decision trees, Kohonen maps and neural networks. Decision trees are a graphical model that helps to analyze and make decisions based on hierarchical data structure. They allow classification and regression analysis, which helps in highlighting optimal strategies and recommendations to improve learner success. Kohonen map are used to highlight key success indicators, find hidden patterns and group data. Neural networks are able to analyze complex relationships and predict outcomes based on input data. The selected machine learning methods allow to optimize the learning process, adapt teaching methods to individual needs and increase the effectiveness of education in general.
Bibliography:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202343105014