| Issue |
E3S Web Conf.
Volume 431, 2023
XI International Scientific and Practical Conference Innovative Technologies in Environmental Science and Education (ITSE-2023)
|
|
|---|---|---|
| Article Number | 05014 | |
| Number of page(s) | 6 | |
| Section | IT and Mathematical Modeling in the Environment | |
| DOI | https://doi.org/10.1051/e3sconf/202343105014 | |
| Published online | 13 October 2023 | |
Machine learning to identify key success indicators
1 Bauman Moscow State Technical University, 105005 Moscow, Russia
2 Peter the Great St.Petersburg Polytechnic University, St. Petersburg, Russia
3 Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia
* Corresponding author: anna_glinskaja@rambler.ru
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.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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