Using Machine Learning to Analyze the Learning Process for Solving Mathematical Problems
Dublin Core
Title
Using Machine Learning to Analyze the Learning Process for Solving Mathematical Problems
Subject
Artificial intelligence
Informatics
Mental schemas
Pedagogy
Description
The relevance of the research under consideration is due to the need to improve the efficiency of the analysis of the quality, and completeness of the knowledge obtained by students when solving computational problems, the example problems in mathematics. The theoretical argumentation is proposed and the practical implementation of an intelligent automated analytical system for analyzing the quality and forecasting the content of educational material and the trajectories of the student's learning direction is described. The relevance of the research is the creation of neural network algorithms that allow analyzing the dynamics of changes in the student's level of formation of skills to solve arithmetic problems. The methods of analyzing the assimilation of educational information and methods of personalized construction of the curriculum for each student are substantiated.
Creator
Bauyrzhan , Nauryzbayev
Sakysh , Baygamitova
Zhanar, Akhmetova
Nikolay, Pak
Ardak , Karipzhanova
Kumys, Urazbaeva
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 21 (2022); pp. 114-124
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2022-11-15
Rights
Copyright (c) 2022 Nauryzbayev Bauyrzhan , Baygamitova Sakysh , Akhmetova Zhanar, Pak Nikola, Karipzhanova Ardak , Urazbaeva Kumy
https://creativecommons.org/licenses/by/4.0
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
Bauyrzhan , Nauryzbayev et al., Using Machine Learning to Analyze the Learning Process for Solving Mathematical Problems, International Association of Online Engineering (IAOE), Vienna, Austria, 2022, accessed November 2, 2024, https://igi.indrastra.com/items/show/2438