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

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