Cargando…

Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background

With the development of Internet technology and the arrival of the knowledge-driven era, the breadth and depth of educational informatization are increasing day by day. Educational technology is not only a subject but also a career adapted to education and teaching. The growth speed of modern educat...

Descripción completa

Detalles Bibliográficos
Autores principales: Hong, Xueqiong, Wang, Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525196/
https://www.ncbi.nlm.nih.gov/pubmed/36188712
http://dx.doi.org/10.1155/2022/1924138
_version_ 1784800655683092480
author Hong, Xueqiong
Wang, Lin
author_facet Hong, Xueqiong
Wang, Lin
author_sort Hong, Xueqiong
collection PubMed
description With the development of Internet technology and the arrival of the knowledge-driven era, the breadth and depth of educational informatization are increasing day by day. Educational technology is not only a subject but also a career adapted to education and teaching. The growth speed of modern educational technology and the size of its benefits determine its management level to a large extent. With new technologies, new ideas, and new social needs, it is difficult for new ideas, new thoughts, and new methods to make the traditional e-learning management to accommodate the demands of the new era. At present, the work efficiency of modern educational technology visualization systems is generally not high, and modern distance teaching has an increasing demand for management informatization. However, there is a lack of a management platform for distance education that adapts to organizational characteristics such as openness, dynamics, flexibility, individualization, and decentralization. Therefore, this study introduces machine learning and BP neural network, establishes a visual modern distance teaching management system model, and uses machine learning algorithms to learn the visual process. The experimental results show that the system efficiency after learning is higher, and the time required for visualization of different groups in the experiment is 14.32 s, 13.18 s, 12.27 s, and 13.64 s, respectively, which effectively improves the efficiency of visualization and reduces the consumption of human resources.
format Online
Article
Text
id pubmed-9525196
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-95251962022-10-01 Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background Hong, Xueqiong Wang, Lin Comput Intell Neurosci Research Article With the development of Internet technology and the arrival of the knowledge-driven era, the breadth and depth of educational informatization are increasing day by day. Educational technology is not only a subject but also a career adapted to education and teaching. The growth speed of modern educational technology and the size of its benefits determine its management level to a large extent. With new technologies, new ideas, and new social needs, it is difficult for new ideas, new thoughts, and new methods to make the traditional e-learning management to accommodate the demands of the new era. At present, the work efficiency of modern educational technology visualization systems is generally not high, and modern distance teaching has an increasing demand for management informatization. However, there is a lack of a management platform for distance education that adapts to organizational characteristics such as openness, dynamics, flexibility, individualization, and decentralization. Therefore, this study introduces machine learning and BP neural network, establishes a visual modern distance teaching management system model, and uses machine learning algorithms to learn the visual process. The experimental results show that the system efficiency after learning is higher, and the time required for visualization of different groups in the experiment is 14.32 s, 13.18 s, 12.27 s, and 13.64 s, respectively, which effectively improves the efficiency of visualization and reduces the consumption of human resources. Hindawi 2022-09-23 /pmc/articles/PMC9525196/ /pubmed/36188712 http://dx.doi.org/10.1155/2022/1924138 Text en Copyright © 2022 Xueqiong Hong and Lin Wang. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hong, Xueqiong
Wang, Lin
Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background
title Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background
title_full Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background
title_fullStr Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background
title_full_unstemmed Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background
title_short Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background
title_sort visual resolve of modern educational technology based on artificial intelligence under the digital background
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525196/
https://www.ncbi.nlm.nih.gov/pubmed/36188712
http://dx.doi.org/10.1155/2022/1924138
work_keys_str_mv AT hongxueqiong visualresolveofmoderneducationaltechnologybasedonartificialintelligenceunderthedigitalbackground
AT wanglin visualresolveofmoderneducationaltechnologybasedonartificialintelligenceunderthedigitalbackground