Evidence map›Paper›PMID 36188712›Full record

ArticleComputational intelligence and neuroscience2022

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

Xueqiong Hong, Lin Wang

RetractedAbstract readRetracted Publication
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Xueqiong HongGongqing College of Nanchang University, Gongqingcheng 332020, Jiangxi, China.
Lin WangAnyang University, Anyang-si, Gyeonggi-do 14028, Republic of Korea.ORCID https://orcid.org/0000-0002-2561-7512

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceEducational TechnologyHumansTechnology

Identifiers

PMID36188712
PMCPMC9525196

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.