Evidence map›Paper›PMID 35990146›Full record

ArticleComputational intelligence and neuroscience2022

The Reform of University Education Teaching Based on Cloud Computing and Big Data Background.

Jing Li, Lei Liu

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 It has been retracted, and should not be counted.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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Jing LiSchool of Management, Hubei University of Chinese Medicine, Wuhan, Hubei 430065, China.ORCID https://orcid.org/0000-0002-5408-927X
Lei LiuHubei University of Technology Engineering and Technology College, Wuhan, Hubei 430065, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the era of big data and cloud computing, traditional college teaching model needs to be revolutionized in order to adapt to the needs of the present generation. The traditional college teaching model is currently facing unprecedented severe challenges which could be optimistically considered as a huge scope of development opportunity. In order to promote the gradual transformation of college teaching toward digitization, intelligence, and modernization, this paper comprehensively analyzes the impact of science and technology on college teaching. It further encourages the omnidirectional and multifaceted amalgamation of education with big data and cloud computing technology with an objective to improve the overall teaching level of colleges and universities. In order to realize the accurate evaluation of university teaching reform and improve teaching quality, the study presents an evaluation method of university teaching reform based on in-depth research network. Then, it further analyzes the main contents of university teaching reform, establishes the evaluation department of university teaching reform, and then establishes the evaluation model of university education reform. This is achieved by analyzing the relationship between university education reform and indicators using in-depth learning network followed by the development of simulation experiments pertinent to evaluation of university education reform. The results show that this method is helpful in improving the teaching quality.

Indexed as

Big DataCloud ComputingHumansTeachingUniversities

Identifiers

PMID35990146
PMCPMC9391095

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.