Evidence map›Paper›PMID 35958380›Full record

ArticleJournal of environmental and public health2022

The Collection and Utilization of Web Resources for Teaching World History Based on Data Mining Technology.

Xia Wu

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Journal of environmental and public health, 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 It has been retracted, and should not be counted.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
0.7field-weighted citation impact, top 25% of its field
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, 5 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Xia WuSchool of History Culture and Tourism of Liaocheng University, Liaocheng 252000, China.ORCID 0000-0002-8126-5056
Liaocheng University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The foundation of research for academics involved in world history education and research is timely access to pertinent foreign information, comprehension of domestic and international academic developments, and access to fundamental historical materials and research outputs from our ancestors. As a result, it is essential to investigate the necessity and viability of using network resources on the basis of analysis in order to respond to the educational philosophy of the new curriculum reform and adapt to the development of modern teaching methods based on network technology. The creation and use of curriculum resources is an essential component of curriculum development and a crucial assurance for curriculum implementation. The growth and acceptance of online learning will undoubtedly influence how history education is practised around the world. The scarcity of online learning resources is currently a bottleneck impeding the growth of online education. On the other hand, data mining (DM) takes the massive amount of incomplete data and extracts the useful knowledge and information hidden within it. This paper explores the "DM" process of utilizing online resources and suggests a method for gathering and utilizing world history education online resources based on DM technology. The experimental results show that the test interval between MapReduce and DM gradually increases with the increase of data volume. The advantage of DM is more obvious, as the average test time of DM is 27.66 seconds shorter than that of MapReduce. Therefore, DM has high application value in the field of search engines and social network analysis.

Indexed as

CurriculumEducation, DistanceData MiningTechnology

Identifiers

PMID35958380
PMCPMC9359857
OpenAlexW4289262298

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.