Evidence map›Paper›PMID 35942141›Full record

ArticleJournal of environmental and public health2022

Digital Construction of Vocal Music Teaching Resource Base Using Data Mining Technology.

Ning Xu

RetractedAbstract 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 It has been retracted, and should not be counted.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
–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

  • Retraction · 2023-09-27Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/6A6AC50DAF0FE021898D000FAF23B1
  • Retracted
5 · Who and what money

Authors and funding

1 author.

Ning XuLiaocheng University Dongchang College, Liaocheng 252000, China.ORCID 0000-0002-1124-4911

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vocal music teaching resource databases can support teachers' instruction and students' learning while also enhancing the school's ability to run a school because they are the main components of digital construction for vocal music teaching. Currently, system application is the main area of research for DM (data mining), which was previously focused on method discovery. A vocal music teaching resource base based on DM technology is created by using the theme as the central organising principle, processing, and organising the resources. Incorporate lesson planning, teaching, self-study, and output resource construction into a digital network resource application environment by using thematic resource development. Students' achievement data, personal basic information, and evaluation data are used for DM based on the enhanced Apriori algorithm in order to uncover hidden rules and find correlations between various factors. This information is then used to support decision-making. When the system load is 1, the results demonstrate that the Apriori algorithm's CPU waste rate and task success rate is 0.144 and 0.896, respectively. The study's conclusion demonstrates that integrating DM technology into the field of education is theoretically and practically possible and that there is significant room for future study and application in this area.

Indexed as

MusicData MiningHumansSchoolsStudentsTechnology

Identifiers

PMID35942141
PMCPMC9356841

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.