Evidence map›Paper›PMID 38707436›Full record

ArticleHeliyon2024

Artificial multi-verse optimisation for predicting the effect of ideological and political theory course.

Xingzhong Zhuang, Zhaodi Yi, Yuqing Wang, Yi Chen, Sudan Yu

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xingzhong ZhuangCollege of Marxism, Wenzhou University, Wenzhou, 325035, China.
Zhaodi YiCollege of Marxism, Wenzhou University, Wenzhou, 325035, China.
Yuqing WangCollege of law, Wenzhou University, Wenzhou, 325035, China.
Yi ChenDepartment of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
Sudan YuDepartment of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enhancing teaching sufficiency is crucial because low teaching efficiency has always been a widespread issue in ideological and political theory course. Evaluating data on the course is obtained from a freshmen class of 2022 using questionnaires. The data is organised and condensed for mining and analysis. Subsequently, an intelligent artificial multi-verse optimizer (AMVO) method s developed to predict the effect of ideological and political theory course. The proposed AMVO approach was tested against various cutting-edge algorithms to demonstrate its effectiveness and stability on the benchmark functions. The experimental results indicated that AMVO ranked first among the 23 test functions. Furthermore, the binary AMVO enhanced k-nearest neighbour classifier had excellent performance in the art ideological and political theory course in terms of error rate, accuracy, specificity and sensitivity. This model can predict the overall evaluation attitude of freshmen towards the course based on the dataset. In addition, we can further analyse the potential correlations between factors that enhance the intellectual and political content of the course. This model can further refine the evaluation of ideological and political courses by teachers and students in our school, thereby achieving the fundamental goal of moral cultivation.

Indexed as

Art ideological and political theory courseArtificial multi-verse optimizerClassificationTeaching sufficiency

Identifiers

PMID38707436
PMCPMC11066315

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.