Evidence map›Paper›PMID 35345795›Full record

ArticleComputational intelligence and neuroscience2022

Application of Machine Learning and Internet of Things Techniques in Evaluation of English Teaching Effect in Colleges.

Wen-Lan Shang

Abstract read
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. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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

1 author.

Wen-Lan ShangBasic Teaching Department, Huanghe Jiaotong University, Wuzhi, Henan 454950, China.ORCID https://orcid.org/0000-0002-3012-3891

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

College English classroom teaching evaluation is one of the key issues discussed by all schools at present. First of all, teachers and students are highly concerned about English learning in the compulsory course of English examination, but there are also many problems. The distance network teaching with computer technology as the main body develops rapidly, a large number of video and audio teaching resources network through the network transmission to present in front of learners, through the network transmission of video and audio aided, expand the audience of network teaching, is conducive to the realization of digitalization, information, lifelong, new education goals. Firstly, this article summarizes the problems of poor effect, uneven performance, and mismatch between evaluation and teaching in college English at present. Then, based on the previous works, the author designed a machine learning-based Internet of things technology and verifies its feasibility. The data of teaching experiments show that the writing performance of the students in the lower section of the experimental class is better than that of the students in the middle and high sections. Among them, significant progresses have been made in grammar, length, expression, and structure, which has optimized students' preclass preview efficiency, autonomous learning motivation, quality of homework completion, and afterclass reflection behavior. Finally, the author summarized the shortcomings of this research and puts forward the prospect of relevant research, in order to provide reference value for the national college English blended teaching and promotes the efficient implementation and vigorous development of college English teaching. In the future, the evaluation index system under the pressure of performance can be further studied.

Indexed as

Internet of ThingsHumansInternetMachine LearningStudentsUniversities

Identifiers

PMID35345795
PMCPMC8957406

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.