Evidence map›Paper›PMID 39478559›Full record

ArticleBMC medical education2024

Influential factors for medical students' classroom concentration-evaluation with speech recognition and face recognition technology.

Xiaohan Chai, Jingwen Yang, Yunsong Liu

Abstract read
In one paragraph

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

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Observational
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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

3 authors.

Xiaohan ChaiDepartment of Prosthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials & Virtual Teaching and Research Section(VTRS) of Prosthodontics , 22 Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China.
Jingwen YangDepartment of Prosthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials & Virtual Teaching and Research Section(VTRS) of Prosthodontics , 22 Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China. jingwen.yang@foxmail.com.
Yunsong LiuDepartment of Prosthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials & Virtual Teaching and Research Section(VTRS) of Prosthodontics , 22 Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China. liuyunsong@hsc.pku.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

statement of the problemThe concentration of medical students in the classroom is important in promoting their mastery of knowledge. Multiple teaching characteristics, such as speaking speed, voice volume, and question use, are confirmed to be influential factors. PURPOSE: This research aims to analyze how teachers' linguistic characteristics affect medical students' classroom concentration based on a speech recognition toolkit and face recognition technology. MATERIALS AND

methodsA speech recognition toolkit, WeNet, is used to recognize sentences during lectures in this study. Face recognition technology (FRT) is used to detect students' concentration in class. The study involved 80 undergraduate students majoring in stomatology. The classroom videos of 5 class hours in the dental anatomy course were collected in October 2022. A quantitative research methodology is used in this study. Pearson correlation, Spearman correlation and multiple linear regression analyses were used to analyze the impact of time and teachers' linguistic characteristics on students' concentration.

resultsAs a result of regression analysis, the explanatory power of the effect of the linguistic characteristics was 7.09% (F = 83.82, P < 0.001), with time, volume and question being significant influencing factors (P < 0.01). The local polynomial smooth of the scatter between the concentration degree and the use of questions with time appears to fluctuate cyclically and suggests a potential inverse relationship between the use of questions and the concentration degree.

conclusionsThe results of this study support the significant positive influence of volume and questioning technique, the negative influence of time, and the insignificant influence of speaking speed and the interval between sentences on students' concentration. This study also suggested that teachers may adjust their questioning frequency based on their observation of students' concentration.

Indexed as

Speech Recognition SoftwareStudents, MedicalAutomated Facial RecognitionEducational MeasurementEducation, Medical, UndergraduateFacial RecognitionFemaleHumansMaleArtificial intellengenceClass concentrationFace recognition technologySpeech recognitionTeaching strategies

Identifiers

PMID39478559
PMCPMC11526676

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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.