Department 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.
BMC Med Educ. 2024 Oct 31;24(1):1236. doi: 10.1186/s12909-024-06204-5.
The 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.
This research aims to analyze how teachers' linguistic characteristics affect medical students' classroom concentration based on a speech recognition toolkit and face recognition technology.
A 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.
As 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.
The 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.
在促进医学生掌握知识方面，学生在课堂上的专注度非常重要。已经证实，语速、音量和提问使用等多种教学特点是影响因素。
本研究旨在基于语音识别工具包和人脸识别技术，分析教师的语言特征如何影响医学生的课堂注意力。
本研究使用语音识别工具包 WeNet 识别讲座中的句子。人脸识别技术（FRT）用于检测学生在课堂上的注意力。研究涉及 80 名口腔医学专业的本科生。2022 年 10 月，收集了口腔解剖学课程 5 个小时的课堂视频。本研究采用定量研究方法。采用 Pearson 相关、Spearman 相关和多元线性回归分析，分析时间和教师语言特征对学生注意力的影响。
回归分析结果表明，语言特征的影响解释力为 7.09%（F=83.82，P<0.001），其中音量和提问具有显著影响（P<0.01）。时间与提问使用之间的浓度散点的局部多项式平滑似乎呈周期性波动，表明提问使用与浓度之间可能存在反比关系。
本研究结果支持音量和提问技巧具有显著的积极影响、时间具有负面影响，以及语速和句子间隔对学生注意力的影响不显著。本研究还表明，教师可以根据观察到的学生注意力来调整提问频率。
