Influential factors for medical students’ classroom concentration—evaluation with speech recognition and face recognition technology

Abstract

Abstract Statement of the problem 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. 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 methods 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. Results 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. Conclusions 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.

Keywords

• Teaching strategies
• Class concentration
• Artificial intellengence
• Speech recognition
• Face recognition technology