In this study on the classroom concentration of medical students, the trends of students’ attention over time and the influence of teachers’ linguistic characteristics on concentration were discussed. The aim was to understand the relationships between various factors and to explore strategies that could improve students' classroom attention, both individually and collectively.
The design of this study relies on the innovative application of two key technologies. The first one is the speech recognition toolkit called WeNet12. It is a great tool for educators to analyze large-scale datasets such as lecture recordings effectively. The other key technology is FRT, which was introduced in 20239. This study provides further evidence supporting the use of FRT in analyzing students’ concentration.
This study reveals several significant influential factors of teachers’ linguistic characteristics for students’ classroom attention through the analysis of sentences, including time, volume, and questioning techniques. Speaking speed and the interval between sentences and students’ concentration are considered to be nonsignificant influential factors. We also discuss the possible explanations of the curves of the changes in the concentrations of the questions and concentrations with time, which cyclically fluctuate, similar to periodic waves.
The research findings indicate that, akin to common knowledge, students' classroom concentration tends to decline as the class progresses13. Some studies suggest that students’ attention could hardly last for more than 30 minutes during a lecture15, 16. However, in Bunce’s statistical study, students pay attention in a shorter cycle of approximately 4.5 minutes, and the attention lapse occurs again at a shorter and shorter cycle through the lecture segment6. In this study, the relationship between the concentration and time was evidenced by an overall negative correlation between the two variables. According to the results of the scatter plots and polynomial smooth plots, this negative correlation may not be linear. Based on this observation, we conducted a practical exploration and found literature supporting the reproducibility of similar conclusions5, 17. Based on the visual results in this study, the cycle of the concentration degree fluctuation period was approximately 10–15 minutes, with a decreasing trend.
Another correlation finding indicates that volume is also a positive factor influencing concentration. This observation is consistent with previous literature, further corroborating each other's claims11.
The statistical insignificance of certain correlations also holds practical significance. Previous research on the impact of video playback speed on students’ attention and memory retention did not find significant relationships, suggesting that playback speeds of ×1.5-2.0 do not interfere with learning outcomes14, 18. In this study, the lack of significance between concentration and speaking speed and time interval further supports this conclusion.
In addition, the factors examined in this study included the change in volume from one sentence to its previous sentence. It is commonly assumed that a lecture without variation in volume may be monotonous, while fluctuations in volume might be more engaging. However, this study did not find significant effects of volume change on the concentration. According to the FRT and WeNet results, the impact of the change in volume on the concentration degree is nonsignificant in our study, while the impact of volume is significantly positive.
The results of the local polynomial smooth plots suggest that teachers may engage in unconscious subjective observations of students’ degree of concentration, relying on real-time feedback in their own mind as a potential basis for adjusting their questioning frequency in an attempt to motivate students to increase their level of attentiveness. When students are more focused, teachers may reduce their questioning frequency by employing fewer questioning strategies to advance classroom progress. As presented in the overlapping curves, if there is a causal relationship between the two variables, the response time of this effect should be relatively rapid.
This study also has certain limitations. First, according to the results of multiple linear regression, with only 7.09% of the R-squared value indicating that although factors such as time, volume, and the use of questioning have a significant impact on students’ concentration, they do not have a decisive effect. This is understandable because factors such as the type and subject of the course, the use of electronic devices, and the application of teaching methods also have relatively certain influences on classroom concentration1. Due to the basic nature of the speech recognition research method used in this paper, these factors were not fully incorporated into the analysis. Our previous study reported a recall rate of 81.4%, which might have led to the introduction of errors in this study9. Therefore, although the FRT was considered reliable in our study, there is still potential for improving its accuracy.
In the future, discussions on other factors affecting classroom attention should continue to expand. This study only conducted a preliminary analysis of the linguistic information of teachers during lectures, with relatively basic factors included. However, in today's context of promising developments in semantic recognition artificial intelligence, more information related to content meaning rather than simple speech features can also be incorporated into analysis11. This may be of significant reference value for teachers in class preparation and classroom strategy design.