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The absence of students' willingness for classroom participation is known to lead to a lose-lose situation where teaching schedules are disrupted, teachers lose enthusiasm, and students hardly learn anything. The problem is plaguing Chinese college EFL teachers as much as it did decades ago. Large quantities of studies have tackled the problem; however, little research has considered how students' psychology might link to classroom reticence. One psychological factor accountable for inaction when in the presence of a group of people is diffusion of responsibility. Thus, this study explores whether diffusion of responsibility plays a part in college EFL classroom reticence and whether there are any associations between diffusion of responsibility and gender, or English proficiency, or question type, or class size. Data from a questionnaire, interviews, and classroom observations showed that diffusion of responsibility was indeed a cause for class reticence. Further, Spearman correlation analyses found that no correlation existed between diffusion of responsibility and gender, or English proficiency. Paired-samples t-tests showed class size did have an impact on diffusion of responsibility while question type did not make a difference. Several suggestions on containing diffusion of responsibility and building rapport were put forward. The results should assist EFL teachers to work out possible solutions to the problem of class reticence.

[Purpose/Significance] As a new means of information dissemination, short videos have become a new favorite of some people in spreading rumors and refuting rumors due to their characteristics of visibility, convenience, and freedom of expression. It is of great significance to understand the content characteristics of health short videos in rumor refuting and improve their spreading effect. [Method/Process] This paper uses Python to conduct data mining, crawling the rumor refuting short videos related to the theme of COVID-19 epidemic prevention and control as samples, and the data set obtained includes the published articles and the original data, such as the number of forwards, and comments. Based on "logos", "ethos" and "pathos" in Aristotle's rhetoric theory, this paper codes the persuasion strategies of sample videos in combination with the coding standard table of 13 persuasion strategies under four categories. At the same time, it divides short videos into seven video elements under two categories and ten themes under five categories, and encodes samples from two aspects of video elements and themes. The information processing was carried out as follows. First, the Apriori algorithm was used to mine the types of persuasion strategies used, the relationships among various types of persuasion strategies, the video elements commonly used in persuasion strategies, and the correlation between persuasion strategies and rumors in the short health video samples with the theme of COVID-19 epidemic prevention and control. Then, short videos using different numbers or types of persuasion strategies were grouped, and one-way ANOVA was used to explore the relationship between persuasion strategies in short videos of refuting rumors and user responses in short videos. Finally, we proposed measures to promote the dissemination effect of anti-rumor short videos and promote the positive development of anti-rumor short videos. [Results/Conclusions] There is a clear correlation between some video elements, rumor types, and the use of persuasive strategies: the persuasive strategies of health related anti-rumor short videos are mainly logical and diverse. When using logical, emotional, reputation, or other persuasive strategies, videos will combine pure text, non-pure text, voice, or music video elements. In refutation short videos, short videos that use both logic and other persuasive strategies are more likely to be accepted by users. In the future, short videos can be used to optimize video transmission and help rumor management. Future studies can further explore the configuration effects of rumor-dispelling strategies, video elements and rumor-dispelling themes, and we will delve into the causal relationship between them and the communication effect.

The use of conversational agents in computer-supported collaborative learning (CSCL) has been identified as a useful tactic for motivational intervention. The purpose of the current study was to design and implement a conversational agent called a motivational online conversational agent (MOCA) that incorporated motivational interviewing (MI) and was based on an intelligent dialog engine to enhance learner engagement in CSCL. Additionally, the study empirically examined the effects of MOCA on promoting positive changes in collaborative learning engagement through multiturn conversation interventions. A prototype system was developed by combining MOCA and an immersive virtual world, and an effectiveness study was conducted with 40 volunteers. A series of multilevel growth models based on the framework of the hierarchical linear model was established through multiwave longitudinal data. The results indicated that the use of MOCA significantly improved student engagement scores (p < 0.001) and that female students performed better on collaborative tasks than male students (p < 0.05, t = 2.97). Additionally, time was an important predictor and significantly interacted with the MOCA-use condition. The study has implications for the design and assessment of conversational agents embodied in virtual reality.

Aim: To investigate whether oral microbiome diversity is associated with all‐cause mortality in the general US population and in individuals with chronic diseases. Materials and Methods: We included 8224 individuals with oral microbiome diversity data from the National Health and Nutrition Examination Survey (2009–2012), representing 164,000,205 US adults, using a survey‐weighted analysis method. Cox regression analyses were performed to identify the association between oral microbiome diversity and all‐cause mortality. Results: During a survey‐weighted mean follow‐up period of 8.86 years, 429 all‐cause deaths (survey‐weighted number: 7,124,920) occurred in 8224 participants. Cox regression analysis revealed that higher oral microbiome diversity was significantly associated with a lower all‐cause mortality risk. Significant differences in all‐cause mortality risk were observed among the different clusters based on oral microbiome β‐diversity (log‐rank p < 0.001). Subgroup analyses revealed that the oral microbiome diversity was independently associated with all‐cause mortality in individuals with diabetes mellitus and hypertension. A multivariate logistic regression model showed that current smoking and antibiotic use were significantly associated with lower oral microbiome α diversity. Conclusions: Higher oral microbiome diversity was significantly associated with a lower all‐cause mortality risk in the general US population and in individuals with diabetes mellitus and hypertension. [ABSTRACT FROM AUTHOR]

With the continuous advancements in three-dimensional scanning technology, point clouds are fundamental data in various fields such as autonomous driving, 3D urban modeling, and the preservation of cultural heritage. However, inherent inaccuracies in instruments and external environmental interference often introduce noise and outliers into point cloud data, posing numerous challenges for advanced processing tasks such as registration, segmentation, classification, and 3D reconstruction. To effectively address these issues, this study proposes a hierarchical denoising strategy based on finite measurable segmentation in spherical space, taking into account the performance differences in horizontal and vertical resolutions of LiDAR systems. The effectiveness of this method was validated through a denoising experiment conducted on point cloud data collected from real outdoor environments. The experimental result indicates that this denoising strategy not only effectively eliminates noise but also more accurately preserves the original detail features of the point clouds, demonstrating significant advantages over conventional denoising techniques. Overall, this study introduces a novel and effective method for denoising point cloud data in outdoor real-world scenes. [ABSTRACT FROM AUTHOR]

At present, the effectiveness of English writing teaching for junior English majors is not satisfactory, with a lack of interactivity in the classroom, inflexible teaching methods, a deficiency of targeted teaching for students’ problems, and insufficient emphasis on writing training. The main reasons for these problems are that teachers have failed to conduct the student-centered English writing teaching model, emphasizing one-way output, ignoring students’ writing problems, and resulting in the shortage of writing training. In this regard, teachers need to transform the teacher-centered English writing teaching model, consider students’ actual problems, establish common writing goals, construct online teaching resources for English writing course, and apply multi-modal writing teaching methods to boost the effectiveness of English writing teaching.

Evaluating the impacts of researchers plays a role in identifying impactful researchers, cultivating talents, and promoting talent exchange. Traditional indicators emphasize researchers’ scholarly impacts and rely on bibliometric data, which take a long time to reveal the impacts. With the popularization of social networks, researchers have gone beyond academia and shown their impacts instantly on the general population. Although altmetrics have been proposed to measure the societal impacts of researchers, they show differences across countries and regions. A comprehensive indicator framework for evaluating the impacts of Chinese researchers is lacking. This study proposes a novel indicator framework based on bibliometrics and altmetrics and uses it to evaluate the impacts of researchers in China. Specifically, the proposed framework consists of 2, 3, and 17 first-level, second-level, and third-level indicators, respectively. We conduct a case study with data from various online platforms. Results demonstrate that the indicator framework can evaluate the scholarly and societal impacts of Chinese researchers. The results also show that researchers’ societal impacts are stronger than their scholarly impacts in China. According to the impacts, the indicator framework can categorize researchers into different groups, among which the largest group contains ordinary researchers with mediocre scholarly and societal impacts.