Schisming as an Interactional Resource for Learning at the Frontiers of Work and Vocational Training-A Secondary Publication

This paper examines the sorts of interactional competencies and institutional demands required from students as they engage in complex forms of participation combining work and training purposes.It focuses on a series of empirical cases,recorded through video data and analyzed from a conversation analytic perspective,in which mentors make the decision to intervene during work sessions moderated by students.Such interventions do not interrupt the student’s activity and lead to the emergence of two distinct but not impermeable interactional spaces.This complex participation framework,known as“schisming,”contributes to overcoming practical issues within multiparty settings.Our study shows how schisming constitutes a particular sequential phenomenon where participants reorganize the interaction and co-construct a social and cognitive interactional space,thus enabling a shared understanding of the specific training context.Empirical data from the practical training of medical radiographers are used to illustrate how schisming may contribute to learning in the conditions of guided practice.

Automated Classification of Inherited Retinal Diseases in Optical Coherence Tomography Images Using Few-shot Learning

Objective To develop a few-shot learning(FSL) approach for classifying optical coherence tomography(OCT) images in patients with inherited retinal disorders(IRDs).Methods In this study, an FSL model based on a student–teacher learning framework was designed to classify images. 2,317 images from 189 participants were included. Of these, 1,126 images revealed IRDs, 533 were normal samples, and 658 were control samples.Results The FSL model achieved a total accuracy of 0.974–0.983, total sensitivity of 0.934–0.957, total specificity of 0.984–0.990, and total F1 score of 0.935–0.957, which were superior to the total accuracy of the baseline model of 0.943–0.954, total sensitivity of 0.866–0.886, total specificity of 0.962–0.971,and total F1 score of 0.859–0.885. The performance of most subclassifications also exhibited advantages. Moreover, the FSL model had a higher area under curves(AUC) of the receiver operating characteristic(ROC) curves in most subclassifications.Conclusion This study demonstrates the effective use of the FSL model for the classification of OCT images from patients with IRDs, normal, and control participants with a smaller volume of data. The general principle and similar network architectures can also be applied to other retinal diseases with a low prevalence.

Anomaly detection and segmentation based on multi-student teacher network

In automated industrial inspection,it is often necessary to train models on anomaly-free images and perform anomaly detection on products,which is also an important and challenging task in computer vision.The student-teacher network trains students to regress the output of the teacher,and uses the difference between the output of the student network and the pre-trained teacher network to locate anomalies,which has achieved advanced results in the field of abnormal segmentation.However,it is slow to predict a picture,and no anomaly detection is performed.A multi-student teacher network is proposed,which uses multiple student networks to jointly regress the output of the teacher network,and the minimum square difference between the output of students and teachers in each dimension is selected as the difference value.The information in the middle layer of the network is used to represent each area of the image and calculate the anomaly distance for anomaly segmentation,and the maximum abnormal score is used to represent the abnormal degree of the image for abnormal detection.Experiments results on MVTec anomaly detection show that the algorithm predicts a picture in 0.17 s and can output anomaly detection results at the same time,with image AUROC reaching 91.1%and Pixel AUROC reaching 94.5%.On the wall tile dataset produced by taking pictures of real scenes,image AUROC reached 89.7%,and Pixel AUROC reached 89.1%.Compared with the original student-teacher network,the proposed method can quickly complete anomaly segmentation and anomaly detection tasks at the same time with better accuracy,and it also has better results in real applications.

《汽车工程概论》“一体多元”课程思政教学方式探讨

“课程思政”教学改革是高等院校贯彻落实“立德树人”根本任务,构建“三全育人”新格局的重要举措。本文分析重庆理工大学通识教育核心课《汽车工程概论》课程特点、教学方式及不足,探讨其“一体多元”课程思政教学方式——以学生为主体,采用多元混合教学方式,为改善其知识传授与思政教育协同效果提供了方法路径,也为其他非思政课课程思政教学改革提供了有益借鉴。

The Influence of Social-Emotional Competence on Academic Achievement of Elementary School Students:Mechanisms and Educational Implications

Social-emotional competence plays a significant role in promoting positive development among adolescents,reducing problem behaviors,and enhancing academic achievement.However,the specific mechanism through which social-emotional competence affects academic achievement remains unclear.This study analyzed data from 539 elementary school students and found that social-emotional competence could predict academic achievement,with academic emotions,learning strategies,and student-teacher relationships serving as mediating factors between social-emotional competence and academic achievement.The research findings suggest that it is important for schools to value and foster the social-emotional competence of elementary school students while integrating social-emotional learning programs to enhance their academic emotions and learning strategies.Furthermore,it is necessary for teachers to establish positive student teacher relationships and provide supportive environments for their students.

Using Cognitive Interviews to Explore the Many Different Meanings Undergraduate Students in China Attribute to the Term“College Teacher”

The cognitive interview method was applied to evaluate survey questions translated and adapted from a US-based college student survey instrument.This paper draws data from cognitive interviews with 45 undergraduate students in China and explores the different meanings they attribute to the term“college teacher.”Students understood college teacher as course instructor,academic advisor,class headteacher and counselor,student organization supervisor,and student service personnel.Students developed the understanding through a socialization process of student-teacher interaction.This paper also discusses the importance of using cognitive interviewing to improve questionnaire design,implications for research on student-teacher relationships,and suggestions on fostering student-teacher interaction in Chinese higher education institutions.