Classroom Behavior Research Articles

Research on the Innovation of University English Teaching Mode Driven by Artificial Intelligence

Abstract With the continuous deepening and development of information technology in the field of education, the combination of artificial intelligence technology and university English teaching has become a trend. This paper constructs a human-machine symbiotic English teaching model based on intelligent speech recognition, which takes into account university English learning abilities and learning objectives. Specifically, the multi-scale attention mechanism is used instead of the Conformer model to extract the global information of speech features and the local information of different scales and fuse them to learn more speech representations of different scales so as to better recognize the teacher and student behaviors in the English classroom. Meanwhile, combining the teaching mechanism of human-computer symbiosis, we construct a human-computer symbiosis English teaching model for speech recognition in an intelligent classroom and receive feedback after teaching experiments. The occupancy rate of teacher and student behaviors in the English classroom is 50.86% and 49.14%, respectively, and the conversion rate of teacher and student behaviors is 29.78%, which achieves a high level of teacher-student interaction in this model. In addition, the class utilizing the English teaching model of this paper was 5.02 points higher than the traditional class after one semester of teaching, P=0.038<0.05, and the results showed a significant difference. Through the comparison of cases and teaching experiments, the reasonableness of the English teaching model based on speech recognition is demonstrated, and the research content and problem areas of this paper are summarized.

Research on Multimodal English Teaching Methods and Practices Leading to Intelligent Generation

Abstract In this paper, we first establish a multimodal knowledge graph and process the course teaching video using by PaddleOCR algorithm to distinguish and understand the teaching text. This paper also improves the traditional classroom behavior recognition method by using the skeletal covariance matrix extraction strategy to aggregate the skeletal sequence features while combining the courseware information features to jointly achieve the recognition of classroom behavior. Based on the above algorithms, this paper summarizes the English multimodal teaching system based on intelligent learning, which improves traditional English teaching methods in terms of learning environment, teaching method, and mode. Finally, this paper conducted an experimental test on the effects of a multimodal English teaching method. The results show that the class scores of the class applying the multimodal English teaching method have been cumulatively improved by 22.06 points, and the rankings have been improved from 4 or 5 to 1 or 2 among the six tested classes. Students’ satisfaction with the pedagogy increased from an initial score of 3.4 to 4.5. The results show that multimodal English teaching promotes the change of English classrooms in the smart era.

Work-Related Stress and Teaching Performance of Public Elementary School Teachers of San Francisco Cluster IV

This study aimed to investigate the relationship between teachers’ work-related stress and their teaching performance in Public Elementary Schools of San Francisco, Cluster IV in Agusan del Sur Division, Agusan del Sur, Caraga Region. The research used a researcher-made questionnaire to rate teachers’ work-related stress, including workload, assessments and evaluation, time management, classroom behavior, resources, the role of ambiguity, emotional demand, support, and work-life balance. Teachers’ teaching performance was assessed based on the rating of teachers in the Individual Performance Commitment and Review Form (IPCRF). The study employed a descriptive-correlation research design with 53 sampled participants. The study indicated that work-related stress among the teachers was rated as moderate and interpreted as extensive. In terms of teaching performance, 46% of the teacher participants had an outstanding rating, while 54% had a very satisfactory rating, with none rated as satisfactory or poor. The study also found a significant difference in work-related stress among teachers when grouped according to age, teaching experience, and grade level currently teaching. Additionally, the data revealed that the study found no significant impact of the stress caused by workloads on the quality of teaching. Despite the lack of a significant relationship, schools should manage workloads to support educators and maintain optimal teaching quality, fostering a healthier environment and better outcomes for all.

Research on the Reform of Educational Management Mode in Big Data Environment

Abstract The arrival of the big data era, which has been widely used in various industries. In today’s education management mode, in order to improve education development, it is necessary to keep up with the times and reform the education management mode. For this reason, this paper designs an education management model based on the classroom behavior recognition model, which manages students in real time by detecting their classroom behavior status. The classroom behavior recognition model is improved based on the deep learning target detection SSD algorithm. A lightweight model for detecting the behavior of SSD-M3-SFP networks has been designed by replacing the SSD backbone network, VGG, with a MobileNetV3 network after pruning SFPs. Both the speed and accuracy of student behavior recognition can be ensured by the model. Through practical analysis, in the before-and-after analysis of SSD-M3-SFP-based education management model and general education management model, the p-values are 0.001, 0.003, 0.000 on the indicators of the three aspects of education management services, all of which are lower than 0.05, and the difference is significant, and the management effect of SSD-M3-SFP-based education management model is higher than that of general education management model.

Multimodal Analysis and Optimisation Strategy of Teaching Behaviour in Physical Education Classroom

Abstract In the context of the new curriculum, physical education has become an important part of school teaching. In this paper, the edge extraction algorithm and TV-L1 optical flow method are utilized to extract key image features in physical education classroom teaching, and the improved TE network structure extracts the skeletal data features of teachers and students in the classroom based on the TEMS module. Then, the MTHA-MDCNN deep migration model is established to fuse the three features to identify multimodal teaching behaviors in the sports classroom. Experiments show that the accuracy of this paper’s method for recognizing teaching behaviors in physical education classrooms can reach 77.63%. In the actual behavior recognition, the average values of teachers’ and students’ behavioral coverage are 32.68% and 67.77% respectively, which is in line with the optimal division of teaching time in physical education classrooms. In contrast, the students’ behavioral coverage of physical practice is low, which is not conducive to their physical and mental health development. The construction of teachers and supervision of teaching should be strengthened in schools, and students’ learning paths should be expanded. The multimodal analysis method of teaching behavior in this paper provides a reference method for targeted improvement of physical education teaching, and the proposed optimization strategy is important for the successful optimization of physical education classroom teaching in schools in the future.

Optimization of Innovative Paths of Physical Education Teaching in Primary and Secondary Schools under Information Integration Technology

Abstract This article utilizes information fusion technology to provide a more diversified and hierarchical teaching process for physical education, making the content of physical education classroom richer and more vivid. The article firstly discusses the multi-source heterogeneous information fusion technology. It proposes a mass function that combines the output results of the cumulative layer of probabilistic neural network to construct the D-S evidence theory. The D-S evidence theory realizes the decision-level information fusion of the primary results of probabilistic neural networks. In the study, the TPACK knowledge framework was combined with the informationization platform to establish an intelligent classroom for physical education teaching applicable to primary and secondary schools, and a teaching experiment was designed for the selected research subjects. The experiment was conducted to analyze the data mainly from three dimensions: teachers’ and students’ behaviors in the smart classroom, teaching effectiveness and teaching evaluation. The results of data analysis showed that the average time of each learning node of students at the school was more than 50 seconds. At the same time, the coding coverage of teachers’ teaching behaviors was 41.85% lower than the mean value of coding coverage of students’ learning behaviors. In the experimental group, students’ jump rope scores improved by about 9.05% to 20.44% after one month of learning. These findings suggest that an intelligent classroom for physical education teaching relying on information fusion technology can effectively integrate multi-source data, thus providing strong support for improving the effectiveness of physical education teaching in primary and secondary schools.

Teachers’ Perspectives on Disruptive Student Behaviors: The Interconnectedness of Environment and Early Childhood Education in Pakistan

This study describes teachers’ perspectives on disruptive student behaviors in early childhood education classrooms in Khyber Pakhtunkhwa, Pakistan. Using an ecological model, this paper describes interviews with 26 teachers working in early childhood education. The findings show that home, school, and society do not play optimal roles in children’s education, which leads to disruption in classrooms. Illiteracy and socioeconomic problems at home prevent parents from playing their required roles, and schools are often overcrowded and have unattractive classrooms. Teachers are minimally qualified and have few teaching-learning materials at their disposal. Fear of violence is another factor. These factors do not support the behavioral development of young children. The three major circles (home, school, society) that comprise a child’s ecology are inadequate, which leads to classroom disruption, lack of motivation, and poor behavior that endangers the continuation of education.

Construction of a Classification Model for Teacher and Student Behavior in Physical Education Classrooms –Based on Multimodal Data

Abstract The analysis of teachers' and students' behaviors in physical education classrooms is an important way to improve the quality of physical education teaching and teaching methods, which helps teachers to check the gaps and improve the teaching level. In this paper, for the problems of data differences between multiple modalities and the conflict between feature extraction modules of different modalities, we designed a dual-stream framework HRformer algorithm based on Transformer, which unifies the skeletal modalities and video modalities in the algorithm. The relationship between skeletal and video modalities is modeled using the self-attention mechanism, and the matching and fusion of skeletal features and video data is performed to construct a behavior recognition model for teachers and students in the sports classroom based on multimodal data. Then, the model is compared with mainstream networks on the dataset to verify its performance. To conduct model application and example analysis, a university collects data on physical education classroom teachers and students for a semester. It is found that the multimodal model in this paper has a classification F1 value of 95.61%, 93.19%, and 93.74% for the three types of behavior recognition, namely, skill training (ST), game activity (GA), and rest, respectively, which are higher than the two methods of single skeletal modality and video modality. The model has the highest recognition accuracy of 97.12% and 98.15% for Game Activity (GA). Based on real physical education classroom data, the practical application of the model in physical education teaching classrooms in this paper is fruitful, and the results of behavioral recognition classification are in line with the design expectation. This study develops an effective method for classifying teacher and student behaviors in a physical education classroom. It provides a useful exploration for the integration and innovation of physical education teaching and information technology.

A novel parameter dense three-dimensional convolution residual network method and its application in classroom teaching.

Improving the rationality and accuracy of classroom quality analysis is crucial in modern education. Traditional methods, such as questionnaires and manual recordings, are resource-intensive and subjective, leading to inconsistent results. As a solution, computer vision (CV) technologies have emerged as powerful tools for real-time classroom monitoring. This study proposes a novel Dense 3D Convolutional Residual Network (D3DCNN_ResNet) to recognize students' expressions and behaviors in English classrooms. The proposed method combines Single Shot Multibox Detector (SSD) for target detection with an improved D3DCNN_ResNet model. The network applies 3D convolution in both spatial and temporal domains, with shortcut connections from residual blocks to increase network depth. Dense connections are introduced to enhance the flow of high- and low-level features. The model was tested on two datasets: the CK+ dataset for expression recognition and the KTH dataset for behavior recognition. The experiments show that the proposed method is highly efficient in optimizing model training and improving recognition accuracy. On the CK+ dataset, the model achieved an expression recognition accuracy of 97.94%, while on the KTH dataset, the behavior recognition accuracy reached 98.86%. The combination of residual blocks and dense connections reduced feature redundancy and improved gradient flow, leading to better model performance. The results demonstrate that the D3DCNN_ResNet is well-suited for classroom quality analysis and has the potential to enhance teaching strategies by providing real-time feedback on student engagement.

A recognition method of learning behaviour in online classroom based on feature data mining

A recognition method of learning behaviour in online classroom based on feature data mining

Practical Innovation of Students’ Civic Education Model Based on Artificial Intelligence Technology

Abstract Integrating Artificial Intelligence (AI) into education, particularly civic education, represents a transformative shift. This study explores the innovative fusion of AI with teaching methodologies, aiming to enhance educational outcomes and foster comprehensive student development. We construct a multidimensional civic education framework by employing theoretical and empirical approaches, examining the dynamics between educators, students, content, and pedagogical strategies. We assess student academic performance and behavior by utilizing the Multi-Task Classroom Behavior Recognition Network (MCBRN) and multivariate analysis of variance (ANOVA). Our findings reveal that the AI-enhanced teaching model significantly boosts student engagement and learning achievements in the experimental group, with behavior recognition accuracy reaching 96.9%. Moreover, these students demonstrated superior examination scores and overall competency levels compared to the control group (P<0.05), highlighting the effectiveness of this novel approach in elevating the quality of civic education through personalized and efficient learning experiences.

外语愉悦情绪对中学生课堂行为参与度的影响

外语愉悦情绪对中学生课堂行为参与度的影响

Practical Teaching and Intellectualization of University Courses Based on the Maximum Information Entropy Model

Abstract Traditional teaching has become a thing of the past, the integration of technology and the classroom has brought about significant changes in education, and the role of information entropy in teaching has gradually manifested itself. In this paper, the behavioral time occupancy of the teaching process of university courses is viewed as a probability distribution event combined with the Lagrange multiplier method to solve the maximum entropy value in the distribution of practical teaching behavior. The practice depth value of classroom behavior can be calculated using the interaction behavior coefficient and interaction behavior entropy. Apply the information entropy to the practical teaching analysis of university courses, verify the practical effect of university courses from different sides, and carry out intelligent course construction according to the current situation of students’ learning behavior. The results show that the average score of class 3 in the practical analysis ability assessment is 4.021, which indicates that the practical analysis ability of students in the class meets the standard, and the students’ practical execution should be mainly cultivated in the later practical teaching. 67.1% of students believe that the intelligent classroom has improved their performance over the traditional classroom according to the intelligent course model.

Analysis of English Classroom Interaction in Colleges and Universities Based on Student Behavioral Data

Abstract With the advancement of the information age, the traditional teacher-led English classroom interaction in colleges and universities has made it challenging to meet the individualized needs of different students. Therefore, this paper takes the current classroom behavior data of college students as the basis, uses the K-means clustering algorithm and I-divergence measure to categorize, analyze, and study the student behavior data, and digs out the correlation between students’ classroom behavior data and teaching interaction. This paper takes all the teachers and students of English majors in college M as the research samples. It conducts experimental research in terms of teachers’ and students’ classroom speech rates, teachers’ and students’ questioning rates, and teachers’ and students’ use of instructional technology, etc. The 10 classes of English majors in college M do not have more than 50% of the students’ speech rate, except for class 2. Class 2 has the highest speech rate (61.64%) among students, while class 5 has the lowest rate (7.5%). From the point of view of active questioning rate, 10 classes had very low SQ ≤ 0.02, and even in classes 1, 3, 4, 5, and 6, there was a situation where students’ active questioning rate was zero. After analyzing the student behavior data in some classes, it was found that the mean score of English test students in the experimental group before and after the test increased by 4.687, corresponding to a significance Sig value of 0.000<0.05, which reaches the level of significance. The analysis of classroom behavior data reveals that enhancing the ratio of teacher-student interaction activities can improve students’ English scores.

Artificial Intelligence Enabled Teaching Optimization for General Studies Elective Courses

Abstract Network general knowledge course is a meaningful way to cultivate comprehensive quality talents in Chinese higher education based on the modern information technology environment, and the optimization of network general knowledge course teaching is directly related to whether the network general knowledge course can be effectively implemented, which affects the overall effect of talent cultivation. In this paper, starting from the purpose and goal of humanities general education courses, the optimization direction of teaching general education courses using artificial intelligence is clarified. Skeletal sequence feature extraction is used to obtain students’ general education classroom behavior data, and a stackable multimodal attention module consisting of a self-attention layer and a multimodal co-attention layer is designed for feature fusion and action prediction based on multimodal characteristics. After data integration and processing, the HGCL-LG network model is proposed to complete the diagnosis of students’ general knowledge cognition. The trend of interest assessment and multimodal model calculation value is consistent. In contrast, the student’s interest attention in general knowledge classroom above 0.33 accounts for more than 40% of the overall information, and the interest attention is good. Students’ scores on the objective questions of general knowledge are good; the average score range is between 1.5 and 2 points, the degree of dispersion is small, and the mastery of basic knowledge of general knowledge is good.

Research on Intelligent Recognition Algorithm of College Students’ Classroom Behavior Based on Improved Single Shot Multibox Detector

Research on Intelligent Recognition Algorithm of College Students’ Classroom Behavior Based on Improved Single Shot Multibox Detector

Application of online teaching-based classroom behavior capture and analysis system in student management

Application of online teaching-based classroom behavior capture and analysis system in student management

Analysis of the changes in the art curriculum of preschool children’s education in the new media era

Abstract Art education for preschool children is the basis for the cultivation of young children’s perceptual ability, and the cultivation of young children’s perceptual ability plays an essential and fundamental role in young children’s understanding of the world, feeling of beauty, and discovery of beauty. This paper uses AR technology and microclassrooms in the context of new media to construct an educational model of preschool children’s art curriculum, and a portrait of preschool children’s scholars is built. It focuses on combining the hierarchical clustering algorithm with the radial function method of multivariate interpolation to analyze the classroom behaviors of preschool children, mining the correlation between the course learning behaviors and the learning effect through the Apriori algorithm, and constructing a personalized art learning path for preschool children based on the ant colony algorithm for practice. The results showed that preschool children were divided into four categories, with the highest proportion being excellent preschool children (52.5%). The mean score of preschool children’s art perception score was 8.225, and their scores were normally distributed, with the highest number of preschool children between 8.00 and 8.25. This study is significant for changing the art curriculum in preschool children’s education. Teachers should be guided in teaching activities and focus on children’s perceptual experiences and expressions.

Student's classroom behaviour recognition method based on abstract hidden Markov model

Student's classroom behaviour recognition method based on abstract hidden Markov model

RESHAPING TEACHER-STUDENT RELATIONS IN THE DIGITISED SOCIETY

This article highlights the need to redefine teacher-student relationships according to the generations of students’ characteristics (Generation Z, Generation Alpha). Each generation has a specific way of learning and interacting with peers or adults. The theoretical premises of the study are to be found in the recent work of psychologists, sociologists, and specialists in educational sciences on how social characteristics influence the life style of people belonging to that generation. The school with all its structure, content, and relationships cannot but be affected by the changes imposed by technology, globalisation, and social dynamism. This study used qualitative methods (interviews and observation of teachers’ classroom behaviour) carried out in the school year 2022-2023. We have taken a constructivist-interpretive approach in conducting the research. A number of 60 participants (20 teachers from primary, 20 secondary, and 20 from high school level) from Braşov were involved in the study. Five teachers from primary schools were observed in the classroom. The results revealed that there are still teachers who have a classical view of relationships with pupils but who are making efforts to understand, accept, and adapt to the generations of pupils. One of the conclusions of the research is that teachers need to restructure their conceptions of the teacher-student interaction to suit the way students in the society of the future will learn and, more importantly, live. In fact, this teacher-student relationship is based on the values of accepting the diversity of students, personalising learning, motivating, and empowering students while ensuring their well-being is the basis of quality education.