Classroom Behavior Research Articles

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.

Impact of Reinforcement Strategies on Students’ Learning Behaviors in Classroom at Primary Level

Impact of Reinforcement Strategies on Students’ Learning Behaviors in Classroom at Primary Level

Research on Intelligent Teaching Mode Based on Classroom Behavior Monitoring of Body Motion

Objective: Classroom behavior detection is of great significance in the field of education, as it can assess students’ participation and focus. Methods: This paper introduces a novel classroom behavior detection method based on PP-YOLOv2. Leveraging computer vision and deep learning, the proposed approach involves the collection and annotation of student sample datasets, accompanied by thorough data preprocessing. The utilization of the Mish Activation function enhances the model’s learning ability and improves the accuracy of behavior detection. Results: This study is of great significance for real-time monitoring, enabling the evaluation of student behavior to improve teaching effectiveness and promote personalized learning. The experimental results show that this method exhibits good performance in classroom environments, providing educators with an efficient and accurate tool for behavior detection. Conclusion: Further research endeavors can expand the application scope and optimize algorithms to enhance performance. In summary, the proposed method shows promise in revolutionizing classroom behavior detection, offering a more efficient and accurate means of assessing and improving students’ educational experiences.

Teachers’ Perceptions Associated with Student Considerations in The Flipped Learning Model

This study explored primary school teachers' opinions on the effects of Flipped Learning (FL) depending on the grade level and subject matter taught in an EFL environment in Kurdistan. Teachers of a Bradost Private School in Raniyah participated in the study. Data were gathered by using a survey tool borrowed and modified by the researcher. 18 teachers participated randomly. Consequently, due to the additional technology needed outside of the classroom, the participants thought that certain students could find it challenging to access. The results show that the mean scores in the area of parent involvement and parent or teacher conferences, the participants strongly agreed that in an FL, video lectures make the class more transparent to parents, and they also believed that discussions with parents focused more on learning than they do on classroom behavior. Accordingly, they believed that time created for in-class activities in the FL allowed for more active learning and increased higher-order thinking for students, based on the following construct mean scores, with instructional considerations in the FL, in the areas of personalized learning, pupils to teacher interaction, active learning, and time for learning. They believe that FL gave teachers more time to improve and tailor education for students. Additionally, they cited favorable opinions of flipped learning in comparison to traditional classrooms.

MultiFusedNet: A Multi-Feature Fused Network of Pretrained Vision Models via Keyframes for Student Behavior Classification

This research proposes a deep learning method for classifying student behavior in classrooms that follow the professional learning community teaching approach. We collected data on five student activities: hand-raising, interacting, sitting, turning around, and writing. We used the sum of absolute differences (SAD) in the LUV color space to detect scene changes. The K-means algorithm was then applied to select keyframes using the computed SAD. Next, we extracted features using multiple pretrained deep learning models from the convolutional neural network family. The pretrained models considered were InceptionV3, ResNet50V2, VGG16, and EfficientNetB7. We leveraged feature fusion, incorporating optical flow features and data augmentation techniques, to increase the necessary spatial features of selected keyframes. Finally, we classified the students’ behavior using a deep sequence model based on the bidirectional long short-term memory network with an attention mechanism (BiLSTM-AT). The proposed method with the BiLSTM-AT model can recognize behaviors from our dataset with high accuracy, precision, recall, and F1-scores of 0.97, 0.97, and 0.97, respectively. The overall accuracy was 96.67%. This high efficiency demonstrates the potential of the proposed method for classifying student behavior in classrooms.

A novel dataset based on indoor teacher-student interactive mode using AIoT

A novel dataset on teacher-student classroom behavior has been designed and implemented in this paper. The dataset, named Posture-based Teacher-Student Behavioral Engagement Pattern Dataset (PTPD), is realized using AIoT technology and consists of over 13,500 videos from more than 1,000 scenarios, providing convenience for spatio-temporal analysis. PTPD also fills the gap of high-quality datasets for classroom behavior analysis of teachers and students. Then, Alpha Pose, Open Pose, and ST-GCN algorithms are employed to evaluate the accuracy of the dataset. Experimental results demonstrate that the pose-based algorithms achieve an average estimation accuracy of 91.14%, while the ST-GCN algorithm achieves a teacher-student behavior recognition rate of 92.15%, confirming the effectiveness and high quality of this dataset.

Exploring teacher classroom behavior and wellbeing from the student perspective

Abstract In recent years, an increasing drumbeat around emotion research in ELT has brought to the fore an appreciation of teacher psychology and the emotional underpinnings of the teacher–student relationship. This study explores how students report their teacher’s classroom behavior and how those observations lead learners to make broader impressions about the teacher’s mental state. As part of a larger study, 129 adult EFL students took a web survey about their teacher’s classroom behavior, perceived overall happiness, and their perceived wellbeing. Results revealed that teacher behavior was strongly correlated with perceived teacher wellbeing and happiness, meaning that based on how teachers behave in the classroom, students make far-reaching assumptions about their wellbeing beyond the classroom. Due to the phenomenon of emotional contagion, when students think their teachers are happy, they may also feel more positive emotions in the classroom. Suggestions for teachers are discussed, such as strategies for making the best of challenging situations and promoting healthy student relationships as a means to promote teacher and learner wellbeing. Suggestions for potential institutional changes are also made.