Analysis Of Student Behavior Research Articles

A Comparative Analysis of Student Behavior and Teacher Experiences in Revised Basic Education Curriculum (RBEC) and K-12: Insights from English Teachers

A Comparative Analysis of Student Behavior and Teacher Experiences in Revised Basic Education Curriculum (RBEC) and K-12: Insights from English Teachers

Design of Fast Mining Algorithm for Educational Sport Course Data Based on Cluster Analysis

In this age of big data, education researchers are reconceptualizing and re-evaluating the value of education data. Therefore, we need to use educational data mining methods for data analysis to better guide teaching. The informatization level of colleges and universities is improving year by year, and the entire training data of students from enrollment to graduation is stored. These datasets are collected, stored, and kept by different departments, contain a large amount of regular and relevant information, and truly record the growth footprint of students. Traditional educational decision-making has not yet fully explored and used the valuable information hidden in data resources. Although some scholars have carried out research related to campus data mining at this stage, there are still many problems that have not yet been solved in the application of decision-making in colleges and universities. This paper is based on the idea of data-driven decision-making, combined with the data characteristics of campus big data, and establishes a model solution for student behavior analysis and behavior prediction by applying multiple machine learning algorithms. On the basis of the analysis of students’ academic behavior performance in the context of multi-category educational data, we proposed a cluster analysis framework for processing multi-type campus big data, and described the group characteristics of the clustering results. By introducing the K-prototype algorithm, we effectively solved the multi-category problem where traditional clustering algorithms (such as K-Means, etc.) cannot adapt to the attributes of educational data. The research results show that innovative educational decision-making models and methods are based on the idea of “data-prediction-decision”, which promotes the application research of big data science in the area of education.

Detection and analysis of student behavior in college labor education courses based on YOLOv5 network

To improve the application of behavior detection technology in college education, the study proposes a new model built on deep CNN, which is used for student behavior detection and analysis in college labor education courses. The study first analyzed the target detection algorithm, and optimized the selected You Only Look Once version 5 (YOLOv5) algorithm and its network structure with a series of improvements, and based on this, embedded the attention module into the algorithm structure to finally obtain a new model, namely YOLOv5-O. After a series of experiments, YOLOv5-O reached an average accuracy of 90.1% on the test set, while the application test in the actual teaching environment showed that its average accuracy was 86.7%. This result is obviously superior to the existing technology, which proves the validity of the study and provides strong data support for the automatic detection of student behavior. In addition, in the teaching experiment, YOLOv5-O assisted teaching achieved the most significant teaching effect, and students’ achievement improved the most. The feasibility of this method is verified.

From Raw Video to Pedagogical Insights: A Unified Framework for Student Behavior Analysis

Understanding student behavior in educational settings is critical in improving both the quality of pedagogy and the level of student engagement. While various AI-based models exist for classroom analysis, they tend to specialize in limited tasks and lack generalizability across diverse educational environments. Additionally, these models often fall short in ensuring student privacy and in providing actionable insights accessible to educators. To bridge this gap, we introduce a unified, end-to-end framework by leveraging temporal action detection techniques and advanced large language models for a more nuanced student behavior analysis. Our proposed framework provides an end-to-end pipeline that starts with raw classroom video footage and culminates in the autonomous generation of pedagogical reports. It offers a comprehensive and scalable solution for student behavior analysis. Experimental validation confirms the capability of our framework to accurately identify student behaviors and to produce pedagogically meaningful insights, thereby setting the stage for future AI-assisted educational assessments.

Squid Examiner: Student Behavior Analysis Exam Surveillance System

The Squid Examiner system is an innovative solution designed to enhance examination security through automated cheating detection using real-time video surveillance. While offering real-time monitoring and detailed reporting, it faces privacy concerns, potential accuracy issues, and high initial costs. The system's future scope includes AI improvements, multi-modal surveillance, and global expansion, with a focus on continuous innovation and feedback from educational institutions and students.

Analysis of Student Behavior Based on the History of Learning Activities in the Learning Management System Using the Pearson Correlation Method

The purpose of this study is to identify student learning behavior in online learning and to determine the relationship between student learning behavior and learning achievement based on learning history data (Learning logs) on the Learning Management System (LMS), including the performance of assignments and quizzes, utilization interaction features (forums and chat), as well as active access to learning resources (files and URLs). Pearson Correlation method is used to analyze the level of relationship between learning behavior and students’ achievement. The research object is 105 students at Muhammadiyah University of Enrekang who programmed introductory information technology (PTI) courses from 5 different classes but taught by the same lecturer. The total number of processed activity histories (after data preprocessing) is 6500 records, while the total number of logs before data preprocessing is 19386 records. Correlation analysis linking student behavior to student learning achievement is quite strong and unidirectional, as evidenced by the correlation value between learning behavior and student final grades which show an average number of 0.80 and all are positive, with confidence interval values reaching95%. This shows that the higher the learning activities that students participate in in online learning (the more active), the stronger the effect on student learning achievement. It also shows that student activity in completing assignments is the variable that most influences learning achievement with a correlation value of 0.88 (very strong).

Research on Student Behavior Analysis and Grade Prediction System Based on Student Behavior Characteristics

In the era of big data, the traditional governance model of student behavior management gradually shows the disadvantage of “post positioning”. Therefore, the research uses relevant data mining algorithms to extract and analyze students’ behavior characteristics, and constructs a SPC system model for students’ behavior analysis and performance prediction. At the same time, the experiment verifies its effectiveness. The experimental results show that in the factor analysis, the overall variance of the first seven indicators is 69.942\%, which shows that there is a significant correlation between students’ learning behavior and academic performance. In the model performance analysis of SPC system, the accuracy of the five algorithms is kept at 40% - 60%, while SVM has higher stability than other methods. In addition, the prediction accuracy and response rate of SPC model reached 86.90% and 81.57% respectively. When the sequence length was increased from 1 to 20, the accuracy of SPC model exceeded 70%; However, when the feature dimension exceeds 50, the model representation ability will decline. Therefore, appropriate feature dimensions are needed to predict student performance. To sum up, the SPC model built by the research is effective in analyzing student behavior and predicting students’ performance, and practical in actual student management.

Modeling Student Management and Civic Education Strategies from an Interdisciplinary Perspective

Abstract This paper issues from an interdisciplinary perspective, combining computer technology, principles of management and other multi-disciplinary knowledge to build a model of student management and civic education strategy. Two modules are part of the student management model: student behavior analysis and prediction. In student behavior analysis, the fuzzy C-means clustering algorithm is improved, and the Gaussian density function is used to determine the initial clustering center. In student behavior prediction, the convolutional network is used to input raw data, and the extracted features are inputted into the CABLSTM model for training and prediction of student behavior. The Civic Education Strategy Model is built on the principle of feedback control and is constructed at the school, family, and individual levels. In the analysis of student management and Civic and Political Education practice, teaching practice was conducted at University C in Lanzhou City, Gansu Province, China. After the practice, the number of students who are very satisfied and satisfied with the overall student management is 2, 356, accounting for 65.81%, and the comprehensive quality of students is in the middle to high range. The students’ life values, family and national sentiment, and thought level dimensions in Civic and political literacy have significant differences (P<0.05), and the level of mastery of Civic and political knowledge is good.

Behavioral Analysis and Educational Management of College Students in Universities by Integrating Multi-source Data

Abstract Big data technology has brought new opportunities for the development of the education industry and can play a positive role in the management of student campus education. In this paper, the behavioral features in the extracted multi-source behavioral data of college students are dimensionality reduced using principal component analysis, and the behavioral features are clustered using density peak clustering and undersampling methods. The Adaboost algorithm is used to identify and classify each behavioral feature to obtain behavioral analysis data for students. Finally, the university’s education management system is constructed based on this behavioral analysis model. The behavioral analysis of students in a university shows that both consumption behavior and book borrowing behavior cluster into three categories. Among them, the type of students who clocked into the library with a medium frequency and borrowed the most number of books had the highest percentage (39.25%) among the students in this university. And there is about a 79.3% probability that students of the university with a high number of absences seldom visit the library. The study also showed that there was a significant difference (p<0.05) in the study behavior of students in both experimental and control classes after the application of the educational management system. The student behavior analysis and management platform proposed in this paper effectively promotes the development and progress of education management in colleges and universities and improves the efficiency of student management work and the scientificity and reliability of the results.

Adaptive Learning in the Future of Educational Management Adapts to Student Needs

Education is the most important foundation for shaping the future of the next generation. In the era of ever-evolving information technology, education must be updated to remain relevant and effective. One approach that has emerged as a solution to future training needs is "adaptive learning". Adaptive learning is a teaching method that uses technology to identify and adapt each student's learning needs individually. Adaptive learning systems use data and analysis of student behavior when processing learning materials. Thus, the learning process becomes more personal, interactive and related to the abilities and learning styles of each student. This summary aims to describe the concept of adaptive learning in future educational administration and focuses on the importance of adapting to student needs. Research shows that each student has a different learning style and speed of understanding. The use of technology in adaptive learning gives teachers and educational institutions the ability to more accurately identify each student's weaknesses and strengths. In this summary, we analyze how adaptive learning can benefit students, teachers and the entire education system. Adaptive learning not only increases student engagement, but also enables teachers to develop more effective teaching strategies based on the information and data they collect. However, this summary also discusses some of the challenges and considerations that must be faced when implementing adaptive learning. These include questions about student information security, the development of high-quality adaptive learning content, and the role and involvement of teachers in the use of educational technology. Facing the Industrial Revolution 4.0 era, adaptive learning offers hope for creating a more inclusive and effective education system. With an approach that addresses students' needs, adaptive learning can become the main pillar in building a generation that is ready to face the complexities of the modern world.

Profiling Students Engagement in Full Online Learning

The emergence of online learning platforms has transformed the landscape of education, offering flexibility and accessibility to learners worldwide. There are extensive datasets encompassing diverse aspects of online learning, including demographic information, course enrollment, participation metrics, and academic performance. Through EDA, we uncover patterns, trends, and anomalies within the data, shedding light on the characteristics of online learners. Machine learning approaches are employed to develop predictive models for various facets of online learning. Furthermore, this study explores the potential of personalized learning recommendations and interventions based on the analysis of student behavior and engagement. we use computers to learn about online students and help them do better. This helps make online learning more enjoyable and useful for everyone.

ANALISIS PERILAKU DALAM MELAKSANAKAN PROGRAM 3R (REDUCE, REUSE, RECYLE) TERHADAP BERBAGAI JENIS SAMPAH BOTOL PLASTIK PADA MAHASISWA UNIVERSITAS JEMBER

This study aims to carefully analyze the social behavior in properly implementing the 3R program (reducing, reusing, recycling) various types of plastic bottle waste. This social problem is indicated to Jember University students who frequently employ various types of plastic bottles in their ordinary life. This published article was carefully compiled using literature study research methods and quantitative surveys. Data collection is typically done by typically taking specific references in peer-reviewed journals, published articles and local news. The survey was conducted by distributing questionnaires to students using a google form via the WhatsApp application. Typically based on the extant literature, it is proven that by implementing the 3R program (reducing, reusing, recycling), students are able to reduce environmental pollution at the University of Jember due to the use of various types of plastic bottle waste. With extensive research on the analysis of student behavior towards various types of plastic bottle waste, we can find out the level of awareness of Jember University students about the importance of preserving the environment and how they are trying to reduce the impact of global warming on the campus environment.

An early warning method for abnormal behavior of college students based on multimodal fusion and improved decision tree

With the development of the Internet and the informatization construction of universities, the massive data accumulated by “campus big data” presents problems such as discreteness and sparseness. Students with abnormal behaviors have become an urgent problem to be solved in student behavior analysis. This paper proposes an early warning method for abnormal behaviour of college students based on multimodal fusion and an improved decision tree (EWMABCS-MFIDT). First, given the insufficient representation of student behavioral portraits and the problems of timeliness and dynamics in behavioral labels, a student behavioral portrait based on the multimodal fusion method is proposed. Second, aiming at the timeliness and backwardness of abnormal behavior prediction, based on student behavior classification prediction, this paper proposes an improved decision tree-based early warning method for abnormal student behavior. Finally, we design a student behavior analysis and early warning framework under the campus big data environment. Taking the abnormal early warning of students’ academic performance as an example, compared with other early warning algorithms, the EWMABCS-MFIDT method can improve the accuracy of early warning and make students’ educational work more targeted, personalized, and predictive.

Retracted: Student Behavior Analysis and Research Model Based on Clustering Technology

Retracted: Student Behavior Analysis and Research Model Based on Clustering Technology

Using AI Chatbot for Math Tutoring

Aim. The aim of this paper is to present the results of a pilot validation of an artificial intelligence chatbot for mathematics tutoring. The subject of the analysis is the behaviour of the students in the course, in particular their approach to solving problems without external motivation through scoring or other assessment. Methods. The analysis of student behaviour was carried out on the basis of records in the application database. The subject of the analysis was not only the pupils' responses, but also the time taken to produce them and the overall trajectory through the course. Results. The results of the analysis show that students who choose to join the course, work intensively in the lessons. Most pupils solve more than one basic task. A significant proportion of pupils also use the prepared help and instructional videos. The analysis of the solution time allowed identification of a group of pupils who do not even read the problems and only confirm the results. Conclusion. The pilot validation confirmed that the chat-bot format is close to the learners, and they are able to use the courses created in this environment. Solution time analysis suggests that this parameter can be used to analyse learner behaviour and system control. Further analyses in this area are needed.

Retracted: Analysis of Teaching Strategies of College English Speculative Reading Based on Big Data Analysis of Student Behavior in Cross-Cultural Education Environment.

[This retracts the article DOI: 10.1155/2022/1385074.].

Application of Artificial Intelligence in Preschool Education Management

With the continuous development of artificial intelligence technology, its application in various fields has gradually expanded, including preschool education management. This paper aims to explore the application of artificial intelligence in preschool education management, focusing on its role in the optimization of educational resources, personalized teaching, student behavior analysis, etc. By analyzing the existing research results, this article will explore the advantages and challenges of artificial intelligence in preschool education management, and look forward to the future development trend.

BiTNet: A lightweight object detection network for real-time classroom behavior recognition with transformer and bi-directional pyramid network

In this paper, we propose BiTNet, a real-time object detection network, for enhancing teaching quality and providing feedback through real-time analysis of student behavior in classroom. BiTNet addresses challenges faced by current methods, such as occlusion and small objects in images. Specifically, it incorporates an Efficient Transformer Block (ETB) and Efficient Convolution Aggregation Block (ECAB) for extracting image features. ETB uses convolutional multi-head self-attention (CMHSA) to capture contextual information thus improves the capability to recoginze occluded objects. Its homogeneous multi-branch design reduces the computational costs. The single aggregation operation of ECAB aggregates the extracted features into a single feature map, improving the accuracy of recognition while reducing the computational cost. Then, the semantic and positional information extracted from the backbone are fused in Bi-directional Feature Pyramid Network (BiFPN). BiTNet also employs a smaller size detection head for recognition of small objects. Experimental results on classroom behavior dataset demonstrate that our proposed method has impressive accuracy (82.9%) and speed (256.7 FPS on GPU, 30.7 FPS on CPU, and 18.9 on camera). We further experiment with our method on Pascal VOC 2007 and VisDrone2021, validating BiTNet’s generalization and its capability to recognize small objects. In conclusion, BiTNet has a simple yet efficient network structure and feature extraction module, which reduce computational cost while maintaining recognition accuracy, making it a contender for real-time classroom behavior recognition tasks.

The Impact of Contemplative Teaching on Fostering Iraqi (High) School Students Willingness to Communicate (WTC) and Speaking Fluency

This study investigates how contemplative education and teaching backs up Iraqi EFL learners to become more willing to speak in class. After data analysis and interpretation of the performance of 72 young Iraqi students in two language schools in Iraq, the researchers found how recruited participants responded to contemplative teaching strategies in their speaking class and developed a higher willingness to interact with their teacher and their cohort Based on the results, those students who received instruction through contemplative teaching did better than students who received the traditional speech program. Moreover, those students who had more communication skills proved to be more fluent than those who had less. Based on the results and the discussion of pre-test and post-test results, the willingness of students to communicate in questionnaires, analysis of student behavior and the classroom behavior of teachers, the researchers concluded that the reflective teaching strategies and techniques used in this study have a positive impact on student’s desire to communicate and speak fluent intermediate level language skills.

Quality Evaluation of Innovation and Entrepreneurship Education Based on Modelling of Students’ Behavior Sequences

In the Innovation and Entrepreneurship (I&E) education of some higher vocational colleges, there’s a common problem: serious disconnection between professional education and practical education, which may result in poor quality of I&E education. With the help of I&E education quality evaluation, we can figure out the distribution and utilization of education resources, discover and solve problems during the teaching process in time, and optimize and adjust teaching content and methods in a targeted manner. Student behavior analysis can reveal the actual needs and questions encountered by students during I&E education, thereby attaining more pertinent and pragmatic evaluation results. For this reason, this paper aims to study the I&E education quality evaluation by means of modelling students’ behavior sequences. At first, a student I&E behavior sequence feature extraction module was created based on attention mechanism, and the student I&E ability level feature extraction layer and student I&E ability level feature evolution layer contained in the module structure were introduced in detail. Then, the data sources of I&E education quality evaluation based on students’ I&E behavior analysis were given, the I&E behavior sequences of students were modeled based on the single-sequence first-order linear differential equation model GM(1,1), and the established model was applied to I&E education quality evaluation. At last, experimental results verified the validity and accuracy of the proposed method.