Online Learning Behavior Research Articles

Factors Affecting Online Teaching and Learning among Chinese High School Students: Education Equality Perspectives

Online learning is significant to promote education equality in high school sector. This research aims to explore the factors affecting students' acceptance of online learning, construct a structural equation model of high school students' online learning behavior, and propose measures to promote educational equity. The study employed quantitative research methods, utilizing online questionnaires to gather 633 data from high school students in Dazhou, Bazhong, and Liangshan regions. A comprehensive approach to data analysis was adopted, including descriptive statistical analysis, reliability and validity tests, confirmatory factor analysis, structural equation modeling, and path analysis. Key findings revealed the significant influence of online teaching quality and course content on students' perceived usefulness, ease of use, subjective norms, attitudes towards online learning, and their subsequent learning intentions and behaviors. The study confirmed the mediating roles of these perceptions and attitudes in shaping students' engagement with online learning platforms. In conclusion, the research provides vital insights into the dynamics of online education in a high school setting. It highlights the need for enhanced teaching quality and course design to improve online learning experiences. The findings offer valuable implications for educators, policymakers, technology developers, and other stakeholders, emphasizing the importance of a collaborative approach to create more effective and equitable online learning environments. This study lays a foundation for future research and strategies aimed at optimizing the potential of online education, ensuring it is accessible and beneficial to all students.

Research on Evaluation of Online Education Effectiveness Based on AI Teaching Aid System

Abstract In the age of technology, the application of intelligent assistive systems in teaching is getting more and more attention. After systematically describing the framework of the AI teaching assistance system and the process of online education effect evaluation, the study selects the course of an AI teaching assistance platform as a research case, collects the students’ learning behavior data, and applies the K-means-based clustering algorithm to divide them into clusters to obtain the learner portrait. The association rule algorithm based on Apriori is used to initially mine the connection between online learning behavior and educational effects. Furthermore, the multivariate linear regression method is applied to determine specific associations between online learning behaviors and educational effects to complete the assessment of online educational effects. Through the analysis, four types of learners were summarized: general (37.89%), negative (6.84%), interactive (9.48%), and positive (45.79%). Significant correlations between students’ online learning behaviors and educational outcomes were obtained, with regression equations between them. The effectiveness of online education is greatly influenced by students’ time commitment and learning motivation. Subsequent teaching can start from these two aspects to promote the improvement of the quality of teaching in online education.

Application and analysis of online and offline blended teaching mode based on online and offline in art theory course civics

Abstract In the context of the new era of socialism with Chinese characteristics, college, and university curricula need to reform the content of curriculum Civics and Politics in keeping with the times and introduce new era Civics and Politics elements into the curriculum content. The study integrates civic and political elements into the art theory classroom in a scientific and refined way according to the concept of civic and political curriculum. Then, data mining of students’ online learning behavior in online-offline blended teaching is carried out by constructing an improved Apriori algorithm. There is no significant difference between the midterm grades of the experimental group and the control group, and the mean score of the experimental group’s final grade is 10.55 points higher than that of the control group, and the significance P-value is less than 0.01. The experimental group and the control group present significant differences in the three aspects of artistic creation, artistic symbolism, and artistic content (P<0.05), and P>0.05 in the artistic genre and artistic performance, and the differences between the two groups are relatively small. There is a high correlation between the number of visits to the learning page, the completion of resource learning, the completion of chapter testing, and the learning effect, and there is no significant effect of homework submission initiative on the students’ learning effect.

The Study on the Impact of Chinese Vocational College Students’ Online Learning Behavior on Their Learning Effectiveness: Intervened by Self Efficacy of Online Learning and Social Support

The Study on the Impact of Chinese Vocational College Students’ Online Learning Behavior on Their Learning Effectiveness: Intervened by Self Efficacy of Online Learning and Social Support

Digital Technology-Driven Music Teaching Model Innovation and Students’ Artistic Literacy Enhancement

Abstract This study examines the use of digital technologies in music teaching, focusing on the combination of musical feature extraction, attention mechanisms, and convolutional neural networks, and the potential impact of these technologies on improving students’ artistic literacy. The study explores digital technology-driven innovations in music teaching models and their impact on improving students’ artistic literacy. The study preprocesses musical features and uses Mel’s inverted spectral coefficients to extract key audio features. The feature extraction process is further optimized by introducing the attention mechanism and convolutional neural network to ensure the effectiveness and relevance of the music teaching content. On this basis, a music teaching aid platform is designed, which concentrates on resource sharing, openness and professionalism. Through questionnaire surveys and empirical analysis, this study conducted an in-depth analysis of the platform’s willingness to use, online learning behavior, and system performance. The results show that relative advantage, perceived quality and perceived usefulness are the main factors affecting the willingness to use. Meanwhile, there are significant differences in task participation behavior among students of different genders. The system performance test shows that the platform can effectively support multi-user concurrent operation and meet music teaching needs. To summarize, the study proves the potential of digital technology in enhancing the effectiveness of music teaching and students’ artistic literacy. It provides a valuable reference for the future application of educational technology.

Research on the Mechanism of Dynamic Monitoring of Undergraduate Students’ Learning Situation and Adaptive Adjustment of Teaching Mode--Based on the Perspective of Educational Evaluation Reform

Abstract Dynamic monitoring of undergraduate learning is a tool for the accurate pulse of education and teaching, and an in-depth study of the quality monitoring data of students’ learning can discover the hidden problems and shortboards in education and teaching. The article utilizes information technology to build a dynamic monitoring platform for undergraduates. Then, it establishes an accurate teaching model for undergraduates with the assistance of this platform. The SHAP interpretable model is used to obtain the online learning behavior characteristics of undergraduates, and the LSTM autocoder is used to construct the time-varying feature sequence of undergraduates’ learning behavior, which is inputted into the LSTM model to establish the undergraduate learning situation early warning model. Taking the data of students’ online behavioral characteristics as an example, the validation of the importance of undergraduate learning behavioral characteristics and early warning is carried out, and the effect of the precise teaching model is also analyzed. The SHAP value of undergraduate students taking online tests is 0.969, which significantly impacts their online learning behavior. The accuracy of undergraduate students’ learning alert was 0.822, which was about 3.53% higher than the FWTS-CNN model with sub-optimal performance, and the results of the learning adaptability retest were 3.24 points higher than the initial test results. From the perspective of educational evaluation reform, combining undergraduate students’ learning dynamic monitoring data can enable adaptive adjustment of teaching content and mode.

Transformation Research on the Teaching Mode of Ideological and Political Courses in Colleges and Universities under Big Data Environment

Abstract The application of big data technology in the teaching of ideology and politics courses in colleges and universities can provide strong support for the precise reform and innovation of ideology and politics work. In this paper, we first sort out the status quo and dilemma of the teaching of ideology and politics courses in colleges and universities and analyze the advantages and feasibility of big data technology in the learning of ideology and politics. Then, based on the big data streaming computing Spark framework, a personalized learning service platform for teaching Civics and Politics courses in colleges and universities was established. Aiming at the online learning behavior of students on the platform, the K-mean algorithm is used to select their behavioral characteristics. The personalized recommendation of learning resources for Civics and Political Science courses is implemented based on the learning behavior. Data was utilized to analyze the effectiveness of the personalized learning platform in transforming the teaching of Civics and Political Science courses in colleges and universities using S University as the research object. The study found that the number of focused learners in category 1 of the online learning behavior reached 303, and the teaching of the Civic and Political Science course based on the personalized learning platform helped the students’ will cultivation score increase by 2.49 points and more than 95% of the students recognized the platform. The customized service learning platform for teaching Civics courses established based on big data can realize the innovative transformation of Civics courses in colleges and universities and improve the teaching quality and level of Civics courses in colleges and universities.

The Impact of Situational Achievement Goals on Online Learning Behavior: Results from Field Experiments

The Impact of Situational Achievement Goals on Online Learning Behavior: Results from Field Experiments

The reform and practice path of “curriculum civics” of medical specialties in colleges and universities on the integration of multiple data

Abstract In the context of the new era, reforming “curriculum and political thinking” in the medical specialty of colleges and universities is a key path to realizing the fundamental task of cultivating moral integrity. The study adopts a blended teaching mode, combines online and offline resources, and focuses on analyzing the learning behavior of medical students. The correlation between learning behavior and performance was analyzed by DH-Kmeans cluster analysis algorithm and Pearson’s correlation coefficient. The experimental data came from the online learning behaviors of students in the 2022-2023 academic year of T Medical College, and a total of 2426 data were collected. The results showed that learning behaviors were highly correlated with grades, especially the number of tasks completed, course video progress, and the number of chapter studies. In addition, the comparative analysis of teaching experiments showed that students in the experimental class were better than the control class regarding ideological values, learning attitudes and grades. The conclusion shows that the blended teaching mode effectively improves medical students’ learning effect and the quality of ideological education.

Online and offline mixed teaching quality assessment method for physical education class based on value assignment method

Abstract The blended teaching mode combines the advantages of online and offline teaching, which can improve the effect of university physical education teaching. The article takes the acquisition path of students’ multidimensional learning behavior data as the entry point, introduces the data preprocessing method after acquiring online learning behavior, and then establishes the online-offline blended teaching quality evaluation index system for physical education classes based on the CIPP model. The subjective and objective weights of the evaluation indexes are solved by using the AHP method and the improved pull-apart grade method, and the comprehensive, integrated assignment method is introduced to weigh the subjective and objective weights in order to obtain the comprehensive weights. Based on the weights of the indicators obtained by AHP-ISD, a teaching quality assessment model for physical education courses was established by combining the SVM model, and the SMO algorithm was selected to optimize the training of the model. To assess the application effectiveness of the model, a comprehensive assessment of its teaching quality was carried out, and the teaching effect of the blended teaching mode was also explored. The weight of the learning outcome evaluation index is 0.438 at most, the accuracy of the SMO algorithm training model is up to 99.99%, and the range of error scores between the model evaluation value and the actual value is between [0.01,0.03] points. The teaching quality assessment model based on the assignment method can obtain more accurate teaching quality assessment results for physical education courses and provide a new direction for optimizing the hybrid teaching assessment method of physical education courses.

Design and Implementation of the Reliable Learning Style Recognition Mechanism Based on Fusion Labels and Ensemble Classification

Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this paper. Firstly, a learning style labeling framework (LSDFA) based on multi-label fusion is proposed, which can obtain learning style labels by mining the potential information of two sets of inventories. Furthermore, a two-layer ensemble model (SRGSML) based on learners' online learning behaviors data to recognize learners' learning styles is proposed, which combines the resampling technology (SMOTE) to solve the unreliable prediction problem caused by class imbalance. The superiority of the proposed mechanism is verified on learning behavior data of 2,056 learners during the online teaching period of Shanghai Normal University. Experimental results show that the recognition accuracy of SRGSML achieves to 0.977, as well as prove the effectiveness of the LSDFA for labeling learning style.

Portrait of College Students’ Online Learning Behavior Based on Artificial Intelligence Technology

Portrait of College Students’ Online Learning Behavior Based on Artificial Intelligence Technology

Deep mining method of online learning behavior data based on big data analysis

Deep mining method of online learning behavior data based on big data analysis

Practice and Exploration of Ideological and Political Education in Colleges and Universities in the Context of Big Data

Abstract Big data is not only a technological innovation but also provides a new value and methodology, which provides new ideas and methods to enhance and improve the effect of education and makes the realization of personalization of ideological and political education possible. In this paper, based on the disciplinary knowledge model, we draw a knowledge structure map, organize the learning resource base, prepare disciplinary quizzes, and obtain effective student assessment data. By analyzing student assessment data, we construct a learner interest model to accurately locate the learners’ ideological and political learning needs. Modeling student behavior during video learning of ideological and political courses on the online course platform of universities. Quantify the quality of online learning from the perspective of learning participation time. Then, a learning behavior sequence is defined to describe ideological and political online learning behavior. Finally, two ideological and political education courses in the online learning platform of a university are analyzed for students’ learning behavior, and optimization suggestions for ideological and political education in colleges and universities are proposed based on the analysis results. The geometric means of play completion of the two ideological and political courses are 0.6391 and 0.7907, and the coefficients of variation of the courses are 0.1738 and 0.2136, which indicate that students’ play completion in the two ideological and political courses deviates from the mean value. The results have strong practical application value and provide a new perspective for carrying out personalized education of ideology and politics in colleges and universities in a digital environment.

An online learning behaviour recognition method based on tag set correlation learning

An online learning behaviour recognition method based on tag set correlation learning

Research on the optimization of teaching content for cloud computing technology in secondary school labor education curriculum

Abstract Labor education is an important educational course for secondary school students to learn and grow comprehensively. This paper first collects the current improvement direction of secondary school labor education through relevant questionnaires and then introduces cloud computing technology to design an online learning platform for secondary school labor education courses, which assists teachers in completing the labor teaching process. The design of the platform adopts MVC development mode, AJAX asynchronous refresh technology, and other key technologies, analyzes the requirements from three aspects of objectives, performance, and functions, and finally realizes five major modules, including user management, course management, and evaluation management. To address the problem of insufficient evaluation of labor courses, this paper constructs a model of students’ online learning behavior based on the K-means algorithm in the application process of the platform and evaluates the behavior of students’ online participation in labor education courses. By standardizing students’ platform learning information data and analyzing and calculating the learning behaviors of 300 students can be divided into 4 categories, accounting for 16.0%, 34.3%, 23.0%, and 26.7%, respectively, and the teachers of the labor courses can formulate personalized teaching plans for each type of labor learning behavior.

An online learning behaviour monitoring of students based on face recognition and feature extraction

An online learning behaviour monitoring of students based on face recognition and feature extraction

Exploring the Feasibility of Integrating Marxist Philosophy into Teaching and Learning Reform in Colleges and Universities under the Architecture of Disciplinary Knowledge Mapping

Abstract This paper centers on the research of integrating the teaching reform of Marxist philosophy. The study first constructs the architecture of disciplinary knowledge mapping, focusing on the design of entity recognition based on the BiLSTM CRF model and the joint entity relationship extraction model based on annotation strategy. Then, Marxist philosophy is incorporated into the construction of disciplinary knowledge ontology. Finally, the discipline-specific course corpus and data layer are constructed to explore the feasibility and nature of integrating Marxist philosophy into discipline knowledge mapping for teaching reform. Feedback survey on the four dimensions of Marxist philosophy from the cognitive, affective, and attitudinal aspects, the difference between the mean and standard deviation of the data of the two classes is slight. The significance of the four dimensions (two-tailed) is more significant than 0.05, and this study is generally applicable to the teaching reform of colleges and universities. After the teaching reform, students’ online learning behaviors increased, and test scores improved significantly, i.e., The teaching reform in colleges and universities integrating Marxist philosophy is feasible and effective.

Exploring the Path of Civic-Constitutional Construction of Civil Aviation Professional English Courses in the Information Age

Abstract The form of online teaching in the information age has become the mainstream of the construction of civil aviation professional English courses in Civics. In this paper, the digital resource module for the English course Civics for Civil Aviation Professionals is screened out by combining it with the teaching design of the Civics course for civil aviation English courses. With the help of the subtitle text selection method and the semi-supervised model of S4VM to label the teaching resources of the English course Civics, the CUMPF model is used to recommend the teaching resources of the English course Civics. Collecting and organizing the online learning behavior data of flipped classrooms to explore the influence of long and short-acting course resource design factors on the continuity of online learning, and recommending teaching resources based on the analysis results. The results of the effectiveness of the construction of the civil aviation professional English course’s Civic and political construction are analyzed from the degree of integration of the course’s Civic and political elements as well as from the four dimensions of course background, course guarantee, course implementation, and course effect, respectively. The integration degree of English course Civics resources for civil aviation majors is the best, which can reach up to 3.9315. The overall construction scores of the English course Civics are 0.907, 0.809, 0.744, and 0.768. The construction of the course implementation dimension needs to be improved, and the teaching materials of the English course Civics need to be improved in terms of writing and design. Civil aviation majors can benefit from the study’s ability to check and fill in the gaps in the Civics and Politics of English course construction.

User Behavior Analysis and Optimization of Japanese Language Online Education Platforms

Abstract Japanese language instruction at universities has gained new life thanks to the quick growth of online learning via the Internet, microclasses, flipped classrooms, and other innovative teaching methods. This is the direction of future educational reform in colleges and universities. After gathering and pre-processing behavioral data of Japanese learners, this study builds a data analysis model of Japanese online learning user behavior based on the Japanese online education platform. In this model, user behavioral features are extracted and classified using the RFG-SVM model, which is based on SVM. Users with similar user behaviors are then clustered together using the FCM algorithm, and the association rule algorithm is utilized to explore the intricate relationship between user online learning behaviors and learning effects. Lastly, the FSQCA approach is used to investigate the optimization path of Japanese online education platforms after combining with example analysis. The most significant aspect of Japanese online learning is its online learning rate (0.7499). Users can be categorized into three groups: close cooperation (52.3%), active participation (6.1%), and weak participation (41.6%). Japanese online learners also exhibit better user behavior. The consistency indexes for Grouping H1: SI*LA*CC*HA, Grouping H2: ~SQ*CQ*SI*~LA*CC, and Grouping H3: SQ*CQ*SI*~LA*HA were 0.929, 0.959, and 0.965, respectively, with both social influence (SI) and habit (HA) serving as important requisites. This study helps to form a mature mechanism that prompts Japanese online education users to develop a continuous willingness to use the program, which contributes to the development of inclusive education to a certain extent.