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The comprehension of mathematical notions can profit from the personal background of a population of students, drawing upon their former knowledge in another field. In particular, the embodiment of mathematics provides us with a bridge to transfer competencies from music to intuitive understanding of non-trivial geometries and topologies. In this study, we analyse an experiment in which music composition students are introduced to the Klein bottle for the first time. Through the Ontological and Semiotic Approach (OSA), a conceptual framework in mathematics education, we evaluate the evolution from an initial intuition of the Klein bottle to a formal understanding of the object via musical renditions and verbal presentations to both neophyte and specialist audiences in mathematics. [ABSTRACT FROM AUTHOR]

Advances in medical imaging and artificial intelligence have led to improvements in diagnosis and non-invasive patient examination accuracy. The use of the fundamental method for Magnetic resonance imaging (MRI) brain scans as a screening tool has increased in recent years. Numerous studies have proposed a variety of feature extraction methods to classify the abnormal growths in MRI scans. Recently, the MRI texture analysis and the use of deep features have resulted in remarkable performance improvements in the classification and diagnosis of challenging pathologies, like brain tumors. This study proposes employing a handcrafted model based on Weierstrass elliptic function (WEF) and deep feature based on DenseNet-201 to classify brain tumors in MRI images. By calculating the energy of each individual pixel, the Weierstrass coefficients of the WEF are used to capture high frequency image details of the brain image. The WEF mode works to extract the nonlinear patterns in MRI images based on the probability of each pixel. While the dense connectivity of DenseNet-201’s architecture allows to learn features at multiple scales and abstraction levels. These features are passed to support vector machines classifier, which classifies the brain tumor. The results of classification accuracy achieved is 98.55% for combined features of WEF with trained DenseNet-201. Findings on the brain tumor segmentation dataset indicated that the proposed method performed better than alternative techniques for classifying brain tumors.

Higher education is the first productive force of science and technology, the first resource of talents, and the first driving force of innovation, and it plays a pivotal strategic role in the historical process of realizing Chinese-style modernization. Accelerating the construction and development of high-quality higher education and comprehensively improving the quality of independent talent cultivation is an inevitable trend in line with the development of the information age. The theoretical definition of the quality and characteristics of higher education management is used to analyze the development and quality needs of higher education in depth. Then, the intelligent education software for higher education is developed and designed by using the Android system in combination with the needs of the higher education model, and the multiculturalism is integrated, and the multicultural integration education model based on the intelligent education software is designed. On this basis, an evaluation model for education management quality was constructed using fuzzy synthesis, and an evaluation index system was established. The weights of the three first-level indicators of teaching organization and management quality, teachers’ comprehensive business quality, and students’ learning and comprehensive skills are 0.124, 0.255, and 0.621, respectively. In conclusion, utilizing the evaluation model to evaluate the effect of this paper’s education model, the comprehensive average score of this paper’s education model is 4.097, which is a high level. This result fully verifies that the multicultural integration education model based on intelligent education software in this paper has excellent performance, and the scientific research provides a reference for the development of higher education management and further explores the high-quality education model of higher education.

This paper introduces AI intelligence technology in English distance education and establishes a learning analytics model that includes information collection and acquisition, data storage, cleaning, integration, analysis, visualization, and action. Each component corresponds to a series of specific technologies and processes, and generates information such as learner portrait modeling profiling learning behaviors, using data mining to provide personalized learning solutions for learning. The assessment system is constructed from three perspectives: contextual, cognitive, and technological, which ensures accurate teaching for the personalized needs of English learners. It has been verified that the contextual interaction effect on students’ performance accounts for 78% of the total sample in the English distance education system after applying AI intelligent technology. Students in the experimental class showed positive interest in the distance education system 47.44±7.15, which was significantly higher than that of the control class 29.65±8.21. This integration model provides a new development direction for English distance education, and also provides strong support for personalized teaching and continuous optimization of the educational process.

Information technology reform is an important way for schools to realize efficient management and information technology should be introduced into the management of student registration files to accelerate the process of informationization of registration file management. This paper builds an informatization database of adult higher continuing education student registration file management based on informatization technology and carries out the design of the main business process of student registration files. The multi-layer stacked LSTM network in the ELMo model is utilized to learn the preprocessing of the text of the school records data, and the CART decision tree algorithm is improved based on the reinforcement learning mechanism in order to improve the classification prediction accuracy of the decision tree in the dataset. In order to verify the feasibility of the school registration file informationization database constructed in this paper, a test analysis is conducted for the application of data mining technology and informationization database. The results show that the check accuracy of the CART decision tree algorithm improved by the reinforcement learning mechanism is around 80%, and the throughput of this paper’s database reaches 4827ops/s when the data writing volume is 1*105GB. When the number of threads reaches 128, the throughput of this paper’s informative database is around 5486ops/s, and when the data volume reaches 12*105 entries, the database in this paper the query response time is around 602ms. Under the background of three-round education, it is necessary to fully integrate information technology with the management of school registration files, which will lead to the innovation of the informationization mode of the management of school registration files in adult higher education.

In this paper, the transfer matrix model was first applied, and the eigenvalues of the matrix were calculated to clarify the cultural nature of tourist attractions. The concatenated form is introduced into the transfer matrix, successfully calculating the matrix’s matching function. The coupled coordinated time-series evolution was then used to derive the comprehensive evaluation index of tourist attractions and cultural and educational properties, and the weights of the indexes’ correlation and difference were analyzed to derive the index entropy value. Finally, the original data are processed by the dimensionless standard, the degree of coupling coordination is evaluated with the help of the coupling concept, and the relative level of coupling is dealt with after the matrix transfer. The analysis found that the highest coupling degree of cultural and educational attributes of tourist attractions in 10 years is 0.7. Therefore, the integration and development of cultural and educational attributes of tourist attractions is the inherent requirement and inevitable result of cultural construction and tourism development and must adhere to the principle of coordinated development of promoting tourism with culture and manifesting culture with tourism.

This paper analyzes the factors affecting the efficiency value of human resource management in higher education by using the Tobit regression model, puts forward the hypotheses of the influencing factors, constructs a model of the factors affecting the efficiency of human resource utilization in higher education, and derives the specific correlation coefficients of the influencing factors. After examining the correlation between each dimension of teacher management culture in higher education and teachers’ organizational commitment and its dimensions using Pearson’s product-difference correlation method, the correlation between educational management and teachers’ teaching commitment is verified through the quantitative measurement of correlation analysis and linear regression analysis. Based on the decision tree algorithm and multiple linear regression method to predict the resource allocation of teachers’ positions, we constructed a model of human resource optimization and allocation strategy for college teachers and carried out the actual analysis of the utilization rate of resources for teachers’ positions and teachers’ schedules by genetic algorithm. Analysis obtained shows that selecting different population sizes for test results in teacher positions and teacher scheduling programs results in higher algorithm efficiency for populations between 30-50. When the value of the population is taken in the range of 30-50, the average time of the experiment is 60380.4ms-153199.2ms.

This paper constructs a teaching model for the integration of Civics and Mental Health Education based on the VAR model, establishes an efficacy function by utilizing the system sequential covariate fitness, and sets the number of district states of the VAR model to be constant, so as to be able to understand the relationship between Civics and Mental Health Education comprehensively. In addition, the fitness function was established to objectively reflect the degree of coordination and fitness of the entire education system. Meanwhile, a composite system synergy degree model was constructed by combining coupling theory. And the gray correlation statistics were introduced, by taking the average of the results, they were able to determine the comprehensive weight of the evaluation indexes. In the LNL order difference variable, its value is 0.262, which is lower than the 1% critical value of 3.556, indicating that there is a basic consistency between college students’ mental health and Civic and Political Education in terms of cultivation objectives.

Under the new round of scientific and technological revolution and industrial change, the integration of vocational education and industry is an important direction of educational development. In this paper, the structure of the learning path network of higher vocational professional courses is analyzed, and through the analysis of the elements of learning behaviors, the learning behaviors are mined by adopting sequence relations, and the behavioral structure model is established. The assessment of the learning path under the integration of industry and education courses was finished using uncertainty association coefficients and time association coefficients. After building the learning path network for vocational education, the subjective characteristics of students under vocational education were detected using isolated point analysis. The detection results show that the correlation coefficient between the first pair of typical variables is 0.488. The Wilks statistical test results for typical correlation coefficients show that the p-value of the first typical correlation coefficient is 0.098, which is less than 10%. The correlation study of the environment of integration of education and industry and the effect of integration can be simplified to study only the relationship between the first pair of typical variables. By analyzing the main characteristics, the main body for vocational education and industry integration is clarified, which creates a digital-driven impetus for the integration of industry and education.

This paper designs a teaching mode for online ideological and political education under deep learning, designing teaching content in a structured, contextualized and activity-based way to enhance teaching effectiveness and learning experience. By mining the learning needs embedded in users’ learning behaviors, customized learning resources are provided for each student to meet the personalized learning needs of different students. It also uses knowledge-forgetting matrix decomposition technology to identify and recommend key knowledge points in teaching content, helping students master important knowledge more effectively. The teaching mode proposed in this paper performs well in resource recommendation, with an average server response time of 15.147ms, while the students’ preference time is above 0.940s, which effectively improves the educational and teaching effect of the theory and method of online ideological and political education for college students.

This paper explores all student related data using educational data mining techniques to draw conclusions about their performance and behavior. The main idea and theoretical basis of Random Forest is described, the importance of each feature is calculated using Random Forest based Important Feature Selection Algorithm, and each feature is ranked and the best feature is selected as the effective feature for constructing the performance prediction model. By Light GBM is to further improve the GBDT algorithm and XGBoost algorithm to construct the Light GBM grade prediction model in order to improve the training speed and the prediction ability of the model. In order to verify the feasibility of the constructed model, the application of sustainable education in Chinese language education is tested from various aspects such as model testing as well as learning behavior. The results show that the accuracy of the Ligth GBM grade prediction model increases from 0.68 to 0.918 when the course progress is raised from 10 to 100, i.e., the accuracy of the Ligth GBM grade prediction model in predicting students’ grades gradually increases as the course progresses, so that it can effectively analyze the application of sustainable education in Chinese education.

In this paper, we use cognitive mapping for four-parameter Logistics model fitting computation and obtain personalized learning resource paths incorporating learners’ fuzzy cognitive mapping after eliminating the influence of blunders and guessing factors on learners’ cognitive assessment. After calculating peer similarity based on the cognitive map, the cognitive change trajectory is tracked, and learning resources are recommended iteratively until students become proficient. Based on the cognitive objective function and knowledge relevance, the cognitive mapping of education management of college students is constructed. The research on the mechanism of higher education management and student cultivation is analyzed from the goal of talent cultivation in colleges and universities. Statistical analysis is utilized to analyze the research on the mechanism of higher education management and student cultivation. The results show that the t-value of the regression results are all greater than 2, the regression results are significant, and its regression coefficient is 0.87, which means that all dimensions of the cultivation mechanism of colleges and universities have a positive impact on external employability. In terms of academic attitude, moral cultivation and academic norms, the supervisor has a greater role in influencing postgraduates, and its percentage values are 49.2%, 52.1% and 46.5%, respectively, and the mean values of the three dimensions are above 4.1, while the standard deviation values are above 1.0. This study provides basic references and effective suggestions to promote the continuous improvement of graduate student training quality and is of great significance to the innovation of educational management and student training mechanisms.

This paper intends to construct a new multi-dimensional preference analysis model for learners by using the improved linear programming method of multi-dimensional preference analysis (LINMAP) to get the weight vector of attributes under the situation that the weights of attributes are completely unknown and for the uncertain multi-attribute decision-making problem that the elements of decision-making matrix are triangular fuzzy numbers, and the preference information is ordered pairs. The data of subjective or objective Civics education learning preferences are aggregated, and the statistical analysis of the data is carried out using SPSS.21 to generate the Civics education learning preference input degree weighting system. The results show that the offline behavioral input weight in the college Civics course is 0.5769, which is greater compared to the online input. And the attribute weights of course Civics preference are calculated to get Zmax = 2.29, Zmin = 0.73. Multiple feasible solutions can be obtained by taking any value between [0,1]. Based on the development law of the times, this paper makes a new thinking on ideological and political education and actively explores a new road suitable for the development of ideological and political education in colleges and universities.

Behaviorist psychology can deeply reflect the perception of digital technology in teacher education in colleges and universities, and the digital transformation of teacher education is an effective path to improve the quality of education. Based on the TAM model, this paper analyzes the factors affecting the digital transformation of teacher education in colleges and universities, selects the relevant variables for research hypotheses, and obtains the relevant data for empirical analysis through questionnaires. In order to deeply analyze the initiatives, effectiveness and hidden worries brought about by the digital transformation of college teacher education from the perspective of behaviorist psychology, the combination of factor analysis and principal component analysis is used to carry out the extraction of common factors, which provides the basis for analyzing the factor loading coefficients of the influencing factors of digital transformation. The validity of the method was assessed using various methods, including descriptive statistics, reliability and validity tests, and structural equation modeling path fitting. The results show that the absolute values of skewness and kurtosis of the survey data in this paper are less than 1, which is in line with normal distribution, and the value of Cronbach’s Alpha coefficient of the reliability test is basically above 0.9, and the standardized factor loading coefficients are greater than 0.6. The direct effect of behavioral imagery on the actual application is 0.448, resulting in a significant difference at the 1% level. The digital transformation of teacher education in higher education needs to pay attention to the relevant applications of external environment and media technology so as to realize the improvement of the quality of teacher education in higher education.

This paper first proposes the direction of constructing a higher mathematics teaching mode supported by intelligent technology and then models the learner portrait of the learning outcome data according to the subject knowledge graph. An improved ant colony optimization algorithm is used to search for the optimal learning path, which is then combined with an improved convolutional neural network to generate a personalized learning path. The Trans R method is used to quantify the relationship between learners and learning resources and a semi-supervised learning conditional random field method based on K-NN is proposed to label learning resources and generate learning accurate evaluation for smart teaching. The smart teaching model of advanced mathematics courses is applied and analyzed in terms of students’ advanced mathematics pre and post-test scores, students’ satisfaction, teachers’ teaching methods and teaching resources in four directions. The analysis obtained that the posttest scores of the learners in the experimental group were 75.631, and the posttest scores of the learners in the control group were 66.314, with a difference of 9.317. The significance level of the variance chi-square test was 0.000

This paper constructs an evolutionary game model by making relevant research hypotheses on higher education management and student cultivation with evolutionary game theory, selecting the evolutionary strategies of higher education management and students, and solving the expected returns of higher education management and student cultivation through the payoff function. A separate equilibrium game is solved for the replicated dynamic equations of the evolutionary game so as to better analyze the dynamic evolutionary game relationship between higher education management and student cultivation. The stability analysis of the evolutionary game model for higher education management and student cultivation was carried out, and numerical validation was performed using MATLAB software. The results show that when P3+P4+ T3 < F, the benefits of both universities and students are higher than the costs, and at this time (1,1) is the evolutionary stability point, the university management and students learning by heart are the evolutionary stability strategy, and both sides tend to take a positive strategy. When the proportion of the university education management department choosing “attentive management” strategy is 0.9, its evolutionary path of students choosing “attentive learning” strategy proportion increases and then tends to 1, so as to reach a stable state. The best evolutionary stability point is achieved when the strategies of higher education management and student training choice are both 0.7 and 0.8 after the university incentive policy is introduced.

This paper mainly analyzes the educational level of vocational education in the context of information technology from the perspective of matching degrees of vocational education specialty settings and talent demand. Combined with the framework of vocational education service supply under information technology, the SARIMA-BP model is utilized to predict the demand for talent training, and then the match between the professional setting and the education level is explored in a quantitative description. The PSO algorithm is used to optimize the linear combination prediction model of the SARIMA-BP neural network, and the prediction accuracy is compared. The degree of vocational education development and talent cultivation mode are analyzed from four aspects. According to the division of secondary vocational school graduates according to the three industries, the proportion of graduates from Guangdong’s primary industry decreased by 0.1053, and the proportion of graduates from the secondary industry decreased by 0.0123. At the same time, the proportion of graduates from the tertiary industry increased by 0.0243. It can be seen that, to cope with the change of industrial structure in the context of information technology, secondary vocational education should adjust the relevant specialties offered to upgrade the industry’s development and the market’s need for the Talent cultivation mode.

Based on the SQL Server2000 database, this paper integrates the advantages of C/S and B/S structure to develop a sports management system oriented to big data. The system includes three parts: teaching resource management, operation management information and quality and evaluation management. Which focuses on the design of the resource management system based on the recommendation algorithm, combined with the linear regression model as well as the user rating to construct a waterfall combination recommendation model to realize the recommendation of sports teaching resources. After the system was configured, the performance of the system, as well as the application, was tested and its usability investigated. Faculty members’ opinions on the system and student usage results were included in the tests. The tests showed that 88.75% of the faculty members were satisfied with the functionality and usability of the system, and only 9 of the 80 faculty members who participated in the tests expressed dissatisfaction. Two people expressed dissatisfaction with functional testing, and 11.25% of the faculty members expressed dissatisfaction with the interface design. Students who selected courses according to the recommendations improved their evaluated performance by 3.1 points in the step test, 39.30% in the standing long jump, and 7.36% in the forward bend. Based on the test results it was proved that the system functionality meets the needs of faculty and youth.

With the help of the dynamics model of the integration of AI and the management of college civic education, this paper investigates the integration of artificial intelligence and civic education in three directions: application specification, application design, and application practice. Using differential equations to represent the non-linear dynamics equations of the system and by means of differential solutions, the evolution mechanism of AI education application and integration is projected in this paper. The integration of AI and college civic education is explored through empirical analysis and comparative analysis. The results show that the average value of the comprehensive score of the three directions in the sample colleges and universities is 77.5828, which is in the bare integration stage, and the integration degree in the “formulation” path is the best, which is 85.698, and is in the complete integration stage. The students in the experimental class of the sample universities scored better than the control class without AI integration in civic education. There were no students with scores below 60, and there were 32 more students than the control class in the 90–100 segment.

Labor education in colleges and universities in the new era is the fundamental task of implementing the principle of establishing morality and educating people, as well as an essential hand in cultivating college students with all-round development of morality, intelligence, physicality, aesthetics, and labor. This paper focuses on the objective evaluation method of college students’ labor education to conduct research. Using the improved binary tree support vector machine multi-class classification algorithm, an evaluation model for college labor education based on a binary tree support vector machine has been constructed, and an evaluation system for labor education has been developed. The results show that the majors that have compulsory labor education courses, elective labor education courses, and have added labor education modules in their major courses are all art and sports, accounting for more than 50% on average. The percentages of liberal arts, science, economics and management, art and sports, medicine and science, and agriculture, which did not offer labor education-related courses, were 26.35%, 22.16%, 19.91%, 13.59%, 11.03%, and 13.19%, respectively. It is evident that labor education courses are not distributed equally in colleges and universities, and there are still some majors that do not offer labor education courses. In terms of labor education, most of the labor education through Civics and Political Science classes is provided to students. Managers are those who choose the most specialized courses. Students also tend to participate in bicultural education and social practice activities, accounting for 29.02% and 43.01%, respectively. It shows that the algorithm in this paper can objectively and effectively evaluate labor education in colleges and universities and identify the deficiencies in education.

This study explores the integration of information technology tools, such as audio signal visualization, score recommendation algorithms based on difficulty features, and sight-singing assessment algorithms, into the development of an intelligent music classroom. It evaluates the impact of these technologies on teaching quality and efficiency, emphasizing their role in enhancing music sight-singing and classroom visualization. Findings reveal that innovative music education significantly improves teaching outcomes. While over half of the students responded positively to the creative teaching approaches, a segment found the material presented through music visualization challenging. However, introducing an intelligent music education classroom markedly benefits students’ musical learning.

With the improvement of the status of music education, the reform of music education mode is imminent. The intersection of tradition and innovation is achieved by combining ecological education and deep learning concepts to construct an environmental music education model based on the traditional music education model. The research mainly focuses on intelligent music generation and its auxiliary effects in ecological music education. The steps are to complete the intelligent generation of multi-track music through the HL-MTMG model by combining the blended learning module and reward feedback module after defining the problems of music rhythm and melody problems. In analyzing the efficacy of ecological music education based on deep learning, it was concluded that the satisfaction rate of the generated music samples ranged from 66% to 72%, and the average score of students’ satisfaction with the classroom teaching of the ecological music teaching model based on deep learning ranged from 3.313 to 4.253. The environmental music education model through deep learning can significantly enhance the impact of music education and encourage students’ interest in music learning.

The development of informatization has increasingly become the transformation direction of the Civic Education in colleges and universities in the new media era. This paper outlines the path of civic and political information technology, in conjunction with campus networks in general, and presents a series of support mechanisms for information technology. Taking S university as a research case, firstly, Fractal Brownian motion is used to scientifically characterize the traffic of campus network users. The network group characteristics are estimated through the determination of the Hurst coefficient. Secondly, the Apriori algorithm is used to extract the association rules of online behaviors hidden in these traffic features to explore the relationship between online behaviors in time series. Finally, by combining the above features and association rules, we developed subspace clustering techniques for college students’ campus Internet usage behavior. We proposed a path for ideological education based on the clustering results. The results show that the online behavior of different students on campus shows significant differences. Class A students like to stay up late on weekends, and the number of such students online fluctuates slightly from 14:30 to 23:30. Class B students generally prefer to go online during the daytime. Class C students also have some students who stay up late, but the average duration is not long, with a maximum of 766 seconds. Class D students all stay up all night, and the peak of the mean value of the online duration occurs at 4:30. Class D students all stay up all night, and the peak of the mean value of the online duration occurs at 4:30. Category D students need to be given more attention in the management of political education.

Aiming at the problems existing in the ideological and political education of college students, this paper puts forward the research on the ideological and political education of college students based on CIPP evaluation. From the CIPP evaluation, the fundamental value of ideological and political education evaluation of “enhancing output” and the evaluation objectives of effect, efficiency and effectiveness are clarified, and the four-dimensional evaluation content framework of ideological and political education of college students is formed. An evaluation index system consisting of 4 first-level indicators, 11 second-level indicators, and 36 third-level indicators was finally created by selecting experts. Through the second round of expert consultation, we obtained the results of assigning the degree of importance of indicators at all levels. Then we finalized the weights of indicators at all levels using the hierarchical analysis method. Based on the evaluation index system, colleges and universities’ ideological and political education is analyzed through examples. The results show that the overall evaluation scores of the three colleges and universities are all in the “ good “ range, with the scores of 78.3005, 80.4134 and 81.6171, respectively. This study aims to provide guidance and reference for the practice of ideological and political education for college students, and to promote the standardization and sustainable development of ideological and political education. This study aims to provide guidance and references for the practice of ideological and political education of college students, and promote the standardization and sustainable development of ideological and political education.

For colleges and universities, it is imperative to actively respond to the current new teaching mode and to put information technology into English teaching in the new situation. This paper improves the random forest algorithm by using the sparrow search algorithm to calculate the Gini index score of each evaluation index feature. By addressing the impurities of nodes in the tree, the importance of each index in the forest can be ranked. Further, the improved random forest algorithm is combined with convolutional neural network to construct the obtained English teaching evaluation method to explore the practical effect of the informationized English teaching integration path established in this paper in teaching. The results show that the average scores of the five colleges and universities’ average scores of “technology-enabled teaching” and “awareness and attitude” indicators are high, respectively 4.27 and 4.16. In contrast, the scores of “social support” indicators are low. The average score for “social support” is low, only 3.24. After the teaching practice, the number of “excellent” scores in teaching expansion increased by 15. This paper’s integration path for informationized English teaching has precise results, a certain degree of universality, and serves as a reference for future exploration of the path.

Industry-teaching integration of vocational education is a systematic and innovative schooling system that effectively integrates the resources of the government, vocational colleges and industrial enterprises to realize the coordinated development of applied talent training. This study focuses on using multi-scale feature fusion to study the classroom attention of students in the talent training classroom. It sets up the classroom facial expression detection and head posture estimation modules to realize the intelligent teaching mode reform of teaching, learning and doing integration. Then based on the background of integration of industry and education, combined with the concept of triangular helix, explore the talent training innovation model of higher vocational education, and utilize the QCA method to carry out empirical analysis to explore the synergistic path of integration of industry and education and talent training. The average accuracy of the constructed model for identifying student expressions has been shown to be 93.56%. The original coverage (0.38651) of big business * project motivation * project constraints is the highest, which explains more than 38% of the resultant variables, which is about to be nearly 40% of the industry-teaching fusion projects obtaining high project effectiveness through this path. The synergistic model of industry-teaching integration and talent cultivation in higher vocational education is provided with new ideas by this study.

This research delves into how early childhood teachers’ understanding of their roles impacts their professional advancement, essential for developing and nurturing children. We gathered data from six kindergartens in G city, employing a multiple linear regression model to scrutinize factors influencing this relationship. Our approach involved separate hypothesis formulation and model building, complemented by validity checks via endogeneity and robustness tests using advanced statistical methods. The analysis highlighted the importance of teachers’ emotional connection to their work, with a notable regression coefficient of 0.403. It demonstrated that enhancing core literacy by 1% leads to a 0.154% increase in professional development levels. The findings advocate focusing on emotional engagement and core skills to foster excellence in early childhood education.

The optimization of talent training structures and the promotion of scientific and technical accomplishments transformation may be achieved via the merger of industry and education with higher vocational education, particularly in the information technology era. This paper uses the fuzzy intuition stacked analysis method to compute the weights of the industry-education integration evaluation indexes and the binary semantics-based comprehensive evaluation method to convert the voice evaluation of experts into binary semantic information. According to the study’s findings, there will be 1,594 faculty members overall in 2021—114 more than there were in 2016. In 2021, there will be 1,012 full-time faculty members, representing 63.32% of the overall faculty members. Of the full-time professors on campus, 65.86% of them held a master’s degree in 2016. This represents the percentage of faculty degrees. 72.14% of all full-time professors on campus obtained their master’s degree in 2020, which is equivalent to 720 full-time faculty members. Furthermore, there is a general downward tendency in Institution B’s title faculty profiles from 2016 to 2020. Senior positions will be earned by 315 full-time faculty members, which is 31.56% of the total number of full-time professors on campus in 2020. In 2016 and 2020, the percentage of mid-level degrees was 48.38% and 45.19%, respectively. Assessing the degree of development of industry-taught integration can help modernize the governance structure and diversify the core integration. These developments will help enhance higher vocational education, enhance the development plan, and advance talent cultivation reform.

In the era of big data, the integration of teaching and information technology has become a new trend in the development of modernized vocational education. Taking the big data majors of vocational college M as an example, this study explores the modernization of vocational education governance system, establishes a vocational education resource recommendation model based on K-Means clustering and FunkSVD algorithm, applies the model to analyze the students’ vocational abilities in terms of user profiles, and verifies the performance stability of the resource recommendation model through continuous iterative training of the model. Comparing the changes in students’ vocational abilities before and after using the resource recommendation model, the effects of the vocational education resource recommendation model are explored. It is finally concluded that the vocational skills of the selected students can be categorized into three types: the first type of students has the most muscular comprehensive strength and the highest proportion, 62.15%. The RMSE value of the resource recommendation model gradually decreases with the increase of data volume and iteration number. The RMSE value decreases by 0.06 when the data volume increases 10 times. The vocational ability of the selected students increases by about 0.05~0.15 after the resource recommendation, which verifies the recommendation effect of the model. The study can be used to improve students’ vocational competence and provide intelligent new ideas for the governance system of vocational education.

Traditional English teaching methods are increasingly seen as outdated, failing to adequately develop students’ language abilities. This study introduces a multimodal teaching approach, evaluated through the innovative use of the sailfish optimization algorithm to gauge student attitudes towards this educational innovation. Additionally, the study leverages a genetically optimized CARMA algorithm to analyze academic achievement data, identifying optimal strategies for enhancing learning outcomes. Results indicate a significant improvement in students’ oral English skills, with approximately 70% reporting progress and 86 students endorsing the multimodal approach. Post-implementation test scores across five classes showed an impressive range of 85-97, with Classes A and B demonstrating significant score improvements (p-values of 0.021 and 0.014, respectively). The successful application of this novel teaching method in collegiate settings underscores its potential for broader adoption and highlights its relevance for modernizing educational practices across various subjects.

The cloud platform teaching system utilizes the optimized load balancing algorithm and DP algorithm to allocate and cluster massive teaching resources respectively, organize massive Chinese cultural knowledge scientifically and rationally into relevant network modules, and present it to English learners in a combination of graphic and text, audio and video. By investigating the selection of resource content and the quality of each functional module on the cloud platform, it can be found that the five primary cultural contents are generally well balanced, with the two that account for the most significant proportion of the content being cultural practices and cultural products, and the proportion of the other three varying little. Most students preferred the functional modules of Learning Center (84.3%), Resource Center, Extracurricular Paradise, and PK Pavilion, but only 12% preferred the Psychological Center module. The correlation coefficient between different factors and the intensity of Chinese culture penetration is the highest, 0.924. The average score of the degree of realization of the five teaching objectives in teaching practice is 3.838, close to “basically realized”. The scores of the four major dimensions of the university students increased significantly (p

Employing the OpenPose model, this research advances the prediction and analysis of human skeletal points in videos, paving the way for refined action differentiation and posture analysis. By isolating unique posture features and developing a DTW-based algorithm for posture matching, we identify deviations in movements for corrective action in sports training. This methodology promotes the digitization of physical education, enabling personalized coaching and a detailed understanding of student performance. Our findings reveal a marked improvement in the experimental group’s movement evaluation scores, surpassing the control group by 82.86 points, with a P value of 0.000, and demonstrating significant skill test advancements (t=3.129, P

This paper introduces mental health education into student management, utilizes ID3 algorithm of data mining technology to realize the classification of student information data, and designs the security algorithm of data storage based on C/S structure to complete the security calculation of mental health assessment. Combining the actual measurement results of students’ mental health development, a proposal is made to strengthen mental health education in student management. Taking the development of mental health education as the central perspective, exploring the specific influencing factors of the development of the level and quality of student management information technology construction, the Analysis shows that there is a significant difference between the 22-year-old students and the 18-19-year-old students in terms of information technology application ability. There is also a substantial difference in the quality of students’ participation in student management work with different levels of mental health, and the quality level of management work participated by students with good mental health status is 137.48±7.56, which is more capable of dealing with student management work than student workers with poor mental health status.

Innovation and entrepreneurship are essential for higher vocational students to escape low-end employment and are vital for vocational education to provide diverse products and services, fulfilling the people’s demand for a better life. This study builds on the theory of innovation and entrepreneurship education, integrating information technology to devise the “Four-Drive-One-Guide” model for vocational colleges. Utilizing Province A as a case study, a survey was conducted to gather data on the effectiveness of this education model, analyzed using ANOVA, Pearson’s correlation coefficient, and multiple linear regression. Furthermore, the operational efficiency of innovation and entrepreneurship education across 30 Chinese regions was assessed using the CCR model within the DEA framework. Findings reveal that the “Four-Drive-One-Guide” model significantly boosts students’ innovative and entrepreneurial capabilities, with an average rating of 4.06. The most influential factor in its effectiveness is students’ attitudes, with a coefficient of 0.238. Moreover, vocational colleges in the eastern region exhibit the highest comprehensive efficiency at 0.875. The study suggests that integrating industry teaching and optimizing the curriculum can further enhance students’ innovative and entrepreneurial thinking.

In the digital transformation era, higher education stands on the cusp of a revolution. This paper dives into the heart of this transformation, presenting a novel model of intelligent party building education that leverages digital technology to forge a new path for learning. Through the lens of ecological construction, we unveil the CFA model and a sophisticated deep knowledge tracking method aimed at enhancing student performance in Civic and Political Sciences. Our journey through empirical analysis and comparative experiments sheds light on the transformative power of digital education techniques. Despite the initial findings suggesting a modest impact of smart party building and teaching methods on knowledge retention (with significance levels of 0.6941 and 0.4963), a deeper exploration reveals a compelling narrative: the synergistic effect of these methods significantly boosts knowledge retention, as demonstrated by an F-value of 5.6941 and a P-value below 0.05. The prowess of Civics teachers, assessed across three dimensions, further narrates the success of this educational innovation, with scores reflecting their deep understanding of the curriculum and a robust theoretical knowledge base. The performance of 32 Civics teachers, with scores ranging from 12 to 24, underscores the efficacy of intelligent party building education in today’s colleges and universities.

Physical education in colleges and universities is of great significance to the development of the whole education chain, to realize the goal of physical education courses in colleges and universities and to promote the physical and mental health development of students, this paper takes the physical education courses and students in Z college and university as the object of research, and proposes a correlation model of the physical education courses in colleges and universities and students’ health status by combining the fuzzy comprehensive evaluation and the Apriori correlation rule algorithm. The weights were calculated after constructing the physical education course quality index system. The quality of physical education courses in college Z was comprehensively evaluated. It was concluded that teaching explanation and demonstration (0.137), venue equipment (0.133) and physical education class schedule (0.113) had the most significant influence on the quality of physical education courses. The quality of physical education courses in the selected colleges and universities was good, scoring 88.93 points. When the quality of physical education courses is excellent and sound, the confidence level of students’ health status corresponding to excellent and sound is greater than 80%, indicating that physical education courses strongly correlate with students’ health. Among the various indicators of courses analyzed using big data, teaching explanation and demonstration, venue equipment, and students’ health status have stronger correlations.

To promote the synergistic development of vocational education and regional economy, this paper proposes to use the separated equilibrium game model to study vocational education and regional economy. Starting from the theoretical foundation of the components of game theory, the separated equilibrium game model oriented to the synergistic development of vocational education and regional economy is constructed by combining the game tree and the separated equilibrium game. According to the calculation formula for evolutionary stabilization strategies and equilibrium analysis, the case analysis of vocational education and regional economies is conducted. The results show that when W2 is taken as 2, 3, or 4, the system evolves towards antagonistic cooperation, but evolution is becoming increasingly slow. When W2=6, the two sides of the school and enterprise choose the strategy tends to (positive cooperation, positive cooperation), which indicates that in the school-enterprise cooperation, the government departments through tax concessions, capital subsidies and other incentives to improve the enthusiasm of enterprises is an effective method. This study analyzes the dispute of interests of school-enterprise cooperation in vocational education development from the game perspective. It provides the basis for the construction of regional economic synergistic development strategy.

Cultural self-awareness reflects a nation’s recognition of its own culture and other related values, which relies on a deep cultural foundation and is of great significance to national unity. This paper firstly analyzes the content and characteristics of cultural self-awareness, discusses the promotion role of college civic education on the cultivation and enhancement of college students’ cultural self-awareness, introduces the four-layer, four-ring model of educating people to innovate the form of civic education teaching and establishes a system of indicators for evaluating the effect of cultural self-awareness in civic education on the cultivation of people. Secondly, the subjective and objective weights of the evaluation indexes were solved by the AHP method and CRITIC method, respectively. The subjective and objective weights were combined by linear weighting method. Then, a fuzzy comprehensive evaluation model was established to comprehensively evaluate the effect of culturally conscious parenting in Civic and Political Education. Finally, five colleges and universities in a province were used as examples, and the research data was obtained through questionnaires and expert assignments to analyze the effect of culturally conscious parenting in each college and university. The results of the study show that in the effect of culturally conscious parenting on college students’ civic education, the weight of strengthening the innovation of cultural teaching content reaches 0.0726, while the weight value of promoting network culture innovation is only 0.0463, and the comprehensive evaluation score for the effect of cultural conscious parenting of civic education in college A is 9.884 points. The effect of cultural self-awareness in college students’ Civic and Political Education needs to rely on excellent culture, adopt richer teaching contents and forms, and encourage college students to internalize cultural knowledge and promote their cultural self-awareness.

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.

In the context of big data’s growing influence on education, our study presents a novel approach to managing higher vocational student data through a model based on the “three-round education” philosophy. We construct a predictive model to dissect and categorize student performance at X higher vocational college by integrating K-prototypes and LSSVM algorithms. Our findings reveal three primary groups: high achievers (43.65%), average performers (23.38%), and those with challenges (32.97%), each showing apparent differences in academic success indicators. Impressively, the model forecasts student enrollment numbers with less than 1.077% error, providing a reliable tool for educational administrators to make informed decisions and tailor student management strategies effectively.

In the context of the information age, Civics education in colleges and universities faces new challenges. Cognitive mapping, an emerging technology, and its application in the design of Civics teaching in college courses is expected to enhance the teaching effect and quality. The purpose of this paper is to explore the use of cognitive mapping in the teaching of Civics in colleges and universities. The study uses the methods of data collection, preprocessing, knowledge extraction, fusion, and map construction to create a cognitive mapping-based model for teaching Civics in colleges and universities. The results show that the model can significantly improve the teaching effectiveness. In the named entity recognition experiments, the precision, recall, and F1 value of the BiLSTM-CRF-GNN model reached 93.47%, 93.58%, and 93.88%, respectively. In addition, the analysis of the hotspots of Civics research shows that the most frequent keywords include “Civics curriculum”, “Civics education” and “teaching practice”. This study not only advances the application of cognitive mapping theoretically, but also provides practical guidance for teaching Civics in colleges and universities, which is of great significance for improving the quality and efficiency of Civics teaching.

At universities and colleges today, physical education is overly regimented and ignores students’ basic health literacy. This study aims to investigate this phenomenon by examining the state of physical education instruction in colleges and universities nowadays using data mining techniques. In order to retrieve information on the essential literacies of health and physical education courses at colleges and universities, the study first builds a data mining framework using data clustering analysis, association rule extraction, and decision tree algorithms. The teaching-coordination-level model of the core literacies of physical education and health courses is established utilizing the entropy power approach, and the development history of physical education is examined. To determine the best teaching technique for integrating the core literacies of physical education and health courses, an experiment was conducted using College A as an example. The findings indicate that following the trial, students in the experimental class had substantial improvements in their physical and health literacy scores, with the exception of physical fitness (P>0.05). In the previous 10 years, the mean value of the coupling coordination was between 0.6683. The integration of the essential literacies of physical education and health courses accounted for more than 90% of the total credit hours earned in the best colleges and universities. In addition to enhancing physical education abilities, the integration of health and physical education curricula with core literacy in colleges and universities supports the general development of students’ health literacy.

This paper summarizes the communication methods of local music culture elements in the Internet perspective, combines the feature extraction and melody recognition of local music culture elements with Internet technology, and analyzes the communication effects and WCI values of music publics in media communication. It briefly outlines integrating local music culture elements with piano performance education, models musical features and piano notes. It analyzes the development of piano performance and education from the perspectives of teachers and students using multi-dimensional evaluation rules. Taking the famous song “Two Fountains Reflecting the Moon” as the experimental material, we analyze the dissemination effect of the works of local music culture elements played by two instruments: erhu and piano. Compared with the other public numbers, the total number of likes of Folk Song China is the highest, which is 12,494 higher than that of the music research public number, and the music works containing local music culture elements are more resonant to the public. The mean value of the piano group is “2.67”, higher than that of the erhu group “2.13”, indicating that the piano version of local musical works is likelier to be liked on the Internet. This suggests that the piano version of local music works is more straightforward to spread in the Internet environment, and its influence is more significant.

Cultural patterns, through the process of socialization, determine the national identity of a nation and influence the way of thinking, value orientation, and behavior of individuals. In this paper, we analyze Japanese values education from the dimensions of “group”, “interpersonal”, and “self”. The information mining algorithm based on web logs is used to establish a correlation matrix of Japanese learners’ values, and the values of Japanese learners are clustered according to the similarity of access subgraphs between the correlation matrices. During the period, the ID3 algorithm is used under the decision tree to eliminate the gap in metric size between clustering subgroups and achieve spatial division of learners’ value types. The three critical dimensions of Japanese values education and their sub-dimensions were empirically analyzed, and the F-values of the three dimensions of values for students of different majors were 0.265, 0.431, and 0.078, respectively, with P-values of 0.05 or higher, so there was no significant difference in the perception of values among students of different majors. In the value analysis of “self” sub-dimension, the descriptive statistics mean prestige, economy, and society values are 4.3592, 4.249, and 3.8696 respectively, which are all above 3. The value of education in Japanese universities is practical.

The transformative development of international Chinese language education has entered a new period, showing emerging development trends such as diversification, marketization, and digitalization. This study creates a unified classification system that links Chinese and English knowledge and complements each other based on the cross-language knowledge mapping of Chinese cultural knowledge. Additionally, a knowledge base for sharing cultural knowledge between Chinese and English is being constructed to provide strong support for information retrieval and machine translation. The data information is stored in plain text format for ontology and knowledge to make it easier to use and process teaching resources. The results show that in the teaching feedback, the average scores are all over 3, indicating that the teaching effect of the resource is positive overall, and the standard deviation values range from 0.1560 to 0.9812, indicating significant differences between different individuals. The above findings can be a valuable resource for constructing and implementing international Chinese language education network resources.

Pre-school education is an integral part of primary education, and with the development of digital technology, the integration of pre-school education and information technology development is deepening. The study first adds category constraints to the CycleGAN network to improve the efficiency of algorithm operations. The facial features of young children are extracted using the LBP method and recognized by improving the CycleGAN network after they are classified using the SOC classification method. On this basis, the improved model is applied to early childhood preschool education to explore the recognition of young children’s characteristics and educational effects. In this paper, the recognition accuracy of the improved CycleGAN network model is improved by 0.02~0.14 in the expression recognition accuracy of seven basic expressions. in early childhood education, the third round of test scores of the experimental group of young children is 93 compared to the first round of test improved by 34 points, compared with the control group scores improved by 15 points. This study provides practical and feasible development directions for constructing and reforming information technology in preschool education.

This paper takes grey correlation analysis as the theoretical basis and improves the original grey proximity correlation calculation method by adding bias terms. Uniformly select multiple sample datasets so that the proportion of positive and negative samples is roughly equal. Use the BiasGA method to calculate the biased grey correlation between users and other users to obtain the biased grey correlation matrix of users. Determine if there is a link between student management and ideological and political education by setting the correlation threshold. Integration of teaching cases and feasibility analysis led to the results being found. Teachers under 35 account for 68.75% of the total proportion. The teacher team tends to be younger, and they lack experience in ideological and political management. In student management, only a small number of teachers can improve the effectiveness of student management with the guidance of ideological and political education. the performance evaluation values of the five teachers were 72, 63, 72, 58, 61, respectively. The teachers did not do a comprehensive job of integrating the concept of ideological and political education into the management of students in colleges and universities. Daily affairs management should be strengthened in managing students in colleges and universities, and the integration of ideological and political education concepts should be emphasised.

Although China’s higher vocational colleges and universities have achieved substantial results in school-enterprise cooperation, numerous problems have surfaced in practice. This paper analyzes the influence of strategic synergy, organizational synergy, knowledge synergy, and synergy effects within the context of school-enterprise cooperation. It also examines the role of equivalence matching factors in enhancing these synergy effects. Utilizing relevant data on school-enterprise cooperation, a synergistic education model was developed using a multilayer linear model to capture the interaction of latent variables. Subsequently, a performance measurement analysis was conducted to verify the scientific validity and effectiveness of collaborative efforts in higher vocational education. The results indicate that the synergistic performance in higher vocational institutions engaged in school-enterprise cooperation is 0.655, representing an increase of 0.107 compared to institutions without such collaboration. Implementing school-enterprise cooperation significantly enhances the synergistic performance of higher vocational institutions, thereby maximizing the benefits of this collaboration.

The rapid ascent of online learning has positioned it as a preeminent method of instruction. This rise prompts a critical examination of how online education can be seamlessly integrated with the development of craftsmanship skills, a pressing issue within the educational realm. This study addresses the challenge by proposing a novel learning analytics model tailored for vocational education. The model, which emphasizes process orientation, continual enhancement, and interactive engagement, is strategically applied to three key areas: identification of learning characteristics, analysis of learning behaviors, and scrutiny of online interactions. Such applications aim to bolster data-driven decisions in nurturing craftsmanship within vocational settings. Further, the research adopts the “Learning by Doing” approach to probe the inherent elements necessary for the endogenization of learning on vocational education online platforms with a focus on craftsmanship. The empirical findings reveal that the integration of online learning with variables such as vocational beliefs, virtues, attitudes, and aspirations—quantified by Beta values of 0.185, 0.142, 0.207, and 0.394, respectively— exerts a uniformly positive influence. These results underscore the efficacy of online vocational education in fostering craftsmanship, thereby enhancing the quality of talent development in vocational disciplines.

The rapid development of social media and information dissemination has profoundly affected the ideology and value orientation of college students and put forward new challenges and requirements for ideological education work in colleges and universities. This paper takes the characteristics of social media as an orientation, studies the problems of ideological education in colleges and universities, and carries out a SWOT analysis of ideological education under the influence of social media. Based on social media, we constructed a complex network, gave a measure of its network topological characteristics, and then combined it with the classical information dissemination model to establish a SEIR information dissemination model of Civic and Political Education in colleges under the influence of social media. Taking the “Tang Palace Night Banquet” in Province H and the Civic and Political Education Skills Competition in University H as research objects, we analyzed the characteristics of the social network of Civic and Political Education information and verified the explanatory ability of the SEIR model in the dissemination of Civic and Political Education information in colleges and universities. People’s Daily has the highest incidence value and middle centrality of 63 and 72 in social media networks, and its middle centrality is 28.57% higher than that of Henan Satellite TV. The SEIR model tends to stabilize on the 180th day of the evolution, when the refusers of the dissemination of information on civic and political education are 700, and the disseminators are 300. Social media can expand the content of Civic Education in colleges and universities, combined with diverse star network nodes, can enhance the ability of Civic Education in colleges and universities, and improve the level of students’ Civic Education.