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The Gram–Schmidt process is a standard topic in beginning linear algebra courses. The computations, while straightforward, often require working with square roots, which can be difficult for students. We demonstrate how rational orthogonal matrices can be used to design questions examining the Gram–Schmidt method which avoid the appearance of square roots. Additionally, we show how these matrices are useful for the creation of geometry questions, which also maintain rational arithmetic. Finally, we discuss ways in which rational orthogonal matrices may be generated as well as give details of databases of generated matrices. [ABSTRACT FROM AUTHOR]

Assessment is among the inevitable components of a curriculum and directs students' learning. E-assessment, as prepared and administered with the use of ICT, provides opportunities to make the process easier in some aspects, but also brings certain challenges. This paper presents an e-assessment framework from a student perspective. Our study lasted for one academic year and incorporated a total of 631 students' responses to questionnaires conducted three times in two different mathematics courses. The courses are compulsory for undergraduate informatics students. The research included a three-step factor analysis: two exploratory factor analyses and a confirmatory factor analysis. It yielded a model of a student perspective on e-assessment consisting of four factors: Transparency and fairness of assessment, Formative and summative assessment and feedback, Meaningful use of technology in assessment and Difficulty of learning outcomes. The students' opinion about the use of e-assessment was generally positive. The students appreciated the student-centred approach based on sound pedagogical alignment of all teaching and learning elements with assessment. They found it important that technology should not distract students during assessment tasks and considered cheating in a controlled e-assessment environment to be no more frequent than in face-to-face assessment. [ABSTRACT FROM AUTHOR]

In this paper, firstly, the core literacy and teaching mechanism of the synergistic development of vocational education and open education is studied based on the school education plan under the requirement of the new engineering education goal to improve the quality of vocational school education. Then, the mechanism of collaborative development of vocational education and open education based on multi-scale integration characteristics is studied, and the teaching correction multi-scale integration network in the collaborative model of vocational education and open education is proposed according to the students’ learning state in the classroom. To avoid the problem of over-fitting the loss function in the prediction of the learning state, it is necessary to introduce the regular term to prevent the model from over-fitting and to analyze the learning state and the satisfaction of the collaborative model analysis. The results showed that the learning status of the two students was in a state of concentration, which was a more objective analysis of the distribution of students’ attention in the classroom. In terms of satisfaction with the collaborative model, the satisfaction level of the first-year students with the factors of the collaborative model of vocational education and open education was lower than that of the second-year and third-year students. This study is important for achieving the ambitious goal of achieving high-quality development of the synergistic mechanism between vocational education and open education.

In this paper, firstly, the process of building a statistical analysis model is studied in detail, the process of calculating the structural equation is described, and the structural equation algorithm is improved. A correlation model was created based on three dimensions: cognitive involvement, behavioral involvement, and affective involvement. After analyzing the model’s reliability and validity, it was modified to examine the correlation between learning engagement and effectiveness. The results showed that the overall effects of behavioral engagement, cognitive engagement, and affective engagement on key competencies were 0.978, 0.769, and -0.819, and the overall effects of behavioral engagement, cognitive engagement, and affective engagement on basic knowledge were 0.668, -0.569, and 0.586. The correlation between learning engagement and learning effectiveness was significant. This paper’s study has significant implications for educational reform in colleges and universities and for students to enhance their learning effectiveness.

This paper explores the multilayer linear relationship between vocational education and artisan skills talent based on a multilayer linear model using statistical methods. By dividing vocational education and artisan skills data into first and second layers, the limitations of traditional linearity or the problem of biased results can be effectively avoided. The multilayer correlation between vocational education and artisanal skills talents is explored by constructing an evaluation. Combining the variables and assignments of vocational education and skilled artisanal talents, the influence mechanism of vocational education on cultivating skilled artisanal talents is explored. The results show that the regression coefficients of vocational education input and students’ situation are high, 0.180 and 0.224, respectively, and significant at P

This paper firstly classifies the ideological and political education course field dimensions, studies the specific composition of the learning field in three dimensions: information, technology and emotion, and discusses the necessity of learning field optimization in secondary school Civics class. Secondly, on the multivariate data chain network, the MIFS model is proposed to extract the ideological and political teaching feature values, followed by the LSTM neural network to train the information related to students’ Civics classroom contents and the average function of adaptive excitation function is used to replace the excitation function in the LSTM neural network to achieve the effect of improving the output accuracy. Then, the characteristics of learners and learning resources are parametrically represented, followed by an experimental analysis of ideological and political education based on multivariate data chain networks using evaluation indexes based on the data set. The results show that there is no difference between the MAE values of CNN and LSTM except for sample one, but the MSE values of LSTM are both lower than CNN, and LSTM is more stable than CNN, indicating that it is reasonable to choose LSTM when ideological and political education based on multivariate data chain network. This study provides a reference and reference for future field optimization in ideology and political science classes.

This paper constructs the architecture of the online teaching platform for education economics based on the MVC pattern coupled with three-tier architecture. The optimal value function is solved through the experience playback mechanism and the strategy function of reinforcement learning, and the value function is used to realize the update of the deep Q learning parameters, which is used to realize the guided teaching of students in the platform. A test analysis was conducted to verify the effectiveness of using the online teaching platform for education economics courses. The results show that the waiting time for teaching resource requests in the 0-3s range of this paper’s platform is 29.71% and 51.56% lower than that of the B/C and Web platforms, respectively. The mean value of students’ learning attitude toward the platform was 108.558, and the highest satisfaction score was 0.7088. The online teaching platform can effectively improve students' learning attitude and promote the innovation of information technology in education economics classroom teaching.

This paper uses big data analysis to determine user similarity and calculate the bias in selecting nearest neighbors. The temporal factor is introduced to fully reflect the changing status of users’ interest degrees so that the recommendation accuracy can be significantly improved. According to the nearest neighbors’ rating of experimental teaching resources, the collected data on the effectiveness of vocal music teaching in colleges and universities are clustered, and the results are reflected using degree weights and biases for updating. It was discovered that seven samples had actual student vocal rating values above 0.5, and the overall vocal test scores could reach 86 or higher. To be more energetic in contemporary times, vocal music teaching in colleges and universities should be reformed and innovated to incorporate big data analysis technology.

The use of an initial state value function and an optimal strategy are used in this paper to solve educational problems based on deep reinforcement learning. Deep reinforcement learning’s approximate function is defined, and the matrix model is created by training tuning using learning methods like gradient descent. To analyze the modeling process of reinforcement learning, reward values are added to the Markov decision transfer matrix and the expected value of cumulative returns is calculated. The weights are trained using the Bellman equation to enhance the algorithm’s stability. In evaluating the effect of reform and innovation in Civic Education, the teacher education concept is rated as 10 points. The reform and innovation of civic education, combined with deep reinforcement learning, can promote the reform of education and teaching modes, improving the efficiency and quality of education.

In this paper, based on the data of the Riiid education platform, the LSTM deep learning model is used to provide accurate prediction and guidance for the education management of colleges and universities. The Gini coefficient is also introduced to simplify the calculation process, focusing on predicting the development of students’ careers. To achieve this goal, the online education platform provided a dataset that was carefully pre-processed and cleaned of data, and feature engineering was performed to obtain more informative features. Comparing the AUC value of the offline area of the ROC curve, the AUC value of the LSTM deep learning model can reach 0.758, and the training time of a single model is about 41.8 seconds. Therefore, a deep learning model based on the LSTM algorithm can be used for innovation research.

This paper compares the characteristics and methods of informatization teaching in higher vocational education with traditional teaching modes, extracts the factors affecting the quality of higher vocational informatization teaching, and constructs a preliminary evaluation index system of higher vocational informatization teaching quality based on evaluation principles and evaluation strategies. At the same time, the qualitative evaluation was transformed into a quantitative evaluation based on the affiliation theory of fuzzy mathematics, the index weight set was established, and the fuzzy comprehensive evaluation model was designed comprehensively. In the evaluation results, the ratios of teachers’ factor, students’ factor, teaching content influence factor, and teaching effect on the quality of information technology education were 0.39, 0.33, 0.34, and 0.36.

To solve the employment problems of college students, this paper integrates Civic Education and employment education, reforms the Civic Education of colleges and universities based on deep learning, and constructs the evaluation index system of Civic Education of colleges and universities to improve the employment and entrepreneurship rate. Based on the established evaluation indexes, a comprehensive evaluation model for college Civic Education based on deep learning is constructed by combining principal component analysis and gray correlation degree. The strategy evaluation is comprehensive by screening and assigning weights to the evaluation indexes. The results show that the maximum value of the gray comprehensive correlation degree is 0.93, and the correlation degrees of five first-level indicators are 0.18, 0.19, 0.2, 0.21 and 0.15, respectively. The quality of college Civic Education based on deep learning is superior and can enhance employment and entrepreneurship abilities.

This paper firstly researches the generation path of innovation and entrepreneurship education ecosystem in applied undergraduate universities from the four aspects of education ecosystem, teacher team, conceptual change and system mechanism. Then, it constructs the ecological index system of innovation and entrepreneurship education and transforms the model into a parametric model by modeling the mean value parameter and dispersion parameter and approximating the smooth function of the model by using the Bayesian P-spline method. Finally, University H is taken as an example to evaluate and test the innovation and entrepreneurship education system based on the parametric model. The results show that education and teaching occupy a large proportion of the ecosystem, indicating that by actively initiating or participating in the innovation and entrepreneurship education ecology, applied undergraduate colleges and universities can rely on social capital endeavors to prompt a constant input of resources into the ecosystem, thus maintaining the benign and sustainable development of the innovation and entrepreneurship education ecosystem.

This paper uses a deep learning algorithm to extract training samples for hybrid teaching resources in the context of Internet+. The support vector machine establishes a linear regression function, and a nonlinear function is used to map the samples to a high-dimensional feature space for solving. To optimize the training problem, a deep neural network is employed to construct a neuronal structure model and calculate the loss function. The text vectorization method was selected as the input layer processing of the convolutional neural network, and the index scores of the hybrid teaching model in the context of Internet+ were all 6 and above, with the highest score reaching 10. Therefore, online and offline hybrid teaching modes can provide a more flexible and optimized teaching method and promote resource sharing and optimization.

This paper develops a cognitive map of the Yan’an spirit that is based on the performance characteristics of the Yan’an spirit. An entity boundary perception model based on multi-task learning is proposed, and the traditional named entity recognition task is decomposed into entity boundary perception and entity classification, which completes the entity recognition based on MTBA-NER. The entity-oriented relationship extraction is based on the Bi-LSTM network to classify the expression of features, and the softmax function is used to extract the classification results. The political value has always been the value of Yan’an spirit, which has a value of 0.274 today, while the economic value, cultural value and educational value have a value of 0.322, 0.367 and 0.245, respectively. The analysis of the connotation of the Yan’an spirit based on cognitive mapping can help reflect and promote its value in contemporary times.

This paper first establishes a structural equation model to reflect the relationship between latent variables and analyzes the influence of parents' psychological capital on children's social skills in four dimensions. The partial least squares regression is applied to the component data, and a new modified weighted symbol weight sum is proposed to obtain the estimated value of the latent variable and the modified weighted symbol weight sum makes the model fit better. The results show that the average values in urban areas are 3.77, 3.60, 3.20 and 4.15, all higher than those in rural areas. It shows regional differences in the influence of parents' psychological capital on children's social skills, and urban children have the strongest social interaction abilities.

This paper firstly researches the integration of the concept of establishing moral integrity into the work of Civic and political education in colleges and universities and analyzes the thinking and path of the integration of the concept of establishing moral integrity into the work of Civic and political education in colleges and universities, to put forward a new system of Civic and political courses under the concept of establishing moral integrity and establishing people. Secondly, under the perspective of reinforcement learning, the Bellman optimal strategy equation, direct strategy search, and proximal strategy optimization algorithm are compared, respectively, and then the Civic and political education based on the proximal strategy optimization algorithm is proposed. Then, the online learning group formation strategy of the Civics course is constructed through the proximal strategy optimization algorithm, and the Civics education in colleges and universities based on the proximal strategy optimization algorithm is empirically analyzed. The results show that in the algorithmic analysis, the PPO algorithm is overall superior to the path-based, embedding-based, and strategy network reinforcement learning-based methods on the FB15K-237 dataset. On analyzing online learning group formation strategies for Civics courses, the first group of learning members tends to prefer audio learning resources, and the fourth group prefers text learning resources, with a clear manifestation of proximity. This study has promoted the concept of “cultivating morality and nurturing people” throughout the work of Civic and Political Education.

This paper first discusses the system and specific tasks of the theory of jurisprudence and analyzes the integration and development of ideological and political education with the theory of jurisprudence. Secondly, it constructed the evaluation indexes of the formation quality of jurisprudence theory, determined the subjective weights of the indexes by using the method of sequential relationship analysis, determined the objective weights by the entropy value method, and made the combination of weights by the game theory. Using the set-pair method, I analyzed the optimization indicators and constructed a set-pair analysis model based on game theory. Finally, an evaluation analysis was conducted to verify the validity of the model and the influence of ideological and political education on the formation of legal theory. The results show that based on ideological and political theory can effectively improve the quality of jurisprudential theory formation, which makes the formation of jurisprudential theory more in line with the reality of social development and can further solve social development problems. The index of jurisprudence ideology has a comprehensive linkage degree of 0.839, and the evaluation grade is excellent. The expansion of jurisprudential thought can be promoted by ideological and political education, which in turn positively impacts the formation of jurisprudential theory.

In this paper, for the innovative teaching of high school project-based courses, an adaptive differential evolutionary algorithm is used as one of the tools of the project to help students solve complex problems. The traditional adaptive differential evolution algorithm has the problems of local optimum and low convergence accuracy; to overcome these limitations, this paper proposes an enhanced reverse learning adaptive differential evolution algorithm. The OL-ADE algorithm is improved by introducing the reverse learning strategy in machine learning to extract the reverse elite individual population from the original individual population and select the better-adapted individuals for mutation operation. After the project-based teaching, the students’ works have high scores in layout color matching, technical operation, organization, information awareness, and information social responsibility, and their scores are 8.6, 9.75, 9, 9.35, and 8.75, respectively. Therefore, this paper proves the Adaptive Difference Evolution algorithm’s effectiveness in innovative teaching of high school project-based courses.

In this paper, a formal view of mental health based on deep learning was constructed by collecting social network data and psychological labeling data of students in colleges and universities, and the social network data model was extracted by using the MANE algorithm and combined with the average degree and other indexes to make a preliminary assessment. Oriented to this data model, a student mental health identification algorithm based on the MAM algorithm was proposed, and the method was combined with psychology education to study its application in the Civics classroom. From the perspective of educators, the influence degrees of teachers’ knowledge, ability, quality, and teaching input in Civics education from the psychological perspective are 0.214, 0.114, 0.043, and 0.048, respectively. Healthy psychology education based on deep learning can effectively analyze the psychology of Civics education and provide a guiding path to the use of psychology in the Civics classroom.

In this paper, we first construct ontology and entity learning to construct a knowledge graph by matching the knowledge to the schema layer. Then, we obtain users’ learning styles, extract interest keywords from user behavioral data, map them into the vector space encoded based on the KgTransH algorithm, and compare them with the original algorithm on multiple datasets. Then, the business English learning path is planned based on learning path length, learning time and achievement ranking. Finally, the effect of the knowledge graph-based business English learning path planning method is analyzed through comparative experiments. Through 50 iterations, the satisfaction of the knowledge graph method, GA method and ACO method are between 4.3-4.8, 4-4.7 and 4.05-4.7, respectively, which indicates that the method of this paper is better than the other methods. This study is important for improving the learning effect of Business English.

In the context of rural revitalization strategy, this paper uses an adaptive density-sensitive similarity measure method to improve based on a spectral clustering algorithm and combines with coordinate visualization technology to cluster rural infrastructure data for visualization. On this basis, a rural infrastructure big data platform is constructed by designing data management, data analysis and data visualization, and the overall performance of the rural infrastructure big data platform is tested in the configured test environment. The results show that when the visualization node is 50000, the running time is only 153.8s, the big data platform can output data stably, the proportion of failure is within 3%, and the corresponding time of each interface on the line is also 1000ms. Based on this research, the infrastructure for a big data platform in rural revitalization strategies can be successfully constructed.

Based on analyzing the curriculum of teacher education in colleges and universities, this paper proposes the key factors of the innovative curriculum system of teacher education, i.e., the innovation of teacher curriculum and teaching materials. Based on the method of correlation analysis to construct a curriculum system oriented to teacher education innovation, the systematic cluster analysis method is used to study the proper classification of the curriculum system by calculating the Euclidean distance for R-type clustering, showing the clustering process and results of the whole curriculum system. The similarity coefficient of student achievement is used to measure the degree of similarity between the variables, and according to the results of clustering analysis of teachers’ educational research situation, it is proposed to meet the needs of teachers’ educational research. The values of educational research results of teachers with external influence are 3.63, 2.32, 2.45, 3.56, 3.34. i.e., the degree of influence of the educational research results of teachers with external influence on the school and outside of the school is much greater than the degree of influence on the classroom team and grade team.

With the further spread of Japanese literature, this paper explores the application of artificial intelligence technology in Japanese literature education, improves the feature extraction TF-IDF algorithm based on information theory, counts the information content of the keywords as the control coefficient of their TF-IDF values in the final result, and constructs the TFIDF-BOIT linguistic feature extraction algorithm for Japanese literary works. The style analysis of literary works was conducted by screening five-dimensional variables based on the extracted linguistic features. A more targeted teaching path is proposed by analyzing the language features and styles of various Japanese literary works. The DR in Japanese literature during the Nara period was 0.345, and the DLS was 0.472. While in the Meiji period, the DR in literature decreased by 0.104, and the DLS increased by 0.085. The refinement of literature education can be achieved through the intelligent analysis of Japanese literature.

Based on SWOT analysis, this paper combines data mining algorithms to analyze the quality of preschool education curriculum Civics and propose corresponding strategies. First, the internal and external strengths, weaknesses, opportunities, and threats to the quality of preschool education curriculum Civics and Politics are identified through SWOT analysis. Then, data mining algorithms are used to extract numerical attribute features and categorical attributes, and adaptive optimization methods are used to derive adaptive weighted edge vectors to obtain the probability density function of the quality of course ideology. Finally, the study found that the overall satisfaction of teachers and students was more than 70% based on a SWOT analysis. Corresponding countermeasures are proposed to provide an important reference for developing preschool education programs.

This paper first explores the composition of visual elements in modern digital media artworks, extracts the graphical element features of visual elements by improved SIFT algorithm, and classifies and recognizes the graphical elements using by SVM algorithm. Secondly, the extracted and categorized graphical elements are represented by Laplace feature vector correlation spectra in combination with spectral graph theory to study the mutual relationships between the graphical elements. Finally, some graphic elements in modern digital media artworks are used as examples to explore the performance and interrelationship of graphic feature extraction, recognition, and classification. The results show that the vector eigenvalues of spectral graph theory are categorized into [0], (0,100], (100,200], [200, ∞), and the corresponding interrelationships are one-to-one, one-to-many, many-to-one, many-to-many, respectively.

Based on the student-oriented teaching practice in the ideological and political reform of the Human Resource Management course, this paper established a teaching evaluation index system with four dimensions: teaching context, teaching input, teaching process and teaching product. Kehlenbach alpha coefficient and split-half reliability were utilized to test the reliability of indicators, and expert validity and content validity were adopted to examine the validity of indicators. Multivariate statistical analysis was used for principal component analysis and factor load matrix estimation to construct a comprehensive evaluation based on principal component analysis and an educational evaluation based on the factor analysis method.

In this paper, firstly, based on the digital construction of education management in colleges and universities, it is proposed to construct an education management system for colleges and universities to realize the digital development of education management and to design the function and database of the education management system of colleges and universities. Secondly, the Apriori algorithm in association rule mining algorithm is used to define the minimum support and minimum confidence in the variable parameter focus, then the support threshold and confidence threshold are set in the process of optimization connection and pruning, and for the shortcomings of traditional algorithms, the relevance metrics are cited here to improve the systematic data mining efficiency of the Apriori algorithm. Then, the simulation experiment environment and data set are selected, the evaluation index system is determined according to the system requirements, and the education management system based on the association rule mining algorithm is simulated and analyzed. The results show that when min_supp=70%, the Apriori algorithm generates a total of 60 frequent itemsets, and the running time is about 21.2s. Overall, the Apriori algorithm only reads the data set twice, so the program running time is shorter than the Eclat algorithm running time.

This paper aims to construct a resource base for adult online education using a multimodal fusion basis. The fusion processing methods of knowledge resources incorporate temporal and non-temporal feature learning. This paper proposes a process for constructing a multimodal subject knowledge space that can aid educators in organizing and presenting multimodal teaching content effectively. Also, the operation process of each major functional module of the adult online education resource library, including the online teaching process, online learning process and online resource production process, is studied in detail. Finally, the performance test of optimizing small file storage, the performance test of de-duplication of redundant data of learning resources and the stress test were conducted on the constructed resource library. The study concluded that the resource memory occupancy of the computer class was reduced by 0.35 after the de-duplication process, and in the stress test, the login response time was less than 8 seconds, and the average CPU utilization of the database server was

This paper combines the nine-factor model with the construction of university think tanks, establishes the strategic positioning of university think tanks, analyzes the methods and measures of cultivating leading talents and innovative talents in university think tanks and classifies the influence of university think tanks into decision-making influence, elite influence and popular influence. Based on the analysis of big data and concerning the fuzzy hierarchical analysis method, the evaluation model of information service quality of university think tanks has been established. The evaluation indexes of the information platform of university think tanks are divided into four indexes: service resources, service content, webpage technology, and service effect. According to the constructed evaluation indexes, two-by-two comparisons are made between each index to obtain the score of the importance of each index. Based on fuzzy mathematics, the qualitative evaluation can be transformed into quantitative evaluation according to the principle of affiliation degree to arrive at the evaluation grade of the information platform capacity of university think tanks and innovative talents.

This paper examines the reality of sports tourism resources by effectively integrating the universal symbiosis and spatial hierarchy of sports tourism resources. Spatial analysis is utilized to view each element of the near-neighboring related element environment to find the spatial distribution probability density value of the random distribution within the sports tourism resources. The overall data distribution can be inferred from the limited samples, and calculations are performed to recognize localized density changes in the distribution of point-like samples. The highest mean value of sports resources density is 0.827, the highest comprehensive value of tourism resources evaluation is 0.82 points, and the maximum value of tourism competitiveness is 0.389. The spatial analysis perspective greatly improves the sports tourism industry’s comprehensive strength and the economy’s sustainable development is realized.

In this paper, we systematically summarize the chord configuration method in harmonic writing, start from the connection principle of functional harmony, and use the knowledge of harmonic theory to research and explain the harmonic variability writing and chord configuration method. Secondly, based on big data algorithms, it is proposed that according to the chord composition law and progression logic, a chord system construction objective function is proposed, and a dynamic planning algorithm for automatic chord arrangement is designed to realize automatic chord arrangement by machine. Then, after reducing the chord repetition rate of the generated music, it was found that the ideal style type of music could not be achieved. For this problem, a hidden Markov-based chord auto-configuration model is proposed for optimization and experimental analysis of the chord configuration method in harmonic writing is carried out. The results show that the emotional response of the major triad music fragment with an octave decrease in overall pitch is a positive emotion, and the intensity of the emotion is significantly reduced compared to the major triad with no pitch change. The emotional response to the music fragment of the major triad with an octave increase in overall pitch was positive, and the emotional intensity of the major triad with no pitch change was significantly increased. This study improved students’ musical aesthetics, cognitive abilities, and experiences.

This paper aims to create a digital platform that uses big data to manage HR performance. Firstly, the FCM algorithm transforms the dataset according to its affiliation degree and constructs a new database. An objective function is established to optimize the classification of organizational information using the correlation between data. Finally, the AHP algorithm is introduced to set indicator weights and factor sets, making the assessment results more hierarchical. The results show that the management platform system constructed in this paper operates stably, keeping the departure rate of university staff at about 5%, and 92% of students believe that the platform can improve teachers’ teaching quality. This shows that big data technology optimizes human resources management in colleges and universities.

This paper analyzes the current situation of physical education teaching in colleges and universities from the perspective of deep learning and innovates the teaching methodology. A multidimensional data array is used to quantify students’ interests and hobbies to create a recommendation module for intelligent sports learning materials. Using the SIFT feature extraction algorithm, identify the extreme value point of the detection target and develop the sports target extraction module. Through distance transformation and morphological features, determine the skeleton image features to construct a college sports teaching model using deep learning. The results show that the model in this paper enables the coverage rate of sports courses to reach over 97.20%, with a fluctuation of no more than 1.5%. Deep learning has contributed to some innovation in college sports teaching.

Due to the current kindergarten preschool education being increasingly emphasized, this paper combines intelligent technology to explore, firstly, analyzes the current intelligent learning environment model and designs the intelligent learning environment based on human-computer interaction. Secondly, the emblem elements are integrated into the physical learning environment, combined with intelligent VR technology, gesture interaction recognition based on feature vectors and pose interaction recognition based on vector machines to realize the construction of the smart learning environment. Finally, the interaction performance in the smart learning environment is analyzed based on simulation experiments, and the changes in learning effects are explored. The application yields that young children’s independent ability and social interaction ability increased by 0.5 and 0.48, respectively, and the mathematical thinking ability, artistic creation ability and Hui culture cognitive ability increased by 0.35, 0.39 and 0.32, respectively.

This paper designs a scheme for a nursing professional teaching quality evaluation system, including constructing an evaluation index system and a nursing teaching quality evaluation model based on a fuzzy comprehensive evaluation. Among them, the establishment of a fuzzy comprehensive evaluation model involves the determination of fuzzy operators and the processing of evaluation indexes. Finally, the constructed method was used to conduct a study on evaluating nursing teaching quality in College H for students of different academic years. The results showed that freshman, sophomore, and junior students were inconsistent in all aspects of faculty teaching effectiveness and overall evaluations. The total rating of freshmen was 18.34±0.17. The total rating of sophomores was 20.50±0.15. The total rating of juniors was 23.46±0.12. And 66.7% of the faculty members thought that the quality of nursing teaching at the university was not up to standard. Therefore, this paper provides a valid assessment method for nursing education.

This paper firstly researches the brand communication strategy and influence enhancement of higher vocational education, according to the strategic layout of vocational education, the SWOT analysis of online brand communication of offline vocational education institutions and online vocational education institutions, and then puts forward the strategy of enhancing the influence of brand communication of higher vocational education. Secondly, based on the multi-information interaction communication mechanism, research on the influence of vocational education brand communication based on the information dissemination model is proposed for the brand communication dilemma of vocational education in the new media environment. Then, considering the influence mechanism inference, a single intelligent body model needs to be extended to the whole multi-vocational education information interaction communication model to make the vocational education information interaction communication more stable, and research and analysis of vocational education brand communication strategy and influence enhancement based on the information communication model. The results show that targeted vocational education information will occupy more storage space, the vector sum will be more, and the trend and evolution state of vocational education information dissemination can be effectively controlled by controlling the vector sum. This study enhances the impact of brand communication on higher vocational education.

This paper first explores human resource management and organizational performance and investigates the multiple linear regression model and its hypothesis conditions, parameter estimation, model testing and variable normalization treatment. Then, the evaluation index system of university organizational performance was constructed, and the relationship between human resource management and organizational performance in universities was analyzed in depth through correlation analysis, the relationship between subjects’ basic conditions and organizational performance, analysis of variance explanation of endogenous latent variables, and empirical analysis of the relationship between human resource management practices and organizational performance. The results show that information sharing has a significant positive relationship with research contribution (β=0.293, Sig.=0.084, T=4.67) and based on human resource planning has a significant positive relationship with research input (β=0.55, Sig.=0.023, T=2.362). Therefore, human resource management in universities has a significant impact on organizational performance.

Multimedia-assisted data mining algorithms are used in this paper to extract information from soccer training teaching data and develop models for the sampling and storing soccer training data. Filtering is used to process the soccer training attribute data stream, and global position optimization is performed using the mutual information analysis method to integrate the teaching system resources. The soccer training teaching model and teaching program were established to finish the teaching system design. The results show that after one semester of teaching, the final assessment, compared to the average score of the entrance assessment, the training performance increased by about 42%, thus proving the effectiveness of the soccer training teaching system based on multimedia-assisted teaching.

This paper firstly researches the role of sports in the cultivation of socialist core values in the context of healthy China, which is mainly embodied in the establishment of the correct goal to strive for in life and the establishment of a sense of fair and just social responsibility, and puts forward the path of socialist core values cultivation. Secondly, the socialist core values are studied by visualizing campus sports data. According to the LSTM neural network with good visualization and computational recognition ability, a campus sports data visualization model based on LSTM is constructed, and sensors are used to obtain the feature extraction of students’ sports. Based on the feature values, the feature state’s output value is computed using the LSTM neural network. Considering the extraction and feature loss problem in the calculation process, it is necessary to select a suitable loss function to optimize it and the example analysis of socialist core values based on LSTM data visualization. The results show that the higher the level of soccer skills, the higher the level of “self-confidence”, “strength”, “respect”, “responsibility”, “law-abiding” and “patriotism” indicators, the higher the level of “self-confidence” (t=3.736, p=0.005) indicators. The difference is significant. This study has contributed to socialist core values in the context of sports.

In this paper, first of all, in the quality assessment of the teaching quality of the translation course, it is proposed to use a multi-attribute decision model to assess the quality of the set of alternatives for all the courses and to use the existing decision ideal points to rank and select the best of a set of optimized solutions for the translation course through the hesitant fuzzy number and objective optimization model. Finally, to explore the optimization problem of the curriculum, a research analysis was conducted on MTI master’s students who recently graduated from the institution. The results show that by optimizing the MTI course specifically, the students’ positive interest (p1 = 0.03) and independent inquiry interest (p2 = 0.01) are less than 0.05, showing significant differences (p

Exploring the innovative model of mental health education talent training is beneficial for aiding students in establishing correct mental health concepts. In this paper, starting from the data mining algorithm based on the random forest algorithm and XGBoost algorithm, the RF-XGBoost hybrid analysis model is jointly constructed by the residual sequence of the random forest model and the prediction sequence of the XGBoost model. The influencing factors of mental health education were described, the integration model of mental health education talent cultivation was given, and the data analysis of the principles and contents of integrated talent cultivation using the RF-XGBoost hybrid model was conducted with the University of Z as an example, from the cultivation principles, wholeness, coordination, and continuity improved by 90.09%, 71.47%, and 90.86%, respectively, compared with 2017. Regarding the training content, the percentages of those who rated very satisfied, generally satisfied, and dissatisfied were 57.36%, 30.01%, and 12.63%, respectively. This shows that the integrated talent training model can help mental health education achieve its cultivation goals and establish the correct concepts for students.

This research firstly analyzes the significance of the construction of a virtual simulation training base for vocational education and takes modern shipping as an example to explore the path of the construction of training. Secondly, through point cloud data processing, combined with the improved SAC-IA algorithm for multi-source data fusion processing, three-dimensional modeling of the training base can be achieved. Finally, a virtual simulation training platform for vocational education is designed by combining the key technologies and equipment for platform development, and the accuracy of 3D modeling with multi-source data fusion and the application of the platform are analyzed. The consequence shows that the significance of learning interest P=0.03

This paper strategically classifies the emotional labor of early childhood teachers and explores the relationship between emotional labor, emotional exhaustion, occupational well-being, and occupational self-efficacy based on a five-dimensional model of teachers’ occupational well-being. A structural equation model was developed to explore the mediating effects of early childhood teachers’ psychological capital between surface behavior, passive depth behavior, and occupational well-being, using psychological capital as a mediating variable. Based on the mediation analysis of the structural equation model, parameter identification and parameter design were performed to conduct descriptive statistics, correlation and regression analyses of emotional labor and occupational well-being of kindergarten teachers occupational well-being, and to test the moderating effect of social support in the multimedia context. Deep play significantly and positively predicted the dimensions of emotional well-being, professional well-being, interpersonal harmony, environmental satisfaction, and total occupational well-being scores, i.e., β= 0.35, 0.42, 0.40, 0.21, 0.36, P

This paper firstly analyzes the developmental problems of the construction of the sports course ideology and politics in colleges and universities, starting from the value function of the construction of the sports course ideology and politics. Secondly, it constructs the evaluation index system for the construction of college sports course ideology and politics, utilizes the subordinate criterion function of the subordinate matrix model for the weight calculation of the index subordinate coefficients, and calculates the positive and negative ideal solutions and relative posting progress of each index through the TOPSIS method. Finally, in order to analyze the transformation of the number-based effectiveness of the construction of the college sports course ideology and politics, an example verification analysis was carried out. The results show that the transformation of digital effectiveness in the construction of the ideology and politics of sports courses in colleges and universities is mainly carried out through the shaping of students’ values, the cultivation of ideals and beliefs, and the analysis of the weights of the indexes points out the direction of the development of the construction of ideology and politics of sports courses. This shows that the transformation of the digital effectiveness of the ideological and political construction of physical education courses needs to start from the spirit of sports, which in turn promotes the ideological and political construction of physical education courses to boost the quality of physical education disciplines.

This paper firstly researches the reform and innovation of higher vocational dance choreography teaching under the background of new media and puts forward the reform and innovation suggestions of higher vocational dance choreography teaching according to the structure and thinking steps of higher vocational dance choreography teaching. Then, research on dance choreography teaching based on virtual environment technology, using quaternion interpolation to define the distance between dance action frames in dance choreography teaching video, selecting public frames of dance choreography teaching video based on the definition of dance action, articulating the keyframes of dance action according to splicing the dance action sequences and analyzing the research on innovation of higher vocational dance choreography teaching. The results showed that in posture, the P-value of students’ shoulder, torso, sitting, standing, walking, and overall posture were less than 0.01, with highly significant differences. In terms of students’ aesthetics, the P value of students’ aesthetic sensibility was 0.026, P0.05, and aesthetic creativity P=0.063>0.05, which shows that students in the control class got a significant improvement in aesthetic sensibility. This study plays a certain role in inspiring and leading generalization for the teaching of dance choreography.

In order to realize the innovation of sports teaching mode, this paper first combines the motion capture technology and three-dimensional modeling technology, based on the objectives and theories of sports teaching in colleges and universities, and constructs a college sports auxiliary teaching system under the fusion network architecture. Then, the three-dimensional modeling of sports action, the extraction of key sports action features, and the use of generalized discriminant analysis and hierarchical SVM construction method for the degradation and classification, respectively, to achieve the recognition of the sport in sports teaching. Finally, the effectiveness of the system and teaching model was tested and evaluated through the use of Loadrunner and SPSS software. The results show that the response time is 2s when the number of concurrent users is 100, the utilization rate of CPU is 64.2%, and the mastery of sports knowledge and movement skills of the experimental class is higher than that of the control class by 0.18 and 0.26, respectively. The innovative teaching mode of physical education in this study is effective.

This paper takes the problems and ways of diversified curriculum construction of vocal music education in colleges and universities as the entry point and constructs a deep blended learning model of vocal music diversified curriculum based on the deep learning model. The deep evolutionary knowledge tracking model is constructed by combining the self-attention mechanism embedding model with vocal music knowledge evolution based on Transformer. To verify the effectiveness of the deep blended learning model constructed in this paper, model comparison experiments and empirical analysis were conducted. The results show that compared with the SAKT model, the DBL model in this paper has a 5.19% improvement in the average ROC-AUC value. The vocal posttest scores of the students in the experimental class were 5.09 points higher than those of the control class, with a two-tailed significance value of 0.027, which is less than 0.05 and a significant difference. This indicates that the deep learning model can effectively promote the innovative design of diversified curricula for vocal music education in colleges and universities and enhance students’ vocal music learning ability.

Based on structural equation modeling, this paper measures and evaluates the transformation of scientific and technological achievements of colleges and universities in terms of implicit and explicit variables. The PLS algorithm is used to measure the intrinsic factor, which is designed to follow the Gaussian distribution and reach the expected value through iterative loops. The indicators and parameters of the explicit variables are evaluated by combining the principal component analysis and the great likelihood method. According to the results, the factors that are influenced by the policy environment account for 16.5%, while the problem of capital investment accounts for 36.3%. The number of invention patents increased by 8.93% after measuring and assessing them using the method presented in this paper. It demonstrates that universities can explore their technological achievements in depth and enhance their scientific research level.

This paper explores the theory and practice characteristics of management accounting, expands upon the function of management accounting, and outlines the business process of strategic management accounting. Then, it constructs the functional effect analysis model of management accounting, establishes the causality analysis model of the structural equation, explores the markov property of the equation, and uses multiple linear regression equations to estimate the regression parameters. Finally, it analyzes the correlation between the overall level of strategic management accounting, the application level of management accounting, and the horizontal integration level of management accounting and enterprise performance. According to regression analysis, the three dimensions have a significant positive correlation with enterprise performance at the 1% level. The structural equation analysis indicates that the path coefficient of management accounting on performance is 0.388, which is significant at a 1% level. The application of management accounting can have a positive impact on the performance level of enterprises, as shown. This research promotes the innovative practice of management accounting and is of great significance for the high-quality development of enterprises.