e-Learning, e-Education, and Online Training

In order to solve the problem of low accuracy in the evaluation of art and design intelligent education systems, design an evaluation model of the intelligent education system of art design based on the gray correlation analysis method, and realize the function evaluation of the system. The evaluation system model of art design intelligent education system is built from the dimensions of system utility, website structure and information organization, and the availability of information content. Analytic hierarchy process is applied to calculate the weight value of each index. The comprehensive evaluation function of the evaluation model of art design intelligent education system is realized based on the gray correlation analysis method. The model test results show that the evaluation accuracy of the model reaches 96%.

With the development of computer technology, in the field of education, a new online teaching model has been formed in order to improve flexibility and convenience, broaden learning resources and opportunities, and provide personalized learning experience. In order to promote the development of online teaching, it is necessary to evaluate the quality of online teaching. However, there are some problems such as low accuracy and long evaluation time in online teaching quality evaluation of instrument analysis course in colleges and universities. Therefore, based on analytic hierarchy process, this paper studies the quality evaluation of instrument analysis course online teaching in colleges and universities. This method uses Shark algorithm to collect online teaching data of instrument analysis courses in colleges and universities. The Analytic Hierarchy Process (AHP) is introduced to construct online teaching quality evaluation, and the evaluation results are converted into scores to reflect the evaluation results intuitively, so as to realize the online teaching quality evaluation of instrument analysis courses in colleges and universities, improve the evaluation accuracy and reduce the evaluation time. The experimental results show that the evaluation accuracy of this method is higher than that of the comparison method under different working conditions, and the highest evaluation accuracy reaches 97%. With the improvement of data integrity, the evaluation time is reduced from 3.0 s to 1.0 s, which shows the feasibility of the method.

With the rapid development of information technology and the popularization of the Internet, distance education has been widely promoted and applied globally. Remote education in universities, as an important component of distance education, is receiving increasing attention. In order to improve the evaluation effect of remote teaching quality in universities, this study conducted a fuzzy AHP based evaluation method for remote teaching quality in universities. Firstly, the theory of fuzzy AHP and teaching quality evaluation is studied, and then the process of remote education is analyzed. Through the quality control model of remote education, a quality evaluation system for remote education in universities is established. Finally, complete the calculation of evaluation index weights and the construction of a comprehensive evaluation model. The experimental results show that the proposed method can comprehensively evaluate the quality of remote education in universities, with an accuracy rate of over 95% and a low evaluation time cost of only 16.7 ms, which is superior to the comparative method and has greater application value.

The evaluation of online education quality in e-commerce logistics can provide theoretical guidance and practical support for improving education quality, promoting industry development, and enhancing talent quality. However, the establishment of traditional evaluation system structures is complex and subjective, which affects the quality and efficiency of e-commerce logistics online education. Therefore, a study on the quality evaluation of e-commerce logistics online education based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed. Firstly, determine the principles for establishing an online education quality evaluation system and construct an e-commerce logistics online education quality evaluation system. Secondly, the entropy weight TOPSIS method is used to quantify the difference between different e-commerce logistics online education quality data samples through the entropy weight amplitude, calculate the weight of e-commerce logistics online education quality evaluation indicators, determine the entropy weight vector of the evaluation indicators according to the weight of the evaluation indicators, calculate the distance from the evaluation object to the best solution and the worst solution, and rank them. When the distance between the object to be evaluated and the best solution is the smallest, it is the best, Thus, a quality evaluation model for e-commerce logistics online education is constructed. Finally, calculate the evaluation scores of various indicators and incorporate them into the evaluation model to achieve the evaluation of the quality of e-commerce logistics online education. The case analysis results show that the method in this paper can evaluate the quality of e-commerce logistics online education, obtain the high and low order of e-commerce logistics online education quality, and has good performance in the evaluation accuracy and time.

The traditional blended teaching reform system is an important teaching mode, which combines the elements of traditional face-to-face teaching and online teaching and improves the teaching effect. However, due to the inaccurate evaluation indicators, the evaluation accuracy rate of this system is low. In order to solve the problem of large deviation and low accuracy between the evaluation results and the actual results of the traditional mixed teaching reform system evaluation method in practical application, this paper studies the mixed teaching reform system evaluation method based on the characteristic analysis of higher vocational colleges. On the basis of analyzing the characteristics of higher vocational colleges, this method designs the primary and secondary indexes of blended teaching evaluation by following the principles of honesty and objectivity, operation and development, and combining the factors affecting blended learning, such as students, teachers, instructional design and teaching process. In order to improve the accuracy of evaluation indicators, weight calculation method is introduced to calculate and determine the weight of evaluation indicators through standardized data matrix. The greater the weight, the greater the impact of indicators on evaluation. The evaluation and judgment matrix of mixed teaching reform system is established according to the evaluation scale of AHP, and the evaluation index is optimized to further judge the importance of each evaluation index and ensure the accuracy of the evaluation index. On this basis, the relevance principle is introduced to design the evaluation and implementation process of the mixed teaching reform system and improve the evaluation method, so as to achieve the purpose of all-round and multi-dimensional accurate evaluation of the mixed teaching reform system. The experimental analysis results show that after applying the new evaluation method, the evaluation scores of each index are closer to the actual scores, and the evaluation accuracy rate is more than 97.7%, indicating that this method can obtain the evaluation results of the mixed teaching reform system of higher vocational schools more accurately.

In order to ensure the education quality of the platform, the teaching quality has been paid more and more attention by people, because the teaching quality directly affects the cultivation of talents. Aiming at the problem that it is difficult to evaluate the teaching effect of network physical education course, this research designs a method of evaluating the teaching effect of network physical education based on fuzzy analytic hierarchy process. Based on the principles of objectivity, scientificity and feasibility, this method constructs the evaluation system framework of network physical education teaching effect. Then the AHP is used to determine the weight of each index in the evaluation system, and finally the membership matrix is calculated by fuzzy AHP to get the comprehensive evaluation result. The test results show that the accuracy of this design method to evaluate the effect of online physical education is more than 95%, which has certain application value.

In recent years, distance education is rising gradually, and it is difficult to evaluate its teaching effect. In this context, based on the data mining of student behavior, a method of evaluating the effect of distance learning is designed. Implement data cleaning, data integration, data reduction and data transformation of teaching data of a distance learning platform. K-means algorithm based on Canopy and maximum minimum distance is designed to implement data mining of student behavior. On this basis, according to the three basic principles of “comprehensiveness”, “objectivity” and “learning-oriented”, through the analysis of data, the corresponding evaluation index is designed, so as to establish a set of effective evaluation system. Through testing, the evaluation method proposed in this paper is nearly 95% correct, and the F score is very low.

In order to improve the operational performance of intelligent education systems in university classrooms and ensure their intelligent teaching functions, a quality evaluation method for intelligent education systems in university classrooms based on OBE + CIPP is proposed. Set quality evaluation standards for intelligent education systems in university classrooms, simulate the operation process of intelligent education systems in university classrooms, and use data mining Apriori algorithm based on association rules to obtain operational data of intelligent education systems in university classrooms. Based on the OBE + CIPP architecture, a quality evaluation model for intelligent education systems in university classrooms is constructed, which comprehensively considers factors such as teaching objectives, background environment, input resources, teaching process, and learning output, comprehensively evaluates all aspects of the education system, more accurately understands the quality of the entire teaching process, pays more attention to students' learning outcomes and ability improvement, as well as the design of teaching activities and environmental support in the teaching process. Under the constraints of OBE + CIPP architecture principles, select system quality evaluation indicators from aspects such as background, input, process, and results, and calculate the weight of evaluation indicators. Based on the results of system operation data collection, evaluation indicators are determined, and compared with the set evaluation standards, the system quality evaluation results are obtained to comprehensively and accurately evaluate and optimize teaching quality, providing meaningful contributions to higher education. The experimental results show that the educational function and operational performance of the proposed method have been improved.

Aiming at the problems of low accuracy and strong subjectivity due to the fact that the existing PE teaching quality evaluation methods fail to take into account all the indicators related to teaching quality and rely on students’ feedback and evaluation data, a correlation analysis of PE teaching quality evaluation based on fuzzy comprehensive evaluation is proposed. The evaluation index system of physical education teaching quality is constructed, the characteristic data set is preprocessed by the CNNa network model, the physical education teaching quality is evaluated by the fuzzy comprehensive evaluation method, the fuzzy evaluation set is established, and the evaluation function of the fuzzy comprehensive evaluation is calculated by combining the fuzzy comprehensive evaluation and the feature weighting algorithm. The relevance analysis of PE teaching quality assessment is carried out. The experimental results show that when the class lasts for 10 min ~ 20 min, the students’ listening rate is higher than the first 10 min, which can reach more than 0.6. The students’ attention is more concentrated, and the quality of physical education is better. The accuracy rate of the evaluation method is above 98.5%, and the maximum evaluation efficiency of physical education teaching quality is 97.1%.

In recent years, the reform of quality education in China has gradually deepened, and more and more vocational colleges are carrying out the reform of teaching mode of “post course certificate contest” in response to the national call for educational reform. The implementation it can significantly enhance students’ language proficiency through increased opportunities for practical language practice. This paper designs an intelligent evaluation method for English teaching quality in vocational colleges. Implement the preprocessing of higher vocational teaching data, and use the frequent item set mining algorithm based on transaction compression to implement the mining of English “post course match certificate” fusion teaching data. Design selection principles, design the indicators of the Rongtong teaching quality intelligent evaluation system according to the mining data, and construct the Rongtong teaching quality intelligent evaluation system. Based on fuzzy analytic hierarchy process and fuzzy comprehensive evaluation method, a fuzzy consistency judgment matrix is constructed to realize intelligent evaluation of teaching quality. The test results show that the evaluation accuracy of this method exceeds 96%.

Physics education is one of the important subjects to cultivate students' Scientific literacy and innovation ability. However, the traditional physics education lacks personalized and diversified teaching methods. At the same time, the conventional parallel integration methods of physics education information usually only focus on the integration of decision-making information, ignoring the parallel integration link, resulting in poor integration effect.Therefore, a parallel integration method of physical education information based on multi machine learning model is designed. Extracting physical education information and integrating the feature of elements, machine learning and classifying multiple physical education information. Based on the multi machine learning model, the integration and transmission mechanism of physical education information is measured, the physical education information and data are transferred in a two-way way, and the physical information flow is integrated and managed in a parallel integration mode, so as to realize the parallel integration of physical education information. The simulation experiment verifies that the integration method has better integration effect and can be applied in real life.

To address the problems of high error rate in feature extraction, low abnormal recognition rate, and long recognition time in traditional methods for sports mobile education platform user behavior data, a new method for anomaly detection of user behavior data on sports mobile education platforms is proposed. This method involves denoising the user behavior data on sports mobile education platforms, extracting features from the denoised data using the STL algorithm, and selecting data features using the MRMR algorithm. By combining the feature selection results with the decision tree algorithm, the dataset is divided into normal and abnormal subsets, thereby achieving anomaly detection of user behavior data on sports mobile education platforms. Experimental results show that the error rate of feature extraction in this method varies between 3% and 5%, the abnormal recognition rate varies between 94% and 98%, and the average recognition time is 0.57s, indicating a high recognition accuracy.

Aiming at the problem that interactive design teaching system is difficult to express interactive information intuitively, a reliability analysis method of three-dimensional light rail landscape interactive design teaching system based on virtual reality is proposed. The reliability analysis framework of the system hardware structure is built with ARM chip as the core, and 3D production software is used to create a virtual display of 3D scene. The scene combines 2D design and 3D representation closely to realize OpenGL dynamic browsing of the scene. In order to improve the effectiveness of landscape interactive design teaching, a reliability analysis framework for the cooperation relationship of structural modules is designed based on real-time state-driven data. Through the reliability analysis module of subsystem state synchronization control, the data synchronization transmission is optimized, and the state data synchronization between multi-level subsystems is realized. The framework difference method is introduced to further optimize the reliability analysis method of three-dimensional light rail landscape interaction design teaching system, perceive abnormal interaction information, and analyze the reliability of three-dimensional light rail landscape information interaction process and interaction behavior between teachers and students in teaching. The experimental results show that this method can successfully realize the information exchange under all interaction nodes.

In the current context of globalization and digitization, college graduates are facing the complexity of the employment situation and the intense competition. Predicting the employment situation of college students is of great significance for formulating relevant policies, providing effective career guidance and support, and helping college students plan their career development paths. When forecasting the employment situation of college students, the reliability of the prediction results is relatively low due to the large number of factor parameters that need to be calculated. Therefore, the research on the employment prediction method of college students under the background of entrepreneurship and innovation education is proposed. In the stage of comprehensive analysis of the factors affecting the employment of college students, the research is carried out from the perspectives of individual graduates, university education system, employers and employment market, and the composition of each factor is refined. Considering the relationship between incomplete labor market information and college students’ employment, this paper analyzes the operation mechanism of the job search model from the perspective of market labor remuneration. Finally, output the parameters of college students’ employment influencing factors to the integrated algorithm W_voting, the job search model is used as a classifier to predict the employment of college students. In the test results, the accuracy rate of the design method for predicting the employment situation of college students reached 0.95, the recall rate reached 0.96, and the F1 value reached 0.96. From this, it can be seen that the prediction accuracy of the method in this article is high and has high application value.

Aiming at the problem of low recommendation cover age rate and low recommendation accuracy due to the small recommendation range of medical rehabilitation teaching resources, a multi-level recommendation method of medical rehabilitation teaching resources based on grey correlation method was proposed. According to the actual recommendation needs and standards, we recommend and classify basic medical rehabilitation teaching resources, expand the scope of recommendation, build a multi-level teaching resource recommendation structure, design a gray relational medical rehabilitation resource recommendation model, and implement resource recommendation through collaborative filtering. The final test results show that for the six objectives, the recommended coverage can reach more than 80%, the recommendation accuracy rate reached 97.5%, indicating that this teaching resource recommendation method is highly targeted and stable, with a large precision and coverage, and has practical application value.

In order to improve the efficiency and quality of automatic generation of English teaching information and achieve the ideal effect of generating accurate teaching information in a fast time, automatic extraction of Web information is introduced, and the research on automatic generation of English teaching information based on automatic extraction of Web information is carried out. First, we use data amplification method to preprocess English teaching information and construct pseudo parallel sentences. Secondly, the context of teaching information is coded, and the distance calculation between any two positions of English teaching information is reduced to a constant through the attention mechanism to solve the problem of long-distance dependence of information. Then, a corpus of English teaching information is built to store and manage the relevant contents, such as morphological reduction, parts-of-speech tagging, syntactic structure tagging, etc. Finally, the semantic relation of English teaching information is automatically extracted from Web information, and the teaching information is automatically generated. The experimental results show that with the increase of test data in English corpus, the F-value reaches more than 96%, and the accuracy rate of information generation and recall rate are higher.

In order to solve the problems of long response time and poor application effectiveness of the intelligent education system teaching quality evaluation system, a system for assessing teaching quality under the OBE+CIPP model has been developed. The OBE model is used to simulate the operation process of the intelligent education system. Under the constraint of determining the construction principle of the teaching quality evaluation system, CIPP model is used to select the teaching quality evaluation indicators from four aspects of background, input, process and results. Through the collection of real-time operation data of intelligent education system, determine the specific value of evaluation indicators, and finally complete the construction of teaching quality evaluation system of intelligent education system through the calculation of the weight of each evaluation indicator. After conducting the experimental application test, it has been deduced that upon implementation of the proposed method, the maximum system response time is 20s, and the students’ assessment scores have been significantly improved, which proves that the teaching quality evaluation system of intelligent education system under OBE+CIPP model has good application effect.

Emotion analysis can clarify the semantic orientation of emotions in social media texts. In order to improve the ability of emotion analysis of social media texts, a method of emotion analysis of social media texts based on comment information mining is proposed. Firstly, preprocess social media comment information to better explore the emotional information within it. Then, based on the linguistic features at different levels of social media texts, including auxiliary features, word level linguistic features, phrase level linguistic features, and sentence level linguistic features, rich linguistic features are extracted. In order to calculate the emotional polarity score of social media texts, the emotional polarity score of characters was considered, and the emotional polarity score of words was combined. At the same time, a sentence level sentiment score influencing factor has been introduced to more accurately calculate the sentiment score of social media texts. Through these calculations, emotional scores of social media texts can be obtained for emotional analysis. In order to better represent the emotional polarity of social media texts, a representation model integrating semantic knowledge was adopted. By integrating semantic knowledge with social media texts, the probability value of emotional polarity in social media texts can be calculated, achieving accurate analysis of social media texts’ emotions. The experimental results show that the method in this paper has a higher accuracy rate for social media text emotion classification.

In order to better understand learners’ emotional orientation and learning status, an online education learners’ emotional engagement assessment method based on multimodal data fusion was proposed. In order to comprehensively evaluate learners’ emotional engagement, two modal data, comment data and facial expression images, were selected as the evaluation basis. Collect comment data using crawler technology and preprocess word segmentation and part of speech tagging; Extract features from comment data using improved TF-IDF and construct a classifier for comment data using the K-nearest neighbor algorithm. Using a camera to capture facial expression images, and performing lighting transformation, graying, and filtering; Extract HOG features of facial expression images using absolute gradient histogram algorithm, and construct a classifier for facial expression images using Adaboost algorithm. By synthesizing the above two parts of the process, two evaluation results were obtained, with different weights set for each type of single modal data. The weighted sum rule is used to fuse multimodal data at the decision-making level to obtain the final evaluation decision result. The results show that the MSE, MAE and MAPE of the evaluation methods based on review text, facial expression and body movements are relatively smaller than those based on body movements, which indicates that the evaluation accuracy is higher.

With the development of globalization and the Internet and digital technology, the protection, inheritance, and dissemination of traditional Chinese culture have become more important, making online teaching of traditional Chinese culture an important topic in contemporary society. The conventional online teaching model of Chinese traditional culture mainly uses ESS (evolutionary stable strategy) to select teaching objectives, which is vulnerable to the dynamic effect of replicators, resulting in low reliability of the model. Therefore, a new online teaching model of Chinese traditional culture needs to be designed under the ELF concept. Based on the ELF concept, the online teaching multi-function module is constructed. The basic characteristics of online teaching are extracted, and the online teaching optimization model of Chinese traditional culture is generated, thus realizing the online teaching of Chinese traditional culture. The case analysis results show that the designed online teaching model of Chinese traditional culture has high reliability, reliability and certain application value, and has made certain contributions to improving the teaching quality of Chinese traditional culture.

Enterprises inevitably involve sensitive information when organizing asset training and education activities. To ensure information security, a machine learning based method for detecting sensitive information in enterprise asset training and education has been proposed. First, preprocess the enterprise asset training and education information text by word segmentation and removing Stop word. Then, the Bayesian network in machine learning is used to extract the characteristic words of the sensitive information of enterprise asset training and education, and then the enterprise asset training and education is expressed through the Vector space model. Finally, using the k-means clustering algorithm in machine learning, a sensitive information detection model is constructed to achieve sensitive information detection in enterprise asset training education. The experimental results indicate that the accuracy and recall of the studied method are higher, and the F-Measure is larger, which proves the effectiveness of the sensitive information detection method studied.