In order to improve the accuracy of online teaching data classification results and provide comprehensive technical guidance and help for the standardized implementation of quality education, the machine learning model was introduced to carry out the design and research of online teaching data classification methods, taking the apron control specialty of a university as an example. Collect the basic information of college students majoring in apron control, the information generated in the teaching process, and the phased achievements of professional online teaching, build the online teaching database of college students majoring in apron control, and preprocess the data according to the specifications; The machine learning model is innovatively introduced to visually process the data. The encoding tool is used to transform the data format, so as to achieve the extraction of data characteristics; Calculate the similarity of the online teaching data characteristics of the apron control specialty in colleges and universities, set the classification criteria for the online teaching data of the apron control specialty in colleges and universities, and when the data similarity exceeds the set criteria, divide the data into the same category to complete the design of the classification method. The experimental results show that the designed classification method has a good application effect, and this method can effectively improve the accuracy of the classification results.
Under the Big data technology, the English intelligent teaching system has accumulated a large amount of learning data. However, due to the lack of accurate utility level definitions of English intelligent teaching indicators, the accuracy of the evaluation of the application effect of the teaching system is low. For this reason, research is carried out on the construction method of English intelligent teaching system evaluation system based on Big data technology. In the Hadoop distributed architecture system, coordinate the transmission behavior of big data samples, and cooperate with Apache server and HBuilder-X editor to realize the development of teaching system evaluation system based on big data technology. On this basis, determine the assessment subject of English intelligent teaching system, improve it according to the necessary conditions of the assessment criteria, so as to achieve the definition of the action mechanism of the assessment instruction, and complete the design of the assessment system of English intelligent teaching system under big data technology in combination with relevant application components. The experimental results show that under the above system, the effectiveness rating results of English intelligent teaching indicators are consistent with their true effectiveness rating, which has outstanding application value in accurately evaluating the application effect of the teaching system.
The current evaluation matrix of medical rehabilitation teaching quality is mostly one-way, and the scope of evaluation is limited, resulting in an increase in the average difference of evaluation. Therefore, the design and research of the evaluation method of medical rehabilitation teaching quality based on historical big data decision tree classification is proposed. According to the actual measurement and analysis, set the basic teaching quality evaluation indicators, use the multi-level form, break the limitation of the evaluation range, develop the multi-level evaluation matrix, design the decision tree classification evaluation structure, build the historical big data decision tree classification evaluation model, and use the top-level improvement analysis to achieve quality evaluation. The final test results show that the analysis of the test results has been completed: after five cycles of measurement, the quality of medical rehabilitation teaching has been evaluated for five items, namely, professional ethics, teaching ability, teaching methods, teaching arrangements, and teaching effects. The final evaluation mean difference has been well controlled below 1.5, indicating that this evaluation method is highly targeted and stable, it has practical application value.
With the expansion of enterprise scale and the increase in information volume, traditional manual retrieval methods are no longer able to meet the needs of rapid and accurate retrieval of enterprise education information. To this end, research is conducted to optimize the design of enterprise education information retrieval models based on machine learning technology. Utilize web scraping techniques to gather comprehensive educational data for the company’s learning and development initiatives, and complete the pre-processing of the initial enterprise education information through word segmentation, semantic tagging, clustering and other steps. The support vector machine technology in machine learning is used to extract the characteristics of enterprise education information, and the output results of enterprise education information retrieval model are obtained through the steps of feature matching, retrieval expansion, etc. Through the model test experiment, it is concluded that the retrieval accuracy and recall rate of the design model are 99.0
The construction of online courses is the key to the modernization of education. Due to the backwardness of the application means of the existing network course automatic generation system, the network course automatic generation takes a long time and the function of the network course is less perfect. In order to solve the problems such as long time and imperfect function of high school physical network course automatic generation, this paper puts forward the design and research of high school physical network course automatic generation system based on multi-agent. Take high school physics as the research subject, determine the principles that need to be followed in the automatic generation of online courses, build the automatic generation framework of online courses, design the database of online courses, construct and configure multi-agent according to the automatic generation requirements of online courses. The multi-agent value decomposition stage and the multi-agent communication mechanism design stage enable the multi-agent to have the corresponding function of automatically generating network courses, which can automatically generate the required high school physics network courses. The experimental data shows that after the application of the design system, the minimum time spent for automatic generation of online courses is 16 s, and the maximum value of functional perfection of online courses is 99
In order to address the shortcomings of low sharing accuracy and long waiting time for shared tasks in existing course resource sharing methods, this paper proposes a teaching course resource sharing method based on WEB data mining. First, sorting and mining the subset of economic law teaching curriculum resources, transforming unstructured data into data warehouse using XML format, and generating the minimum association rule set based on Apriori algorithm; Secondly, resource data cleaning is carried out using group by syntax, and wavelet coefficients are used to remove noise from resource data, achieving preprocessing of economic law teaching course resource data; Once again, the information gain algorithm is used to extract the information features of economic law teaching course resource data; Finally, use polynomial naive Bayes to classify resources and optimize resource sharing requests, completing the design of resource sharing methods for economic law teaching courses. The experimental results show that the accuracy of resource sharing using the method proposed in this article is between 90
There is a problem of resource redundancy in the conventional resource sharing methods of distance online education in higher vocational colleges, which affects the final quality of resource sharing. Therefore, a resource sharing method of distance online education in higher vocational education based on sparse clustering algorithm is designed. Construct the framework of online education resource sharing in higher vocational education, and analyze the demand for online education resource sharing. Based on the sparse clustering algorithm, the constraint factors of the shared space of educational resources are determined, and the optimal sharing code of remote online educational resources is obtained, thus eliminating the shared redundant resources. Establish a remote online education resource sharing mechanism to carry out remote management and coordination of online education resources, so as to achieve effective sharing of remote online education resources. The example analysis verifies that the method has better sharing performance and can be applied in real life.
With the advent of the digital era of network information, online teaching has become one of the main teaching methods for aircraft mechanical and electrical equipment maintenance majors. However, there are currently a large number of online teaching resources scattered across different platforms and websites, which requires students to spend a lot of time and energy searching and screening suitable resources for themselves. Therefore, a study on the integration method of multimodal online teaching resources for aircraft electromechanical equipment maintenance majors is proposed. Firstly, conduct a thorough analysis of the characteristics of multimodal online teaching resources and classify and process teaching resources. Then, based on this, establish a judgment and evaluation mechanism for the teaching resource website of aircraft electromechanical equipment maintenance major. Finally, crawler technology is used to automatically collect online teaching resources, and improved fish swarm algorithm and fuzzy C-means clustering algorithm are applied to achieve the integration of online teaching resources. The experimental data shows that under two experimental conditions, the integration time of teaching resources obtained after the application of the proposed method is the smallest, which is 5.2 s and 4.0 s, respectively, fully confirming that the integration performance of multimodal online teaching resources is more high-quality.
In order to enhance the personalized recommendation effect of Korean online education course resources and improve the accuracy of recommendations. This article designs a Korean online education course resource recommendation method based on user preference model. The Korean online education curriculum data is extracted by HtmlParser, the Korean online education curriculum text data is classified by Text segmentation, the Korean online education curriculum resource characteristics are obtained by the subjective clause extraction method, the Korean online education curriculum user preference model is constructed according to the user evaluation information, and the Korean online education curriculum resource recommendation is realized according to the Collaborative filtering recommendation algorithm. The experimental results show that the average recommendation accuracy of this method reaches 16.702
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.
In order to improve the allocation performance of Python online teaching resources, improve the utilization rate of Python online teaching resources, and reduce the allocation delay, an online allocation method of Python online teaching resources based on the global search algorithm was proposed. This method firstly designs the critical path chain of Python online teaching shared resources, reasonably allocates and adjusts Python online teaching resources, constructs the Python online teaching resource sharing model, and develops the optimal sharing mechanism of Python online education resources. Secondly, the delay model of teaching resource allocation is established, the actual load difference between each teaching resource processing node is calculated, and the teaching resource demand of virtual machine is constrained based on Euclidean distance to achieve the purpose of reducing the allocation delay. Finally, the resource scheduling problem in Python online teaching is modeled as a nonlinear optimization problem by using the global search algorithm to improve resource utilization and realize online allocation of Python online teaching resources. The experimental results show that the method proposed in this paper is more reasonable in the allocation of Python online teaching resources, and the utilization and allocation delay of teaching resources are controlled above 90
With the rapid development of information technology, multimedia distance education plays an increasingly important role in modern education. In order to solve the problems existing in the existing teaching resource management system, a multimedia distance education resource management system based on knowledge mapping is proposed. The hardware configuration of the system is improved based on at 45db80 chip. In order to ensure the operation of the hardware configuration, the information classification management algorithm of the multimedia distance education resource management system is optimized combined with the knowledge map algorithm, and the information classification algorithm and information management process are improved according to the knowledge map algorithm, so as to realize the design of the multimedia distance education resource management system. Finally, the experiment proves that the multimedia distance education resource management system based on knowledge map is more effective than the traditional management system, and the detection results can reach more than 90
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
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.
The rapid development of online teaching has led to a surge in the number of online learning resources, but students are also prone to the problem of “information loss”. To solve this problem, Collaborative filtering recommendation is an effective method. However, traditional similarity calculation methods take into account the differences in user interests over time, resulting in low recommendation accuracy. Therefore, a recommendation model of online learning resources Collaborative filtering for e-commerce logistics talent training is proposed. Firstly, collect data on users’ online learning behavior and introduce time factors to simulate dynamic changes in user preferences. Then, an improved user resource rating matrix is constructed, the similarity between users is calculated and sorted, and a set of user neighbors is constructed. Finally, predict the target score by setting the target user’s neighbor’s score for a resource and the first N resources with the maximum value, and recommend this resource to the target user, thus completing the Collaborative filtering recommendation of online learning resources. The experimental results indicate that the recommendation model studied has a greater coverage and smaller PMAE in applications, demonstrating stronger recommendation capabilities.
With the rapid development of Internet+education and the implementation of the “Three Connections and Two Platforms Project”, online education resources are becoming more and more abundant, and society is increasingly dependent on online education resources. How to efficiently and reasonably classify resources and help users quickly access education resources is a research hotspot of online education. Therefore, an automatic generation method of multidimensional labels of educational resources based on gray clustering is proposed. The method first preprocesses educational resources, including word segmentation and de stop word processing. Feature words are extracted by combining TF-IDF and TextRank. Based on the extracted feature words, the improved KNN clustering algorithm CLKNN is used to cluster educational resources. The method of grey correlation degree is used to filter the text feature words that are clustered into the same type, and the selected feature words are used as their tag words to complete the automatic generation of multi-dimensional tags of educational resources. The results show that the accuracy P (Precision), recall rate R (Recall rate) and F-Measure of the automatic generation method of multi-dimensional labels of educational resources based on gray clustering are higher, which indicates that the research method can generate labels more comprehensively and accurately.
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.
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
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
Under the background of Sunshine Sports, students can carry out more abundant sports, while the amount of online teaching resources is limited. Therefore, under the background of Sunshine Sports, research the allocation method of sports online video teaching resources. Use Analytic Hierarchy Process to analyze the reasons for imbalanced resource allocation, and provide a constraint range for resource allocation requirements. Build a cloud resource allocation model to maximize the Utility maximization problem of resource allocation. Calculate the proportion of each dimension of resources in each resource processing node’s total resources, as well as the ratio and dynamic weight between the allocated resources in each dimension of the resource processing node and all allocated resources in its nodes, to achieve balanced allocation of sports online video teaching resources. According to the experimental results, the maximum resource loss rates of the three groups of users in this method are 1.0
