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
