Model For Evaluation Of Effects Research Articles

Blocking Mechanism and Evaluation Model of Insensitive Effect Under Multi-Frequency Electromagnetic Radiation

Due to the insensitive blocking effect on frequency equipment, the current effect evaluation model based on the third-order power series expansion is disabled to the insensitive blocking effect evaluation under multi-frequency electromagnetic radiation. We adopt the fifth-order power series expansion to analyze the insensitive blocking interference effect. In addition to revealing the mechanism of the insensitive blocking effect under multi-frequency electromagnetic radiation, we also build an improved effect evaluation model in this research. It defines the effect evaluation index. Subsequently, a ranging radar and a communication radio station are employed to verify the effectiveness of the proposed effect evaluation model separately. The results show that the proposed model can be applied to the electromagnetic radiation effect evaluation, regardless of whether the multi-frequency insensitive jamming effect exists on tested equipment or not. All effect evaluation indexes of the new model are smaller than that of the previous model based on the third-order power series expansion method. The absolute value of relative errors could be less than 1 dB. Not only is the theoretical analysis result of the multi-frequency insensitive blocking effect mechanism verified, but also the problem of poor universality of the current electromagnetic radiation effect evaluation model is figured out.

Evaluation of the Gas Drainage Effect in Deep Loose Coal Seams Based on the Cloud Model

The gas drainage effect is one of the important elements in the study of gas drainage in coal mines. It is critical to establish an effective evaluation model of the gas drainage effect for coal mines because the result of gas drainage is directly related to the safety of the coal mines. Through the research related to the safety evaluation in the existing coal mining process, we discovered that there are few studies on the evaluation of the impact of deep and soft gas drainage, and the evaluation methods are not sufficiently effective to resolve the complex problems arising in the process of gas drainage. This paper took “three soft” coal seams in the Lugou Coal Mine as the research object and constructed the evaluation index system on the basis of thoroughly analyzing the factors of coal seam drainage. We then employed a combination weighting method to attain the optimal weight by organically integrating the Analysis Hierarchical Process subjective weighting method and the Criteria Importance Through Interaction Correlation objective weighting method and utilized the cloud model to compute the numerical characteristic value of the evaluation index. In the end, this method obtained an evaluation result of the gas drainage effect evaluation. The evaluation result grade is good. Additional analysis was performed according to the evaluation factors, and corresponding improvement measures were proposed. This is of great importance in promoting safe production and improving the efficiency of gas drainage.

The English Teaching Mode under the Environment of Computer Technology

The development of Internet information technology has opened up new potential for the education sector, encouraging the development of innovative teaching and learning methods as well as the optimal use of available educational resources. In this environment, the online plus offline mixed teaching model emerged, which can not only get around time and space restrictions and link smoothly inside and outside the classroom but also enhance the form of teaching and learning and strengthen teacher-student relationships. However, to assure the quality of instruction, this research first examines the successful ways of developing an online plus offline hybrid teaching mode from the perspective of college English professors. Second, in this paper, the support vector machine and particle swarm method are used to build an evaluation model of the English teaching effect. The model has the potential to increase the accuracy and efficiency of English teaching impact evaluation while also meeting the standards of English teaching effect evaluation. The experimental findings show that, while evaluating the effects of college English instruction, the technique beats the comparison algorithm in terms of evaluation accuracy and algorithm time consumption.

An Evaluation Model of Carbon Emission Reduction Effect of Prefabricated Buildings Based on Cloud Model from the Perspective of Construction Supply Chain

Prefabricated buildings are the future direction of the construction industry. The carbon reduction in prefabricated buildings has attracted increasing attention due to its importance to low-carbon and energy savings in the construction industry and achieving China’s “dual carbon” goal. Although research has been conducted on carbon reduction in prefabricated buildings, the use of cloud models for carbon reduction effect evaluation has not been explored. This study therefore aims to develop a cloud model-based evaluation of the carbon emission reduction effect for prefabricated buildings incorporating the characteristics of prefabricated buildings and the building supply chain. The developed model can support assessments of the whole life cycle phases of a prefabricated building. Firstly, carbon flow analysis is carried out from the perspective of the construction supply chain, and the carbon emission reduction effect evaluation index system of prefabricated buildings is established, which contains 5 guideline layers and 26 carbon emission reduction indicators. Secondly, the Continuous Ordered Weighted Averaging operator (C-OWA) is used to calculate the index weight calculation, and the cloud model is applied to conduct a comprehensive evaluation of the carbon emission reduction effect. Finally, this model is applied to evaluate the carbon emission reduction effect in the case of a building. The case study validated the efficiency of the developed model. This study extends the knowledge of carbon emission reduction by addressing specific characteristics of prefabrication and the construction supply chain. This validated model will enhance the willingness to apply prefabricated buildings to reduce carbon emissions and achieve the “dual carbon” goal.

Evaluation Model of Music Therapy's Auxiliary Effect on Mental Health Based on Artificial Intelligence Technology.

The reason why music can affect people's emotional experience is that the stimulation can be transmitted to the brain through hearing, such as the thalamus and lenticular nucleus. Music therapy has a positive auxiliary treatment effect on mental health. Therefore, an evaluation model of the auxiliary effect of music therapy on mental health based on artificial intelligence technology is proposed. We construct the constraint index parameters for the evaluation of music therapy's auxiliary effect on mental health, take parent pressure, self-pressure, teacher pressure, and social pressure as the questionnaire object parameters, take class type as the independent variable, carry out an independent sample t-test, and construct an adaptive information extraction model for the evaluation of music therapy's auxiliary effect on mental health. Paired sample t-test is used to analyze whether there is a difference between the experimental group and the control group on the learning stress scale. According to the analysis of the difference between the experimental group and the control group, combined with the difference test analysis of the data of the stress release of music therapy on mental health, the quantitative evaluation of the auxiliary effect of music therapy on mental health is realized through artificial intelligence optimization control. The experimental results show that the accuracy and reliability of this method to analyze the auxiliary effect of music therapy on mental health are high. There are obvious changes in the data of students' self-pressure, and the difference between the average value and standard deviation of the data before and after the course is obvious. From the perspective of the effectiveness of the course, the students in the class who implement the four relaxation experience courses in the course are under the pressure of parents, self-pressure, teacher pressure, and social pressure. There are obvious changes in the five aspects of learning pressure compared with that before the implementation of the course. After the course experience, the pressure value of most students decreases, and the course intervention effect is obvious.

Computer-Aided Design & Applications, 20(S5), 2023, 106-115 © 2023 CAD Solutions, LLC, http://www.cad-journal.net 106 Effect Evaluation Model of Computer Aided Physical Education Teaching and Training Based on Artificial Intelligence

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

Evaluation Method of Music Teaching Effect Based on Fusion of Deep Neural Network under the Background of Big Data

Music is an art course that is different from traditional subjects. It not only needs to teach basic music knowledge but also needs to show the timbre contained in music itself, the form of music creation, or the effects expressed by music. The teaching effect evaluation model for traditional subjects can no longer be applied to the teaching effect evaluation of music subjects. If the traditional subject teaching effect evaluation model is used to evaluate the music subject teaching effect, it will easily lead to the inaccuracy of the effect evaluation. Because there is a strong subjectivity in the evaluation of the teaching effect of music subjects, this study combines deep neural network technology to study the feasibility and accuracy of the convolutional neural network (CNN) and long short-term memory neural network (LSTM) method in the evaluation of music teaching effects. It mainly reflects the evaluation under the three characteristics of music basic knowledge, music expression effect, and music innovation effect. The research results show that the CNN and LSTM methods can better extract the three features in the evaluation of music teaching effect, and the maximum prediction error is only 2.23%. Hybrid CNN-LSTM with LSTM neural network has higher accuracy in predicting music teaching effect than single neural network technique. The linear correlation coefficients also all exceeded 0.955.

Optimization Method of University English Guidance Based on Enhanced Decision Tree Model in the Context of Big Data

The development of economy has propelled the university’s English guidance system to pay attention to students’ development which further impacts the development of the country because students are the future of any nation. The college English function is rather complicated, and thus, reunderstanding and positioning of the university’s English guidance plays a significant role in the field of education. The existing teaching guidance system puts more emphasis on teaching content and teaching mechanism resulting in lack of application of the knowledge gained. Thus, it is extremely important to clarify and highlight the objectives of college English learning and on the basis of these design personalized curriculum which could convert the skilled talents into compound ones. The present paper explores the evaluation model of the University English Guidance effect based on an enhanced decision tree algorithm. The model has the potential to improve accuracy and efficiency of University English Guidance effect evaluation system and meet the requirements of University English Guidance effect evaluation. The framework constructs a multi-index University English Guidance effect evaluation system constituting of teachers and students as the main entities. It considers the University English Guidance effect evaluation index data as the input sample and implements the least squares support vector machine to realize the University English Guidance effect evaluation. The effectiveness of this model is verified by experiments, which lays a foundation for the optimization of University English Guidance.

Effectiveness Assessment of College Ideological and Political Courses Using BP Neural Networks in Network Environment.

We will not be able to provide educators with the assistance they require to implement the network IPECU and boost the effectiveness of the network IPE until we have established a trustworthy and effective assessment system. By first identifying the problems with the current ideological course assessment system, this study builds an indicator system for evaluating IPECU's effectiveness. An evaluation model of IPECU effectiveness based on the BPNN is designed and built using this as the basis. In this research, GA with adaptive mutation is used to optimise the initial weights and thresholds of the BPNN. As a result, the training termination conditions are satisfied by the weights and thresholds for the BPNN's assessment of the instructional quality more quickly, increasing the prediction accuracy and convergence speed. The simulation and comparative analyses presented in this study use MATLAB to verify the effectiveness of the method's evaluation. Experiments show that this algorithm's prediction accuracy can be as high as 95%, which is comparable to GA and the traditional BPNN algorithm. This method could provide some technical assistance for the network IPECU validity assessment.

Special Physical Characteristic and Training Strategy of Badminton Based on Machine Learning Algorithm

Machine learning algorithms involve many fields such as statistics and probability theory. It is mainly aimed at computer simulation or execution of human learning behaviors to acquire new knowledge and skills. And the existing knowledge is to be integrated and then continue to be improved. This study aims to introduce a machine learning algorithm to establish a corresponding training effect evaluation model, to use the model to research and analyze the special physical fitness training strategy of badminton, and to discuss the appropriate badminton physical fitness training method. At the same time, the Markov model is used to compare the effect of the machine learning algorithm model. The experimental results show that the combination of four kinds of training of phosphate energy supply capacity, speed, sensitivity, and core strength can improve the training performance of badminton players by 10 points and the training efficiency by 4%. Therefore, it is necessary to comprehensively and reasonably arrange the four physical training methods to achieve better training effects. The average evaluation and prediction accuracies of the machine learning algorithm model were 86% and 87%, respectively, and the computing time was 26.7 ms and 13.6 ms. The average evaluation and prediction accuracies of the Markov model were 79% and 82%, respectively, and the computing time was 19.5 ms and 11.8 ms. It can be seen that the model based on the machine learning algorithm is more accurate than the Markov model.

Fuzzy gray clustering evaluation of green building operation effect: a case study of Shenzhen Bay One, China

PurposeThis paper improves the evaluation index system of green building operation effect and establishes the evaluation model of green building operation effect. It is expected to promote energy saving and emission reduction and provide a more scientific evaluation method for green building operation effect evaluation.Design/methodology/approachFirst, 20 key evaluation indexes are selected to establish the operation effective evaluation index system. Then, the combined weight method is proposed to determine the weight of each evaluation index. Next, the gray clustering-fuzzy comprehensive evaluation method is used to construct the green building operation effective evaluation model. Finally, the feasibility and validity of the selected model were verified by taking Shenzhen Bay One green building in Shenzhen as an example.FindingsThis paper establishes the evaluation system of green building operational effect, and evaluates green building from the angle of operational effect. Taking Shenzhen Bay One project as an example, the rationality and applicability of the model are verified.Originality/valueIn this paper, for the first time, relevant indexes of user experience are included in the evaluation system of green building operational effect, which makes the evaluation system more perfect. In addition, a more scientific fuzzy gray clustering method is used to evaluate the operational effect of green building, and a new evaluation model is established.

Street Landscape Planning and Design Guided by Artificial Intelligence Interactive Experience.

As an important part of the city, urban streets have always played an active role in carrying public needs and optimizing public living spaces. They are also important to open public spaces in the city. In addition, with technology integration and life, the interactive experience of artificial intelligence (AI) in Street View is gradually increasing. A streetscape planning and design method based on AI interactive experience is proposed. CSiXRevit 2022 software builds street simulation models for better street planning and design. Street digital imaging technology is used to analyze the original streetscape. Landscape features are integrated with the street model. Based on intelligent technology, urban landscape design is studied, and AI interactive experience is analyzed. Analytic hierarchy process (AHP) and AI technology evaluate the effect of landscape design. Firstly, AHP is used to construct the evaluation index system of the rural landscape design effect, and the index weight is determined. Back propagation neural network (BPNN) is used to construct an evaluation model of landscape design effect. Three indexes of target, project, and indicator layer are used to evaluate the effect of rural landscape design. The results show that (1) the variety and richness of streetscapes in the survey area should be increased. Interactive buildings should be placed on the street. (2) The comprehensive evaluation score of the street landscape is 0.60, and the design effect is excellent. The comprehensive evaluation values of the three project layers are 0.65, 0.48, and 0.67, respectively. The data show that the carrying capacity index of the study area is the best, and the vitality index is the weakest. Therefore, when designing the street landscape, the economic development has been strengthened by attracting investment, introducing large-scale exhibition shops, increasing street income, strengthening the construction of street public facilities, and paying attention to the public street experience. This work can provide references for related streetscape designs.

Mathematical Modeling and Simulation of Online Teaching Effect Evaluation Based on Decision Tree Algorithm

With the support of big data technology, the field of education is also facing new problems and opportunities. Network teaching has become the mainstream means of higher education. In order to explore the changes of students’ learning effect in the process of online teaching, this paper proposes to build an online teaching effect evaluation model with the support of data mining technology and decision tree algorithm. This paper records the factors and objects that reflect the teaching effect in network teaching and traditional teaching, respectively. A decision tree algorithm is used to divide the attributes of influencing factors from relevant rules. Using the Kirschner model to build the evaluation system, add two attribute elements: students’ teaching evaluation and teachers’ self-evaluation. Data mining technology is used to preprocess and clean up the sample set, which improves the accuracy of the calculation results. In the evaluation model, the association rule algorithm is also constructed to classify the data of the same element type and delete the data of different elements after marking. Through this evaluation model, teachers can accurately judge students’ learning interests and improve students’ academic performance. The results show that compared with the traditional data mining algorithm, the decision tree algorithm has obvious advantages in computing speed and accuracy.

A Model to Evaluate the Effectiveness of the Maritime Shipping Risk Mitigation System by Entropy-Based Capability Degradation Analysis.

Accurate evaluation of the risk mitigation status of navigating ships is essential for guaranteeing navigational safety. This research mainly focuses on the feasibility and accuracy of evaluating the real effectiveness of a risk mitigation system for navigating ships, including addressing the problem of immeasurableness for risk mitigation capability and determining the degradation regulation of risk mitigation capability over time. The proposed method to solve the problem is an effectiveness evaluation model based on the capability perspective, composed of a capability measurement algorithm based on entropy theory and capability degradation regulation analysis based on numerical process fitting. First, combined with the theoretical framework of a comprehensive defence system, the risk mitigation system designed for navigating ships is reconstructed based on capability building. Second, using a numerical fitting method, the degradation regulation of risk mitigation capability with time is obtained to improve the accuracy of the dynamic evaluation. Finally, referring to entropy theory, the uncertainty of capability is calculated, and then the model is constructed based on this uncertainty to realize the effectiveness evaluation from a capability perspective. The results obtained in an application test of the proposed model indicate that using the entropy of capability can realize an accurate effectiveness evaluation of a risk mitigation system for navigating ships, with a 9% improvement in accuracy, and the Weibull curve fitting is more consistent with capability degradation regulation, with a signification level of 2.5%. The proposed model provides a new path for evaluating the effectiveness of a risk mitigation system for navigating ships from the entropy of capability, and compared with the traditional probabilistic method, the model is more realistic and accurate in actual applications.

OUTCOMES OF MANAGEMENT DEVELOPMENT PROGRAM FOR EMPLOYEES OF NADHLATUL ‘ULAMA HOSPITAL

Introduction: The various management development interventions that have been implemented seem to have not identified the characteristics of the role model, perceived control, and understanding of the Management Development Program (MDP) itself. The MDP effectiveness evaluation model also seems to have not received much attention. Analyze the influence of the characteristics of the Management Development Program, i.e., role models, feelings of control, and understanding that result in organizational commitment, utility, and participatory Behavior. Methods: The research population was 130 employees of NUH Jombang. Samples were taken using a simple random sampling technique of 100 employees from various work units. Statistical analysis used is path analysis to determine the direct and indirect effect on organizational commitment, utility, and participation behavior. Results: The higher MDP characteristics that consist of role model, perceived control, and understanding will be followed by high organizational commitment, utility, and participative behavior as positive outcomes. Conclusion: The study results can improve understanding of the characteristics of the relevant Management Development Program to improve employee outcomes. Employee outcome, i.e., organizational commitment, Utilities which include intrinsic – extrinsic – organizational benefits, behavior that includes the willingness to participate in development activities, innovative behavior that is beneficial to the organization, and sharing opinions.

Evaluation model of electromagnetic pulse welding effect based on Vc-β trajectory curve

As a non-heat input solid-state joining technology, electromagnetic pulse welding (EMPW) has many advantages in the connection of dissimilar metals. Finite element (FE) methods are often used to design the EMPW equipment, explore the parameters and analyze the transient process. However, it is difficult to reflect the welding effect directly. This work proposed a welding effect evaluation model based on the welding window theory and the Vc-β trajectory curve obtained in the FE simulation. A FE model coupling the circuit-magnetic-structure field was established in COMSOL software to simulate the EMPW process of the Cu plate and Al plate. The Vc-β trajectory curve and welding traces were obtained according to the simulation results. An experimental platform WD-28 was used to carry out the welding experiments of Cu–Al plates at different discharging voltages. The welding traces were measured through SEM method and compared with the simulation results. According to the comparative result, the empirical constant of the calculation formula for the lower boundary of the welding window was adjusted into 1.6, so that the evaluation result was consistent with the experimental result. This work can provide a new method for evaluating directly the welding effect through the FE simulation results.

Improved Convolutional Neural Networks for Course Teaching Quality Assessment

The cultivation of innovative talents is closely related to the quality of course teaching, and there is a correlation between facial expressions and the effectiveness of classroom teaching. In this paper, a separate long-term recursive convolutional network (SLRCN) microexpression recognition algorithm is proposed using deep learning technology for building a course teaching effectiveness evaluation model. Firstly, facial image sequences are extracted from microexpression data sets, and the transfer learning method is introduced to extract spatial features of facial expression frames through pretrained convolutional neural network model to reduce the risk of overfitting in network training. The extracted features of video sequences were input into long short-team memory (LSTM) to process temporal features. Experimental results show that SLRCN algorithm has the best performance in training set and test set. It has the best performance in ROC curve. This effectively distinguishes between seven different expressions in the database. The model proposed in this paper can obtain the changes of students’ facial expressions in class and evaluate students’ learning status, thus promoting the improvement of teaching quality. It provides a new method of course teaching quality evaluation.

Establishment and assessment of urban meteorological disaster emergency response capability based on modeling methods

Emergency response capability assessments can be performed to determine the state of emergency management and identify weaknesses, thus increasing preparedness and strengthening emergency rescue capabilities. Multiple linear regressions (MLRs), support vector machines (SVMs), and artificial neural networks (ANNs) are widely used for assessment and prediction. However, few studies have used them to evaluate urban meteorological disaster emergency response capabilities. This study used these mathematical models to evaluate urban meteorological disaster emergency response capabilities, and the model verification parameters and deviations were compared. The analytic hierarchy process formulated a comprehensive weighting system and a quantitative assessment standard. The meteorological disaster cases from 2010 to 2020 were used as the data source, and then 50 sets of sample data were obtained to enhance the operability and scientificity. MLR, SVM, and ANN were used to establish an urban meteorological disaster emergency response capability assessment and prediction model. The results indicate that all three models are effective. SVMs (mean squared error = 0.1074) provide excellent prediction, MLRs (mean squared error = 0.1184) provides satisfactory prediction, and ANNs (mean squared error = 0.3211) provides poor prediction. The proposed estimation methods provide an effective predictive evaluation model that reflects the actual conditions and can enable the government and other groups to perform inspections conveniently. It can not only check one by one according to the quantitative evaluation standards but also continuously improve the emergency management ability according to the forecast results and then enhance the ability to resist meteorological disasters in time.

Application of Medical-Nursing-Assistance Integration Model Based on Theoretical Basis of Behavioral Psychology in Management of Children's ICU.

The children's intensive care unit is a closed management area with limited visiting time and no accompanying persons. It fails to systematically reflect and summarize the opinions and needs of the families of the children. The more critically ill the family members are, the higher the requirements for medical care. Good relationship between doctors, nurses, assistant, and patients can promote the rehabilitation of children's diseases and achieve the advanced medical model level of “seamless management and no loopholes.” In order to aid the complete intensive care process, it is vital to understand children's psychological and physical development based on children's behavioral psychology when the medical-nursing-assistance (MNA) integration model is used in the children's intensive care unit. Therefore, this paper has completed the following tasks: (1) the development status of the domestic and foreign MNA integration model in the quality management of children's intensive care units is introduced, and the MNA integration model based on the theoretical basis of behavioral psychology is proposed for the following article in children's intensive care. The effect evaluation system of room management provides a theoretical basis. (2) The principle of BP neural network is introduced, and the effect evaluation model of the integrated mode of MNA based on BPNN in the management of children's intensive care unit is constructed. (3) The relevant data collected are used to form an available data set for the model accuracy test. The experimental results show that, after the research in this paper, the BPNN model proposed in this paper is introduced into the MNA integration model to evaluate the effect of the management of children's intensive care units which is practical and effective.

EXPLORING AND CONSTRUCTING AN EFFECTIVE EVALUATION MODEL OF NETWORK TEACHING FROM THE PERSPECTIVE OF EMOTION REGULATION

BackgroundAffected by the epidemic, the online teaching of “Internet + intelligence” technology has broken the traditional teaching mode and realized the multi-dimensional, intelligent and in-depth interaction and integration between learners, teachers and online learning resources. It has become an important development direction of higher education in China and even the world. Network teaching has changed from “fresh” to “new normal”.Topics and MethodsUsing the methods of literature review, system analysis and PDCA cycle model, determine the design and evaluation of network teaching, and explore an effective evaluation model of network teaching from the perspective of educational psychology, so as to promote teaching practice. This study takes 200 freshmen in a university as the research object to study the College Chinese anxiety, self-efficacy and achievement level in online teaching. The score of the Chinese final exam is used to divide participants into pass group and fail group. Both gender and major are considered as variables of language achievement. This study includes four elements of self-efficacy: learning ability, courage to overcome challenges, effort and overcoming setbacks. Through t-test, univariate and multivariate analysis of variance and regression analysis, we investigated the relationship between foreign language anxiety, self-efficacy and academic achievement of students of different majors and genders.ResultsGenerally speaking, students' attention to online teaching evaluation fluctuated and increased, but there is still no core author group and close institutional cooperation network. The frontier fields of network teaching evaluation research mainly include evaluation theory and technology research, quality and effect research, quality influencing factors research, teaching design evaluation research and evaluation methods research. The research hotspots of network teaching evaluation mainly include the research on the concept and connotation of network teaching evaluation, the evaluation of network teaching environment and technology, the evaluation of network teaching curriculum and teaching process, and the evaluation of network teaching effect. However, teaching quality has become the bottleneck restricting the development of network teaching. It is difficult to see the whole picture of effective network teaching. Considering the space limitations of network teaching, there are many blind spots in the evaluation of teachers' network teaching effect. This paper formulates an effective network teaching evaluation model framework, constructs an effective network teaching standard evaluation content, and puts forward the implementation path of standard evaluation, which provides a strong guarantee for the high-quality development of network teaching. The results show that the learning anxiety level of the improvement group is significantly lower than that of the failure group, and the self-efficacy level of the pass group is significantly higher than that of the failure group. Compared with the male group, the female group's language achievement and self-efficacy level are much higher. However, there was no significant difference in anxiety levels between men and women. In addition, the study also found that foreign language anxiety was negatively correlated with foreign language achievement. Similarly, there is a significant negative correlation between foreign language anxiety and self-efficacy. State anxiety, trait anxiety and foreign language learning anxiety are positively correlated. The anxiety component of learners' personality characteristics plays an important role in the generation of foreign language learning anxiety. There was no significant difference between the experimental group (M = 2.528, SD = 0.540) and the control group (M = 2.695, SD = 0.858); There was no significant difference between the experimental group (M = 2.778, SD = 0770) and the control group (M = 2.556, SD = 0.808); There was no significant difference in the total score of emotion regulation self-efficacy between the experimental group (M = 35.083, SD = 5.931) and the control group (M = 35.500, SD = 5.551).ConclusionRealizing the standardization and standardization of the whole chain of the purpose, process and results of online teaching will help teachers clarify this process and knowledge system, improve the standards and practical ability of effective online teaching, and integrate new technologies such as artificial intelligence into the process of online teaching and online teaching evaluation, which has attracted the attention of more and more researchers and can greatly improve the learning quality of students, It provides a reference for the orderly development of online teaching activities.AcknowledgementThis research was supported by Heilongjiang Higher Education Teaching Reform Project “Exploration and Practice of Online Teaching Mode for Effective Learning” (Grant No. SJGZ20200094), Heilongjiang Postdoctoral Scientific Research Developmental Fund (Grant No. LBH-Q20107), Heilongjiang Basic Scientific Research Project (Grant No. 2020KYYWF-FC12), and Harbin Sport University Doctoral Scientific Research Start-up Fee Project (Grant No. RCYJ-2104).