Intelligent teaching practical training system and method for entrepreneurship guidance

Abstract

The invention provides an intelligent teaching practical training system and method for entrepreneurship guidance. The content matching degree of teaching practical training content in the entrepreneurship guidance practical training docking process is determined based on historical teaching practical training content data; obtaining practical training feedback data of the teaching practical training content, determining a feedback trend of opinion feedback after practical training of a student according to the practical training feedback data, and determining an index category according to the feedback trend and a content matching degree; determining a plurality of co-occurrence distances among the teaching keywords in the teaching practical training content, and determining a pushing frequency through all the co-occurrence distances and the practical training feedback data; and according to the pushing frequency and the index category, determining a practical training scheduling level, and according to the practical training scheduling level, carrying out level division on teaching practical training contents used for entrepreneurship guidance. According to the scheme, the teaching and practical training content can be intelligently managed in a unified manner, so that the practical training feasibility of the teaching and practical training content is improved.

Description

Technical Field

[0001] The present application relates to the technical field of entrepreneurship guidance, and more specifically, to an intelligent teaching training system and method for entrepreneurship guidance. Background Art

[0002] Entrepreneurial guidance refers to helping entrepreneurs avoid common mistakes, accelerate business development, and increase the chances of entrepreneurial success by sharing experiences, imparting knowledge and skills, while teaching practice is a practical learning activity in the education and training process, which aims to help learners master theoretical knowledge and improve practical skills through actual operations and applications, and enhance learners' practical operation ability and problem-solving ability. Smart teaching practice used for entrepreneurship guidance combines modern technology and teaching methods, aiming to help entrepreneurs master the skills, knowledge and experience required for entrepreneurship, enable learners to cope with practical challenges, and increase the possibility of entrepreneurial success.

[0003] In the existing entrepreneurial guidance smart teaching and training process, the teaching and training process is often evaluated through past experience and qualitative analysis to complete the management of the teaching and training content. However, the unified teaching and training content is difficult to meet the personalized needs of entrepreneurial projects in different industries and at different development stages. In addition, due to the different cognitions of each training manager, it is difficult to accurately judge the applicability and training effect of the teaching and training content for different entrepreneurial projects. Therefore, how to intelligently manage the teaching and training content in a unified manner to improve the feasibility of the teaching and training content has become a problem faced by the industry. Summary of the invention

[0004] The present application provides a smart teaching and training system and method for entrepreneurship guidance, which can intelligently manage the teaching and training content in a unified manner, thereby improving the feasibility of the teaching and training content.

[0005] In a first aspect, the present application provides a smart teaching training method for entrepreneurship guidance, comprising the following steps: Determine the teaching and training content for entrepreneurship guidance; Determine the content matching degree of the teaching and training content in the process of entrepreneurship guidance and training docking based on the historical teaching and training content data; Acquire the training feedback data of the teaching training content, and then determine the feedback trend of the teaching training content after the trainees’ training based on the training feedback data, classify the entrepreneurial guidance effect of the teaching training content according to the feedback trend and the content matching degree, and obtain the index category of the teaching training content in the training process; Determine the co-occurrence distances between the teaching keywords in the teaching practice content, and predict the push frequency of the teaching practice content in the entrepreneurship guidance process through all the co-occurrence distances and the practice feedback data; The training scheduling level of the teaching training content is determined according to the push frequency and the index category, and the teaching training content used for entrepreneurship guidance is evaluated by the training scheduling level to obtain the content level of the teaching training content during the scheduling process.

[0006] In some embodiments, determining the content matching degree of the teaching and training content in the entrepreneurship guidance and training docking process based on the historical teaching and training content data specifically includes: Obtaining historical teaching training content data; Determine a plurality of similar training items of the teaching training content according to the history teaching training content data; Selecting a similar training project as a selected similar training project, and determining a common vocabulary set between the selected similar training project and the teaching training content; Determining the similarity confidence between the selected similar training items and the teaching training content through the same vocabulary set; Continue to determine the same confidence levels between the remaining same-type practical training items and the teaching and training content, and obtain multiple same-type confidence levels of the teaching and training content; The content matching degree of the teaching training content in the process of entrepreneurship guidance and training docking is determined according to all similar confidence levels.

[0007] In some embodiments, determining the content matching degree of the teaching training content in the entrepreneurship guidance training docking process according to all similar confidence levels specifically includes: Obtain all similar practical training projects of the teaching and training content; Determine the number of students entropy for all similar practical training programs; Determining the content adjustment degree of the teaching training content according to all similar confidence levels; The content matching degree of the teaching training content in the process of connecting the entrepreneurship guidance and training is determined according to the student quantity entropy and the content adjustment degree.

[0008] In some embodiments, determining the feedback trend of the teaching training content from the training feedback data specifically includes: Dividing the training feedback data into positive feedback data and negative feedback data; Determine the feedback positive entropy of the positive opinion feedback data; Determine the feedback negative entropy of the negative opinion feedback data; The feedback trend of the teaching training content in response to the trainees' feedback after the training is determined according to the feedback positive entropy and the feedback negative entropy.

[0009] In some embodiments, the entrepreneurial guidance effect of the teaching and training content is classified according to the feedback trend and the content matching degree, and the index category of the teaching and training content in the training process is obtained, which specifically includes: Obtaining the teaching time of the teaching training content; Determine the degree of practical training effectiveness of the entrepreneurial guidance effect of the teaching and training content according to the content matching degree and the teaching time; The index category of the teaching training content in the training process is determined according to the training effectiveness and the feedback trend.

[0010] In some embodiments, predicting the push frequency of the teaching and training content in the entrepreneurship guidance process through all co-occurrence distances and the training feedback data specifically includes: Determine the content gap of the teaching training content according to all co-occurrence distances; Determining the content resolution of the training feedback data; The push frequency of the teaching training content in the entrepreneurship guidance process is determined according to the content gap, the content resolution and the preset initial push frequency.

[0011] In some embodiments, determining the training scheduling level of the teaching training content according to the push frequency and the index category specifically includes: Determine the category proportion of the teaching and training content in the entrepreneurship guidance according to the index category; Determine the impact of the training according to the push frequency and the feedback trend; The training scheduling level of the teaching training content is determined according to the category proportion and the training impact.

[0012] In a second aspect, the present application provides a smart teaching training system for entrepreneurship guidance, the smart teaching training system for entrepreneurship guidance includes an evaluation unit, and the evaluation unit includes: A determination module is used to determine the teaching and training content for entrepreneurship guidance; A processing module, used to determine the content matching degree of the teaching and training content in the process of entrepreneurship guidance and training docking based on the historical teaching and training content data; The processing module is further used to obtain the training feedback data of the teaching training content, and then determine the feedback trend of the teaching training content after the trainees' feedback from the training feedback data, classify the entrepreneurial guidance effect of the teaching training content according to the feedback trend and the content matching degree, and obtain the index category of the teaching training content in the training process; The processing module is further used to determine the co-occurrence distance between each teaching keyword in the teaching practice content, and predict the push frequency of the teaching practice content in the entrepreneurship guidance process through all the co-occurrence distances and the practice feedback data; An execution module is used to determine the training scheduling level of the teaching training content according to the push frequency and the index category, and to evaluate the level of the teaching training content used for entrepreneurship guidance through the training scheduling level to obtain the content level of the teaching training content during the scheduling process.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned smart teaching training method for entrepreneurship guidance.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned smart teaching training method for entrepreneurship guidance.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In a smart teaching and training system and method for entrepreneurship guidance provided by the present application, the teaching and training content for entrepreneurship guidance is first determined; based on historical teaching and training content data, the content matching degree of the teaching and training content in the process of entrepreneurship guidance and training docking is determined; the training feedback data of the teaching and training content is obtained, and then the feedback trend of the teaching and training content in the feedback of trainees after the training is determined from the training feedback data, and the entrepreneurship guidance effect of the teaching and training content is classified according to the feedback trend and the content matching degree, so as to obtain the index category of the teaching and training content in the training process; the co-occurrence distance between each teaching keyword in the teaching and training content is determined, and the push frequency of the teaching and training content in the entrepreneurship guidance process is predicted through all the co-occurrence distances and the training feedback data; the training scheduling level of the teaching and training content is determined according to the push frequency and the index category, and the teaching and training content for entrepreneurship guidance is graded through the training scheduling level to obtain the content level of the teaching and training content in the scheduling process.

[0016] It can be seen that in the process of smart teaching and training for entrepreneurship guidance, this application analyzes the teaching and training content in combination with the historical teaching and training content data, determines multiple historical teaching and training contents that are similar to the teaching and training contents, predicts the degree of student participation in the teaching and training contents during the training docking process through all similar historical teaching and training contents, and then obtains the content matching degree, which is used to predict the student situation of the teaching and training contents during the training process, so as to realize the unified management of the teaching and training contents; secondly, by analyzing the feedback data of the trainees on the teaching and training contents after the training, obtains the feedback trend, which is used to evaluate the training effect of the teaching and training contents; thereby determining the classification standard of the entrepreneurship guidance effect of the teaching and training contents through the content matching degree and the feedback trend, and then And obtain the index category, which is used for the unified management and evaluation of the teaching training content, thereby improving the collaborative efficiency, resource utilization, communication effect and overall project or task execution effect of the teaching training content; secondly, the frequency of the push degree of the teaching training content in the entrepreneurship guidance process is evaluated through the correlation between the various teaching keywords in the teaching training content and the feedback data, and then the push frequency is obtained. The push frequency is used to predict the push situation of the teaching training content, so as to judge the entrepreneurship guidance effect of the teaching training content; finally, the training scheduling level of the teaching training content is determined according to the push frequency and the index category, and the teaching training content used for entrepreneurship guidance is evaluated by the training scheduling level to obtain the content level of the teaching training content in the scheduling process. The above scheme can intelligently manage the teaching training content in a unified manner, thereby improving the feasibility of the training of the teaching training content. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flow chart of a smart teaching training method for entrepreneurship guidance according to some embodiments of the present application; Figure 2 is an exemplary flow chart of determining content matching according to some embodiments of the present application; Figure 3 is an exemplary flow chart of determining an index category according to some embodiments of the present application; Figure 4 is a schematic diagram of exemplary hardware and / or software of an evaluation unit according to some embodiments of the present application; Figure 5 It is a structural diagram of a computer device for implementing a smart teaching training method for entrepreneurship guidance according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] The core of this application is to determine the teaching and training content used for entrepreneurship guidance; determine the content matching degree of the teaching and training content in the process of entrepreneurship guidance and training docking based on historical teaching and training content data; obtain the training feedback data of the teaching and training content, and then determine the feedback trend of the teaching and training content in the feedback of trainees after the training from the training feedback data, classify the entrepreneurship guidance effect of the teaching and training content according to the feedback trend and the content matching degree, and obtain the index category of the teaching and training content in the training process; determine the co-occurrence distance between each teaching keyword in the teaching and training content, and predict the push frequency of the teaching and training content in the process of entrepreneurship guidance through all the co-occurrence distances and the training feedback data; determine the training scheduling level of the teaching and training content according to the push frequency and the index category, and evaluate the teaching and training content used for entrepreneurship guidance through the training scheduling level to obtain the content level of the teaching and training content in the scheduling process. The above scheme can intelligently manage the teaching and training content in a unified manner, thereby improving the feasibility of the training of the teaching and training content.

[0019] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a smart teaching training method for entrepreneurship guidance according to some embodiments of the present application. The smart teaching training method 100 for entrepreneurship guidance mainly includes the following steps: In step 101, the teaching and training content for entrepreneurship guidance is determined.

[0020] In specific implementation, after starting the smart teaching training for entrepreneurship guidance, the guidance demand information of the entrepreneurial projects for entrepreneurship guidance is obtained from the database of the smart teaching training system, and the teaching training content for entrepreneurship guidance is determined by combining the guidance demand information with the problem-oriented learning method in the prior art, wherein the guidance demand information includes market demand, capital demand, technical demand, operation demand, etc., and the teaching training content includes the teaching content when conducting training on entrepreneurship guidance; it should be noted that the problem-oriented learning method in this application is a problem-centered teaching method that promotes students' active learning and critical thinking by solving practical problems. This method emphasizes that students master knowledge and skills by exploring and cooperating to solve complex problems in real or simulated situations; the teaching training content represents the content of practical activities that help students or participants master entrepreneurial skills and knowledge.

[0021] In step 102, the content matching degree of the teaching and training content in the process of connecting with the entrepreneurship guidance and training is determined based on the historical teaching and training content data.

[0022] In some embodiments, determining the content matching degree of the teaching and training content in the process of entrepreneurship guidance and training docking based on the historical teaching and training content data can be achieved by the following steps: Obtaining historical teaching training content data; Determine a plurality of similar training items of the teaching training content according to the history teaching training content data; Selecting a similar training project as a selected similar training project, and determining a common vocabulary set between the selected similar training project and the teaching training content; Determining the similarity confidence between the selected similar training items and the teaching training content through the same vocabulary set; Continue to determine the same confidence levels between the remaining same-type practical training items and the teaching and training content, and obtain multiple same-type confidence levels of the teaching and training content; The content matching degree of the teaching training content in the process of entrepreneurship guidance and training docking is determined according to all similar confidence levels.

[0023] It should be noted that the history teaching training content data in this application is a collection of all history teaching training content. The history teaching training content in the history teaching training content data represents the training projects used for teaching and entrepreneurship guidance. As a preferred embodiment, the history teaching training content data can be obtained from the database of the smart teaching training system. In other embodiments, other methods can also be used to obtain it, which will not be repeated here.

[0024] In specific implementation, determining multiple similar training projects of the teaching training content according to the historical teaching training content data can be implemented in the following manner, namely: obtaining the project category of the teaching training content and the historical project category of each historical teaching training content in the historical teaching training content data from the database of the smart teaching training system, wherein the project category represents the category of the field in which the teaching training content is located, selecting one of the historical teaching training contents in the historical teaching training content data as the selected historical teaching training content, and if the historical project category corresponding to the selected historical teaching training content is the same as the project category of the teaching training content, then the selected historical teaching training content will be used as the similar training project of the teaching training content, and multiple similar training projects of the teaching training content will continue to be determined, wherein the similar training project represents a training project in the same field as the teaching training content; determining the same vocabulary set between the selected similar training project and the teaching training content can be implemented in the following manner, namely: using a file comparison tool in the prior art (WinMerge, Beyond Compare) obtains all the same words between the selected similar training project and the teaching training content, and takes the set of all the same words as the same word set between the selected similar training project and the teaching training content, wherein the same word set represents the set of the same words between the two projects, and is used to analyze the association between the two projects; determining the similarity confidence between the selected similar training project and the teaching training content through the same word set can be achieved in the following manner, namely: obtaining the number of words in the teaching training content through the text analysis software (AntConc, Textalyser) in the prior art, dividing the number of words by the total number of all the same words in the same word set, performing a logarithm operation with a base of 2 on the value obtained by the division, and taking the value obtained by the logarithm operation as the similarity confidence between the selected similar training project and the teaching training content. In other embodiments, other methods can also be used for determination, which are not limited here.

[0025] It should be noted that the same type of confidence in this application represents the parameter value of the confidence level between the same type of historical teaching training content and teaching training content, that is, the similarity between the historical teaching training content and the teaching training content, which is used to predict the training situation of the teaching training content, and then obtain the training effect of the teaching training content.

[0026] Wherein, in some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of the process of determining the content matching degree in some embodiments of the present application. In this embodiment, the content matching degree of the teaching training content in the process of entrepreneurship guidance training docking can be determined according to all similar confidence levels, which can be achieved by the following steps: First, in step 1021, all similar practical training items of the teaching practical training content are obtained; Secondly, in step 1022, the number entropy of students in all similar practical training projects is determined; Then, in step 1023, the content adjustment degree of the teaching training content is determined according to all similar confidence levels; Finally, in step 1024, the content matching degree of the teaching training content in the process of connecting the entrepreneurship guidance and training is determined according to the student quantity entropy and the content adjustment degree.

[0027] In specific implementation, determining the student number entropy of all similar training projects can be achieved in the following manner, namely: obtaining the number of students of each similar training project from the database of the smart teaching training system, and using the entropy of all student numbers as the student number entropy of all similar training projects, wherein the student number entropy represents the parameter value of the distribution of the number of students participating in the same type of training projects corresponding to the teaching training content, and is used to analyze the student participation in the teaching training content; determining the content adjustment degree of the teaching training content according to all similar confidences can be achieved in the following manner, namely: dividing the maximum similar confidence among all similar confidences by the minimum similar confidence, performing a logarithmic operation with a base of 2 on the value obtained by the division, multiplying the value obtained by the logarithmic operation by the average value of all similar confidences, and using the multiplied value as the content adjustment degree of the teaching training content, wherein the content adjustment degree represents the degree of adjustment of the teaching training content after the training. , which is used to adjust the teaching and training projects; determining the content matching degree of the teaching and training content in the process of entrepreneurship guidance and training docking according to the student quantity entropy and the content adjustment degree can be achieved in the following way, namely: initializing a content matching degree model, taking the student quantity entropy as the constraint parameter of the content matching degree model, and taking the content adjustment degree as the initialization parameter of the content matching degree model, and then obtaining the content matching degree of the teaching and training content in the process of entrepreneurship guidance and training docking through the content matching degree model, wherein the content matching degree model is a participation model of content matching established by a machine learning algorithm (such as a regression algorithm, a neural network, etc.), and the participation model is, for example: content matching degree = content adjustment degree * A + student quantity entropy * B, wherein A and B are weight coefficients, A and B can be determined based on a large number of content matching degrees, and can also be determined in other ways in other embodiments, which are not limited here.

[0028] It should be noted that the content matching degree in this application is a parameter value that reflects the degree of matching of the teaching training content in the training docking process, that is, the training docking represents the teaching process of the teaching training content, which is used to judge the content of the teaching training content in the training, so as to facilitate the analysis of the training effect of the entrepreneurial projects of entrepreneurial guidance corresponding to the teaching training content.

[0029] In step 103, the training feedback data of the teaching training content is obtained, and then the feedback trend of the teaching training content in terms of the feedback from the trainees after the training is determined based on the training feedback data. The entrepreneurial guidance effect of the teaching training content is classified according to the feedback trend and the content matching degree, and the index category of the teaching training content in the training process is obtained.

[0030] It should be noted that the training feedback data in the present application represents the feedback data of relevant personnel after the teaching training content is provided to relevant personnel of the entrepreneurial project under entrepreneurship guidance. The training feedback data includes feedback on the training content, methods, effects and overall experience. As a preferred embodiment, the training feedback data of the teaching training content can be obtained from the management system of the entrepreneurial project under entrepreneurship guidance. In other embodiments, other methods can also be used to obtain it, which will not be repeated here.

[0031] In some embodiments, determining the feedback trend of the teaching training content from the training feedback data after the training can be achieved by the following steps: Dividing the training feedback data into positive feedback data and negative feedback data; Determine the feedback positive entropy of the positive opinion feedback data; Determine the feedback negative entropy of the negative opinion feedback data; The feedback trend of the teaching training content in response to the trainees' feedback after the training is determined according to the feedback positive entropy and the feedback negative entropy.

[0032] In specific implementation, the training feedback data can be divided into positive feedback data and negative feedback data in the following manner, namely, the training feedback data is divided into positive feedback data and negative feedback data by using a text analysis tool (TextBlob, VADER, etc.) in the prior art, wherein the positive feedback data is a collection of all positive feedback sentences, and the positive feedback sentences in the positive feedback data indicate feedback sentences with positive, active, and supportive vocabulary, such as: students perform well, the project goes smoothly, and the expected goals are achieved, etc. The negative feedback data is a collection of all negative feedback sentences, and the negative feedback sentences in the negative feedback data indicate feedback sentences with negative, critical, and need-to-improve vocabulary, such as: students perform poorly, the project has problems, and the expected goals are not achieved, etc. Other methods of division may also be used in other embodiments, which are not limited here.

[0033] In a specific implementation, determining the feedback positive entropy of the positive opinion feedback data can be achieved in the following manner, namely: obtaining a positive keyword set of positive opinion feedback of teaching training content from the management system of entrepreneurial projects of entrepreneurial guidance, wherein the positive keyword set is a collection of all positive keywords, selecting a positive opinion feedback sentence in the positive opinion feedback data as a selected positive opinion feedback sentence, extracting all positive keywords in the selected positive opinion feedback sentence through text analysis software in the prior art (Jieba library in Python, TextRank, etc.) combined with the positive keyword set, and summing up all positive keywords The number of words in the selected positive feedback sentence is divided by the total number of words in the selected positive feedback sentence, and the value obtained by the division is used as the positive keyword ratio of the selected positive feedback sentence, and the positive keyword ratio of the remaining positive feedback sentences in the positive feedback data is continued to be determined, wherein the positive keyword ratio represents the parameter value of the ratio of positive keywords in the positive feedback sentence, and the entropy of all positive keyword ratios is used as the feedback positive entropy of the positive feedback data, wherein the feedback positive entropy represents the parameter value of the distribution of the ratio of positive keywords in the positive feedback data, which is used to analyze the teaching training content; determine the negative opinion feedback The negative entropy of the feedback data can be realized in the following way, namely: a positive keyword set of negative feedback on teaching training content is obtained from the management system of entrepreneurial projects of entrepreneurial guidance, wherein the negative keyword set is a collection of all negative keywords, a negative feedback sentence in the negative feedback data is selected as the selected negative feedback sentence, all the words of the positive keywords in the selected negative feedback sentence are extracted by combining the text analysis software in the prior art (Jieba library in Python, TextRank, etc.) with the positive keyword set, and the total number of all words is divided by the number of words in the selected negative feedback sentence. The total number of the negative feedback data is obtained by dividing the total number of the negative keywords in the selected sentence, and the negative keyword ratio of the remaining sentences in the negative feedback data is further determined, wherein the negative keyword ratio represents the parameter value of the ratio of negative keywords in the negative feedback sentence, and the entropy of all negative keyword ratios is used as the feedback negative entropy of the negative feedback data, wherein the feedback negative entropy represents the parameter value of the distribution of the ratio of negative keywords in the negative feedback data, which is used to analyze the teaching training content; in other embodiments, other division methods can also be used, which are not limited here.

[0034] In specific implementation, the feedback trend of the trainees' feedback on the teaching practice content after the training can be determined according to the feedback positive entropy and the feedback negative entropy in the following manner, namely: comparing the feedback positive entropy and the feedback negative entropy, if the feedback positive entropy is greater than the feedback negative entropy, then taking the positive feedback as the feedback trend of the trainees' feedback on the teaching practice content after the training; if the feedback positive entropy is less than the feedback negative entropy, then taking the negative feedback as the feedback trend of the trainees' feedback on the teaching practice content after the training; if the feedback positive entropy is equal to the feedback negative entropy, then taking the neutral feedback as the feedback trend of the trainees' feedback on the teaching practice content after the training; in other embodiments, other methods may also be used for determination, which are not limited here.

[0035] It should be noted that the positive and negative feedback in this application includes positive feedback data and negative feedback data. The feedback trend reflects the parameter value of the feedback trend of the trainees' feedback on the teaching training content after the training, which is used to evaluate the training effect of the teaching training content, and then obtain the training effect of the teaching training content.

[0036] In some embodiments, reference Figure 3 As shown, this figure is a schematic diagram of the process of determining the index category in some embodiments of the present application. In this embodiment, the entrepreneurial guidance effect of the teaching and training content is classified according to the feedback trend and the content matching degree, and the index category of the teaching and training content in the training process can be obtained by the following steps: First, in step 1031, the teaching time of the teaching training content is obtained; Secondly, in step 1032, the practical training effectiveness of the entrepreneurial guidance effect of the teaching and training content is determined according to the content matching degree and the teaching time; Finally, in step 1033, the index category of the teaching training content in the training process is determined according to the training effectiveness and the feedback trend.

[0037] In specific implementation, the teaching time of the teaching training content is obtained from the database of the teaching training content, wherein the teaching time represents the time required for the teaching training content from the beginning to the end of the training; the training effectiveness of the entrepreneurial project under entrepreneurship guidance in the training process is determined according to the content matching degree and the teaching time, which can be achieved in the following way, namely: performing a natural exponential operation on the teaching time, multiplying the inverse of the natural exponential operation by the content matching degree, and using the multiplied value as the training effectiveness of the entrepreneurial project under entrepreneurship guidance in the training process, wherein the training effectiveness represents the parameter value of the actual effect and influence of the teaching training content on the entrepreneurial project under entrepreneurship guidance; determining the teaching practice according to the training effectiveness degree and the feedback trend The index category of the training content during the training process can be implemented in the following way, namely: initialize an index category prediction model, use the training effectiveness as the constraint parameter of the index category prediction model, use the feedback trend as the initialization parameter of the index category prediction model, and then obtain the index category of the teaching training content during the training process through the index category prediction model, wherein the index category prediction model is a prediction model of the index category established by a machine learning algorithm (such as a regression algorithm, a neural network, etc.), and the prediction model is, for example: index category = feedback trend * C + training effectiveness * D, wherein C and D are weight coefficients, C and D can be determined based on a large number of index categories, and can also be determined in other ways in other embodiments, which are not limited here.

[0038] It should be noted that the index categories described in this application are classification standards that reflect the entrepreneurial guidance effects of teaching and training contents, among which the classification standards include: project progress, project feedback, etc., which are used to classify and manage the entrepreneurial guidance process of teaching and training contents, so as to improve collaboration efficiency, resource utilization, communication effects and the execution effects of the overall project or task.

[0039] In step 104, the co-occurrence distances between the teaching keywords in the teaching practice content are determined, and the push frequency of the teaching practice content in the entrepreneurship guidance process is predicted through all the co-occurrence distances and the practice feedback data.

[0040] In some embodiments, determining the co-occurrence distance between the teaching keywords in the teaching training content can be achieved by using the following steps: Obtain keyword information of entrepreneurial projects for entrepreneurial guidance; Determine multiple keyword distances of content in the teaching training content through the keyword information; The co-occurrence distances between the teaching keywords in the teaching training content are determined according to all keyword distances.

[0041] In specific implementation, the keyword information of the entrepreneurial project under entrepreneurship guidance can be obtained in the following manner, namely: the keyword information of the entrepreneurial project under entrepreneurship guidance is obtained from the database of the entrepreneurial project under entrepreneurship guidance, the keyword information is a collection of all keywords, and the keywords in the keyword information represent keywords related to the entrepreneurial project under entrepreneurship guidance, which are used to judge the guidance direction of the entrepreneurial project under entrepreneurship guidance; the distance between multiple keywords in the content of the teaching training content is determined by the keyword information, namely: the text analysis platform in the prior art (Python Libraries, Voyant Tools, etc.) determine the position of each keyword in the teaching practice content in combination with keyword information, and use the total number of words between each adjacent keyword in the teaching practice content as the keyword distance of the corresponding adjacent keyword, wherein the keyword distance represents the distance between adjacent keywords in the teaching practice content; determining the co-occurrence distance between each teaching keyword in the teaching practice content according to all keyword distances can be achieved in the following manner, namely: selecting a keyword distance as the selected keyword distance, performing a natural exponential operation on the selected keyword distance, and using the inverse of the value obtained by the natural exponential operation as the co-occurrence distance of the selected keyword distance, continuing to determine the co-occurrence distance of the remaining keyword distances, and using all co-occurrence distances as the co-occurrence distance between each teaching keyword in the teaching practice content. In other embodiments, other methods may also be used for determination, which are not limited here.

[0042] It should be noted that the co-occurrence distance in this application is a distance that reflects the degree of connection between keywords in the teaching training content. It is used to analyze the distribution of teaching training content on keywords, and then judge the training situation of the teaching training content, so as to facilitate the evaluation of the training effect of the teaching training content.

[0043] In some embodiments, predicting the push frequency of the teaching practice content in the entrepreneurship guidance process by all co-occurrence distances and the practice feedback data can be achieved by the following steps: Determine the content gap of the teaching training content according to all co-occurrence distances; Determining the content resolution of the training feedback data; The push frequency of the teaching training content in the entrepreneurship guidance process is determined according to the content gap, the content resolution and the preset initial push frequency.

[0044] In specific implementation, determining the content gap of the teaching training content according to all co-occurrence distances can be achieved in the following manner, namely: arranging all co-occurrence distances according to the order of the corresponding adjacent keywords in the teaching training content, using the arranged sequence as the co-occurrence distance sequence, selecting a group of adjacent co-occurrence distances in the co-occurrence distance sequence as the selected adjacent co-occurrence distances, subtracting the second co-occurrence distance from the first co-occurrence distance in the selected adjacent co-occurrence distances, and using the subtracted value as the distance difference value of the selected adjacent co-occurrence distances, and continuing to determine the distance difference values ​​of the remaining adjacent co-occurrence distances in the co-occurrence distance sequence, wherein the distance difference value represents a parameter value of the degree of difference between adjacent co-occurrence distances, and is used to judge the content of the teaching training content, and the average value of all distance difference values ​​is used as the content gap of the teaching training content, wherein the content gap represents a parameter value of the degree of gap between keywords in the teaching training content; determining the content resolution of the training feedback data can be achieved in the following manner, namely: obtaining the total number of words in the training feedback data through Python software in the prior art, and the total number of positive keywords in the training feedback data Divide by the total number of negative keywords in the training feedback data, multiply the value obtained by the division by the total number of words, and use the multiplied value as the content resolution of the training feedback data, wherein the content resolution represents the parameter value of the degree of discrimination of the content in the training feedback data; determining the push frequency of the teaching training content in the entrepreneurship guidance process according to the content gap, the content resolution and the preset initial push frequency can be implemented in the following manner, namely: multiply the content gap and the content resolution, perform a natural exponential operation on the multiplied value, subtract the reciprocal of the value obtained by the natural exponential operation from 1, and use the subtracted value as the management difference amount of the content in the teaching training content, wherein the management difference amount represents the parameter value of the degree of difference of the content in the teaching training content, and is used to manage the content in the teaching training content, obtain the preset initial push frequency from the database of entrepreneurial projects of entrepreneurship guidance, multiply the management difference amount by the preset initial push frequency, and use the multiplied value as the push frequency of the teaching training content in the entrepreneurship guidance process, and other methods can also be used in other embodiments to determine, which is not limited here.

[0045] It should be noted that the push frequency in this application reflects the frequency of the push of teaching and training content in the entrepreneurship guidance process, and is used to predict the push situation of teaching and training content, so as to facilitate the evaluation of the entrepreneurship guidance effect of teaching and training content.

[0046] In step 105, the practice scheduling level of the teaching practice content is determined according to the push frequency and the index category, and the teaching practice content for entrepreneurship guidance is evaluated according to the practice scheduling level to obtain the content level of the teaching practice content during the scheduling process.

[0047] In some embodiments, determining the training scheduling level of the teaching training content according to the push frequency and the index category can be implemented by the following steps: Determine the category proportion of the teaching and training content in the entrepreneurship guidance according to the index category; Determine the impact of the training according to the push frequency and the feedback trend; The training scheduling level of the teaching training content is determined according to the category proportion and the training impact.

[0048] In specific implementation, the category proportion of the teaching practice content can be determined according to the index category in the following manner, namely: the category proportion of the teaching practice content in the entrepreneurship guidance is determined by combining the index category and the guidance effect of the relevant entrepreneurship guidance project with the project management software in the prior art, wherein the project management software is, for example, Microsoft Project software, Asana software, Trello software, etc., wherein the category proportion represents the parameter value of the proportion of the entrepreneurial effect of the teaching and training content in the entrepreneurship guidance (i.e., the proportion in the relevant entrepreneurship guidance), which is used to evaluate the teaching and training effect of the teaching and training content; the training influence can be determined according to the push frequency and the feedback trend. The following method can be used to achieve it, namely: according to the regression analysis in the prior art (i.e., linear regression, multivariate regression), combined with the feedback trend, training content, and teaching method, the feedback influence coefficient of the teaching and training content is determined, wherein the feedback influence coefficient represents the parameter of the influence degree on the feedback data, the push frequency is multiplied by the feedback influence coefficient, the multiplied value is subjected to natural exponential operation, and the value obtained by the natural exponential operation is used as the training influence, wherein the training influence represents the parameter of the influence degree of the teaching and training content after teaching. A value is used to judge the training effect of the teaching training content; determining the training scheduling level of the teaching training content according to the category proportion and the training influence can be achieved in the following way, namely: adding the category proportion and the training influence, dividing the added value by the product of the category proportion and the training influence, and using the divided value as the grade marking coefficient of the teaching training content, wherein the grade marking coefficient represents a parameter of the degree of scheduling of the teaching training content in the entrepreneurship guidance process, and is used to predict the scheduling level of the teaching training content. The training scheduling level is divided by the grade marking coefficient. For example: when the grade marking coefficient is between 0 and 3, the training scheduling level is level one; when the grade marking coefficient is between 3 and 7, the training scheduling level is level two; when the grade marking coefficient is greater than 7, the training scheduling level is level three. Other methods may also be used to determine in other embodiments, which are not limited here.

[0049] It should be noted that the training scheduling level in this application reflects the level that needs to be scheduled during the training process of the teaching training content. It is used to schedule the training process of the teaching training content, so as to determine whether the teaching training content meets the needs of the entrepreneurial projects of entrepreneurship guidance, so as to improve the feasibility of the training of the teaching training content.

[0050] In some embodiments, the teaching practice content for entrepreneurship guidance is evaluated by the practice scheduling level, and the content level of the teaching practice content in the scheduling process can be obtained by the following steps: The practical training scheduling level is used as the content level of the teaching practical training content for entrepreneurship guidance in the scheduling process; When the training scheduling level is level one, the teaching training content is high-frequency push content; When the training scheduling level is level 2, the teaching training content is medium frequency push content; When the training scheduling level is level three, the teaching training content is low-frequency push content.

[0051] It should be noted that in this application, when the teaching practice training content is a high-frequency push content, it means that the entrepreneurship guidance effect of the teaching practice training content is very good; when the teaching practice training content is a medium-frequency push content, it means that the entrepreneurship guidance effect of the teaching practice training content is average; when the teaching practice training content is a low-frequency push content, it means that the entrepreneurship guidance effect of the teaching practice training content is very poor.

[0052] In addition, in another aspect of the present application, in some embodiments, the present application provides a smart teaching training system for entrepreneurship guidance, the smart teaching training system for entrepreneurship guidance includes an evaluation unit, the evaluation unit is used to grade the teaching training content, reference Figure 4 , which is a schematic diagram of exemplary hardware and / or software of an evaluation unit according to some embodiments of the present application, the evaluation unit 400 includes: a determination module 401, a processing module 402 and an execution module 403, which are described as follows: Determination module 401, in this application, determination module 401 is mainly used to determine the teaching and training content for entrepreneurship guidance; Processing module 402, in this application, processing module 402 is used to determine the content matching degree of the teaching and training content in the process of entrepreneurship guidance and training docking based on the historical teaching and training content data; It should be noted that the processing module 402 in the present application is also used to obtain the training feedback data of the teaching training content, and then determine the feedback trend of the teaching training content after the trainees' feedback from the training feedback data, classify the entrepreneurial guidance effect of the teaching training content according to the feedback trend and the content matching degree, and obtain the index category of the teaching training content in the training process; In addition, it should be noted that the processing module 402 in the present application is also used to determine the co-occurrence distance between each teaching keyword in the teaching practice content, and predict the push frequency of the teaching practice content in the entrepreneurship guidance process through all the co-occurrence distances and the practice feedback data; Execution module 403. In this application, execution module 403 is mainly used to determine the training scheduling level of the teaching training content according to the push frequency and the index category, and to evaluate the level of the teaching training content used for entrepreneurship guidance through the training scheduling level to obtain the content level of the teaching training content during the scheduling process.

[0053] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned smart teaching training method for entrepreneurship guidance.

[0054] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a smart teaching training method for entrepreneurship guidance according to some embodiments of the present application. Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0055] The processor 501 may be a general-purpose central processing unit (CPU) or an application specific integrated circuit (ASIC).

[0056] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0057] The memory 503 may be a read only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read only memory (EEPROM), a compact disc read only memory (CD ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0058] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0059] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0060] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0061] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0062] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned smart teaching training method for entrepreneurship guidance.

[0063] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0064] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for evaluating teaching training content, used in a smart teaching training system for entrepreneurship guidance to grade teaching training content, characterized in that: The method includes: Determine the teaching and training content for entrepreneurship guidance; Determine the content matching degree of the teaching and training content in the process of entrepreneurship guidance and training docking based on the historical teaching and training content data; Acquire the training feedback data of the teaching training content, and then determine the feedback trend of the teaching training content after the trainees’ training based on the training feedback data, classify the entrepreneurial guidance effect of the teaching training content according to the feedback trend and the content matching degree, and obtain the index category of the teaching training content in the training process; Determine the co-occurrence distances between the teaching keywords in the teaching practice content, and predict the push frequency of the teaching practice content in the entrepreneurship guidance process through all the co-occurrence distances and the practice feedback data; The training scheduling level of the teaching training content is determined according to the push frequency and the index category, and the teaching training content used for entrepreneurship guidance is evaluated by the training scheduling level to obtain the content level of the teaching training content during the scheduling process.

2. The method according to claim 1, characterized in that Determining the content matching degree of the teaching and training content in the process of entrepreneurship guidance and training docking based on the historical teaching and training content data specifically includes: Obtaining historical teaching training content data; Determine a plurality of similar training items of the teaching training content according to the history teaching training content data; Selecting a similar training project as a selected similar training project, and determining a common vocabulary set between the selected similar training project and the teaching training content; Determining the similarity confidence between the selected similar training items and the teaching training content through the same vocabulary set; Continue to determine the same confidence levels between the remaining same-type practical training items and the teaching and training content, and obtain multiple same-type confidence levels of the teaching and training content; The content matching degree of the teaching training content in the process of entrepreneurship guidance and training docking is determined according to all similar confidence levels.

3. The method according to claim 2, characterized in that Determining the content matching degree of the teaching training content in the process of entrepreneurship guidance training docking based on all similar confidence levels specifically includes: Obtain all similar practical training projects of the teaching and training content; Determine the number of students entropy for all similar practical training programs; Determining the content adjustment degree of the teaching training content according to all similar confidence levels; The content matching degree of the teaching training content in the process of connecting the entrepreneurship guidance and training is determined according to the student quantity entropy and the content adjustment degree.

4. The method according to claim 1, characterized in that The feedback trend of the teaching training content determined by the training feedback data after the trainees’ training specifically includes: Dividing the training feedback data into positive feedback data and negative feedback data; Determining the feedback positive entropy of the positive opinion feedback data; Determine the feedback negative entropy of the negative opinion feedback data; The feedback trend of the teaching training content in response to the trainees' feedback after the training is determined according to the feedback positive entropy and the feedback negative entropy.

5. The method according to claim 1, characterized in that The entrepreneurial guidance effect of the teaching and training content is classified according to the feedback trend and the content matching degree, and the index categories of the teaching and training content in the training process are obtained, which specifically include: Obtaining the teaching time of the teaching training content; Determine the degree of practical training effectiveness of the entrepreneurial guidance effect of the teaching and training content according to the content matching degree and the teaching time; The index category of the teaching training content in the training process is determined according to the training effectiveness and the feedback trend.

6. The method according to claim 1, characterized in that Predicting the push frequency of the teaching training content in the entrepreneurship guidance process through all co-occurrence distances and the training feedback data specifically includes: Determine the content gap of the teaching training content according to all co-occurrence distances; Determining the content resolution of the training feedback data; The push frequency of the teaching training content in the entrepreneurship guidance process is determined according to the content gap, the content resolution and the preset initial push frequency.

7. The method according to claim 1, characterized in that Determining the training scheduling level of the teaching training content according to the push frequency and the index category specifically includes: Determine the category proportion of the teaching and training content in the entrepreneurship guidance according to the index category; Determine the impact of the training according to the push frequency and the feedback trend; The training scheduling level of the teaching training content is determined according to the category proportion and the training impact.

8. A smart teaching training system for entrepreneurship guidance, the smart teaching training system for entrepreneurship guidance includes an evaluation unit, characterized in that: The evaluation unit comprises: A determination module is used to determine the teaching and training content for entrepreneurship guidance; A processing module, used to determine the content matching degree of the teaching and training content in the process of entrepreneurship guidance and training docking based on the historical teaching and training content data; The processing module is further used to obtain the training feedback data of the teaching training content, and then determine the feedback trend of the teaching training content after the trainees' feedback from the training feedback data, classify the entrepreneurial guidance effect of the teaching training content according to the feedback trend and the content matching degree, and obtain the index category of the teaching training content in the training process; The processing module is further used to determine the co-occurrence distance between each teaching keyword in the teaching practice content, and predict the push frequency of the teaching practice content in the entrepreneurship guidance process through all the co-occurrence distances and the practice feedback data; An execution module is used to determine the training scheduling level of the teaching training content according to the push frequency and the index category, and to evaluate the level of the teaching training content used for entrepreneurship guidance through the training scheduling level to obtain the content level of the teaching training content during the scheduling process.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the smart teaching training method for entrepreneurship guidance as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the smart teaching training method for entrepreneurship guidance as described in any one of claims 1 to 7 is implemented.

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