Teaching and training feature evaluation method and device for online classroom

Abstract

The invention discloses a teaching and training feature evaluation method and device for an online classroom, relates to the technical field of computer development, and mainly aims to solve the problem of low use efficiency caused by poor online classroom feature evaluation effect in the prior art. Comprising the following steps: in response to a teaching and training feature selection instruction of an online classroom application program, obtaining basic grading data matched with a target teaching and training feature and a grading weight; step-by-step feature operation is carried out on the basic grading data and the grading weight based on a cloud reverse generator, cloud feature values are obtained, and the cloud feature values are used for representing numerical value evaluation results of different sub-features in corresponding grades; and generating an evaluation cloud picture of the target teaching and training features based on the cloud feature value, and displaying the evaluation cloud picture.

Description

Technical Field

[0001] This application relates to the field of computer development technology, and particularly to a method and device for evaluating teaching and training characteristics of an online classroom. Background Art

[0002] With the diversified development of computer application development scenarios, when developing enterprise training / teaching training online classroom applications, consideration will be given to how to provide users with classroom teaching or training quality evaluation services, such as evaluating the learning situation of students in the classroom.

[0003] Currently, when evaluating the quality of online teaching and training, features such as whether there is an absence from class and the number of participations are usually used as evaluation bases. However, due to the large number of students, simply evaluating features from the perspective of participation cannot effectively provide accurate feedback on classroom quality and also reduces the usage efficiency of online classroom applications. Summary of the Invention

[0004] In view of this, this application provides a method and device for evaluating teaching and training characteristics of an online classroom, mainly aiming to solve the problem of poor evaluation effect of existing online classroom characteristics, resulting in low usage efficiency.

[0005] According to one aspect of this application, a method for evaluating teaching and training characteristics of an online classroom is provided, including:

[0006] Responding to a teaching and training characteristic selection instruction of an online classroom application, obtaining basic grading data and grading weights that match the target teaching and training characteristics;

[0007] Performing hierarchical feature operations on the basic grading data and the grading weights based on a cloud inverse generator to obtain cloud feature values, where the cloud feature values are used to represent the numerical evaluation results of different sub-features in the corresponding grading;

[0008] Generating an evaluation cloud map of the target teaching and training characteristics based on the cloud feature values and displaying it.

[0009] Further, the performing hierarchical feature operations on the basic grading data and the grading weights based on a cloud inverse generator to obtain cloud feature values includes:

[0010] Determining the number of gradings and sub-features corresponding to the basic grading data, and performing weighted operations on the basic grading data and the grading weights one by one according to the number of gradings through the cloud inverse generator to obtain cloud feature values corresponding to the sub-features;

[0011] Wherein, the number of gradings corresponds to the number of features of the sub-features.

[0012] Further, the obtaining basic grading data that matches the target teaching and training characteristics includes:

[0013] Determine the sub-features corresponding to the target education and training feature based on the constructed feature standard hierarchy relationship, and retrieve the basic classification data matching the sub-features. The feature standard hierarchy relationship includes the sub-features corresponding to different education and training features in different classifications;

[0014] Perform cleaning and filling processing on the basic classification data to perform hierarchical feature operations based on the processed basic classification data.

[0015] Further, the obtaining of the basic classification data matching the target education and training feature includes:

[0016] Output the weight configuration items of the target education and training feature;

[0017] Respond to the weight configuration instruction for the weight configuration item to determine the classification weight;

[0018] Wherein, the sum of the classification weights of each classification is a preset weight threshold.

[0019] Further, the generating and displaying the evaluation cloud map of the target education and training feature based on the cloud feature value includes:

[0020] Determine the evaluation coordinates, and generate sub-cloud maps corresponding to different classifications based on the cloud feature value and the evaluation coordinates respectively;

[0021] Obtain the evaluation weight of the target education and training feature, and generate the evaluation cloud map of the target education and training feature based on the sub-cloud map and the evaluation weight in the evaluation coordinates;

[0022] Respond to the evaluation display instruction of the target education and training feature, and display the evaluation cloud map in the evaluation coordinates;

[0023] The method further includes:

[0024] Respond to the evaluation result display instruction of the sub-feature, and display the sub-cloud map in the evaluation coordinates.

[0025] Further, the method further includes:

[0026] Display multiple education and training features to be selected, and the education and training features include sub-features of multiple classifications;

[0027] If it is detected that the target education and training feature is selected, display all the sub-features corresponding to the target education and training feature;

[0028] Respond to the confirmation instruction of the sub-feature, and generate the education and training feature selection instruction; or,

[0029] In response to the change instruction of the sub-feature, when it is queried that the changed feature is configured with a corresponding grading weight, generate the teaching and training feature selection instruction;

[0030] Wherein, the teaching and training feature selection instruction carries the selected target teaching and training features, and the target teaching and training features include the confirmed sub-features or the changed sub-features.

[0031] Further, the method further includes:

[0032] In response to the adjustment instruction of the target teaching and training feature, determine the evaluation result of the evaluation cloud map;

[0033] Retrieve the teaching and training feature adjustment strategy that matches the target teaching and training feature and the evaluation result, and display it.

[0034] According to another aspect of the present application, there is provided an evaluation device for teaching and training features of an online classroom, including:

[0035] An acquisition module, configured to acquire the basic grading data and grading weight that match the target teaching and training features in response to the teaching and training feature selection instruction of the online classroom application;

[0036] An operation module, configured to perform step-by-step feature operations on the basic grading data and the grading weight based on a cloud inverse generator to obtain cloud feature values, where the cloud feature values are used to represent the numerical evaluation results of different sub-features in the corresponding grading;

[0037] A generation module, configured to generate and display the evaluation cloud map of the target teaching and training feature based on the cloud feature value.

[0038] Further, the operation module is specifically configured to determine the grading number and sub-features corresponding to the basic grading data, and perform weighted operations on the basic grading data and the grading weight one by one according to the grading number through the cloud inverse generator to obtain the cloud feature values corresponding to the sub-features; wherein, the grading number corresponds to the number of features of the sub-features.

[0039] Further, the acquisition module is specifically configured to determine the sub-features corresponding to the target teaching and training feature based on the constructed feature standard level relationship, and retrieve the basic grading data that matches the sub-features, where the feature standard level relationship includes the sub-features corresponding to different teaching and training features in different gradings; perform cleaning and filling processing on the basic grading data, so as to perform step-by-step feature operations based on the processed basic grading data.

[0040] Further, the obtaining module is specifically configured to output a weight configuration item of the target education and training feature; in response to a weight configuration instruction for the weight configuration item, determine the hierarchical weight; wherein the sum of the hierarchical weights of each level is a preset weight threshold.

[0041] Further, the generating module is specifically configured to determine an evaluation coordinate, and respectively generate sub-cloud maps corresponding to different levels based on the cloud feature value and the evaluation coordinate; obtain an evaluation weight of the target education and training feature, and generate an evaluation cloud map of the target education and training feature based on the sub-cloud map and the evaluation weight in the evaluation coordinate; in response to an evaluation display instruction for the target education and training feature, display the evaluation cloud map in the evaluation coordinate; in response to an evaluation result display instruction for the sub-feature, display the sub-cloud map in the evaluation coordinate.

[0042] Further, the apparatus further includes: a display module,

[0043] The display module is configured to display a plurality of education and training features to be selected, and the education and training features include sub-features of multiple levels;

[0044] The display module is further configured to, if it is detected that the target education and training feature is selected, display all sub-features corresponding to the target education and training feature;

[0045] The generating module is further configured to, in response to a confirmation instruction for the sub-feature, generate an education and training feature selection instruction; or, in response to a change instruction for the sub-feature, generate an education and training feature selection instruction when it is queried that the changed feature is configured with a corresponding hierarchical weight; wherein the education and training feature selection instruction carries the selected target education and training feature, and the target education and training feature includes the confirmed sub-feature or the changed sub-feature.

[0046] Further, the apparatus further includes:

[0047] A determination module, which determines an evaluation result of the evaluation cloud map in response to an adjustment instruction for the target education and training feature;

[0048] An extraction module, which is configured to extract an education and training feature adjustment strategy that matches the target education and training feature and the evaluation result, and display it.

[0049] According to another aspect of the present application, there is provided a storage medium, in which at least one executable instruction is stored, and the executable instruction causes a processor to perform operations corresponding to the above-mentioned method for evaluating education and training features of an online classroom.

[0050] According to another aspect of the present application, a terminal is provided, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0051] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations corresponding to the above-mentioned evaluation method for the teaching and training characteristics of the online classroom.

[0052] By means of the above technical solution, the technical solution provided by the embodiments of the present application has at least the following advantages:

[0053] The present application provides an evaluation method and device for the teaching and training characteristics of an online classroom. Compared with the prior art, in the embodiments of the present application, in response to a teaching and training characteristic selection instruction of an online classroom application program, basic grading data and grading weights matching the target teaching and training characteristics are obtained; based on a cloud inverse generator, step-by-step feature operations are performed on the basic grading data and the grading weights to obtain cloud feature values, and the cloud feature values are used to represent the numerical evaluation results of different sub-characteristics in the corresponding grading; based on the cloud feature values, an evaluation cloud map of the target teaching and training characteristics is generated and displayed, so as to achieve the purpose of accurately evaluating the teaching and training characteristics through multiple dimensions and multiple levels. At the same time, a cloud inverse operation method is adopted to increase the integrated evaluation method for characteristics at different levels, greatly improving the flexibility of evaluating the teaching and training characteristics, meeting the personalized evaluation needs of different users for the teaching and training characteristics, effectively improving the accuracy of evaluating the teaching and training characteristics, and thus improving the use efficiency of the online classroom application program.

[0054] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the following specific embodiments of the present application are specifically given. Description of the Drawings

[0055] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present application. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0056] Figure 1 Shows a flowchart of an evaluation method for the teaching and training characteristics of an online classroom provided by an embodiment of the present application;

[0057] Figure 2 Shows a schematic diagram of a cloud map of an evaluation result provided by an embodiment of the present application;

[0058] Figure 3Shows a schematic diagram of a comprehensive cloud map provided by an embodiment of the present application;

[0059] Figure 4 Shows a block diagram of a composition of an evaluation device for teaching and training characteristics of an online classroom provided by an embodiment of the present application;

[0060] Figure 5 Shows a schematic diagram of the structure of a terminal provided by an embodiment of the present application. Detailed implementation manners

[0061] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art.

[0062] An embodiment of the present application provides a method for evaluating teaching and training characteristics of an online classroom. As Figure 1 shown, the method includes:

[0063] 101. In response to a teaching and training characteristic selection instruction of an online classroom application, obtain basic grading data and grading weights that match the target teaching and training characteristics.

[0064] In an embodiment of the present application, the current execution end, as the execution subject for evaluating teaching and training characteristics by the online classroom application, can be a client device or a server device, and the present application does not make specific limitations. Among them, the teaching and training characteristics are used to characterize the characteristics that need to evaluate the teaching and training quality of the online classroom, including but not limited to the cognitive level of students, learning attitude, mental health, teaching duration of teachers, knowledge point coverage, etc. The current execution end first outputs the teaching and training characteristics that need to be selected by the user, thereby triggering the teaching and training characteristic selection instruction to receive the characteristics that the user hopes to evaluate the quality, that is, the target teaching and training characteristics. In addition, the basic grading data is the basic data collected by grading multiple sub-characteristics belonging to the target teaching and training characteristics when evaluating the target teaching and training characteristics. For example, if the target teaching and training characteristic is the cognitive level, the corresponding grading includes three levels, that is, the cognitive level is the top level, and the sub-characteristics included are knowledge mastery, problem-solving ability, and thinking development, as the second level. The sub-characteristics of knowledge mastery include basic concept understanding, knowledge point memory, and knowledge application ability, as the third level. Furthermore, the data collected as the student's basic concept understanding is used as the basic grading data, such as the score of the concept text written in the online classroom application. The present application does not make specific limitations, and other gradings are not specifically limited.

[0065] It should be noted that the current execution end can pre-configure the basic hierarchical data to be collected for different sub-feature configurations for collection during evaluation. Additionally, the hierarchical weight is the weight value for each hierarchical operation, which can be selected and configured based on user requirements or set by default based on evaluation rules. The embodiments of the present application do not make specific limitations in this regard.

[0066] 102. Perform step-by-step feature operations on the basic hierarchical data and the hierarchical weight based on the cloud inverse generator to obtain cloud feature values.

[0067] Among them, the cloud feature value is used to represent the numerical evaluation result of different sub-features in the corresponding hierarchy. The cloud inverse generator can perform weighted sum calculations one by one according to each hierarchy to obtain the cloud feature value of each hierarchy until the evaluation result of the target education and training feature is obtained.

[0068] It should be noted that the cloud inverse generator represents the process of converting from quantitative to qualitative in the cloud model and is a mapping model that converts numerical features into cloud points. At this time, the cloud model is an operation model constructed by the cloud server based on data processing technologies such as operation, reasoning, and control. The cloud inverse generator takes the basic hierarchical data as input and converts it into feature values represented by the expected value Ex, entropy En, and hyperentropy He, and then generates the corresponding cloud feature value, represented as a cloud point.

[0069] 103. Generate an evaluation cloud map of the target education and training feature based on the cloud feature value and display it.

[0070] To meet the visualization display effect of the evaluation result of the education and training feature, after obtaining the cloud feature value of the target education and training feature, an evaluation cloud map is generated and displayed. Among them, since the coordinate dimensions of different education and training features are different, when generating the evaluation cloud map, the weighted sum can be performed based on the cloud feature value corresponding to the sub-feature to obtain the cloud point corresponding to the target education and training feature, which is plotted in the cloud map to visualize the evaluation result of the target education and training feature. It can also be plotted and then overlaid and displayed for the cloud maps corresponding to different hierarchies. The embodiments of the present application do not make specific limitations in this regard.

[0071] The embodiment of the present application provides an evaluation method for the teaching and training characteristics of an online classroom. Compared with the prior art, in the embodiment of the present application, in response to the teaching and training characteristic selection instruction of the online classroom application, basic grading data and grading weights matching the target teaching and training characteristics are obtained; based on the cloud inverse generator, step-by-step characteristic operations are performed on the basic grading data and the grading weights to obtain cloud characteristic values, and the cloud characteristic values are used to represent the numerical evaluation results of different sub-characteristics in the corresponding grading; based on the cloud characteristic values, an evaluation cloud map of the target teaching and training characteristics is generated and displayed, so as to achieve the purpose of accurate evaluation of teaching and training characteristics through multiple dimensions and multiple levels. At the same time, the cloud inverse operation method is adopted to increase the integrated evaluation method for characteristics at different levels, greatly improving the flexibility evaluation purpose of teaching and training characteristics, meeting the personalized evaluation needs of different users for teaching and training characteristics, effectively improving the accuracy of teaching and training characteristics evaluation, and thus improving the use efficiency of the online classroom application.

[0072] In another embodiment of the present application, for further illustration and limitation, the step of performing step-by-step characteristic operations on the basic grading data and the grading weights based on the cloud inverse generator to obtain cloud characteristic values includes:

[0073] Determine the grading number and sub-characteristics corresponding to the basic grading data, and perform weighted operations on the basic grading data and the grading weights one by one according to the grading number through the cloud inverse generator to obtain cloud characteristic values corresponding to the sub-characteristics.

[0074] In order to realize the cloud characteristic value of the top-level target teaching and training characteristic after reverse calculating the cloud characteristic values one by one using multiple levels, so as to achieve the purpose of multi-dimensional characteristic value evaluation, when the current execution end performs step-by-step characteristic operations, it first determines the grading number and sub-characteristics corresponding to the basic grading data. At this time, the grading number corresponds to the number of sub-characteristics, that is, the more the grading number, the more the number of sub-characteristics, but it is not limited that the number of sub-characteristics at the second level must be equal to 2. For example, the number of sub-characteristics at the second level can be greater than 2, and the number of sub-characteristics at the third level can be greater than 3. The embodiment of the present application does not make specific limitations.

[0075] It should be noted that after determining the grading number and sub-characteristics, the cloud inverse generator performs weighted operations on the basic grading data and the grading weights of the corresponding grading one by one from the lowest grading according to the grading number to obtain cloud characteristic values of the sub-characteristics corresponding to each grading.

[0076] In a specific implementation scenario, as shown in Table 1 below, the inverse generator calculates cloud parameters including expectation Ex, entropy En, and hyperentropy He according to the basic grading data, combined with the grading weights w A11 、w A12 、w A12Calculate the cloud feature values (evaluation values) A11 of the third-level sub-features such as etc., and further, combine the second-level classification weights w A1 , w A2 , w A3 Calculate the cloud feature value A1 of the second-level sub-feature, and finally calculate the cloud feature value A of the final training feature by combining the evaluation weight of the target training feature.

[0077] Table 1

[0078]

[0079]

[0080] In a specific scenario of the embodiment of the present application, as shown in Table 1, if C11 is the attendance rate feature, the number of students is N, and the attendance rate of each student is expressed as x i , and the attendance rate data of N students is recorded as (x1, x2,..., x n ), calculate the expectation, entropy, and hyper-entropy of the attendance rate of this class, which are E x , E n , H e , and the calculation formula is expressed as: Among them,

[0081] In a specific embodiment, the sub-features corresponding to different classifications and the selectable target training features may include the content shown in Table 2 below.

[0082] Table 2

[0083]

[0084]

[0085] In the above embodiment, calculate the cloud feature value of the attendance rate for the third classification. Further, the second-level cloud feature value of the sub-feature C1 in the final calculation is Finally, calculate the cloud feature value of the target training feature C according to the evaluation weight determined by the user, and the calculation method is Furthermore, combine the cloud model to generate a cloud map, which is not specifically limited in the embodiment of the present application.

[0086] In another embodiment of the present application, for further illustration and limitation, the step of obtaining the basic classification data matching the target training feature includes:

[0087] Determine the sub-features corresponding to the target education and training feature based on the constructed feature standard level relationship, and retrieve the basic grading data matching the sub-features;

[0088] Perform cleaning and filling processing on the basic grading data, so as to perform hierarchical feature operations based on the processed basic grading data.

[0089] In order to perform reverse calculation based on the basic grading data to obtain the evaluation values of different grading features, when obtaining the basic grading data and grading weights, determine the sub-features corresponding to the target education and training feature based on the constructed feature standard level relationship. Among them, the sub-features corresponding to different education and training features in different grades are included in the feature standard level relationship. At this time, the feature standard level relationship can be configured according to the education and training quality evaluation requirements of the online classroom. For example, for the education and training feature of learning attitude, the sub-features in the second grade are learning interest motivation and self-learning ability, and the sub-features in the third grade are learning interest and motivation recognition. The embodiments of the present application do not make specific limitations. In addition, when retrieving the basic grading data matching the sub-features, it can be retrieved from the server database of the online education and training application program. The embodiments of the present application do not make specific limitations. In addition, in order to ensure the availability of the basic grading data, the current execution end performs cleaning and filling processing on the basic grading data, that is, performs abnormal judgment on the basic grading data according to the abnormal data threshold, deletes the data exceeding the abnormal data extreme value, and at the same time, there may be a situation where the basic grading data collected at different collection times has collection blanks. Therefore, for the blank data, the filling method is used for supplementation, and the average value can be selected for filling for different sub-features. The embodiments of the present application do not make specific limitations.

[0090] In another embodiment of the present application, for further illustration and limitation, the step of obtaining the grading weight matching the target education and training feature includes:

[0091] Output the weight configuration item of the target education and training feature;

[0092] Respond to the weight configuration instruction for the weight configuration item to determine the grading weight.

[0093] To meet the user's evaluation requirements for different sub - features, reverse cloud computing is performed on the sub - features through hierarchical weight restrictions. Currently, the execution end first outputs the weight configuration items of the target education and training feature in the front - end interface. Among them, the weight configuration items include the weight configuration items of each sub - feature, and may also include the weight configuration items of the evaluation weight for the user to select and configure. The sum of the hierarchical weights of each level is a preset weight threshold. Preferably, the preset weight threshold is 1, which is not specifically limited in the embodiments of the present application. When the user determines the weights, the current execution end enters them through the weight configuration items, and then determines the hierarchical weights of each sub - feature to meet the limitations of different users' evaluation dimensions for different sub - features.

[0094] In another embodiment of the present application, for further illustration and limitation, the steps of generating the evaluation cloud map of the target education and training feature based on the cloud feature value and displaying it include:

[0095] Determine the evaluation coordinates, and generate sub - cloud maps corresponding to different levels respectively based on the cloud feature value and the evaluation coordinates;

[0096] Obtain the evaluation weight of the target education and training feature, and generate the evaluation cloud map of the target education and training feature based on the sub - cloud map and the evaluation weight in the evaluation coordinates;

[0097] In response to the evaluation display instruction of the target education and training feature, display the evaluation cloud map in the evaluation coordinates.

[0098] To achieve the purpose of flexibly displaying the evaluation results of different education and training features to the user, when generating the evaluation cloud map, first determine the evaluation coordinates, and generate sub - cloud maps of different levels respectively based on the calculated cloud feature values of each sub - feature. Among them, the abscissa of the evaluation coordinates is the interval division of the preset evaluation results (the corresponding standard evaluation intervals for different interval divisions are shown in Table 3), and the ordinate can be the membership degree of the students. At the same time, since different levels correspond to different sub - features, in order to better display the evaluation results of the features, the current execution end generates sub - cloud maps in the evaluation coordinates according to the cloud feature values, and the evaluation results of the sub - features at the corresponding level can be determined from the sub - cloud maps. For example, the evaluation result of the problem - solving ability is not specifically limited in the embodiments of the present application. Since the abscissa and ordinate of the sub - cloud maps of different sub - features represent different units, in order to effectively display the cloud map of the target education and training feature, the evaluation coordinates of the final evaluation cloud map can be determined based on the sub - cloud maps. For example, the abscissa is the cloud feature value and the ordinate is the membership degree, as Figure 2As shown, to determine that the distribution of each cloud point (each cloud point represents a trainee) as the evaluation result of sub-feature a is medium (determined in combination with the evaluation scores in Table 3), the embodiments of the present application do not make specific limitations. Further, obtain the evaluation weight of the target education and training feature. At this time, it can be obtained based on the user input method or based on the default configuration method, so as to generate the evaluation cloud map of the target education and training feature based on the sub-cloud map and the evaluation weight in the evaluation coordinate. At this time, the cloud points shown in the evaluation cloud map are obtained by adding the weight values of the cloud feature values in the sub-cloud map and the evaluation weight, that is, the cloud feature value of the final grading (target education and training feature) is generated by the cloud generator, and the embodiments of the present application do not make specific limitations. After generating the evaluation cloud map, an instruction to prompt the user to view can be generated in the front-end interface, so that the user can trigger the evaluation display instruction of the target education and training feature, and the current execution end displays the evaluation cloud map in the determined evaluation coordinate. At this time, only the cloud points of the target education and training feature can be shown in the final evaluation cloud map to separately display the evaluation result of the target education and training feature.

[0099] In the embodiments of the present invention, as shown in Table 3, for different sub-features, the corresponding interval division can be determined based on the calculated cloud feature values, and finally the evaluation result of the education and training feature can be obtained as the evaluation basis for classroom quality, and the embodiments of the present application do not make specific limitations.

[0100] Table 3

[0101]

[0102] Among them, for different evaluation intervals, the corresponding cloud feature values can be calculated by using the cloud model to generate a cloud map as shown in Figure 3 . For the i-th classroom quality evaluation interval , the cloud features E xi , E ni , H e need to be calculated as the standard evaluation features to draw the cloud map. Among them, the calculation formulas are respectively H e can be set based on different visualization requirements, such as set to 0.5, 0.6, etc., and the embodiments of the present application do not make specific limitations. At this time, corresponding standard cloud maps are generated for different evaluation intervals, as shown in Figure 3 . While showing the cloud points of the standard evaluation interval in the comprehensive cloud map, the position of the comprehensive evaluation cloud map corresponding to the cloud feature value of the education and training feature for calculating the attendance rate in each standard cloud map can also be shown, so as to achieve the purpose of visualizing the evaluation result.

[0103] To meet the visualization requirements of different users for evaluating sub-features, specifically, the steps further include:

[0104] In response to the instruction for displaying the evaluation result of the sub-feature, display the sub-cloud map at the evaluation coordinates.

[0105] Specifically, since the cloud eigenvalues of each sub-feature can be obtained when the sub-features at each level are subjected to cloud reverse generation, when the user selects the evaluation result of the sub-feature to be viewed, the instruction for displaying the evaluation result of the sub-feature can be triggered, so that the current execution end can retrieve the cloud eigenvalues of the sub-feature that have been calculated to generate a sub-cloud map for display. The embodiments of the present application do not make specific limitations.

[0106] In another embodiment of the present application, for further illustration and limitation, the steps further include:

[0107] Display a plurality of training features to be selected;

[0108] If it is detected that the target training feature is selected, display all the sub-features corresponding to the target training feature;

[0109] In response to the confirmation instruction of the sub-feature, generate the training feature selection instruction.

[0110] In order to meet the evaluation requirements of different users for personalized training features, thereby improving the flexibility and effectiveness of the evaluation of online training features, the current execution end also provides a training feature selection function for users. Specifically, first display the target training features to be selected by the user. Each training feature may include multiple levels of sub-features. When the user determines the selected target training feature, it can be entered through the training feature selection instruction to determine the target training feature, that is, the training feature selection instruction carries the selected target training feature. At this time, since the target training feature is selected by the user, the target training feature includes at least one confirmed sub-feature.

[0111] In another embodiment of the present invention, in order to meet the user's need to select and change the training features at any time to meet the personalized evaluation requirements of the training features, the current execution end can also obtain the features to be changed by receiving the change instruction for the sub-feature. Among them, in response to the change instruction of the sub-feature, in order to avoid invalid changes, it is necessary to query whether there is a hierarchical weight corresponding to the changed feature before the weight calculation is performed by the cloud reverse generator. If the changed hierarchical weight is found, the training feature selection instruction can be directly generated. At this time, the training feature selection instruction carries the changed-to feature.

[0112] In another embodiment of the present application, for further illustration and limitation, the steps further include:

[0113] In response to the adjustment instruction of the target training feature, determine the evaluation result of the evaluation cloud map;

[0114] Retrieve a teaching and training feature adjustment strategy that matches the target teaching and training feature and the evaluation result, and display it.

[0115] To meet the diverse processing requirements of users for teaching and training feature evaluation, the current execution end can also, after displaying the evaluation cloud map, enable the user to trigger an adjustment instruction for the target teaching and training feature to retrieve the numerical evaluation result and the corresponding adjustment strategy. Among them, the numerical evaluation result is the evaluation score or result obtained by calculating the weight according to the evaluation weight and the cloud feature value of the sub-features at the next level for matching, including but not limited to excellent, good, medium, poor, etc. At this time, it can be divided and set according to different numerical intervals, and the embodiments of the present application do not make specific limitations.

[0116] It should be noted that after determining the evaluation result of the evaluation cloud map, while outputting and displaying this evaluation result, the current execution end can retrieve a teaching and training feature adjustment strategy that matches the target teaching and training feature and the evaluation result, and display it. Among them, the teaching and training feature adjustment strategy is used to represent the specific method for improving the teaching and training feature evaluation result. Different teaching and training feature corresponding adjustment strategies are pre-configured in the current execution end. For example, when the evaluation result of the attendance rate feature is poor, the retrieved adjustment strategy is to strengthen communication between home and school, understand the reasons for absenteeism and provide necessary support, etc. Another example is the feature of inactive classroom participation. The corresponding adjustment strategy is to stimulate their enthusiasm for participation through group discussions, role-playing, more interactions, etc. The embodiments of the present application do not make specific limitations.

[0117] The embodiments of the present application provide another evaluation method for teaching and training features in an online classroom, achieving the purpose of accurately evaluating teaching and training features through multiple dimensions and multiple levels. At the same time, the cloud reverse operation method is adopted to increase the integrated evaluation method for features at different levels, greatly improving the purpose of flexible evaluation of teaching and training features, meeting the personalized evaluation needs of different users for teaching and training features, effectively improving the accuracy of teaching and training feature evaluation, and thus improving the usage efficiency of the online classroom application program.

[0118] Further, as an implementation of the above Figure 1 shown method, the embodiments of the present application provide an evaluation device for teaching and training features in an online classroom, as Figure 4 shown. The device includes:

[0119] An acquisition module 21, configured to obtain basic classification data and classification weights that match the target teaching and training feature in response to a teaching and training feature selection instruction of the online classroom application program;

[0120] An operation module 22, configured to perform step-by-step feature operations on the basic classification data and the classification weights based on a cloud inverse generator to obtain cloud feature values, where the cloud feature values are used to represent the numerical evaluation results of different sub-features in the corresponding classification;

[0121] A generation module 23, configured to generate an evaluation cloud map of the target education and training feature based on the cloud feature values and display it.

[0122] Further, the operation module is specifically configured to determine the number of classifications and sub-features corresponding to the basic classification data, and perform weighted operations on the basic classification data and the classification weights one by one according to the number of classifications through the cloud inverse generator to obtain cloud feature values corresponding to the sub-features; where the number of classifications corresponds to the number of features of the sub-features.

[0123] Further, the acquisition module is specifically configured to determine sub-features corresponding to the target education and training feature based on a constructed feature standard level relationship, and retrieve basic classification data matching the sub-features, where the feature standard level relationship includes sub-features corresponding to different education and training features in different classifications; perform cleaning and filling processing on the basic classification data to perform step-by-step feature operations based on the processed basic classification data.

[0124] Further, the acquisition module is specifically configured to output a weight configuration item of the target education and training feature; in response to a weight configuration instruction for the weight configuration item, determine the classification weights; where the sum of the classification weights of each classification is a preset weight threshold.

[0125] Further, the generation module is specifically configured to determine evaluation coordinates, and respectively generate sub-cloud maps corresponding to different classifications based on the cloud feature values and the evaluation coordinates; obtain an evaluation weight of the target education and training feature, and generate an evaluation cloud map of the target education and training feature based on the sub-cloud maps and the evaluation weight in the evaluation coordinates; in response to an evaluation display instruction of the target education and training feature, display the evaluation cloud map in the evaluation coordinates; in response to an evaluation result display instruction of the sub-feature, display the sub-cloud map in the evaluation coordinates.

[0126] Further, the device further includes: a display module,

[0127] The display module is configured to display a plurality of education and training features to be selected, where the education and training features include sub-features of multiple classifications;

[0128] The display module is further configured to, if it detects that the target education and training feature is selected, display all sub-features corresponding to the target education and training feature;

[0129] The generating module is further configured to generate the education and training feature selection instruction in response to the confirmation instruction of the sub-feature; or, in response to the change instruction of the sub-feature, generate the education and training feature selection instruction when it is queried that the changed feature is configured with a corresponding grading weight; wherein, the education and training feature selection instruction carries the selected target education and training feature, and the target education and training feature includes the confirmed sub-feature or the changed sub-feature.

[0130] Further, the device further includes:

[0131] A determining module, configured to determine the evaluation result of the evaluation cloud map in response to the adjustment instruction of the target education and training feature;

[0132] A retrieval module, configured to retrieve and display the education and training feature adjustment strategy that matches the target education and training feature and the evaluation result.

[0133] The embodiment of the present application provides an evaluation device for the education and training features of an online classroom. Compared with the prior art, the embodiment of the present application obtains the basic grading data and grading weights that match the target education and training features by responding to the education and training feature selection instruction of the online classroom application; performs hierarchical feature operations on the basic grading data and the grading weights based on the cloud inverse generator to obtain cloud feature values, and the cloud feature values are used to represent the numerical evaluation results of different sub-features in the corresponding grading; generates and displays the evaluation cloud map of the target education and training feature based on the cloud feature values, realizes the purpose of accurately evaluating the education and training features through multi-dimensions and multi-grades, and at the same time, adopts the cloud inverse operation method to increase the integrated evaluation method for features at different levels, greatly improves the purpose of flexibly evaluating the education and training features, meets the personalized evaluation needs of different users for the education and training features, effectively improves the accuracy of evaluating the education and training features, and thus improves the use efficiency of the online classroom application.

[0134] According to an embodiment of the present application, there is provided a storage medium storing at least one executable instruction, and the computer executable instruction can execute the evaluation method for the education and training features of the online classroom in any of the above method embodiments.

[0135] Figure 5 FIG. shows a schematic structural diagram of a terminal according to an embodiment of the present application. The specific implementation of the terminal is not limited in the specific embodiment of the present application.

[0136] As Figure 5 shown, the terminal may include: a processor 302, a communication interface 304, a memory 306, and a communication bus 308.

[0137] Among them: The processor 302, the communication interface 304, and the memory 306 communicate with each other through the communication bus 308.

[0138] The communication interface 304 is used to communicate with network elements of other devices such as clients or other servers.

[0139] The processor 302 is used to execute the program 310, and specifically can execute the relevant steps in the embodiments of the evaluation method of the teaching and training characteristics of the above online classroom.

[0140] Specifically, the program 310 may include program codes, and the program codes include computer operation instructions.

[0141] The processor 302 may be a central processing unit (CPU), or a specific integrated circuit (ASIC, Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application. One or more processors included in the terminal may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0142] The memory 306 is used to store the program 310. The memory 306 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0143] The program 310 is specifically used to cause the processor 302 to perform the following operations:

[0144] In response to the teaching and training feature selection instruction of the online classroom application program, obtain the basic grading data and grading weights matching the target teaching and training features;

[0145] Based on the cloud inverse generator, perform hierarchical feature operations on the basic grading data and the grading weights to obtain cloud feature values, and the cloud feature values are used to represent the numerical evaluation results of different sub-features in the corresponding grading;

[0146] Generate an evaluation cloud map of the target teaching and training feature based on the cloud feature value and display it.

[0147] Obviously, those skilled in the art should understand that the various modules or steps of the present application described above can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed over a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a sequence different from that here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0148] The foregoing is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. An evaluation method for the teaching and training characteristics of an online classroom, characterized in that, including: In response to the teaching and training feature selection instruction of the online classroom application, obtaining the basic grading data and grading weights matching the target teaching and training features; Based on the cloud inverse generator, performing hierarchical feature operations on the basic grading data and the grading weights to obtain cloud feature values, where the cloud feature values are used to represent the numerical evaluation results of different sub-features in the corresponding grading; Generating an evaluation cloud map of the target teaching and training feature based on the cloud feature values and displaying it.

2. The method according to claim 1, wherein The performing hierarchical feature operations on the basic grading data and the grading weights based on the cloud inverse generator to obtain cloud feature values includes: Determining the grading number and sub-features corresponding to the basic grading data, and performing weighted operations on the basic grading data and the grading weights one by one according to the grading number through the cloud inverse generator to obtain cloud feature values corresponding to the sub-features; Wherein, the grading number corresponds to the number of features of the sub-features.

3. The method according to claim 1, wherein The obtaining the basic grading data matching the target teaching and training features includes: Based on the established feature standard level relationship, determining the sub-features corresponding to the target teaching and training features, and retrieving the basic grading data matching the sub-features, where the feature standard level relationship includes the sub-features corresponding to different teaching and training features in different gradings; Performing cleaning and filling processing on the basic grading data to perform hierarchical feature operations based on the processed basic grading data.

4. The method according to claim 1, wherein The obtaining the basic grading data matching the target teaching and training features includes: Outputting the weight configuration items of the target teaching and training features; In response to the weight configuration instruction for the weight configuration items, determining the grading weights; Wherein, the sum of the grading weights of each grading is a preset weight threshold.

5. The method according to claim 1, wherein The generating an evaluation cloud map of the target teaching and training feature based on the cloud feature values and displaying it includes: Determining the evaluation coordinates, and respectively generating sub-cloud maps corresponding to different gradings based on the cloud feature values and the evaluation coordinates; Obtaining the evaluation weights of the target teaching and training features, and generating an evaluation cloud map of the target teaching and training feature based on the sub-cloud maps and the evaluation weights in the evaluation coordinates; In response to the evaluation display instruction of the target teaching and training feature, displaying the evaluation cloud map in the evaluation coordinates; The method further includes: In response to the evaluation result display instruction of the sub-features, displaying the sub-cloud maps in the evaluation coordinates.

6. The method according to claim 1, wherein The method further includes: Displaying multiple teaching and training features to be selected, where the teaching and training features include sub-features of multiple gradings; If it is detected that the target teaching and training feature is selected, displaying all the sub-features corresponding to the target teaching and training feature; In response to the confirmation instruction of the sub-features, generating the teaching and training feature selection instruction; or, In response to the change instruction of the sub-features, generating the teaching and training feature selection instruction when it is queried that the changed feature is configured with corresponding grading weights; Wherein, the teaching and training feature selection instruction carries the selected target teaching and training feature, and the target teaching and training feature includes the confirmed sub-features or the changed sub-features.

7. The method according to claim 1, wherein The method further includes: In response to the adjustment instruction of the target teaching and training feature, determining the evaluation result of the evaluation cloud map. Retrieve a teaching and training feature adjustment strategy that matches the target teaching and training feature and the evaluation result, and display it.

8. An evaluation device for the teaching and training characteristics of an online classroom, characterized in that, Including: An acquisition module, configured to obtain basic grading data and grading weights that match the target teaching and training features in response to a teaching and training feature selection instruction of an online classroom application program; An operation module, configured to perform step-by-step feature operations on the basic grading data and the grading weights based on a cloud inverse generator to obtain cloud feature values, where the cloud feature values are used to represent the numerical evaluation results of different sub-features in the corresponding grading; A generation module, configured to generate an evaluation cloud map of the target teaching and training feature based on the cloud feature values and display it.

9. A computer-readable storage medium having computer programs / instructions stored thereon, characterized in that, When the computer program / instructions are executed by a processor, the steps of the evaluation method for the teaching and training features of the online classroom described in claim 1 are implemented.

10. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the evaluation method for the teaching and training features of the online classroom described in claim 1.

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