Lecture effect evaluation method, device and electronic equipment

Abstract

The disclosed embodiments disclose a method, device and electronic device for evaluating the effect of lectures. The method for evaluating the effect of lectures includes: obtaining audio data of lectures in a standard environment; using a pre-trained model to recognize the audio data and converting the audio data into text data; comparing the text data with standard text; and evaluating the effect of lectures according to the comparison results. By collecting audio data of lectures in a standard environment, recognizing the audio data, and comparing the recognition results with standard text, the effect of lectures is evaluated according to the comparison results. Thus, the evaluation standards are unified. The purpose of unifying the standards for evaluating the effect of lectures is achieved.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, and in particular to a method and device for evaluating the effectiveness of a lecture. Background Art

[0002] In the prior art, the evaluation of the teaching effect of teachers is often conducted by listening to the lectures, that is, a group of experienced teachers conduct subjective evaluation of the teaching effect. Because each person has different evaluation standards, even if the same person evaluates the teaching effect of different teachers, their evaluation standards are also different. That is, the inventor found that the prior art has the problem of inconsistent standards for evaluating the teaching effect of teachers in the process of developing this embodiment. Summary of the invention

[0003] In view of this, the embodiments of the present disclosure provide a method, device and electronic device for evaluating the effectiveness of a lecture, which at least partially solve the problem of inconsistent evaluation standards in the prior art.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for evaluating the effect of a lecture, comprising:

[0005] Obtaining audio data of lectures in a standard environment;

[0006] Use the pre-trained model to recognize audio data and convert it into text data;

[0007] Comparing the text data with standard text;

[0008] Evaluate the teaching effectiveness based on the comparison results.

[0009] Optionally, before the step of obtaining the lecture audio data in the standard environment, the step further includes:

[0010] Obtain sample data of the lecture environment;

[0011] Analyzing the environmental sample data;

[0012] Based on the analysis results, standard environmental data parameters are obtained.

[0013] Optionally, obtaining standard environmental data parameters based on the analysis results includes:

[0014] Input standard environmental parameters into the pre-built model for simulation runs;

[0015] The standard environmental parameters are adjusted based on the operating results.

[0016] Optionally, the using a pre-trained model to recognize the audio data and convert the audio data into text data includes:

[0017] Decomposing the audio data according to natural language rules;

[0018] Convert the decomposed audio data into text data.

[0019] Optionally, after the step of evaluating the teaching effect according to the comparison result, the method further includes:

[0020] Mark the data whose evaluation results do not meet the standards;

[0021] Find the corresponding audio data according to the tag;

[0022] Diagnose audio data to analyze factors that do not meet standards.

[0023] Optionally, the step of obtaining the lecture audio data in a standard environment includes:

[0024] Evaluating the lecture audio data;

[0025] When the evaluation result reaches the set condition, the lecture audio data is sent to the pre-trained model;

[0026] When the evaluation structure fails to meet the set conditions, a prompt message is sent.

[0027] Optionally, evaluating the lecture audio data includes:

[0028] The audio data is calculated based on the integral algorithm to obtain an integral result;

[0029] The audio data is evaluated based on the integration result.

[0030] In a second aspect, the present disclosure also provides a teaching effect evaluation device, including:

[0031] An audio acquisition module is used to acquire audio data of lectures in a standard environment;

[0032] A recognition module, used to recognize audio data using a pre-trained model and convert the audio data into text data;

[0033] A comparison module, used for comparing the text data with standard text;

[0034] The evaluation module is used to evaluate the teaching effect according to the comparison results.

[0035] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0036] at least one processor; and,

[0037] a memory communicatively connected to the at least one processor; wherein,

[0038] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the teaching effectiveness evaluation method described in any one of the first aspects.

[0039] In a fourth aspect, an embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the teaching effectiveness evaluation method described in any one of the first aspects of the claims.

[0040] The present disclosure provides a method, device, electronic device, and non-transitory computer-readable storage medium for evaluating the effect of a lecture, wherein the method for evaluating the effect of a lecture collects audio data of the lecture in a standard environment, recognizes the audio data, and compares the recognition result with the standard text, thereby evaluating the result according to the comparison result. Thus, the evaluation standard is unified. The purpose of unifying the standard for evaluating the effect of a lecture is achieved.

[0041] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 A flow chart of a method for evaluating the effect of a lecture provided by an embodiment of the present disclosure;

[0044] Figure 2 A principle block diagram of a teaching effect evaluation device provided by an embodiment of the present disclosure;

[0045] Figure 3 A functional block diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0046] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0047] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0048] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0049] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0050] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0051] like Figure 1 As shown, this embodiment discloses a method for evaluating the effect of a lecture, comprising:

[0052] Step S101: Acquire lecture audio data in a standard environment;

[0053] Obtain sample data of the lecture environment;

[0054] Environmental sample data includes parameters that affect audio collection, such as the size of the lecture venue, electromagnetic environment, and noise. The size of the venue will affect echoes, etc., thus affecting sound collection.

[0055] Analyzing the environmental sample data;

[0056] That is, the risks such as the size of the venue, electromagnetic environment, noise, etc. are analyzed to determine their impact on audio acquisition, and then the weights of various parameters are assigned according to the impact, thereby increasing the accuracy of the subsequent standard environment establishment.

[0057] Based on the analysis results, standard environmental data parameters are obtained.

[0058] Based on the weights of the above parameters, we can get the parameters of the standard data, such as electromagnetic interference, noise, the location of the audio acquisition equipment, etc. Based on the obtained environmental data parameters, we can adjust the corresponding parameters of the lecture venue to get a standard environment. That is, the parameters that affect the audio acquisition in each teacher's lecture place are unified to avoid errors in the final effect evaluation caused by environmental factors.

[0059] Environmental parameters can be set according to different standard environmental data in different regions to adapt to the local teaching environment. For example, the standard environmental parameters for the southern and northern regions can be set separately.

[0060] Step S102: using a pre-trained model to recognize audio data and convert the audio data into text data;

[0061] The pre-trained model is built based on pre-collected sample data and is implemented using artificial intelligence algorithms. When recognizing audio data, the pre-trained model does not simply recognize the audio as text, but also recognizes the volume, pitch, speech speed, etc. of the audio. If any of the parameters does not reach the set threshold, the recognition cannot be correct. The recognition of audio data adopts standard Mandarin level recognition, that is, the samples used by the pre-trained model are trained using standard Mandarin samples.

[0062] Step S103: comparing the text data with standard text;

[0063] The recognized text is compared with the standard text in the lecture lesson plan to obtain the accuracy of the recognized text.

[0064] Step S104: Evaluate the teaching effect according to the comparison result.

[0065] The teaching effect is evaluated based on the accuracy of text recognition. If the recognition rate reaches 90%, it is excellent.

[0066] Optionally, obtaining standard environmental data parameters based on the analysis results includes:

[0067] Input standard environmental parameters into the pre-built model for simulation runs;

[0068] The standard environmental parameters are adjusted based on the operating results.

[0069] Optionally, the using a pre-trained model to recognize the audio data and convert the audio data into text data includes:

[0070] Decomposing the audio data according to natural language rules;

[0071] That is, the audio data is decomposed into corresponding sentences and words according to natural language rules.

[0072] Convert the decomposed audio data into text data.

[0073] Optionally, after the step of evaluating the teaching effect according to the comparison result, the method further includes:

[0074] Mark the data whose evaluation results do not meet the standards;

[0075] Find the corresponding audio data according to the tag;

[0076] Diagnose audio data to analyze factors that do not meet standards.

[0077] Analyze the factors that fail to meet the standards so as to improve the teaching methods. For example, if the failure to meet the standards is caused by insufficient volume or too fast speaking speed, if the speaking speed is too fast, then the speed can be slowed down in the later stage.

[0078] In a specific implementation scenario, improvement suggestions will be presented to the instructor in real time, allowing the instructor to make timely adjustments.

[0079] Optionally, the step of obtaining the lecture audio data in a standard environment includes:

[0080] Evaluating the lecture audio data;

[0081] When the evaluation result reaches the set condition, the lecture audio data is sent to the pre-trained model;

[0082] When the evaluation structure fails to meet the set conditions, a prompt message is sent.

[0083] Optionally, evaluating the lecture audio data includes:

[0084] The audio data is calculated based on the integral algorithm to obtain an integral result;

[0085] The audio data is evaluated based on the integration result.

[0086] The integral calculation formula is as follows:

[0087] c is the integral, n is the total number of students and teachers in the standard environment configuration, T is the class duration in minutes, k1 is the recognition rate, and k2 is the environmental error adjustment parameter. The model recognizes audio data in real time.

[0088] The value range of k2 is 0.2 to 0.55. When the value is not within this range, the evaluation method will have a large error.

[0089] like Figure 2 As shown, a teaching effect evaluation device comprises:

[0090] An audio acquisition module is used to acquire audio data of lectures in a standard environment;

[0091] A recognition module, used to recognize audio data using a pre-trained model and convert the audio data into text data;

[0092] A comparison module, used for comparing the text data with standard text;

[0093] The evaluation module is used to evaluate the teaching effect according to the comparison results.

[0094] Optionally, before the step of obtaining the lecture audio data in the standard environment, the step further includes:

[0095] Obtain sample data of the lecture environment;

[0096] Analyzing the environmental sample data;

[0097] Based on the analysis results, standard environmental data parameters are obtained.

[0098] Optionally, obtaining standard environmental data parameters based on the analysis results includes:

[0099] Input standard environmental parameters into the pre-built model for simulation runs;

[0100] The standard environmental parameters are adjusted based on the operating results.

[0101] Optionally, the using a pre-trained model to recognize the audio data and convert the audio data into text data includes:

[0102] Decomposing the audio data according to natural language rules;

[0103] Convert the decomposed audio data into text data.

[0104] Optionally, after the step of evaluating the teaching effect according to the comparison result, the method further includes:

[0105] Mark the data whose evaluation results do not meet the standards;

[0106] Find the corresponding audio data according to the tag;

[0107] Diagnose audio data to analyze factors that do not meet standards.

[0108] Optionally, the step of obtaining the lecture audio data in a standard environment includes:

[0109] Evaluating the lecture audio data;

[0110] When the evaluation result reaches the set condition, the lecture audio data is sent to the pre-trained model;

[0111] When the evaluation structure fails to meet the set conditions, a prompt message is sent.

[0112] Optionally, evaluating the lecture audio data includes:

[0113] The audio data is calculated based on the integral algorithm to obtain an integral result;

[0114] The audio data is evaluated based on the integration result.

[0115] This embodiment also discloses an electronic device, which includes:

[0116] at least one processor; and,

[0117] a memory communicatively connected to the at least one processor; wherein,

[0118] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the teaching effect evaluation method of this embodiment.

[0119] Figure 3 is a hardware block diagram of an electronic device according to an embodiment of the present disclosure. Figure 3 As shown, the electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0120] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0121] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory, so that the electronic device performs all or part of the steps of the teaching effect evaluation method of each embodiment of the present disclosure.

[0122] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0123] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0124] This embodiment also discloses a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the teaching effect evaluation method disclosed in this embodiment.

[0125] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by the processor, all or part of the steps of the teaching effect evaluation method of each embodiment of the present disclosure are executed.

[0126] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0127] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0128] Various embodiments of the teaching effect evaluation method proposed in the present disclosure can be implemented using a computer-readable medium such as computer software, hardware, or any combination thereof. For hardware implementation, various embodiments of the teaching effect evaluation method proposed in the present disclosure can be implemented by using at least one of a specific application integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein. In some cases, various embodiments of the teaching effect evaluation method proposed in the present disclosure can be implemented in a controller. For software implementation, various embodiments of the teaching effect evaluation method proposed in the present disclosure can be implemented with a separate software module that allows the execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any appropriate programming language, and the software code can be stored in a storage unit and executed by a controller.

[0129] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0130] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0131] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0132] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0133] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0134] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0135] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0136] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for evaluating the effect of a lecture, characterized in that: include: Obtain lecture audio data in a standard environment; Use pre-trained models to perform real-time recognition of audio data and convert it into text data; Comparing the text data with standard text; Evaluate the teaching effect based on the comparison results; The obtaining of the lecture audio data in the standard environment includes: evaluating the lecture audio data; when the evaluation result meets the set conditions, sending the lecture audio data to the pre-trained model; when the evaluation result does not meet the set conditions, sending a prompt message; The evaluating of the lecture audio data includes: calculating the audio data based on an integration algorithm to obtain an integration result; and evaluating the audio data according to the integration result; The integration algorithm includes: , where c is the integral, n is the sum of the number of students and teachers in the standard environment configuration, T is the class duration, k1 is the recognition rate, and k2 is the environmental error adjustment parameter; After the step of evaluating the teaching effect according to the comparison results, the method further includes: Mark the data whose evaluation results do not meet the standards; Find the corresponding audio data according to the tag; Diagnose audio data to analyze factors that fail to meet standards.

2. The teaching effect evaluation method according to claim 1, characterized in that: Before the step of obtaining the lecture audio data in the standard environment, the method further includes: Obtain sample data of the lecture environment; analyzing the environmental sample data; Standard environmental data parameters are obtained based on the analysis results.

3. The teaching effect evaluation method according to claim 2, characterized in that: The standard environmental data parameters obtained based on the analysis results include: Input standard environmental parameters into the pre-built model for simulation runs; The standard environmental parameters are adjusted based on the operating results.

4. The teaching effect evaluation method according to claim 1, characterized in that: The method of using a pre-trained model to recognize audio data and converting the audio data into text data includes: Decomposing the audio data according to natural language rules; Convert the decomposed audio data into text data.

5. A teaching effect evaluation device, characterized in that: include: Audio acquisition module, used to obtain lecture audio data in a standard environment; The recognition module is used to perform real-time recognition of audio data using a pre-trained model and convert the audio data into text data; A comparison module, used for comparing the text data with standard text; Evaluation module, used to evaluate the teaching effect based on the comparison results; The obtaining of the lecture audio data in the standard environment includes: evaluating the lecture audio data; when the evaluation result meets the set conditions, sending the lecture audio data to the pre-trained model; when the evaluation result does not meet the set conditions, sending a prompt message; The evaluating of the lecture audio data includes: calculating the audio data based on an integration algorithm to obtain an integration result; and evaluating the audio data according to the integration result; The integration algorithm includes: , where c is the integral, n is the sum of the number of students and teachers in the standard environment configuration, T is the class duration, k1 is the recognition rate, and k2 is the environmental error adjustment parameter; After the step of evaluating the teaching effect according to the comparison results, the method further includes: Mark the data whose evaluation results do not meet the standards; Find the corresponding audio data according to the tag; Diagnose audio data to analyze factors that fail to meet standards.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the teaching effect evaluation method described in any one of claims 1-4.

7. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the teaching effect evaluation method described in any one of claims 1-4.

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