Test question scoring method and system, electronic equipment and storage medium

Abstract

The invention provides a test question scoring method and system, electronic equipment and a storage medium, and belongs to the field of examination evaluation. The method comprises the steps that a word frequency table is constructed for examination subjects, and the word frequency table comprises the occurrence frequency of all vocabularies of the subjects in the subjects; performing word frequency statistics on the desensitized test question data, and converting the desensitized test question data into corresponding vocabularies in a word frequency table according to frequency by utilizing the word frequency table to generate a test question pseudo text; scoring the generated test question pseudo text based on a pre-constructed test question scoring model to obtain a test question scoring result; wherein the test question scoring model is a joint scoring model based on a classification task and a regression task, and the joint scoring model is constructed on a base model after field adaptability pre-training. Understanding of the base model on domain knowledge is improved by adopting modes of pseudo-text conversion and domain adaptability pre-training; and a joint scoring model is introduced to carry out machine scoring mutual verification, so that the scoring accuracy and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of examination and evaluation, and in particular to a test question scoring method, system, electronic equipment and storage medium. Background Art

[0002] In the current field of test assessment technology, with the development of educational informatization, automatic scoring of test questions has become increasingly important. However, traditional scoring methods often rely on manually set rules or simple statistical methods, which are difficult to accurately reflect the true difficulty of the test questions and the actual level of the examinees. In addition, in order to protect the privacy of examinees and prevent cheating, test question data usually needs to be desensitized, which further increases the difficulty of automatic scoring.

[0003] Desensitized data means replacing or encrypting personal information, sensitive information, or even all answers in the test questions to protect data privacy. However, desensitized data often loses some of the original information, making it difficult to effectively apply traditional rule-based or statistical scoring methods. Therefore, how to intelligently score desensitized test data has become an urgent problem to be solved.

[0004] However, since test data is usually subject-specific, it is often difficult to achieve ideal scoring results by directly using a general language model. In addition, the scoring of test questions usually involves multiple dimensions, such as difficulty, knowledge point coverage, and problem-solving steps, which are difficult to accurately evaluate through a single scoring model. Therefore, it is necessary to build a scoring model that can comprehensively consider multiple dimensions to improve the accuracy and reliability of scoring. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a test question scoring method, system, electronic device and storage medium, which are used to completely or at least partially solve the problem of low scoring accuracy of desensitized test question data existing in the above-mentioned prior art.

[0006] In order to achieve the above object, an embodiment of the present invention provides a test question scoring method, comprising: Constructing a word frequency table for each test subject, wherein the word frequency table includes the frequency of each word in the subject appearing in the subject; Perform word frequency statistics on the desensitized test data, and use the word frequency table to convert the desensitized test data into corresponding words in the word frequency table according to the frequency, so as to generate test pseudo text; The generated test question pseudo text is scored based on a pre-built test question scoring model to obtain a test question scoring result; wherein the test question scoring model is a joint scoring model based on classification tasks and regression tasks constructed on a base model after domain adaptability pre-training.

[0007] Optionally, the desensitized test question data is characterized by replacing or encrypting personal information, sensitive information and all answer contents in the test questions.

[0008] The domain adaptation pre-training process includes: Combine the test subject corpus and test question pseudo text to form a domain-adaptive pre-training dataset; A Chinese pre-trained language model PTM is selected as a starting model for domain adaptive pre-training, some texts in a data set are randomly selected for occlusion processing, and the Chinese pre-trained language model PTM is subjected to domain adaptive pre-training through a task of predicting occluded words.

[0009] Furthermore, on the base model after domain adaptation pre-training, the process of constructing a joint scoring model based on classification tasks and regression tasks includes: Leverage domain-adaptive pre-trained feature extraction base model For input Extract features from pseudo text sequences to obtain text sequence features :

[0010] Text sequence features Perform average pooling and maximum pooling, and concatenate the pooled features to obtain text features :

[0011] In the formula, represents average pooling, represents the maximum pooling, Represents a splicing operation; Classification branch utilization

[0012] Make score predictions, Convert the output into the probability of each category, Represents the probability value of each score category, is a classification fully connected layer, whose input dimension is ,in is the dimension of the model semantic embedding, and the output dimension is the number of types of scores ; Regression branch exploitation

[0013] Make score predictions, is the regression result, is a regression fully connected layer, whose input dimension is , the output dimension is 1.

[0014] Furthermore, before scoring the generated test question pseudo text based on the pre-built test question scoring model to obtain the test question scoring result, the test question scoring method further includes fine-tuning the joint scoring model based on manual scoring: Construct a training data set, wherein the training data set is a triple data including test question pseudo text, category data converted from manual scores, and normalized manual scoring data , in is the pseudo text of the test question, represents categorical data converted from artificial scores, Represents normalized manual scoring data:

[0015] in, Indicates the maximum score, represents the average score, Indicates manual expert rating.

[0016] The triplet data is used to train the joint scoring model based on a random small batch training method, wherein the classification task uses cross entropy to calculate the training loss, the regression task uses root mean square error to calculate the training loss, and the Adam optimizer is used to update the parameters of the joint scoring model.

[0017] Furthermore, the generated test question pseudo text is scored based on the pre-built test question scoring model to obtain the test question scoring results, including: Use the fine-tuned test scoring model to score, and the model output and ; choose The score corresponding to the category with the highest probability is used as the scoring result of the classification branch:

[0018] In the formula, Indicates the position index for obtaining the maximum value; right Perform the inverse transformation as the scoring result of the regression branch: , In the formula, represents the rounding function; Calculate the score difference between the classification task and the regression task. If the score difference is within the specified threshold, the score is valid. The final score result The average of the two:

[0019] Select both The maximum probability is used as the confidence level of the scoring result; If the score difference is greater than the specified threshold, the machine score is invalid.

[0020] In a second aspect, the present invention further provides a test question scoring system, comprising: A construction unit, used to construct a word frequency table for an examination subject, wherein the word frequency table includes the frequency of occurrence of each word in the subject; A generating unit is used to count the word frequency of the desensitized test data, and use the word frequency table to convert the desensitized test data into corresponding words in the word frequency table according to the frequency, so as to generate a test pseudo text; The scoring unit is used to score the generated test question pseudo-text based on a pre-built test question scoring model to obtain a test question scoring result; wherein the test question scoring model is a joint scoring model based on classification tasks and regression tasks constructed on a base model after domain adaptability pre-training.

[0021] In a third aspect, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned test scoring method when executing the program.

[0022] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned test question scoring method.

[0023] Through the above technical solution, pseudo-text conversion and domain-adaptive pre-training are used to improve the base model's understanding of domain knowledge; and a joint scoring model is introduced to perform mutual verification of machine scoring to improve scoring accuracy and reliability.

[0024] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings: Figure 1 It is an implementation flow chart of a test question scoring method provided by an embodiment of the present invention; Figure 2 is a structural diagram of a joint scoring model provided by an embodiment of the present invention; Figure 3It is a schematic diagram of a process of scoring a test question pseudo text using a fine-tuned model provided by an embodiment of the present invention; Figure 4 It is a structural diagram of a test question scoring system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0027] See also Figure 1 As shown, it is a flowchart of an implementation method of a test question scoring method provided by an embodiment of the present invention, including the following execution steps: Step 100: Construct a word frequency table for a test subject, wherein the word frequency table includes the frequency of each word in the subject appearing in the subject.

[0028] Step 101: Perform word frequency statistics on the desensitized test data, and use a word frequency table to convert the desensitized test data into corresponding words in the word frequency table according to the frequency, so as to generate a test pseudo text.

[0029] It should be understood that the desensitized test data refers to data in which personal information, sensitive information and all answer contents in the test questions are replaced or encrypted.

[0030] For example, for a short-answer question in a professional qualification exam, the full score is 20 points, with a total of 11,651 answers and corresponding scores, where the scores are given by professional examiners. The anonymized data sample is shown in Table 1, which is only used as an example and has no actual content.

[0031] Table 1: Anonymous data

[0032] Step 102: Score the generated test question pseudo-text based on a pre-constructed test question scoring model to obtain a test question scoring result; wherein the test question scoring model is a joint scoring model based on classification tasks and regression tasks constructed on a base model after domain adaptability pre-training.

[0033] The joint scoring model includes a classification branch and a regression branch, wherein the classification branch is used to predict the score category, and the regression branch is used to predict the specific score value, and the joint scoring model is fine-tuned and trained based on manual scoring data.

[0034] Specifically, the domain adaptive pre-training process includes: merging the test subject corpus and the test question pseudo-text to form a data set for domain adaptive pre-training; selecting the Chinese pre-trained language model PTM as the starting model for domain adaptive pre-training, randomly selecting part of the text in the data set for occlusion processing, and performing domain adaptive pre-training on the Chinese pre-trained language model PTM through the task of predicting the occluded words.

[0035] In some embodiments, the process of constructing a joint scoring model based on a classification task and a regression task on a domain-adaptive pre-trained base model includes: S1: Use the Chinese pre-trained language model PTM that has been pre-trained for domain adaptability to extract features from the input test pseudo-text sequence to obtain text sequence features.

[0036] S2: Perform average pooling and maximum pooling on the text sequence features, and concatenate the features after average pooling and maximum pooling to obtain text features.

[0037] S3: For the text features, the classification fully connected layer is used to output the probability of each category, and the regression fully connected layer is used to output the regression result.

[0038] In a specific embodiment, a joint scoring model based on classification tasks and regression tasks is constructed on the BERT base model after domain adaptation pre-training. Figure 2 As shown, the feature extraction base model is pre-trained with domain adaptation. For input Extract features from pseudo text sequences to obtain text sequence features :

[0039] Text sequence features Perform average pooling and maximum pooling, and concatenate the pooled features to obtain text features :

[0040] In the formula, represents average pooling, represents the maximum pooling, Represents a splicing operation; Classification branch utilization

[0041] Make score predictions, Convert the output into the probability of each category, Represents the probability value of each score category, is a classification fully connected layer, whose input dimension is ,in is the dimension of the model semantic embedding, and the output dimension is the number of types of scores ; Regression branch exploitation

[0042] Make score predictions, is the regression result, is a regression fully connected layer, whose input dimension is , the output dimension is 1.

[0043] In some embodiments, before executing step 102, the steps are also executed: fine-tuning the joint scoring model based on manual scoring: constructing a training data set, wherein the training data set is triple data including pseudo-text of test questions, category data converted from manual scores, and normalized manual scoring data; using the triple data, the joint scoring model is trained based on a random small batch training method, wherein the classification task uses cross entropy to calculate the training loss, the regression task uses root mean square error to calculate the training loss, and the Adam optimizer is used to update the parameters of the joint scoring model.

[0044] For example, a training data set is constructed. The data set is a triple , in is the pseudo text of the test question, represents categorical data converted from artificial scores, Represents normalized manual scoring data:

[0045] in, Indicates the maximum score, represents the average score, Indicates manual expert rating.

[0046] Model fine-tuning training. Using the three original group data, the model is trained based on the random small batch training method. The classification task uses cross entropy to calculate the training loss, the regression task uses the root mean square error to calculate the loss, and the Adam optimizer is used to update the model parameters.

[0047] In a specific implementation, when executing step 102, the following steps may be specifically performed: S1020: Select the classification fully connected layer to output the score corresponding to the category with the highest probability among the probabilities of each category as the scoring result of the classification branch.

[0048] Specifically, the fine-tuned model is used to score the test question pseudo text, and the scoring results are processed to obtain the final machine score, see Figure 3 shown.

[0049] Use the fine-tuned model for scoring, and the model output and ; choose The score corresponding to the category with the highest probability is used as the scoring result of the classification branch:

[0050] in, Indicates the position index where the maximum value is obtained.

[0051] S1021: Perform an inverse transformation on the regression result output by the regression fully connected layer and use it as the scoring result of the regression branch.

[0052] Specifically, the regression result output by the regression fully connected layer is inversely transformed according to the following formula and used as the scoring result of the regression branch:

[0053] In the formula, represents the rounding function, represents the regression results, Indicates the maximum score, Indicates the average score.

[0054] S1022: Calculate the score difference between the scoring result of the classification branch and the scoring result of the regression branch. If the score difference is within a preset threshold range, the scoring is valid, and take the average of the scoring result of the classification branch and the scoring result of the regression branch as the scoring result.

[0055] Specifically, the score difference between the classification task and the regression task is calculated. If the score difference is within the specified threshold, the score is valid. The final score result The average of the two:

[0056] Select both The maximum probability is used as the confidence level of the scoring result. If the score difference is greater than the specified threshold, the machine scoring is invalid.

[0057] In this embodiment, the scoring threshold of the two branches is 1 point, that is, if the classification score and regression score are less than or equal to 1 point, the machine scoring is valid, and if they are greater than 1 point, the machine scoring is invalid. The scoring results include: classification score, regression score, validity, machine score and confidence. The scoring result examples are shown in Table 2: Table 2: Scoring results

[0058] According to the above implementation plan, the review of all candidates' answers to be reviewed is completed. Table 3 is the experimental calculation results obtained using the method of this application.

[0059] Table 3: Experimental calculation results

[0060] The above 2-point consistency rate is calculated by first counting the number of cases where the difference between the machine score and the human score is less than or equal to 2 points, and then dividing it by the valid number.

[0061] By constructing a subject word frequency table, converting the desensitized test data into pseudo-text, conducting domain adaptability pre-training, and building a joint scoring model, we can achieve intelligent scoring of desensitized data and improve the accuracy and reliability of scoring.

[0062] See also Figure 4 FIG. 1 is a schematic diagram of a structure of a test question scoring system provided by an embodiment of the present invention, comprising: A construction unit 40 is used to construct a word frequency table for the test subject, wherein the word frequency table includes the frequency of each word in the subject appearing in the subject; The generating unit 41 is used to count the word frequency of the desensitized test data, and use the word frequency table to convert the desensitized test data into corresponding words in the word frequency table according to the frequency, so as to generate a test pseudo text; The scoring unit 42 is used to score the generated test question pseudo-text based on a pre-constructed test question scoring model to obtain a test question scoring result; wherein the test question scoring model is a joint scoring model based on classification tasks and regression tasks constructed on a base model after domain adaptability pre-training.

[0063] The test question scoring system includes a processor and a memory. The above-mentioned construction unit, generation unit and scoring unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0064] The processor contains a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to achieve intelligent scoring of desensitized data, thereby improving the accuracy and reliability of the scoring.

[0065] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0066] An embodiment of the present invention provides a storage medium on which a program is stored. When the program is executed by a processor, the test question scoring method is implemented.

[0067] An embodiment of the present invention provides a processor, which is used to run a program, wherein the test question scoring method is executed when the program is run.

[0068] The embodiment of the present invention provides a device, the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: constructing a word frequency table for the test subject, wherein the word frequency table contains the frequency of each vocabulary of the subject in the subject; performing word frequency statistics on the desensitized test data, and using the word frequency table, converting the desensitized test data into the corresponding vocabulary in the word frequency table according to the frequency, and generating a test pseudo text; scoring the generated test pseudo text based on a pre-constructed test scoring model to obtain a test scoring result; wherein the test scoring model is a joint scoring model based on a classification task and a regression task constructed on a base model after domain adaptability pre-training. The device in this article can be a server, a PC, a PAD, a mobile phone, etc.

[0069] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that is initialized with the following method steps: constructing a word frequency table for the examination subject, wherein the word frequency table contains the frequency of each word of the subject appearing in the subject; performing word frequency statistics on the desensitized test data, and using the word frequency table, converting the symbols in the desensitized test data into corresponding words in the word frequency table according to the frequency, and generating a test pseudo-text; scoring the generated test pseudo-text based on a pre-constructed test scoring model to obtain a test scoring result; wherein the test scoring model is a joint scoring model based on classification tasks and regression tasks constructed on a base model after domain adaptability pre-training.

[0070] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0071] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0072] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0074] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0075] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0076] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0077] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0078] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A test question scoring method, characterized in that: include: Constructing a word frequency table for each test subject, wherein the word frequency table includes the frequency of each word in the subject appearing in the subject; Perform word frequency statistics on the desensitized test data, and use the word frequency table to convert the desensitized test data into corresponding words in the word frequency table according to the frequency, so as to generate test pseudo text; The generated test question pseudo text is scored based on a pre-built test question scoring model to obtain a test question scoring result; wherein the test question scoring model is a joint scoring model based on classification tasks and regression tasks constructed on a base model after domain adaptability pre-training.

2. The test question scoring method according to claim 1, characterized in that: The desensitized test data refers to data in which personal information, sensitive information and all answer contents in the test questions are replaced or encrypted.

3. The test question scoring method according to claim 1, characterized in that: The domain adaptation pre-training process includes: Combine the test subject corpus and test question pseudo text to form a domain-adaptive pre-training dataset; A Chinese pre-trained language model PTM is selected as a starting model for domain adaptive pre-training, some texts in a data set are randomly selected for occlusion processing, and the Chinese pre-trained language model PTM is subjected to domain adaptive pre-training through a task of predicting occluded words.

4. The test question scoring method according to claim 3, characterized in that: On the domain-adaptive pre-trained base model, the process of building a joint scoring model based on classification and regression tasks includes: Leverage domain-adaptive pre-trained feature extraction base model For input Extract features from pseudo text sequences to obtain text sequence features : Text sequence features Perform average pooling and maximum pooling, and concatenate the pooled features to obtain text features : In the formula, represents average pooling, represents the maximum pooling, Represents a splicing operation; Classification branch utilization Make score predictions, Convert the output into the probability of each category, Represents the probability value of each score category, is a classification fully connected layer, whose input dimension is ,in is the dimension of the model semantic embedding, and the output dimension is the number of types of scores ; Regression branch exploitation Make score predictions, is the regression result, is a regression fully connected layer, whose input dimension is , the output dimension is 1.

5. The test question scoring method according to claim 1, characterized in that: Before scoring the generated test question pseudo text based on the pre-built test question scoring model to obtain the test question scoring result, the test question scoring method further includes fine-tuning the joint scoring model based on manual scoring: Construct a training data set, wherein the training data set is a triple data including test question pseudo text, category data converted from manual scores, and normalized manual scoring data ,in, is the pseudo text of the test question, represents categorical data converted from artificial scores, Represents normalized manual scoring data: In the formula, Indicates the maximum score, represents the average score, represents manual expert rating; The triplet data is used to train the joint scoring model based on a random small batch training method, wherein the classification task uses cross entropy to calculate the training loss, the regression task uses root mean square error to calculate the training loss, and the Adam optimizer is used to update the parameters of the joint scoring model.

6. The test question scoring method according to claim 4, characterized in that: The generated test question pseudo text is scored based on the pre-built test question scoring model to obtain the test question scoring results, including: Use the fine-tuned test scoring model to score, and the model output and ; choose The score corresponding to the category with the highest probability is used as the scoring result of the classification branch: In the formula, Indicates the position index for obtaining the maximum value; right Perform the inverse transformation as the scoring result of the regression branch: ， In the formula, represents the rounding function; Calculate the score difference between the classification task and the regression task. If the score difference is within the specified threshold, the score is valid. The final score result The average of the two: Select both The maximum probability is used as the confidence level of the scoring result; If the score difference is greater than the specified threshold, the machine score is invalid.

7. A test scoring system, characterized in that: include: A construction unit, used to construct a word frequency table for an examination subject, wherein the word frequency table includes the frequency of occurrence of each word in the subject; A generating unit is used to count the word frequency of the desensitized test data, and use the word frequency table to convert the desensitized test data into corresponding words in the word frequency table according to the frequency, so as to generate a test pseudo text; The scoring unit is used to score the generated test question pseudo-text based on a pre-built test question scoring model to obtain a test question scoring result; wherein the test question scoring model is a joint scoring model based on classification tasks and regression tasks constructed on a base model after domain adaptability pre-training.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the test question scoring method as described in any one of claims 1-6 are implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the test question scoring method according to any one of claims 1 to 6 are implemented.

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