A cross-domain cognitive diagnosis method based on prompting technology

Abstract

The present invention discloses a cross-domain cognitive diagnosis method based on prompt technology, comprising: 1. constructing cross-domain data, including data sets of source and target domains, exercise-knowledge point correlations, etc.; 2. constructing a cognitive diagnosis model with soft prompts for the source domain, and using soft prompts to learn common representations between different source domains; 3. constructing a cognitive diagnosis model with soft prompts for the target domain, and migrating the soft prompts from the source domain model to the target domain model; 4. pre-training the cognitive diagnosis model in the source domain and fine-tuning the target domain until convergence, thereby obtaining a trained target domain cognitive diagnosis model for students' exercise recommendations and knowledge tracking. The present invention utilizes the idea of ​​cross-domain soft prompts, migrates the prompts to the target domain after training in the source domain, and uses a small amount of data in the target domain for fine-tuning, thereby improving the accuracy of cross-domain diagnosis.

Description

Technical Field

[0001] The present invention belongs to the field of cognitive diagnosis of smart education, and specifically is a cross-domain cognitive diagnosis method based on prompt technology. Background Art

[0002] In the field of smart education, cognitive diagnosis plays a fundamental role, providing a foundation for exercise recommendations and learning guidance. In cognitive diagnosis, student and exercise representations are essential for capturing the student's cognitive state. Therefore, the core challenge lies in obtaining high-quality student and exercise representations. With the rapid development of deep learning technology, significant efforts have been made to design complex models to learn these embeddings. For example, the neurocognitive diagnostic model (NCDM) sets the dimensionality of student and exercise embeddings to correspond to the number of knowledge points. During the interaction process, NCDM utilizes the correlation matrix (Q matrix) between exercises and knowledge points for alignment. The relational graph-based cognitive diagnostic model (RCD) constructs a multi-layered graph structure containing student, exercise, and knowledge concepts to obtain their embeddings. It then combines diagnostic and predictive modules to capture the interactions between these three entities and facilitate cognitive diagnosis.

[0003] Despite the significant achievements of existing research, most of the models developed so far are domain-specific. These models assume that exercises and knowledge points are from the same subject and that students come from a specific region or school. However, these assumptions limit the generalizability of the models in practical applications. In real-world scenarios, when encountering a new subject or a new group of students from different schools (called a new domain), the model needs to be retrained to make it applicable. However, if the data in this new domain is scarce, the retraining process becomes challenging. Therefore, cross-domain cognitive diagnosis is a challenging and promising research direction with important practical value in the field of intelligent education.

[0004] However, only a few studies have addressed cross-domain cognitive diagnosis models. One such study is TechCD, which proposes a cross-domain cognitive diagnosis method using transferable knowledge concept graph embeddings in cold-start scenarios. Another method, ZeroCD, addresses this challenge by leveraging a batch of student data from earlier participants. However, the TechCD method has high computational costs and is difficult to apply to real-time or large-scale scenarios. It also relies heavily on high-quality knowledge concept graphs, and incomplete or inaccurate data can seriously affect the effectiveness of the algorithm. In the ZeroCD method, the quality of the simulation log depends on the reliability of early student behavior. If there is noise or bias in the behavioral data, it may mislead subsequent cognitive diagnosis. Therefore, how to use the historical answer records of students in the source domain to accurately assess the target domain students' mastery of knowledge points and answer predictions remains a problem worthy of in-depth study. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the present invention proposes a cross-domain cognitive diagnosis method based on prompt technology, which aims to solve the problem that the existing cognitive diagnosis model performs poorly in cross-domain scenarios. By establishing a connection between the source domain and the target domain, it promotes the transfer of student and practice features learned from the source domain to the target domain, thereby improving the accuracy of cross-domain diagnosis.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solutions:

[0007] The cross-domain cognitive diagnosis method based on prompting technology of the present invention is characterized in that it is carried out according to the following steps:

[0008] Step 1: Construct cross-domain data;

[0009] Define the source domain collection and target areas ,in, Indicates the Source fields, Indicates the number of source fields;

[0010] definition The set of students on 、The set of exercises is , the knowledge point set is ;in, Any student in , The Exercises are recorded as , The Knowledge points are recorded as ; and the exercises in each source domain are different;

[0011] Define target areas The set of students on 、The set of exercises is , the knowledge point set is ;in, Any student in , No. Exercises are recorded as , The Knowledge points are recorded as ; and the exercises between the source domain set S and the target domain T are different;

[0012] Using formula (1) to get the source domain set and target areas Overlapping student sets between , and Middle Students are recorded as :

[0013] (1)

[0014] Defining the Source Domain Pre-training dataset on is a set of triples, where Any triple in express right The answer result of ,express Correct answer , , indicating other situations;

[0015] from Randomly select a certain proportion of students’ answer data as the fine-tuning dataset ;in, Any triple in express right The answer result of ,express Correct answer , , indicating other situations;

[0016] Will The relationship matrix between the above exercises and knowledge points is recorded as , where, let Middle Row vector express and The correlation between each knowledge point above, where 1 or 0 respectively indicates and relevant or irrelevant; express The number of exercises in express The number of knowledge points in

[0017] Will The relationship matrix between the above exercises and knowledge points is recorded as , where, let Middle Row vector express and The correlation between each knowledge point above, where 1 or 0 respectively indicates and relevant or irrelevant; express The number of exercises in express The number of knowledge points in

[0018] Step 2: Construct a source domain cognitive diagnosis model with soft prompts and exist The original embedding and The original embedding of exist Upper pair Predicted answer results , thereby constructing the binary cross entropy loss , used to train the source domain cognitive diagnosis model, and obtain The best personalized tips for any source domain and The best shared hint for all exercises in ;

[0019] Step 3: Construct a target domain cognitive diagnosis model with soft prompts and and Process and obtain exist Top pair Predicted answer results , thus constructing exist Top pair The binary cross entropy loss , used to train the target domain cognitive diagnosis model to achieve students' exercise recommendation and knowledge tracking.

[0020] The cross-domain cognitive diagnosis method based on prompting technology of the present invention is also characterized in that step 2 is performed as follows:

[0021] Step 2.1: We can obtain exist Composite representation on as well as Composite representation :

[0022] (3)

[0023] In formula (3), express Personalized tips on any source field, express Shared hints for all exercises in and They represent the random initialization exist The original embedding and The original embedding of Represents horizontal concatenation of vectors;

[0024] Step 2.2: We can obtain exist The final representation of as well as The final representation :

[0025] (4)

[0026] In formula (4), represents a linear layer for personalized hints on the source domain, represents a linear layer over the source domain for shared cues;

[0027] Step 2.3: Get by formula (5) exist Top pair Predicted answer results :

[0028] (5)

[0029] In formula (5), represents the interaction function that predicts the answer based on the student and exercise representations;

[0030] Step 2.4: Construct through formula (6) exist Top pair The binary cross entropy loss :

[0031] (6)

[0032] In formula (6), yes exist Top pair The actual answer results;

[0033] Step 2.5: Use formula (7) to construct the source domain cognitive diagnosis model for the objective function of all source domains :

[0034] (7)

[0035] Step 2.6: All pre-training datasets based on the source domain , the source domain cognitive diagnosis model is trained using the Adam optimizer to update the model parameters until the objective function Until convergence, the cognitive diagnosis model trained into the source domain is obtained. The best personalized tips for any source domain and The best shared hint for all exercises in .

[0036] Furthermore, step 3 is performed as follows:

[0037] Step 3.1: Get by formula (8) In the target area Personalized tips on :

[0038] (8)

[0039] Step 3.2: Get the target domain through formula (9) Shared hints for all exercises in :

[0040] (9)

[0041] In formula (9), represents the vertical splicing operation of shared cues in the source domain, Represents a dimension The row vector of ;

[0042] Step 3.3: Get by formula (10) exist The original embedding on :

[0043] (10)

[0044] Step 3.4: We can obtain exist Composite representation on as well as Composite representation :

[0045] (11)

[0046] In formula (11), Represents the random initialization The original embedding of

[0047] Step 3.5: Use formula (12) to obtain exist The final representation of as well as The final representation :

[0048] (12)

[0049] In formula (12), represents a linear layer for personalized hints on the target domain, represents a linear layer for shared cues on the target domain;

[0050] Step 3.6: Use formula (13) to get exist Top pair Predicted answer results :

[0051] (13)

[0052] Step 3.7: Construct using formula (14) exist Top pair The binary cross entropy loss :

[0053] (14)

[0054] Step 3.8: Construct the objective function of the source domain cognitive diagnosis model for the target domain through formula (15): :

[0055] (15)

[0056] Step 3.9: Fine-tune the dataset based on the target domain dataset , the target domain cognitive diagnosis model is trained based on the Adam optimizer to update the model parameters until the objective function Convergence is achieved, thus obtaining the trained target domain cognitive diagnosis model.

[0057] The electronic device of the present invention includes a memory and a processor, and is characterized in that the memory is used to store a program that supports the processor to execute the cross-domain cognitive diagnosis method, and the processor is configured to execute the program stored in the memory.

[0058] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program executes the steps of the cross-domain cognitive diagnosis method when executed by a processor.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. The present invention effectively solves the problem of model performance degradation and poor performance of existing models in cross-domain scenarios through soft prompt transfer technology, thereby significantly improving diagnostic accuracy, especially when there is a large difference between the source domain and the target domain.

[0061] 2. The present invention adopts a two-stage model of pre-training and fine-tuning, which enables the diagnostic model to quickly adapt to new fields, avoiding the overfitting and catastrophic forgetting problems that may result from retraining the model, thereby enhancing the generalization ability of the diagnostic model between different fields.

[0062] 3. This invention is applicable to cross-disciplinary cognitive diagnostic tasks with diverse exercise content, effectively addressing scenarios across different disciplines and difficulty levels. This multi-dimensional adaptability ensures that the invention is effective in a variety of educational environments and contexts. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a specific flow chart of the cross-domain cognitive diagnosis method based on prompting technology proposed in the present invention;

[0064] Figure 2a This is an architecture diagram of the source domain model when the present invention is applied to the NCDM model;

[0065] Figure 2b This is an architecture diagram of the target model when the present invention is applied to the NCDM model. DETAILED DESCRIPTION

[0066] In this embodiment, a cross-domain cognitive diagnosis method based on prompt technology is proposed for diverse exercise content: first, cross-domain data is constructed: including data sets of source and target domains, and exercise-knowledge point correlations; second, a common cognitive diagnosis model with soft prompts is constructed for all source domains. Because relying solely on entity representation may not adapt to the diverse interactions between different domains, soft prompts are used to learn common representations between different source domains; then, a cognitive diagnosis model with soft prompts is constructed for the target domain, and a method for migrating soft prompts from the source domain model to the target domain model is defined. Personalized prompts are transferred in a one-to-one manner, and shared prompts are mapped back to the original dimension after splicing, so that the information in the source domain can be maximized. In addition, the method of the present invention uses a linear layer to learn the mapping relationship between personalized prompts and the original embedding of entities in the target domain, replacing the random initialization of the original embedding of entities in the target domain, making full use of existing data to capture potential information. Finally, two stages of model pre-training and fine-tuning are performed. In the pre-training stage, personalized and shared prompts are updated using source domain data. In the fine-tuning phase, a small amount of data from the target domain is used to fine-tune the model parameters to achieve knowledge transfer and adaptation from the source domain to the target domain. The two phases are trained sequentially until convergence. Specifically, Figure 1 As shown, the method is carried out in the following steps:

[0067] Step 1: Construct cross-domain data;

[0068] Define the source domain collection and target areas ,in, Indicates the Source fields, Indicates the number of source fields;

[0069] definition The set of students on 、The set of exercises is , the knowledge point set is ;in, Any student in , The Exercises are recorded as , The Knowledge points are recorded as ; and the exercises in each source domain are different;

[0070] Define target areas The set of students on 、The set of exercises is , the knowledge point set is ;in, Any student in , No. Exercises are recorded as , The Knowledge points are recorded as ; and the exercises between the source domain set S and the target domain T are different.

[0071] Using formula (1) to get the source domain set and target areas Overlapping student sets between , and Middle Students are recorded as :

[0072] (1)

[0073] Defining the Source Domain Pre-training dataset on is a set of triples, where Any triple in express right The answer result of ,express Correct answer , , indicating other situations;

[0074] Define target areas Dataset is a set of triples, from A certain proportion of the answer data of each student is randomly selected as the fine-tuning dataset ;in, Any triple in express right The answer result of ,express Correct answer , , indicating other situations.

[0075] Will The relationship matrix between the above exercises and knowledge points is recorded as , where, let Middle Row vector express and The correlation between each knowledge point above, where 1 or 0 respectively indicates and relevant or irrelevant; express The number of exercises in express The number of knowledge points in

[0076] Will The relationship matrix between the above exercises and knowledge points is recorded as , where, let Middle Row vector express and The correlation between each knowledge point above, where 1 or 0 respectively indicates and relevant or irrelevant; express The number of exercises in express The number of knowledge points in .

[0077] Step 2: Build a source domain cognitive diagnosis model with soft prompts:

[0078] Step 2.1: Considering the characteristics of overlapping and non-overlapping entities in cross-domain scenarios, relying solely on entity representation may not be able to adapt to the diverse interactions between different domains. Therefore, the present invention designs two learnable soft prompts: personalized prompts and shared prompts.

[0079] In a cross-domain cognitive diagnosis scenario involving different disciplines, since the students are the same in the source and target domains, each student can be associated with a personalized prompt, thereby achieving more information transfer. In contrast, shared prompts are used for all exercises in the subject in the source domain, ensuring a universal representation of domain-specific knowledge. exist Composite representation on as well as Composite representation :

[0080] (3)

[0081] In formula (3), express Personalized tips on any source field, express Shared hints for all exercises in and They represent the random initialization exist The original embedding and The original embedding of Represents horizontal concatenation of vectors.

[0082] Step 2.2: In order to integrate the splicing features and extract the joint information, we can obtain exist The final representation of as well as The final representation :

[0083] (4)

[0084] In formula (4), represents a linear layer for personalized hints on the source domain, This represents a linear layer targeting shared cues on the source domain. Compared to the original embedding, the final representation combines personalized and shared cues to more effectively capture information transfer and domain-specific knowledge in cross-domain scenarios. Furthermore, the composite representation enhances the model's adaptability, improving the accuracy and generalization of cognitive diagnosis.

[0085] Step 2.3: Get by formula (5) exist Top pair Predicted answer results :

[0086] (5)

[0087] In formula (5), represents the interaction function that predicts the answer result based on the student and exercise representations.

[0088] Step 2.4: Construct through formula (6) exist Top pair The binary cross entropy loss :

[0089] (6)

[0090] In formula (6), yes exist Top pair The actual answer results;

[0091] Step 2.5: Use formula (7) to construct the source domain cognitive diagnosis model for the objective function of all source domains :

[0092] (7)

[0093] Step 2.6: All pre-training datasets based on the source domain , the source domain cognitive diagnosis model is trained using the Adam optimizer to update the model parameters until the objective function Until convergence, the trained source domain cognitive diagnosis model and its corresponding The best personalized tips for any source domain and The best shared hint for all exercises in ,because ,so There is also an optimal personalized prompt in any source field .

[0094] Step 3: Build a target domain cognitive diagnosis model with soft prompts:

[0095] Step 3.1: Get by formula (8) In the target area Personalized tips on , the personalized prompts corresponding to students are transferred from the source domain to the target domain in a one-to-one manner to maintain the integrity of the cross-domain connection information, because these prompts are also related to the target domain:

[0096] (8)

[0097] Step 3.2: In order to effectively capture the commonalities between different source domains, the shared prompts corresponding to the exercises are weighted and combined using formula (9) to obtain the target domain Shared hints for all exercises in :

[0098] (9)

[0099] In formula (9), represents the vertical splicing operation of shared cues in the source domain, Represents a dimension Through weighted combination, different weights can be assigned according to the importance of each source field to the target field, so as to make full use of the relevant information of each source field and improve the generalization ability and effect of the model.

[0100] Step 3.3: Get by formula (10) exist The original embedding on , using linear layers to learn personalized hints and target areas middle The original embedding of The mapping relationship between them:

[0101] (10)

[0102] Contains trainable parameters that are optimized using a small amount of interaction data in the target domain. This strategy replaces random initialization and exploits available data to capture the underlying original information.

[0103] Step 3.4: We can obtain exist Composite representation on as well as Composite representation :

[0104] (11)

[0105] In formula (11), Represents the random initialization The original embedding of

[0106] Step 3.5: Use formula (12) to obtain exist The final representation of as well as The final representation :

[0107] (12)

[0108] In formula (12), represents a linear layer for personalized hints on the target domain, Represents a linear layer for shared cues on the target domain.

[0109] Step 3.6: Use formula (13) to get exist Top pair Predicted answer results :

[0110] (13)

[0111] Step 3.7: Construct using formula (14) exist Top pair The binary cross entropy loss :

[0112] (14)

[0113] Step 3.8: Construct the objective function of the source domain cognitive diagnosis model for the target domain through formula (15): :

[0114] (15)

[0115] Step 3.9: Fine-tune the dataset based on the target domain dataset , the target domain cognitive diagnosis model is trained based on the Adam optimizer to update the model parameters until the objective function Convergence is achieved, thus obtaining a trained target domain cognitive diagnosis model, which can be used in scenarios such as student exercise recommendation, knowledge tracking, and computer adaptive testing.

[0116] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0117] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are executed.

[0118] Example:

[0119] The proposed cross-domain cognitive diagnosis method based on prompting technology is compatible with most existing cognitive diagnosis models. In this example, the proposed cross-domain cognitive diagnosis method is applied to the NCDM model, a classic neural network-based model in the field of cognitive diagnosis, to address diverse exercise content.

[0120] Figure 2a The cross-domain cognitive diagnosis model based on NCDM in the source domain is presented. The model uses personalized prompts and shared prompts to learn the common representation of subjects and students in multiple source domains. Its construction process can be interpreted by formula (3) and formula (4): First, the student In the source field The original embedding on With personalized tips After horizontal splicing and mapping, we get student exist The final representation of ; Secondly, the exercises The original embedding of Share Tips with After horizontal splicing and mapping, we get The final representation , and then input the final representation into the cognitive diagnosis interaction function In the function The corresponding parameters of the neural network are all positive; the model is based on its own prediction results And the real results ,Finally, the source domain cognitive diagnosis model is trained according to the objective function of formula (7);

[0121] Figure 2b The cross-domain cognitive diagnosis model based on NCDM in the target domain is presented. The model first transfers the personalized prompts and shared prompts trained in the source domain. In formula (5), the personalized prompts are transferred in a one-to-one manner, that is, the student Individualized prompts in target areas Equivalent to the best personalized tips in the source field , in formula (6), each source domain The corresponding optimal sharing prompt is optimal After being spliced ​​and mapped, the target area is obtained Sharing tips on . In addition, this method uses a linear layer to learn the student Personalized tips Rather than in the target area The original embedding in The mapping relationship between them is applied to the original embedding corresponding to the unseen students, and then the representation is strengthened according to formulas (3) and (4) to obtain the target domain Upper school student The final representation and exercises The final representation , and then input the final representation into the cognitive diagnosis interaction function In the function The corresponding parameters of the neural network are all positive; the model is based on its own prediction results And the real results ,Finally, the target domain cognitive diagnosis model is trained according to the objective function of formula (15);

[0122] The above embodiments only express the case of applying the present invention to the NCDM model. The present invention can be applied to most cognitive diagnosis models. The core idea of ​​the present invention is to use soft prompts to enhance cognitive diagnosis representation in cross-domain scenarios. This idea is the scope of protection to be applied for the patent of this invention.

Claims

1. A cross-domain cognitive diagnosis method based on prompt technology, characterized in that: The steps are as follows: Step 1: Construct cross-domain data; Define the source domain collection and target areas ,in, Indicates the Source fields, Indicates the number of source fields; definition The set of students on 、The set of exercises is , the knowledge point set is ;in, Any student in , The Exercises are recorded as , The Knowledge points are recorded as ; and the exercises in each source domain are different; Define target areas The set of students on 、The set of exercises is , the knowledge point set is ;in, Any student in , No. Exercises are recorded as , The Knowledge points are recorded as ; and the exercises between the source domain set S and the target domain T are different; Using formula (1) to get the source domain set and target areas Overlapping student sets between , and Middle Students are recorded as : (1) Defining the Source Domain Pre-training dataset on is a set of triples, where Any triple in express right The answer result of ,express Correct answer , , indicating other situations; from Randomly select a certain proportion of students’ answer data as the fine-tuning dataset ;in, Any triple in express right The answer result of ,express Correct answer , , indicating other situations; Will The relationship matrix between the above exercises and knowledge points is recorded as , where, let Middle Row vector express and The correlation between each knowledge point above, where 1 or 0 respectively indicates and relevant or irrelevant; express The number of exercises in express The number of knowledge points in Will The relationship matrix between the above exercises and knowledge points is recorded as , where, let Middle Row vector express and The correlation between each knowledge point above, where 1 or 0 respectively indicates and relevant or irrelevant; express The number of exercises in express The number of knowledge points in Step 2: Construct a source domain cognitive diagnosis model with soft prompts and exist The original embedding and The original embedding of exist Top pair Predicted answer results , thereby constructing the binary cross entropy loss , used to train the source domain cognitive diagnosis model, and obtain The best personalized tips for any source domain and The best shared hint for all exercises in ; Step 3: Construct a target domain cognitive diagnosis model with soft prompts and and Process and obtain exist Top pair Predicted answer results , thus constructing exist Top pair The binary cross entropy loss , used to train the target domain cognitive diagnosis model to achieve students' exercise recommendation and knowledge tracking.

2. The cross-domain cognitive diagnosis method based on prompt technology according to claim 1 is characterized in that: Described step 2 is carried out as follows: Step 2.1: We can obtain exist Composite representation on as well as Composite representation : (3) In formula (3), express Personalized tips on any source field, express Shared hints for all exercises in and They represent the random initialization exist The original embedding and The original embedding of Represents horizontal concatenation of vectors; Step 2.2: We can obtain exist The final representation of as well as The final representation : (4) In formula (4), represents a linear layer for personalized hints on the source domain, represents a linear layer over the source domain for shared cues; Step 2.3: Get by formula (5) exist Top pair Predicted answer results : (5) In formula (5), represents the interaction function that predicts the answer based on the student and exercise representations; Step 2.4: Construct through formula (6) exist Top pair The binary cross entropy loss : (6) In formula (6), yes exist Top pair The actual answer results; Step 2.5: Use formula (7) to construct the source domain cognitive diagnosis model for the objective function of all source domains : (7) Step 2.6: All pre-training datasets based on the source domain , the source domain cognitive diagnosis model is trained using the Adam optimizer to update the model parameters until the objective function Until convergence, the cognitive diagnosis model trained into the source domain is obtained. The best personalized tips for any source domain and The best shared hint for all exercises in .

3. The cross-domain cognitive diagnosis method based on prompt technology according to claim 2, characterized in that: Described step 3 is carried out as follows: Step 3.1: Get by formula (8) In the target area Personalized tips on : (8) Step 3.2: Get the target domain through formula (9) Shared hints for all exercises in : (9) In formula (9), represents the vertical splicing operation of shared cues in the source domain, Represents a dimension The row vector of ; Step 3.3: Get by formula (10) exist The original embedding on : (10) Step 3.4: We can obtain exist Composite representation on as well as Composite representation : (11) In formula (11), Represents the random initialization The original embedding of Step 3.5: Use formula (12) to obtain exist The final representation of as well as The final representation : (12) In formula (12), represents a linear layer for personalized hints on the target domain, represents a linear layer for shared cues on the target domain; Step 3.6: Use formula (13) to get exist Top pair Predicted answer results : (13) Step 3.7: Construct using formula (14) exist Top pair The binary cross entropy loss : (14) Step 3.8: Construct the objective function of the source domain cognitive diagnosis model for the target domain through formula (15): : (15) Step 3.9: Fine-tune the dataset based on the target domain dataset , the target domain cognitive diagnosis model is trained based on the Adam optimizer to update the model parameters until the objective function Convergence is achieved, thus obtaining the trained target domain cognitive diagnosis model.

4. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the cross-domain cognitive diagnosis method according to any one of claims 1 to 3, and the processor is configured to execute the program stored in the memory.

5. A 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 cross-domain cognitive diagnosis method according to any one of claims 1 to 3 are executed.

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