Test and evaluation method for English learning process

Abstract

The invention discloses a test evaluation method for an English learning process, and relates to the field of adaptive learning systems. The method comprises the following steps: S1, constructing a hierarchical knowledge graph based on a knowledge point dependency relationship; after the student completes the task or the test, the knowledge point mastery degree in the knowledge graph is updated in real time; s2, the updated knowledge graph is input into a DTN network model, and the DTN network model combines a direct dependency relationship and an indirect dependency relationship to automatically judge a task currently needing to be learned by a student; and S3, by calculating the knowledge mastery degree and the path dependency matrix of the student, recommending learning content and practice most suitable for the current level of the student. According to the method, by introducing the path dependency matrix and the depth sequential network model, indirect dependency modeling between knowledge points is realized, and the intelligent and personalized level of an adaptive language learning system is improved.

Description

Technical Field

[0001] The invention relates to the field of adaptive learning systems, in particular to a test evaluation method for an English learning process. Background Art

[0002] With the rapid development of educational technology, especially breakthroughs in artificial intelligence and deep learning, many adaptive learning systems have entered practical applications, especially in the field of language learning. Computerized adaptive tests (CAT) based on item response theory have gradually replaced classical tests and are becoming more and more popular at home and abroad. CAT is a test method that adapts to the ability level of the examinee. Computer-assisted tests tailor test questions to the ability of the examinee. In the field of computerized adaptive testing (CAT), item response theory is often used to select the most suitable items based on the individual ability of the examinee. In computerized adaptive testing (CAT), each examinee takes a unique test tailored to his or her ability level. The selection of computer-assisted translation subjects is a selection that targets the individual ability level of the examinee, rather than a single fixed test. After each response, the ability estimate is updated, and the next item is selected to have the optimal properties according to the new estimate.

[0003] Although the existing adaptive learning system has achieved many results, it still has some limitations, which are mainly reflected in the following aspects. First, the existing technology is mostly limited to personalized recommendations and learning path adjustments based on direct dependencies, and lacks effective modeling of indirect dependencies between knowledge points. Most systems recommend learning content based on simple test results and students' mastery, but rarely consider the complex dependencies between knowledge points, especially indirect dependencies. In the actual learning process, the mastery of some knowledge points is not directly judged by test scores, but depends on the mastery of other related knowledge. For example, if students do not master the "grammar" knowledge point well, it may affect their performance in "reading comprehension". The existing technology ignores this indirect dependency, resulting in the recommended learning content not being able to fully meet the actual needs of students, which in turn affects the learning effect. Summary of the invention

[0004] The present invention proposes a test evaluation method for the English learning process. Based on the dynamic learning path adjustment capability of the deep learning model, it can respond to changes in students' learning status in real time, flexibly adjust the learning path, and solve the inefficiency problem caused by the static learning path in the prior art.

[0005] Among them, a test evaluation method for English learning process includes the following steps: S1. Construct a hierarchical knowledge graph based on knowledge point dependencies, which organizes the core knowledge points of language learning hierarchically and dynamically updates the mastery of each knowledge point; when students complete a task or test, the mastery of the knowledge points in the knowledge graph is updated in real time; S2. Input the latest updated knowledge graph into the DTN network model, which combines direct dependencies with indirect dependencies to identify potential knowledge points that the students have not yet mastered, and automatically determines the tasks that the students currently need to learn; S3. Based on the intelligent diagnosis results, by calculating the students’ knowledge mastery and path dependency matrix, we recommend the most suitable learning content and exercises for the students’ current level and optimize the learning path; Wherein, the step S1 specifically includes the following sub-steps: S101. Initialize and construct a knowledge graph, and represent the knowledge graph as a directed graph consisting of a node set and an edge set, where the nodes represent core knowledge points and the edges represent dependency relationships between knowledge points; S102. Based on the initialized knowledge graph information, a path dependency matrix is ​​constructed to represent the direct dependency relationship between the core knowledge points; S103. Obtaining indirect dependency through matrix exponentiation; S104. Update the mastery of knowledge points based on students’ performance in tests and learning tasks.

[0006] Furthermore, the core knowledge points include basic information, pre-knowledge point information and post-knowledge point information; the basic information is basic attributes, including a unique identifier, a name, and the hierarchical position of the knowledge point in the knowledge system; the pre-knowledge point information is the knowledge point that needs to be mastered before learning the current knowledge point; the post-knowledge point information is other knowledge points that depend on the current knowledge point.

[0007] Furthermore, the path dependency matrix is ​​a A matrix where each element Indicates core knowledge points Is it a core knowledge point? Prerequisite knowledge points; when knowledge points For knowledge points When the previous knowledge points are ,on the contrary .

[0008] Furthermore, in step S103, the specific calculation process of the indirect dependency is represented by the transfer of the step dependency: ; Among them, the Represents the index of the power, Represents the path dependency matrix.

[0009] Furthermore, the step S2 specifically includes the following sub-steps: S201. Establish a DTN network model by taking the core knowledge points as task nodes, the dependencies between the knowledge points as edges, and the dependency strength calculated based on the mastery degree and the indirect dependency as weights; S202. DTN network model performs direct dependency analysis based on direct dependencies and indirect dependency analysis based on indirect dependencies; S203. Push learning tasks based on the analysis results and indirect dependencies.

[0010] Furthermore, in step S202, the specific calculation process of direct dependency analysis is to evaluate the impact of direct dependency on each knowledge point according to the path dependency matrix and calculate the learner's mastery of the directly dependent knowledge point, that is: ; Among them, the represents the weighted sum of the mastery of the knowledge points that the knowledge point directly depends on, represents the index of the target knowledge point, Represents the direct dependency relationship between the starting knowledge point and the target knowledge point. Indicates the mastery of the target knowledge point.

[0011] Furthermore, in step S202, the specific calculation process of the indirect dependency analysis is to calculate the mastery of each knowledge point through the indirect path dependency, and calculate the potential indirect knowledge points mastered by the students based on the indirect dependency relationship: ; Among them, the represents the mastery of potential knowledge points through k-step dependencies, Represents the index of the power, Represents the indirect dependency relationship from the starting knowledge point to the target knowledge point. Indicates the mastery of the target knowledge point.

[0012] Furthermore, the step S3 specifically includes the following sub-steps: S301. Calculate the learning path that best suits the student's current learning level based on the student's current knowledge mastery and path dependency matrix; S302. Recommend corresponding learning content and exercises according to the learning path; S303. Dynamically adjust the learning path based on the students’ learning progress.

[0013] Furthermore, in step S301, the specific calculation process of calculating the learning path that best suits the student's current learning level is to calculate the path from the knowledge point reached at the current learning level to the target knowledge point for each knowledge point, that is: ; Among them, the represents the learning path score from the starting knowledge point to the target knowledge point. Indicates the mastery of knowledge points achieved at the current learning level. Represents the dependency relationship from the starting knowledge point to the knowledge point reached at the current learning level. Represents the dependency relationship from the knowledge points reached at the current learning level to the target knowledge points.

[0014] Furthermore, step S4 is also included: After each learning cycle, the DTN network model is trained and updated through the students' historical learning data, and the path dependency matrix is ​​optimized according to the students' learning progress and feedback. Combined with the output of the DTN network model and the dynamically adjusted path dependency matrix, the students' learning status is re-evaluated, and the learning path and content recommendations are adjusted.

[0015] The beneficial effects of the invention are: The present invention realizes indirect dependency modeling between knowledge points by introducing the path dependency matrix and the deep temporal network (DTN) model, which greatly improves the intelligence and personalization level of the adaptive learning system. By closely combining the path dependency relationship with the student's learning dynamics, the system can not only more comprehensively evaluate the student's knowledge mastery, but also accurately predict the student's possible learning difficulties in the future. This method can ensure that the learning content recommendation is more in line with the student's actual learning needs, thereby improving learning efficiency and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A method flow chart of a test and evaluation method for an English learning process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0018] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention. It should be noted that relational terms such as the terms "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0020] Moreover, the terms "comprises," "comprising," or any other variation thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or machine that includes a list of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such process, method, article, or machine. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or machine that includes the element.

[0021] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0022] Among them, Figure 1 , a test evaluation method for an English learning process, comprising the following steps: S1. Construct a hierarchical knowledge graph based on knowledge point dependencies, which organizes the core knowledge points of language learning hierarchically and dynamically updates the mastery of each knowledge point; when students complete a task or test, the mastery of the knowledge points in the knowledge graph is updated in real time; S2. Input the latest updated knowledge graph into the DTN network model, which combines direct dependencies with indirect dependencies to identify potential knowledge points that the students have not yet mastered, and automatically determines the tasks that the students currently need to learn; S3. Based on the intelligent diagnosis results, by calculating the students’ knowledge mastery and path dependency matrix, we recommend the most suitable learning content and exercises for the students’ current level and optimize the learning path; Wherein, the step S1 specifically includes the following sub-steps: S101. Initialize and construct a knowledge graph, and represent the knowledge graph as a directed graph consisting of a node set and an edge set, where the nodes represent core knowledge points and the edges represent dependency relationships between knowledge points; S102. Based on the initialized knowledge graph information, a path dependency matrix is ​​constructed to represent the direct dependency relationship between the core knowledge points; S103. Obtaining indirect dependency through matrix exponentiation; S104. Update the mastery of knowledge points based on students’ performance in tests and learning tasks.

[0023] Furthermore, the core knowledge points include basic information, pre-knowledge point information and post-knowledge point information; the basic information is basic attributes, including a unique identifier, a name, and the hierarchical position of the knowledge point in the knowledge system; the pre-knowledge point information is the knowledge point that needs to be mastered before learning the current knowledge point; the post-knowledge point information is other knowledge points that depend on the current knowledge point.

[0024] Specifically, in step S1 of the above embodiment, mastery refers to the student's mastery of a certain knowledge point, which is measured by test scores or completion of learning tasks. Mastery is a specific value (such as a score between 0 and 1) that indicates the student's mastery of a certain knowledge point. In step S2, the DTN (deep temporal network) model is used to predict the knowledge points that the student may master in the future learning process. The DTN network model analyzes the evolution of the learning state based on the student's historical learning data (such as previous mastery, task performance, learning progress, etc.) to predict which knowledge points the student will master next. The mastery in step S1 is a static real-time indicator that reflects the student's mastery of a specific knowledge point and is calculated and updated based on the student's actual performance (such as test results, task completion, etc.). The prediction of the DTN network model is a dynamic prediction model that predicts which knowledge points the student will master in the future learning process based on the student's historical learning data and path dependency, and determines the student's learning tasks based on the prediction results.

[0025] Furthermore, the path dependency matrix is ​​a A matrix where each element Indicates core knowledge points Is it a core knowledge point? Prerequisite knowledge points; when knowledge points For knowledge points When the previous knowledge points are ,on the contrary .

[0026] For example, in English learning, a certain "grammar" knowledge point depends on the mastery of "vocabulary", while "reading comprehension" depends on "grammar". In addition, the structure of the path dependency matrix is ​​as follows: , where A represents the path dependency matrix.

[0027] For example, there are the following knowledge points: vocabulary (v1), grammar (v2), and reading comprehension (v3). Among them, vocabulary is the prerequisite knowledge point for grammar and reading comprehension; grammar is the prerequisite knowledge point for reading comprehension. Then the path dependency matrix can be expressed as: ; This matrix represents: Vocabulary (v1) is a prerequisite for grammar (v2) and reading comprehension (v3); Grammar (v2) is a prerequisite for reading comprehension (v3); Reading Comprehension (v3) has no prerequisites.

[0028] Furthermore, in step S103, the specific calculation process of the indirect dependency is represented by the transfer of the step dependency: ; Among them, the Represents the index of the power, Represents the path dependency matrix. For example, based on the initial matrix (i.e., the path dependency matrix), the indirect dependency relationship can be obtained by matrix power operation. Elements Representing knowledge points arrive Therefore, it is necessary to calculate the second power of the path dependency matrix, that is: ; For the calculated When the elements in , then it means Indirect impact ; For example, when the path dependency matrix is ​​as above Matrix, then: ; in, This means that vocabulary (v1) indirectly affects reading comprehension (v3) through grammar (v2). Therefore, in the subsequent knowledge point learning, if the student has mastered "vocabulary", the system can assume that "reading comprehension" has also been indirectly mastered.

[0029] Furthermore, the step S2 specifically includes the following sub-steps: S201. Establish a DTN network model by taking the core knowledge points as task nodes, the dependencies between the knowledge points as edges, and the dependency strength calculated based on the mastery degree and the indirect dependency as weights; S202. DTN network model performs direct dependency analysis based on direct dependencies and indirect dependency analysis based on indirect dependencies; S203. Push learning tasks based on the analysis results and indirect dependencies.

[0030] Specifically, when constructing a deep temporal network (DTN), the core knowledge points are first used as nodes, and the dependencies between each knowledge point are connected by directed edges. The strength of the dependency is not only determined by the direct relationship between the knowledge points, but also affected by the students' mastery. For example, knowledge points with a higher mastery of the students have a stronger influence and can directly help the learning of subsequent knowledge points. In this way, the DTN network model can dynamically represent the dependencies between knowledge points and adjust the recommendation priority of tasks according to the students' mastery. Furthermore, after the students' learning data (such as test scores, learning task feedback, etc.) are input into the DTN network model, it is first analyzed based on the direct dependencies between knowledge points. The direct dependency determines whether a certain knowledge point needs to be mastered before learning the next knowledge point. For example, if the students have not mastered the basic grammar points, the following advanced sentence patterns will be difficult to understand. The system will also conduct a deeper analysis through indirect dependencies to find weak links that the students may overlook. Through these two analyses, the system can comprehensively identify the students' knowledge mastery status, including potential knowledge points that the students have not mastered but should master. Finally, after analyzing the student's learning status, the most suitable learning tasks are automatically recommended based on the student's mastery and dependencies. When recommending tasks, the knowledge points that the student has not mastered and the dependencies between these knowledge points and other knowledge points are considered. Priority is given to recommended learning content that is weak in the student, has close dependencies, and is moderately difficult to learn. In this way, task recommendations can be adjusted dynamically to ensure that students can learn in a targeted manner while avoiding repeating content that they have already mastered.

[0031] Furthermore, in step S202, the specific calculation process of direct dependency analysis is to evaluate the impact of direct dependency on each knowledge point according to the path dependency matrix and calculate the learner's mastery of the directly dependent knowledge point, that is: ; Among them, the represents the weighted sum of the mastery of the knowledge points that the knowledge point directly depends on, represents the index of the target knowledge point, Represents the direct dependency relationship between the starting knowledge point and the target knowledge point. Indicates the mastery of the target knowledge point.

[0032] Furthermore, in step S202, the specific calculation process of the indirect dependency analysis is to calculate the mastery of each knowledge point through the indirect path dependency, and calculate the potential indirect knowledge points mastered by the students based on the indirect dependency relationship: ; Among them, the represents the mastery of potential knowledge points through k-step dependencies, Represents the index of the power, Represents the indirect dependency relationship from the starting knowledge point to the target knowledge point. Indicates the mastery of the target knowledge point.

[0033] Furthermore, the step S3 specifically includes the following sub-steps: S301. Calculate the learning path that best suits the student's current learning level based on the student's current knowledge mastery and path dependency matrix; S302. Recommend corresponding learning content and exercises according to the learning path; S303. Dynamically adjust the learning path based on the students’ learning progress.

[0034] Specifically, first, according to the student's current knowledge mastery and dependency, the most suitable learning path for the student is calculated. The calculation of the learning path depends not only on the student's knowledge mastery, but also on the dependency between knowledge points. For example, if the student has not mastered certain basic knowledge points, the system will give priority to learning these basic knowledge points and skip irrelevant or too advanced content. In addition, the system will design a shortest and most efficient learning path for the student based on the priority and dependency between knowledge points to ensure that the student can gradually master the knowledge system. Further, after calculating the learning path, personalized learning content will be recommended based on the path. Personalized recommendation is not only to recommend students to learn new knowledge, but also to review the content that has been mastered, especially those knowledge points that are crucial for subsequent learning. According to the current knowledge status of the student and the priority of the learning path, the recommended learning content will ensure that the student will not waste time on the content that has been mastered, and will not miss the core knowledge points that are crucial for subsequent learning. Finally, each time the student completes a test or task, the student's knowledge mastery is updated according to the student's performance, and the learning path is re-evaluated and optimized. When students perform poorly on a certain knowledge point, the system will adjust the path and recommend more learning tasks related to that knowledge point; if students have already mastered a certain content, the system will skip this part of the content and push them to the next learning stage. Through the above dynamic optimization, it can continuously adapt to the students' learning progress and provide a personalized learning experience.

[0035] Furthermore, in step S301, the specific calculation process of calculating the learning path that best suits the student's current learning level is to calculate the path from the knowledge point reached at the current learning level to the target knowledge point for each knowledge point, that is: ; Among them, the represents the learning path score from the starting knowledge point to the target knowledge point. Indicates the mastery of knowledge points achieved at the current learning level. Represents the dependency relationship from the starting knowledge point to the knowledge point reached at the current learning level. Represents the dependency relationship from the knowledge points reached at the current learning level to the target knowledge points.

[0036] Furthermore, step S4 is also included: After each learning cycle, the DTN network model is trained and updated through the students' historical learning data, and the path dependency matrix is ​​optimized according to the students' learning progress and feedback. Combined with the output of the DTN network model and the dynamically adjusted path dependency matrix, the students' learning status is re-evaluated, and the learning path and content recommendations are adjusted.

[0037] Specifically, in the training and updating of the DTN network model through the historical learning data of the students, the DTN network model parameters are updated through the gradient descent optimization algorithm.

[0038] Furthermore, after each learning cycle, the learning data of the students are collected, including test scores, completion of learning tasks, learning time, etc. The above data helps the system update the parameters of the DTN network model so that it can more accurately reflect the learning trends of the students. Through historical learning data, it is possible to identify which knowledge points have a greater impact on the students' learning progress, and adjust the learning recommendation strategy accordingly, thereby improving the optimization accuracy of the learning path. Furthermore, after the DTN network model is updated, the path dependency matrix is ​​optimized according to the students' learning progress and feedback. The optimization of the path dependency matrix means re-evaluating the dependency relationship and dependency strength between knowledge points. For example, the learning of a certain knowledge point may become less important in some cases, or the dependency relationship between certain knowledge points may become closer. By optimizing the path dependency matrix, the system can more accurately adjust the learning path and recommended tasks, so that the learning path is more in line with the actual learning needs of the students. Combined with the updated DTN network model and the optimized path dependency matrix, the learning status of the students is re-evaluated. By analyzing the students' historical learning data and current learning progress, it is determined whether the students have mastered new knowledge points in the current learning cycle, whether there are new weak links, and adjust the learning path and recommended tasks based on this information. If the student excels in a certain area, the system can speed up the learning progress appropriately; if the student is still confused in some areas, the system will extend the learning time of that part and provide more targeted learning resources. Finally, after re-evaluating the learning status, adjust the student's learning path and task recommendations to ensure that each learning cycle can maximize the learning effect. Dynamically adjust the recommended learning content and tasks based on the student's mastery, dependencies, and learning status. The learning path will be optimized based on the new mastery, dependencies, and student feedback, and recommend learning tasks and content suitable for the student's current status, thereby ensuring that students can get the most appropriate learning guidance throughout the learning process.

[0039] Furthermore, as a preferred implementation method of the above embodiment, a test evaluation system for an English learning process is proposed, comprising an adaptive test subsystem, a learning diagnosis subsystem and an adaptive personalized learning subsystem, wherein: The adaptive testing subsystem is used for students to take tests and build a hierarchical knowledge graph based on knowledge point dependencies. The hierarchical knowledge graph organizes the core knowledge points of language learning hierarchically and dynamically updates the mastery of each knowledge point. When students complete a task or test, the mastery of the knowledge points in the knowledge graph is updated in real time. The learning diagnosis subsystem is used to diagnose the test results of students and input the latest updated knowledge graph into the DTN network model. The DTN network model predicts the knowledge points mastered by students and identifies the potential knowledge points that students have not mastered by combining indirect dependencies. The adaptive personalized learning subsystem is used to recommend the most suitable learning content and exercises for students’ current level and optimize the learning path by calculating students’ knowledge mastery and path dependency matrix based on intelligent diagnosis results; The adaptive test subsystem specifically includes: The knowledge graph module is used to initialize and construct the knowledge graph, which is represented as a directed graph consisting of a set of nodes and a set of edges. The nodes represent core knowledge points, and the edges represent the dependencies between knowledge points. The dependency calculation module constructs a path dependency matrix based on the initialized knowledge graph information to represent the direct dependency between core knowledge points; and obtains the indirect dependency through matrix power operations; The student testing module is used to provide tests and update the mastery of knowledge points based on the students' performance in tests and learning tasks.

[0040] Furthermore, the learning diagnosis subsystem specifically includes: The DTN network model module is used to establish a DTN network model by taking the core knowledge points as task nodes, the dependencies between the knowledge points as edges, and the dependency strength calculated based on the mastery degree and the indirect dependencies as weights; Dependency analysis module, which is used to analyze the mastery of the input knowledge points, and conducts direct dependency analysis based on direct dependency relationships and indirect dependency analysis based on indirect dependency relationships; The result diagnosis module is used to identify potential knowledge points that students have not yet mastered based on the analysis results.

[0041] Furthermore, the adaptive personalized learning subsystem specifically includes: The learning path calculation module is used to calculate the learning path that best suits the student's current learning level based on the student's current knowledge mastery and path dependency matrix; Adaptive recommendation module, used to recommend corresponding learning content and exercises based on the learning path; The adaptive path adjustment module dynamically adjusts the learning path according to the students' learning progress.

[0042] Specifically, the beneficial effects of the above implementation scheme are: Based on the existing transfer learning and path learning, by applying indirect dependencies to the learning diagnosis step (S2), future learning can be diagnosed and evaluated; The DTN dynamic task transfer network model is adopted, including a shared encoding layer and three task-specific output layers (testing, diagnosis, and learning). DTN dynamically switches task priorities according to the input knowledge graph status and student performance, which is more suitable for language learning task requirements.

[0043] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A test and evaluation method for an English learning process, characterized in that: The following steps are involved: S1. Construct a hierarchical knowledge graph based on knowledge point dependencies, which organizes the core knowledge points of language learning hierarchically and dynamically updates the mastery of each knowledge point; when students complete a task or test, the mastery of the knowledge points in the knowledge graph is updated in real time; S2. Input the latest updated knowledge graph into the DTN network model, which combines direct dependencies with indirect dependencies to identify potential knowledge points that the students have not yet mastered, and automatically determines the tasks that the students currently need to learn; S3. Based on the intelligent diagnosis results, by calculating the students’ knowledge mastery and path dependency matrix, we recommend the most suitable learning content and exercises for the students’ current level and optimize the learning path; Wherein, the step S1 specifically includes the following sub-steps: S101. Initialize and construct a knowledge graph, and represent the knowledge graph as a directed graph consisting of a node set and an edge set, where the nodes represent core knowledge points and the edges represent dependency relationships between knowledge points; S102. Based on the initialized knowledge graph information, a path dependency matrix is ​​constructed to represent the direct dependency relationship between the core knowledge points; S103. Obtaining indirect dependency through matrix exponentiation; S104. Update the mastery of knowledge points based on students’ performance in tests and learning tasks.

2. A test and evaluation method for an English learning process as claimed in claim 1, characterized in that: The core knowledge point includes basic information, pre-knowledge point information and post-knowledge point information; the basic information is basic attributes, including a unique identifier, a name, and the hierarchical position of the knowledge point in the knowledge system; The pre-condition knowledge point information refers to the knowledge points that need to be mastered before learning the current knowledge point; the post-condition knowledge point information refers to other knowledge points that depend on the current knowledge point.

3. A test and evaluation method for English learning process as claimed in claim 2, characterized in that: The path dependency matrix is ​​a A matrix where each element Indicates core knowledge points Is it a core knowledge point? Prerequisite knowledge points; when knowledge points For knowledge points When the previous knowledge points are ,on the contrary .

4. A test and evaluation method for English learning process as claimed in claim 1, characterized in that: In step S103, the specific calculation process of the indirect dependency is represented by the transfer of the step dependency: ； Among them, the Represents the index of the power, represents the path dependency matrix, Indicates the number of times.

5. The test and evaluation method for English learning process as claimed in claim 1, characterized in that: The step S2 specifically includes the following sub-steps: S201. Based on the latest updated knowledge graph, the core knowledge points are used as task nodes, the dependencies between the knowledge points are used as edges, and the dependency strength calculated based on the mastered knowledge points and indirect dependencies is used as weight to establish a DTN network model; S202. DTN network model performs direct dependency analysis based on direct dependencies and indirect dependency analysis based on indirect dependencies; S203. Push learning tasks based on the analysis results and indirect dependencies.

6. A test and evaluation method for English learning process as claimed in claim 5, characterized in that: In step S202, the specific calculation process of direct dependency analysis is to evaluate the impact of direct dependency on each knowledge point according to the path dependency matrix and calculate the learner's mastery of the directly dependent knowledge point, that is: ； Among them, the represents the weighted sum of the mastery of the knowledge points that the knowledge point directly depends on, represents the index of the target knowledge point, Represents the direct dependency relationship between the starting knowledge point and the target knowledge point. Indicates the mastery of the target knowledge point.

7. A test and evaluation method for English learning process as claimed in claim 5, characterized in that: In step S202, the specific calculation process of the indirect dependency analysis is to calculate the mastery of each knowledge point through the indirect path dependency, and calculate the mastery of the student's potential knowledge points based on the indirect dependency relationship: ； Among them, the represents the mastery of potential knowledge points through k-step dependencies, Represents the index of the power, Represents the indirect dependency relationship from the starting knowledge point to the target knowledge point. Indicates the mastery of the target knowledge point.

8. The test and evaluation method for English learning process as claimed in claim 1, characterized in that: The step S3 specifically includes the following sub-steps: S301. Calculate the learning path that best suits the student's current learning level based on the student's current knowledge mastery and path dependency matrix; S302. Recommend corresponding learning content and exercises according to the learning path; S303. Dynamically adjust the learning path based on the students’ learning progress.

9. A test and evaluation method for English learning process as claimed in claim 8, characterized in that: In step S301, the specific calculation process of calculating the learning path that best suits the student's current learning level is to calculate the path from the knowledge point reached at the current learning level to the target knowledge point for each knowledge point, that is: ； Among them, the represents the learning path score from the starting knowledge point to the target knowledge point. Indicates the mastery of knowledge points achieved at the current learning level. Represents the dependency relationship from the starting knowledge point to the knowledge point reached at the current learning level. Represents the dependency relationship from the knowledge point reached at the current learning level to the target knowledge point. The index representing the power.

10. The method for testing and evaluating the English learning process as claimed in claim 1, characterized in that: It also includes step S4: After each learning cycle, the DTN network model is trained and updated through the students' historical learning data, and the path dependency matrix is ​​optimized according to the students' learning progress and feedback. Combined with the output of the DTN network model and the dynamically adjusted path dependency matrix, the students' learning status is re-evaluated, and the learning path and content recommendations are adjusted.

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