Teaching material generation method and device, storage medium and electronic equipment

Abstract

The invention provides a textbook generation method and device, a storage medium and electronic equipment. The method comprises the following steps: extracting a first important knowledge point cluster from a textbook, extracting a second important knowledge point cluster from a historical question bank, and extracting a third important knowledge point cluster from a feature data set; performing similarity evaluation on the second important knowledge point cluster and the third important knowledge point cluster by using a first similarity algorithm, and evaluating first candidate knowledge point clusters with similar importance degrees in a knowledge point range corresponding to the first important knowledge point cluster based on a similarity evaluation result; evaluating second candidate knowledge point clusters with similar importance degrees in the knowledge point range by using a second similarity algorithm, and selecting same first target knowledge points; and updating the first important knowledge point cluster based on the first target knowledge point to obtain an updated first target important knowledge point cluster, and generating an electronic textbook according to the first target important knowledge point cluster.

Description

Technical Field

[0001] The present disclosure relates to the field of smart education, and in particular to a teaching material generation method, device, storage medium and electronic device. Background Art

[0002] In recent years, with the continuous advancement of computer technology and educational informatization, computer and artificial intelligence technology have been gradually applied to daily educational and teaching activities. The optimization of knowledge point clusters can effectively help teachers improve the hit rate of important knowledge points, thereby improving teachers' teaching efficiency and students' learning effects.

[0003] The textbook generation method in the current related technology is based on textbook knowledge points, and the knowledge points are scored by calculating the knowledge point scores through the knowledge tree structure. However, in fact, when using the knowledge tree structure to calculate the knowledge point scores to determine the important knowledge points, there are cases where knowledge points are missed or the importance of knowledge points is not high, which leads to low accuracy of the knowledge points recommended to users. For example, in the teacher's lesson preparation scenario, the low accuracy of the knowledge points recommended to teachers will lead to low efficiency in teacher preparation. Summary of the invention

[0004] The present disclosure provides a teaching material generation method, device, storage medium and electronic device.

[0005] According to a first aspect of the present disclosure, a teaching material generation method is provided, the method comprising: Based on the smart education terminal, the first important knowledge point cluster is extracted from the textbook, the second important knowledge point cluster is extracted from the history question bank, and the third important knowledge point cluster is extracted from the feature data set containing the factors affecting students' wrong answers; Using a first similarity algorithm, similarity evaluation is performed on the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster, and based on the similarity evaluation result, a first candidate knowledge point cluster with similar importance is evaluated within the knowledge point range corresponding to the first important knowledge point cluster; Using a second similarity algorithm, evaluating a second candidate knowledge point cluster with similar importance within the knowledge point range, and selecting the same first target knowledge point from the first candidate knowledge point cluster and the second candidate knowledge point cluster; The first important knowledge point cluster is updated based on the first target knowledge point to obtain an updated first target important knowledge point cluster, and an electronic teaching material is generated based on the first target important knowledge point cluster.

[0006] In some embodiments of the present disclosure, extracting a first important knowledge point cluster from a teaching material based on a smart education terminal includes: Conduct text analysis on textbooks based on smart education terminals to obtain knowledge points in the textbooks; The knowledge points are graded according to the associations between the knowledge points to obtain graded labels corresponding to the knowledge points; Encoding the hierarchical identification corresponding to the knowledge point to obtain the identification coding value of the knowledge point at each level; Based on the hierarchical identification corresponding to the knowledge point and the identification code value of the knowledge point at each level, determine the importance identification vector set corresponding to the knowledge point, and generate a basic knowledge point cluster according to the identification vector set; A first important knowledge point cluster is determined based on the importance of knowledge points in the basic knowledge point cluster.

[0007] In some embodiments of the present disclosure, determining a first important knowledge point cluster based on the importance of knowledge points in a basic knowledge point cluster includes: According to the degree of refinement of the knowledge points in the basic knowledge point cluster, determine the difficulty level corresponding to the knowledge points in the basic knowledge point cluster; Using the pre-trained evaluation model, determine the importance of the knowledge points in the basic knowledge point cluster; The basic knowledge point cluster is screened based on the importance of the knowledge points and a first preset screening threshold, and the screened knowledge point set is determined as a first important knowledge point cluster. The first preset screening threshold is an initial value set for the importance of the knowledge points.

[0008] In some embodiments of the present disclosure, extracting the second important knowledge point cluster from the historical question bank includes: Based on all the knowledge points involved in the history question bank and the pre-trained important knowledge point evaluation model, determine the difficulty level score of each knowledge point in all the knowledge points involved in the history question bank; Based on the difficulty level score of each knowledge point in all the knowledge points involved in the history question bank and the second preset screening threshold, all the knowledge points involved in the history question bank are screened, and the screened knowledge point set is determined as the second important knowledge point cluster, and the second preset screening threshold is the initial value set for the difficulty level corresponding to the knowledge point.

[0009] In some embodiments of the present disclosure, a third important knowledge point cluster is extracted from a feature data set containing factors affecting students' wrong answers, including: Based on a feature data set containing factors influencing students' wrong answers, a preset wrong answer correlation evaluation model is used to determine the correlation between each wrong answer influencing factor and the wrong answer in the feature data set containing factors influencing students' wrong answers, and the wrong answer influencing factors include subjective factors and objective factors; From the feature data set containing the factors influencing students' mistakes, select the features of objective factors that lead to students' mistakes, and sort the features of objective factors that lead to students' mistakes from large to small according to the relevance; The set of knowledge points corresponding to the features with the greatest correlation is determined as the third important knowledge point cluster.

[0010] In some embodiments of the present disclosure, after updating the first important knowledge point cluster based on the first target knowledge point to obtain the updated first target important knowledge point cluster, the method further includes: Using the first similarity algorithm, the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster are evaluated for similarity, and based on the similarity evaluation result, a third candidate knowledge point cluster with similar importance is evaluated outside the knowledge point range corresponding to the first important knowledge point cluster; A fourth candidate knowledge point cluster with similar importance is evaluated outside the knowledge point range using a second similarity algorithm, and the same second target knowledge point is selected from the third candidate knowledge point cluster and the fourth candidate knowledge point cluster.

[0011] If the importance identification vector corresponding to the second target knowledge point is the same as the importance identification vector corresponding to the knowledge point in the basic knowledge point cluster, the first target important knowledge point cluster is updated using the importance identification vector corresponding to the second target knowledge point to obtain an updated second target important knowledge point cluster; Generate electronic teaching materials based on the first target important knowledge point cluster, including: An electronic textbook is generated based on the second target important knowledge point cluster to obtain an updated electronic textbook.

[0012] In some embodiments of the present disclosure, after generating the electronic teaching material according to the second target important knowledge point cluster, the method includes: The knowledge points in the updated electronic teaching material are marked with emphasis using the knowledge points in the second important knowledge point cluster and / or the knowledge points in the third important knowledge point cluster.

[0013] According to a second aspect of the present disclosure, a teaching material generating device is provided, the device comprising: An extraction unit, used to extract a first important knowledge point cluster from the textbook, a second important knowledge point cluster from the history question bank, and a third important knowledge point cluster from a feature data set containing factors affecting students' wrong answers based on the smart education terminal; An evaluation unit, configured to perform similarity evaluation on the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster using a first similarity algorithm, and to evaluate a first candidate knowledge point cluster with similar importance within the knowledge point range corresponding to the first important knowledge point cluster based on the similarity evaluation result; A screening unit, configured to evaluate a second candidate knowledge point cluster with similar importance within the knowledge point range by using a second similarity algorithm, and select the same first target knowledge point from the first candidate knowledge point cluster and the second candidate knowledge point cluster; The generating unit is used to update the first important knowledge point cluster based on the first target knowledge point to obtain the updated first target important knowledge point cluster, and generate the electronic teaching material according to the first target important knowledge point cluster.

[0014] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in the first aspect is implemented.

[0015] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0016] The teaching material generation method, device, storage medium and electronic device provided by the present disclosure extract a first important knowledge point cluster from the teaching material based on a smart education terminal, extract a second important knowledge point cluster from a history question bank, and extract a third important knowledge point cluster from a feature data set containing factors affecting students' wrong answers; use a first similarity algorithm to perform a similarity evaluation on an importance identification vector corresponding to the second important knowledge point cluster and an importance identification vector corresponding to the third important knowledge point cluster, and based on the similarity evaluation result, evaluate a first candidate knowledge point cluster with similar importance within the knowledge point range corresponding to the first important knowledge point cluster; use a second similarity algorithm to evaluate a second candidate knowledge point cluster with similar importance within the knowledge point range, and select a first candidate knowledge point cluster from the first candidate knowledge point cluster. and selecting the same first target knowledge point from the second candidate knowledge point cluster; updating the first important knowledge point cluster based on the first target knowledge point to obtain the updated first target important knowledge point cluster, and generating an electronic teaching material based on the first target important knowledge point cluster. This scheme updates the first important knowledge point cluster based on the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster to obtain the first target knowledge point cluster, thereby solving the problems of missing knowledge points and low importance in the first important knowledge point cluster and improving the accuracy of the first important knowledge point cluster. Finally, by generating an electronic teaching material based on the first target important knowledge point cluster, teachers are provided with knowledge points with higher accuracy, thereby improving the efficiency of teachers' lesson preparation.

[0017] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. Figure 1 A flowchart of a teaching material generation method provided by an embodiment of the present disclosure; Figure 2 A schematic diagram of a flow chart of a method for determining a first important knowledge point cluster provided by an embodiment of the present disclosure; Figure 3 A schematic diagram of a knowledge point network provided by an embodiment of the present disclosure; Figure 4 A schematic diagram of the screening process provided by the embodiment of the present disclosure; Figure 5 A schematic diagram of a flow chart of a method for determining a first important knowledge point cluster provided by an embodiment of the present disclosure; Figure 6A schematic diagram of a flow chart of a method for determining a second important knowledge point cluster provided by an embodiment of the present disclosure; Figure 7 A schematic diagram of a flow chart of a method for determining a third important knowledge point cluster provided by an embodiment of the present disclosure; Figure 8 A flowchart of a method for obtaining an updated electronic teaching material provided by an embodiment of the present disclosure; Fig. 9 A schematic diagram of a flow chart of a method for obtaining a first important knowledge point cluster update provided by an embodiment of the present disclosure; Fig.10 A schematic diagram of the structure of a teaching material generating device provided in an embodiment of the present disclosure; Fig.11 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0020] In recent years, with the continuous advancement of computer technology and educational informatization, computer and artificial intelligence technology have been gradually applied to daily educational and teaching activities. The optimization of knowledge point clusters can effectively help teachers improve the hit rate of important knowledge points, thereby improving teachers' teaching efficiency and students' learning effects.

[0021] The textbook generation method in the current related technology is based on textbook knowledge points, and the knowledge points are scored by calculating the knowledge point scores through the knowledge tree structure. However, in fact, when using the knowledge tree structure to calculate the knowledge point scores to determine the important knowledge points, there are cases where knowledge points are missed or the importance of knowledge points is not high, which leads to low accuracy of the knowledge points recommended to users. For example, in the teacher's lesson preparation scenario, the low accuracy of the knowledge points recommended to teachers will lead to low efficiency in teacher preparation.

[0022] In order to solve the problems in the related art, the teaching material generation method provided by the present disclosure extracts a first important knowledge point cluster from the teaching material based on a smart education terminal, extracts a second important knowledge point cluster from a history question bank, and extracts a third important knowledge point cluster from a feature data set containing factors affecting students' wrong answers; uses a first similarity algorithm to perform a similarity evaluation on the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster, and based on the similarity evaluation result, evaluates a first candidate knowledge point cluster with similar importance within the knowledge point range corresponding to the first important knowledge point cluster; uses a second similarity algorithm to evaluate a second candidate knowledge point cluster with similar importance within the knowledge point range, and selects a first candidate knowledge point cluster from the first candidate knowledge point cluster. and selecting the same first target knowledge point from the second candidate knowledge point cluster; updating the first important knowledge point cluster based on the first target knowledge point to obtain the updated first target important knowledge point cluster, and generating an electronic teaching material based on the first target important knowledge point cluster. This scheme updates the first important knowledge point cluster based on the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster to obtain the first target knowledge point cluster, thereby solving the problems of missing knowledge points and low importance in the first important knowledge point cluster and improving the accuracy of the first important knowledge point cluster. Finally, by generating an electronic teaching material based on the first target important knowledge point cluster, teachers are provided with knowledge points with higher accuracy, thereby improving the efficiency of teachers' lesson preparation.

[0023] The teaching material generating method, device, electronic device and storage medium of the embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0024] Figure 1 The following is a flow chart of a teaching material generation method provided by an embodiment of the present disclosure. Figure 1 As shown, the method includes: Step 101: extracting a first important knowledge point cluster from the textbook, a second important knowledge point cluster from the history question bank, and a third important knowledge point cluster from a feature data set containing factors affecting students' wrong answers based on the smart education terminal; In some embodiments, the smart education terminal can be implemented based on hardware devices (such as tablets, smart whiteboards), or it can be based on software applications and installed on ordinary computers or mobile devices. Usually these terminals can be connected to cloud services to access rich educational resources and perform data synchronization and other functions.

[0025] In some embodiments, the content in the textbook can be uploaded to the first knowledge point analysis module of the smart education terminal, and text analysis can be performed on the content in the textbook to obtain the knowledge points in the textbook, and then the first important knowledge point cluster can be determined according to the importance of the knowledge points.

[0026] In one embodiment, a second important knowledge point cluster can be extracted from the historical question bank according to the importance of the knowledge points, wherein the historical question bank refers to a collection of test questions accumulated in the past by the online education platform, including exercises and test questions for various subjects, grades, and knowledge points, etc., recording information such as the test knowledge points, difficulty, and student answers. The historical question bank can be a question bank that is updated in real time.

[0027] In one embodiment, the data acquisition scope in the history question bank can be based on classes, so that teachers can prepare lessons according to the learning levels of students in the class.

[0028] In some embodiments, a feature data set including factors affecting students' incorrect answers can be obtained from log data of a learning management system.

[0029] In some embodiments, since there are many factors that affect students' incorrect answers, including reading the questions incorrectly, writing the questions incorrectly, and really not knowing how to do them, it is impossible to effectively obtain students' mastery of knowledge points. Therefore, it is necessary to filter out knowledge points that students really cannot do from the feature data set that contains the factors that affect students' incorrect answers, so as to obtain the third important knowledge point cluster, thereby facilitating teachers to understand students' mastery of knowledge points.

[0030] Step 102: using a first similarity algorithm to perform similarity evaluation on the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster, and based on the similarity evaluation result, evaluating a first candidate knowledge point cluster with similar importance within the knowledge point range corresponding to the first important knowledge point cluster; In some embodiments, the first similarity algorithm may be one of a Jaccard similarity algorithm, a cosine similarity algorithm, a Euclidean distance algorithm, a Manhattan distance algorithm, and the like.

[0031] In some embodiments, the importance identification vector corresponds one-to-one to the knowledge point, where the importance can include level one, level two, level three, etc. The identification vector is determined by the classification and encoding of the knowledge point. This application does not limit the number of importance levels.

[0032] In some embodiments, the first similarity algorithm takes the Jaccard similarity algorithm as an example, and the mathematical expression of using the Jaccard similarity algorithm to evaluate the similarity between the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster is as follows: Formula 1 In formula 1, G represents the second most important knowledge point cluster, H represents the third most important knowledge point cluster, represents the number of knowledge points in the intersection between the second most important knowledge point cluster and the third most important knowledge point cluster, represents the number of knowledge points in the union of the second most important knowledge point cluster and the third most important knowledge point cluster, Represents the similarity between the second most important knowledge point cluster and the third most important knowledge point cluster.

[0033] In some embodiments, based on the above similarity evaluation results, a first candidate knowledge point cluster with similar importance is evaluated within the scope of knowledge points corresponding to the first important knowledge point cluster. Specifically, the first important knowledge point cluster is obtained by preliminary screening of the basic knowledge point cluster, and contains a large number of important knowledge points, which may include most of the knowledge points in the second important knowledge point cluster and the third important knowledge point cluster. Therefore, within the large scope of the first important knowledge point cluster, knowledge points with similar importance in the second important knowledge point cluster and the third important knowledge point cluster, i.e., the knowledge points in the first candidate knowledge point cluster, may be determined, wherein knowledge points with similar importance may include primary knowledge points, secondary knowledge points, and tertiary knowledge points.

[0034] Step 103: using a second similarity algorithm to evaluate a second candidate knowledge point cluster with similar importance within the knowledge point range, and selecting the same first target knowledge point from the first candidate knowledge point cluster and the second candidate knowledge point cluster; In some embodiments, the second similarity algorithm may also be one of a plurality of algorithms such as a Jaccard similarity algorithm, an improved cosine similarity algorithm, an Euclidean distance algorithm, and a Manhattan distance algorithm. However, in order to make up for the defects of a single similarity algorithm, preferably, a similarity algorithm different from that in step 102 is used here. Specifically, as in the aforementioned step 102, the Jaccard similarity algorithm is used. The Jaccard similarity algorithm is used to calculate the similarity between individuals of symbolic metrics. The disadvantage is that the size of the specific value of the difference cannot be measured, and only the same or different conclusions can be drawn. Since the knowledge points in the present application include multiple levels, the step-by-step matching principle must be followed when determining similar knowledge points. If only the Jaccard similarity is used, for example, q (1, 0, 1) will exist, and the output result is also similar, but the second level is 0, which does not meet the consistency standard. Therefore, the cosine similarity algorithm can be selected in this step for further verification. The improved cosine similarity algorithm has been improved to a certain extent in measuring the difference in the values ​​of each dimension, but the accuracy still has certain shortcomings. The two methods can complement each other.

[0035] In some embodiments, an improved cosine similarity algorithm is used to evaluate the second candidate knowledge point clusters with similar importance within the knowledge point range, wherein the mathematical expression of the improved cosine similarity algorithm is as follows: Formula 2 In formula 2, represents the similarity between the second important knowledge point cluster G and the third important knowledge point cluster H, n is the nth knowledge point, N is the total number of knowledge points after the second important knowledge point cluster G and the third important knowledge point cluster H are aligned, is the nth knowledge point vector in the second most important knowledge point cluster G, is the mean of the nth knowledge point vector after the second most important knowledge point cluster G and the third most important knowledge point cluster H are aligned, is the nth knowledge point vector in the third important knowledge point cluster H.

[0036] In some embodiments, the same first target knowledge point is selected from the first candidate knowledge point cluster and the second candidate knowledge point cluster. Specifically, the Jaccard similarity algorithm and the cosine similarity algorithm are used to respectively obtain the similarity of the second important knowledge point cluster and the third important knowledge point cluster. If the judgment results of the two similarity algorithms are consistent, the knowledge point with the consistent similarity judgment results is determined as the first target knowledge point.

[0037] Step 104: Update the first important knowledge point cluster based on the first target knowledge point to obtain an updated first target important knowledge point cluster, and generate an electronic teaching material based on the first target important knowledge point cluster.

[0038] In some embodiments, knowledge points in the first important knowledge point cluster may be missing, inaccurate, or not very important. By updating the first important knowledge point cluster based on the first target knowledge point to obtain an updated first target important knowledge point cluster, the problems of missing knowledge points and low importance in the first important knowledge point cluster are solved, and the accuracy of the first important knowledge point cluster is improved.

[0039] In some embodiments, by generating electronic teaching materials based on the first target important knowledge point cluster, teachers can be provided with knowledge points with higher accuracy, thereby improving the efficiency of teachers' lesson preparation.

[0040] As a possible implementation, Figure 2 The flowchart of a method for determining the first important knowledge point cluster shown in the figure, based on the above embodiment, the specific process of extracting the first important knowledge point cluster from the teaching material based on the smart education terminal includes the following steps: Step 201: Perform text analysis on the textbook based on the smart education terminal to obtain knowledge points in the textbook; In some embodiments, the content in the textbook can be uploaded to the first knowledge point analysis module, and the knowledge points corresponding to the content in the textbook can be extracted using text analysis. Specifically, the knowledge points corresponding to the content in the textbook can be extracted using keywords. Before performing text analysis, the content in the textbook can also be preprocessed, such as word segmentation, removal of stop words, etc.

[0041] Step 202: classify the knowledge points according to the association relationship between the knowledge points to obtain the classification identification corresponding to the knowledge points; In some embodiments, hierarchical identification refers to assigning specific marks to knowledge points at each level in a multi-level classification structure for easy distinction and management. Through the hierarchical identification of knowledge points, the hierarchical relationship of knowledge points can be clearly displayed, which is helpful for the construction of a knowledge point network. The knowledge points are used as nodes in the knowledge network, and the knowledge points classified by chapter in the textbook are used as first-level classification nodes, corresponding to the main nodes in the knowledge point network; the knowledge points classified by section in the textbook are used as second-level classification nodes, corresponding to the slave nodes of the first-level classification nodes in the knowledge point network, used to represent each subsection in each chapter; the knowledge points classified by unit results can be used as third-level classification nodes, and the corresponding knowledge points are slave nodes of the second-level classification nodes in the network, which are used to further subdivide the specific knowledge points under each unit. The association relationship between knowledge points refers to the above-mentioned chapters, sections and units.

[0042] In some embodiments, the grading identifiers corresponding to the knowledge points may be English letters, such as A for a first-level classification node, B for a second-level classification node, and C for a third-level classification node.

[0043] Step 203: Encode the hierarchical identification corresponding to the knowledge point to obtain the identification code value of the knowledge point at each level; In some embodiments, each knowledge point corresponds to a unique identification code value, and the identification code value can be a combination of one or more of a variety of identification forms such as numbers, letters, special symbols, etc.

[0044] In some embodiments, when English letters are used as hierarchical identifiers, different knowledge points on the same hierarchical node can be encoded using numbers. For example, if there are three first-level classification nodes, they can be encoded as A1, A2, and A3 respectively. If there are two second-level classification nodes, they can be encoded as B1 and B2. At the same time, the first-level classification nodes associated with B1 and B2 also need to be encoded.

[0045] Step 204: determine a set of importance identification vectors corresponding to the knowledge points based on the classification identifications corresponding to the knowledge points and the identification code values ​​of the knowledge points at each classification, and generate a basic knowledge point cluster based on the identification vector set; In some embodiments, a first-level classification node appears in the form of an identification vector of a single unit, and a multi-level classification node appears in the form of a combination of identification vectors of multiple units.

[0046] In some embodiments, the knowledge points in the teaching material can be divided into n levels, such as Figure 3 As shown, Figure 3 Schematic diagram of the knowledge point network provided in the embodiment of the present disclosure. When n is 3, the corresponding knowledge point cluster U=[An, Bm, Ck]. Specifically, the first-level classification An (A in the figure) in the knowledge point network is used as the main node, and is classified by chapter to obtain An=[A1, A2...An]; the second-level classification Bm is used as the slave node of An, and is classified by section to obtain Bm=[A1b1, A1b2, A2b1...Anbm]; the third-level classification Ck is used as the slave node of Bm, and is classified by unit results to obtain Ck=[A1b1c1, A1b2c2, A1b2c3...Anbmck].

[0047] In some embodiments, a corresponding identification vector is generated according to the identification code, and a knowledge point network is constructed based on the identification vector. The knowledge point network is used to represent the relationship between knowledge points, and then multiple knowledge point networks are combined into a comprehensive identification vector set, namely the basic knowledge point cluster Uori.

[0048] Step 205: Determine a first important knowledge point cluster based on the importance of the knowledge points in the basic knowledge point cluster.

[0049] In some embodiments, a pre-trained evaluation model may be used to evaluate the importance of each knowledge point in the basic knowledge point cluster, and then the first important knowledge point cluster may be determined based on the importance.

[0050] In some embodiments, Figure 4 As shown, Figure 4 This is a schematic diagram of the screening process provided in the embodiment of the present disclosure, specifically screening out knowledge points with higher importance from the knowledge points in the basic knowledge point cluster Uori to obtain the first important knowledge point cluster Unew.

[0051] As a possible implementation, Figure 5 The flowchart of a method for determining a first important knowledge point cluster is shown. Based on the above embodiment, the specific process of determining the first important knowledge point cluster based on the importance of knowledge points in the basic knowledge point cluster includes the following steps: Step 501: Determine the difficulty level corresponding to the knowledge points in the basic knowledge point cluster according to the refinement degree of the knowledge points in the basic knowledge point cluster; In some embodiments, the degree of refinement of a knowledge point refers to the level of the knowledge point corresponding to the knowledge point in the knowledge point network. The higher the level corresponding to the knowledge point, the higher the degree of refinement of the knowledge point. For example, if knowledge points A1B1 and A1B1C1 are the secondary classification nodes and tertiary classification nodes in the aforementioned knowledge point network respectively, then the degree of refinement of knowledge point A1B1 is lower than that of knowledge point A1B1C1.

[0052] In some embodiments, the knowledge points in the basic knowledge point cluster are classified in detail, and their difficulty levels are determined according to the degree of refinement of the knowledge points. For example, the degree of refinement corresponding to the knowledge points can be determined according to the chapters, sections, and units to which the knowledge points belong in the textbook text; or the degree of refinement corresponding to the knowledge points can be determined according to the form of the identification vectors corresponding to the knowledge points (in the form of a single unit or a combination of multiple units).

[0053] In some embodiments, the difficulty level of a knowledge point is determined based on the degree of refinement of the knowledge point. The higher the degree of refinement of the knowledge point, the higher the difficulty level corresponding to the knowledge point, which can provide a basis for the subsequent generation of questions of different difficulty levels.

[0054] Step 502: using a pre-trained evaluation model, determine the importance of the knowledge points in the basic knowledge point cluster; In some embodiments, the importance of the knowledge points in the basic knowledge point cluster can be calculated based on a pre-trained evaluation model, and its mathematical expression is as follows: Formula 3 In Formula 3, Z, X, V, and W are the number of identification vectors corresponding to the first-level, second-level, third-level, and n-level knowledge points in the basic knowledge point cluster, respectively. , , , are the weight coefficients corresponding to the importance of the first-level, second-level, third-level and n-level identification vectors, , , , are the weights corresponding to the importance of 1, 2, 3 and n level identification vectors, and , because as the knowledge points become more detailed, their importance accounts for a larger proportion.

[0055] In some embodiments, the size of the weight coefficient corresponding to the importance of the identification vector can reflect the importance of each graded knowledge point, and the size of the weight coefficient can be adjusted according to actual conditions.

[0056] Step 503: Filter the basic knowledge point clusters based on the importance of the knowledge points and a first preset filtering threshold, and determine the filtered knowledge point set as a first important knowledge point cluster. The first preset filtering threshold is an initial value set for the importance of the knowledge points.

[0057] In some embodiments, the selection of the first preset screening threshold is related to the importance of the knowledge points in the first important knowledge point cluster. If the first preset screening threshold is too small, it will result in a large number of knowledge points in the first important knowledge point cluster, and the importance is not high. If the first preset threshold is too large, it will result in too few knowledge points in the first important knowledge point cluster. The knowledge points in the first important knowledge point cluster are highly important, but some relatively important knowledge points may be excluded. Furthermore, for example, in the scenario of teacher preparation, if the first preset threshold is too large, the scope of teacher preparation will become smaller, which is insufficient to cover most of the important knowledge points. For example, in the review scenario, if the first preset threshold is too large, some important test points will not be covered.

[0058] In some embodiments, the first preset screening threshold may be set as an initial threshold by using methods such as customization, averaging, and median, and this embodiment does not limit this.

[0059] In some embodiments, a first preset screening threshold T1 may be determined, and when G>T1, the screened knowledge point identification vectors are used as important knowledge point clusters.

[0060] In some embodiments, more important knowledge points can be obtained by screening the basic knowledge point cluster based on the importance of the knowledge points and a first preset screening threshold, and determining the screened knowledge point set as the first important knowledge point cluster.

[0061] As a possible implementation, Figure 6 The flowchart of a method for determining the second important knowledge point cluster shown in the figure, based on the above embodiment, the specific process of extracting the second important knowledge point cluster from the history question bank includes the following steps: Step 601: Determine the difficulty level score of each of the knowledge points in the history question bank based on all the knowledge points in the history question bank and the pre-trained important knowledge point evaluation model; In some embodiments, based on the historical question bank, a basic knowledge point cluster Zori=[Wn, Rm, Hk] corresponding to the question bank can be generated according to the identification vectors (Wn, Rm, Hk) corresponding to the knowledge points in the question bank.

[0062] In some embodiments, the difficulty level score of each knowledge point in all knowledge points involved in the history question bank can be calculated by a pre-trained important knowledge point evaluation model, and its mathematical expression is as follows: Formula 4 In formula 4, Gj is the number of times the test point knowledge point appears, Gq is the number of times the error frequency of the test point knowledge point appears, Gr is the number of branches of the test point knowledge point, is the weight score corresponding to each indicator, Rate the difficulty level of the knowledge point.

[0063] Step 602: Screen all the knowledge points involved in the history question bank based on the difficulty level score of each knowledge point in the history question bank and the second preset screening threshold, and determine the screened knowledge point set as the second important knowledge point cluster, where the second preset screening threshold is the initial value set for the difficulty level corresponding to the knowledge point.

[0064] In some embodiments, the second preset screening threshold can be obtained by the same method as the first preset screening threshold, or by a method different from the first preset screening threshold, that is, one of the methods such as custom, averaging, median, etc., or by a plurality of methods such as custom, averaging, median, etc., and then averaging multiple values ​​obtained based on multiple methods. This embodiment does not limit this.

[0065] In some embodiments, a second preset screening threshold T2 may be determined, and when yG>T2 in the aforementioned step 601, important knowledge points are screened out to obtain a second important knowledge point cluster.

[0066] In some embodiments, the selection of the second preset screening threshold is related to the importance of the knowledge points in the second important knowledge point cluster. If the second preset screening threshold is too small, it will result in a large number of knowledge points in the second important knowledge point cluster, and the importance is not high. If the second preset threshold is too large, it will result in too few knowledge points in the second important knowledge point cluster, and the importance of the knowledge points in the second important knowledge point cluster is high, but some relatively important knowledge points may be excluded. Furthermore, for example, in the scenario of teacher preparation, if the second preset threshold is too large, the scope of teacher preparation will become smaller, which is insufficient to cover most of the important knowledge points. For example, in the review scenario, if the second preset threshold is too large, some important test points will not be covered.

[0067] As a possible implementation, Figure 7 The flowchart of a method for determining the third important knowledge point cluster is shown in FIG. Based on the above embodiment, the specific process of extracting the third important knowledge point cluster from the feature data set containing the factors affecting students' wrong answers includes the following steps: Step 701: Based on the feature data set containing the influencing factors of students' wrong answers, a preset wrong answer correlation evaluation model is used to determine the correlation between each wrong answer influencing factor and the wrong answer in the feature data set containing the influencing factors of students' wrong answers, where the wrong answer influencing factors include subjective factors and objective factors. In some embodiments, subjective factors may include emotional characteristics including positive, bland, negative, etc.; concentration characteristics including focus, distraction, susceptibility to external factors, etc.; question-solving style characteristics including carefulness, carelessness, lack of patience, etc.; objective factors may include knowledge point mastery characteristics including proficiency, proficiency, unproficiency, etc.; question-solving efficiency characteristics including fast, medium, slow, etc.; question difficulty level characteristics: simple, ordinary, difficult, etc.

[0068] In some embodiments, by using a preset wrong question correlation evaluation model, the correlation between each wrong question influencing factor and the wrong question can be obtained, and the influence of the wrong question influencing factor on the student's wrong question can be known based on the size of the correlation.

[0069] In some embodiments, the preset wrong question relevance assessment model may be a pre-trained relevance assessment model.

[0070] Step 702: Screen out the features of objective factors that cause students to make mistakes from the feature data set containing the factors that affect students' mistakes, and sort the features of objective factors that cause students to make mistakes from large to small according to relevance; In some embodiments, algorithms such as hierarchical clustering algorithms, principal component analysis methods, or correlation coefficient matrices can be used to filter out features of objective factors that cause students to make mistakes in answering questions from a feature data set containing factors affecting students' mistakes in answering questions.

[0071] In some embodiments, by sorting the features that are objective factors causing students to make mistakes in answering questions from large to small according to relevance, the speed of screening the features with the greatest subsequent relevance can be increased.

[0072] Step 703: determine the knowledge point set corresponding to the feature with the greatest correlation as the third important knowledge point cluster.

[0073] In some embodiments, the feature with the greatest correlation is the feature that has the greatest impact on students' wrong answers among the objective factors. By determining the knowledge points corresponding to the objective factors that have the greatest impact on students' wrong answers as the third important knowledge point cluster, the knowledge points that cause students to make mistakes due to subjective factors can be eliminated.

[0074] As a possible implementation, Figure 8The flowchart of a method for obtaining an updated electronic teaching material is shown in FIG. 1 . Based on the above embodiment, after the first important knowledge point cluster is updated based on the first target knowledge point and the updated first target important knowledge point cluster is obtained, the teaching material generation method further includes the following steps: Step 801: using a first similarity algorithm to perform similarity evaluation on the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster, and based on the similarity evaluation result, evaluating a third candidate knowledge point cluster with similar importance outside the knowledge point range corresponding to the first important knowledge point cluster; In some embodiments, based on the above similarity evaluation results, a first candidate knowledge point cluster with similar importance is evaluated outside the scope of knowledge points corresponding to the first important knowledge point cluster. Specifically, the first important knowledge point cluster is obtained by preliminary screening of the basic knowledge point cluster, and contains a large number of important knowledge points, which may include most of the knowledge points in the second important knowledge point cluster and the third important knowledge point cluster, but there are cases where knowledge points are missed during the preliminary screening process. Therefore, knowledge points with similar importance in the second important knowledge point cluster and the third important knowledge point cluster, i.e., the knowledge points in the third candidate knowledge point cluster, may be determined outside the scope of the first important knowledge point cluster.

[0075] Step 802: Use the second similarity algorithm to evaluate a fourth candidate knowledge point cluster with similar importance outside the knowledge point range, and select the same second target knowledge point from the third candidate knowledge point cluster and the fourth candidate knowledge point cluster.

[0076] In some embodiments, this step differs from the aforementioned step 103 in that the scope of the correlation evaluation is different, and the implementation method of the remaining contents is the same as that of the aforementioned step 103, which will not be repeated here.

[0077] Step 803: If the importance identification vector corresponding to the second target knowledge point is the same as the importance identification vector corresponding to the knowledge point in the basic knowledge point cluster, the first target important knowledge point cluster is updated using the importance identification vector corresponding to the second target knowledge point to obtain an updated second target important knowledge point cluster; In some embodiments, although the importance identification vector corresponding to the second target knowledge point is not in the first important knowledge point cluster, it is still in the basic knowledge point cluster. Therefore, it is still necessary to perform similarity matching with the knowledge points in the basic knowledge point cluster. If the similarity matching is successful, the importance identification vector corresponding to the second target knowledge point is used to update the first target important knowledge point cluster.

[0078] In some embodiments, Fig. 9 As shown, Fig. 9A flowchart of a first important knowledge point updating method of an embodiment of the present disclosure is provided. Specifically, taking two knowledge points as three-level classification nodes as an example, first, the aforementioned steps 102 to 103 are executed on the three-level classification node to obtain whether the two knowledge points are similar. If the judgment result is similar, the knowledge point is bound to the question bank; if the judgment result is not similar, the aforementioned steps 102 to 103 are executed on the corresponding second-level classification node in the three-level classification node to obtain whether the two knowledge points are similar. If the judgment result is similar, the knowledge point is bound to the question bank; if the judgment result is not similar, the aforementioned steps 102 to 103 are executed on the corresponding first-level classification node in the three-level classification node to obtain whether the two knowledge points are similar. If the judgment result is similar, the knowledge point is bound to the question bank (corresponding to Fig. 9 If the result of the determination is not similar, the above steps 801 to 803 (corresponding to Fig. 9 In the cross-matching process), if the judgment result is dissimilar, the dissimilar knowledge points are stored in the alternative library; if the judgment result is similar, the similarity is judged with the first basic knowledge point cluster (corresponding basic knowledge point cluster) (based on the same-level matching implementation), if the judgment result is dissimilar, the dissimilar knowledge points are stored in the alternative library, if the judgment result is similar, the knowledge points are bound to the question bank and the first important knowledge point cluster is updated.

[0079] In some embodiments, the cross matching is performed to calibrate the accuracy of the first set of important knowledge points.

[0080] Generate electronic teaching materials based on the first target important knowledge point cluster, including: Step 804: Generate an electronic textbook based on the second target important knowledge point cluster to obtain an updated electronic textbook.

[0081] In some embodiments, by generating an electronic teaching material based on the second target important knowledge point cluster, an updated electronic teaching material is obtained, which can provide teachers with knowledge points with higher accuracy, thereby further improving the teacher's lesson preparation efficiency.

[0082] In some embodiments, in a student review scenario, the updated electronic teaching material can also provide students with more accurate knowledge points to improve the students' review efficiency.

[0083] As a possible implementation, based on the above embodiment, after generating the electronic teaching material according to the second target important knowledge point cluster, the teaching material generating method further includes: The knowledge points in the updated electronic teaching material are marked with emphasis using the knowledge points in the second important knowledge point cluster and / or the knowledge points in the third important knowledge point cluster.

[0084] In some embodiments, by using the knowledge points in the second important knowledge point cluster and / or the knowledge points in the third important knowledge point cluster to highlight the knowledge points in the updated electronic teaching materials, teachers can be helped to adjust the content of lesson plans according to the overall learning level of the class.

[0085] Corresponding to the above teaching material generation method, the present invention also provides a teaching material generation device. Since the device embodiment of the present invention corresponds to the above method embodiment, details not disclosed in the device embodiment can be referred to the above method embodiment, and will not be repeated in the present invention.

[0086] Fig.10 A structural diagram of a teaching material generating device provided by an embodiment of the present disclosure is shown in FIG. Fig.10 As shown, the teaching material generating device 1000 includes: The extraction unit 1001 is used to extract the first important knowledge point cluster from the textbook, the second important knowledge point cluster from the history question bank, and the third important knowledge point cluster from the feature data set containing the factors affecting students' wrong answers based on the smart education terminal; An evaluation unit 1002 is used to perform similarity evaluation on the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster using a first similarity algorithm, and evaluate a first candidate knowledge point cluster with similar importance within the knowledge point range corresponding to the first important knowledge point cluster based on the similarity evaluation result; A screening unit 1003 is used to evaluate a second candidate knowledge point cluster with similar importance within the knowledge point range by using a second similarity algorithm, and select the same first target knowledge point from the first candidate knowledge point cluster and the second candidate knowledge point cluster; The generating unit 1004 is used to update the first important knowledge point cluster based on the first target knowledge point to obtain an updated first target important knowledge point cluster, and generate an electronic teaching material based on the first target important knowledge point cluster.

[0087] In some embodiments of the present disclosure, the extraction unit 1001 is used to: Conduct text analysis on textbooks based on smart education terminals to obtain knowledge points in the textbooks; The knowledge points are graded according to the associations between the knowledge points to obtain graded labels corresponding to the knowledge points; Encoding the hierarchical identification corresponding to the knowledge point to obtain the identification coding value of the knowledge point at each level; Based on the hierarchical identification corresponding to the knowledge point and the identification code value of the knowledge point at each level, determine the importance identification vector set corresponding to the knowledge point, and generate a basic knowledge point cluster according to the identification vector set; A first important knowledge point cluster is determined based on the importance of knowledge points in the basic knowledge point cluster.

[0088] In some embodiments of the present disclosure, the extraction unit 1001 is used to: According to the degree of refinement of the knowledge points in the basic knowledge point cluster, determine the difficulty level corresponding to the knowledge points in the basic knowledge point cluster; Using the pre-trained evaluation model, determine the importance of the knowledge points in the basic knowledge point cluster; The basic knowledge point cluster is screened based on the importance of the knowledge points and a first preset screening threshold, and the screened knowledge point set is determined as a first important knowledge point cluster. The first preset screening threshold is an initial value set for the importance of the knowledge points.

[0089] In some embodiments of the present disclosure, the extraction unit 1001 is used to: Based on all the knowledge points involved in the history question bank and the pre-trained important knowledge point evaluation model, determine the difficulty level score of each knowledge point in all the knowledge points involved in the history question bank; Based on the difficulty level score of each knowledge point in all the knowledge points involved in the history question bank and the second preset screening threshold, all the knowledge points involved in the history question bank are screened, and the screened knowledge point set is determined as the second important knowledge point cluster, and the second preset screening threshold is the initial value set for the difficulty level corresponding to the knowledge point.

[0090] In some embodiments of the present disclosure, the extraction unit 1001 is used to: Based on a feature data set containing factors influencing students' wrong answers, a preset wrong answer correlation evaluation model is used to determine the correlation between each wrong answer influencing factor and the wrong answer in the feature data set containing factors influencing students' wrong answers, and the wrong answer influencing factors include subjective factors and objective factors; From the feature data set containing the factors influencing students' mistakes, select the features of objective factors that lead to students' mistakes, and sort the features of objective factors that lead to students' mistakes from large to small according to the relevance; The set of knowledge points corresponding to the features with the greatest correlation is determined as the third important knowledge point cluster.

[0091] In some embodiments of the present disclosure, the teaching material generating apparatus 1000 further includes an updating unit, which is used to: Using the first similarity algorithm, the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster are evaluated for similarity, and based on the similarity evaluation result, a third candidate knowledge point cluster with similar importance is evaluated outside the knowledge point range corresponding to the first important knowledge point cluster; A fourth candidate knowledge point cluster with similar importance is evaluated outside the knowledge point range using a second similarity algorithm, and the same second target knowledge point is selected from the third candidate knowledge point cluster and the fourth candidate knowledge point cluster.

[0092] If the importance identification vector corresponding to the second target knowledge point is the same as the importance identification vector corresponding to the knowledge point in the basic knowledge point cluster, the first target important knowledge point cluster is updated using the importance identification vector corresponding to the second target knowledge point to obtain an updated second target important knowledge point cluster; In some embodiments of the present disclosure, the generating unit 1004 is configured to: An electronic textbook is generated based on the second target important knowledge point cluster to obtain an updated electronic textbook.

[0093] In some embodiments of the present disclosure, the teaching material generating device 1000 further includes a marking unit, which is used to: The knowledge points in the updated electronic teaching material are marked with emphasis using the knowledge points in the second important knowledge point cluster and / or the knowledge points in the third important knowledge point cluster.

[0094] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is not limited in this embodiment.

[0095] Based on the above Figures 1 to 8 The method shown in the figure, accordingly, this embodiment also provides a computer program product, including a computer program, which implements the above-mentioned Figures 1 to 8 The method shown.

[0096] Based on the above Figures 1 to 8 The method shown in the embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Figures 1 to 8 The method shown.

[0097] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0098] like Fig.11 The figure shows a hardware structure diagram of an electronic device of the present invention, including: at least one processor 1101; and, A memory 1102 is communicatively connected to at least one of the processors 1101; wherein: The memory 1102 stores instructions that can be executed by at least one of the processors. The instructions are executed by at least one of the processors so that the at least one processor can execute the teaching material generation method as described above.

[0099] Fig.11 A processor 1101 is taken as an example.

[0100] The electronic device may further include: an input device 1103 and a display device 1104 .

[0101] The processor 1101, the memory 1102, the input device 1103 and the display device 1104 may be connected via a bus or other means, and the figure takes the connection via a bus as an example.

[0102] The memory 1102 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the review content generation method in the embodiment of the present application, for example, Figures 1 to 8 The processor 1101 executes various functional applications and data processing by running the non-volatile software programs, instructions and modules stored in the memory 1102, that is, the teaching material generation method in the above embodiment is implemented.

[0103] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the review content generation method, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 1102 may optionally include a memory remotely arranged relative to the processor 1101, and these remote memories may be connected to the device for executing the review content generation method via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0104] The input device 1103 may receive user clicks and generate signal inputs related to user settings and function controls of the review content generation method. The display device 1104 may include display devices such as a display screen.

[0105] The one or more modules are stored in the memory 1102, and when executed by the one or more processors 1101, the teaching material generation method in any of the above method embodiments is executed.

[0106] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0107] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0108] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0109] Through the description of the above implementation methods, the technicians in this field can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the scheme of this embodiment, compared with the current existing technology, this embodiment obtains a first historical data set of multiple history students, and based on the first historical data set, constructs a learning performance prediction model, the first historical data set includes the learning performance of multiple history students; obtains a second historical data set of multiple history students, and based on the second historical data set, constructs a learning performance prediction model, the second historical data set includes the learning performance of multiple history students; constructs a teaching material generation model based on the learning performance prediction model and the learning performance prediction model; based on the teaching material generation model, determines the learning ability of the target student, and realizes the construction of the teaching material generation model by constructing the learning performance prediction model and the learning performance prediction model, so that the teaching material generation model can comprehensively evaluate the learning performance of the target student in terms of objective aspects and subjective aspects of learning performance, improves the accuracy and diversity of the teaching material generation model, and helps schools or parents to more intuitively and clearly understand the all-round development of students' learning ability, timely discover the reasons for the decline in students' grades, and intervene as soon as possible to guide targeted learning counseling programs.

[0110] It should be noted that, in this article, relational terms such as "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. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0111] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features applied for herein.

Claims

1. A teaching material generation method, characterized in that: include: Based on the smart education terminal, the first important knowledge point cluster is extracted from the textbook, the second important knowledge point cluster is extracted from the history question bank, and the third important knowledge point cluster is extracted from the feature data set containing the factors affecting students' wrong answers; Using a first similarity algorithm, a similarity evaluation is performed on the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster, and based on the similarity evaluation result, a first candidate knowledge point cluster with similar importance is evaluated within the knowledge point range corresponding to the first important knowledge point cluster; Using a second similarity algorithm to evaluate a second candidate knowledge point cluster with similar importance within the knowledge point range, and selecting the same first target knowledge point from the first candidate knowledge point cluster and the second candidate knowledge point cluster; The first important knowledge point cluster is updated based on the first target knowledge point to obtain an updated first target important knowledge point cluster, and an electronic teaching material is generated based on the first target important knowledge point cluster.

2. The method according to claim 1, characterized in that The step of extracting the first important knowledge point cluster from the teaching material based on the smart education terminal includes: Performing text analysis on the teaching material based on the smart education terminal to obtain knowledge points in the teaching material; The knowledge points are graded according to the associations between the knowledge points to obtain graded identifiers corresponding to the knowledge points; Encoding the hierarchical identification corresponding to the knowledge point to obtain an identification coding value of the knowledge point at each level; Determine a set of importance identification vectors corresponding to the knowledge points based on the classification identifications corresponding to the knowledge points and the identification code values ​​of the knowledge points at each classification, and generate the basic knowledge point cluster according to the identification vector set; The first important knowledge point cluster is determined based on the importance of knowledge points in the basic knowledge point cluster.

3. The method according to claim 2, characterized in that The determining the first important knowledge point cluster based on the importance of the knowledge points in the basic knowledge point cluster includes: Determining the difficulty level corresponding to the knowledge points in the basic knowledge point cluster according to the degree of refinement of the knowledge points in the basic knowledge point cluster; Using a pre-trained evaluation model, determining the importance of the knowledge points in the basic knowledge point cluster; The basic knowledge point cluster is screened based on the importance of the knowledge points and a first preset screening threshold, and the screened knowledge point set is determined as the first important knowledge point cluster, wherein the first preset screening threshold is an initial value set for the importance of the knowledge points.

4. The method according to claim 1, characterized in that The step of extracting the second most important knowledge point cluster from the history question bank includes: Determine the difficulty level score of each of the knowledge points involved in the history question bank based on all the knowledge points involved in the history question bank and the pre-trained important knowledge point evaluation model; Based on the difficulty level score of each knowledge point in all the knowledge points involved in the historical question bank and the second preset screening threshold, all the knowledge points involved in the historical question bank are screened, and the screened knowledge point set is determined as the second important knowledge point cluster, and the second preset screening threshold is the initial value set for the difficulty level corresponding to the knowledge point.

5. The method according to claim 1, characterized in that The third important knowledge point cluster is extracted from the feature data set containing the factors affecting students' wrong answers, including: Based on the feature data set containing the influencing factors of students' wrong answers, a preset wrong answer correlation evaluation model is used to determine the correlation between each wrong answer influencing factor and the wrong answer in the feature data set containing the influencing factors of students' wrong answers, wherein the wrong answer influencing factors include subjective factors and objective factors; Screening out features of objective factors that cause students to make mistakes in the questions from the feature data set containing factors affecting students' mistakes in the questions, and sorting the features of objective factors that cause students to make mistakes in the questions from large to small according to the correlation; The knowledge point set corresponding to the feature with the greatest correlation is determined as the third important knowledge point cluster.

6. The method according to claim 2, characterized in that After the first important knowledge point cluster is updated based on the first target knowledge point to obtain an updated first target important knowledge point cluster, the method further includes: Using a first similarity algorithm, a similarity evaluation is performed on the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster, and based on the similarity evaluation result, a third candidate knowledge point cluster with similar importance is evaluated outside the knowledge point range corresponding to the first important knowledge point cluster; Using a second similarity algorithm to evaluate a fourth candidate knowledge point cluster with similar importance outside the knowledge point range, and selecting the same second target knowledge point from the third candidate knowledge point cluster and the fourth candidate knowledge point cluster; If the importance identification vector corresponding to the second target knowledge point is the same as the importance identification vector corresponding to the knowledge point in the basic knowledge point cluster, then the first target important knowledge point cluster is updated using the importance identification vector corresponding to the second target knowledge point to obtain an updated second target important knowledge point cluster; The step of generating an electronic teaching material based on the first target important knowledge point cluster includes: An electronic textbook is generated based on the second target important knowledge point cluster to obtain an updated electronic textbook.

7. The method according to any one of claims 4 to 6, characterized in that: After the electronic teaching material is generated according to the second target important knowledge point cluster, the method comprises: The knowledge points in the updated electronic teaching material are highlighted by using the knowledge points in the second important knowledge point cluster and / or the knowledge points in the third important knowledge point cluster.

8. A teaching material generating device, characterized in that: include: An extraction unit, used to extract a first important knowledge point cluster from the textbook, a second important knowledge point cluster from the history question bank, and a third important knowledge point cluster from a feature data set containing factors affecting students' wrong answers based on the smart education terminal; an evaluation unit, configured to perform similarity evaluation on the importance identification vector corresponding to the second important knowledge point cluster and the importance identification vector corresponding to the third important knowledge point cluster using a first similarity algorithm, and to evaluate a first candidate knowledge point cluster with similar importance within the knowledge point range corresponding to the first important knowledge point cluster based on the similarity evaluation result; a screening unit, configured to evaluate a second candidate knowledge point cluster with similar importance within the knowledge point range by using a second similarity algorithm, and select the same first target knowledge point from the first candidate knowledge point cluster and the second candidate knowledge point cluster; A generating unit is used to update the first important knowledge point cluster based on the first target knowledge point to obtain an updated first target important knowledge point cluster, and generate an electronic teaching material based on the first target important knowledge point cluster.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the teaching material generating method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the teaching material generating method according to any one of claims 1 to 7 is implemented.

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