Learning path determination method and device, storage medium and electronic equipment

Abstract

The invention discloses a learning path determination method and device, a storage medium and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: obtaining operation behavior data generated by learning of a student in a key learning region in a learning map; determining an incidence relation network among a plurality of behavior factors contained in the operation behavior data; performing clustering processing on the operation behavior data based on the incidence relation network so as to cluster the operation behavior data into a clustering category corresponding to the behavior factor which has the greatest influence on the operation behavior data; feature information of students is extracted according to the operation behavior data in the multiple clustering categories, and the number of target students corresponding to each learning grid in the key learning area is determined based on the feature information; according to the number of the target students, learning grids contained in the key learning areas are updated, and learning paths of the students in the learning map are determined based on the updated key learning areas. The learning efficiency and experience of students can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, storage medium and electronic device for determining a learning path. Background Art

[0002] In question-solving software, the way of determining the learning path usually relies on multiple factors and indicators to comprehensively reflect the user's learning status, level of mastery, and progress.

[0003] At present, when students learn through question-answering software, their learning path is usually determined based on the order in which they have completed questions.

[0004] However, since the knowledge points that students need to learn may include knowledge points that need to be fully mastered and knowledge points that only need to be understood, the learning path obtained using this method of determining the learning path contains the entire trajectory of the student's learned knowledge points. When there are many knowledge points that have been learned, it will not be possible to clearly present the learning path of the content that students actually need to master, resulting in students being unable to truly understand their own learning status of the content that needs to be mastered, affecting the student experience. Summary of the invention

[0005] In view of this, the present application provides a method, device, storage medium and electronic device for determining a learning path, the main purpose of which is to improve the current existing technology. Since the knowledge points that students need to learn may include knowledge points that need to be fully mastered and knowledge points that only need to be understood, the learning path obtained using this learning path determination method contains the student's entire trajectory for the knowledge points that have been learned. When there are many knowledge points that have been learned, the learning path of the content that students actually need to master cannot be clearly presented, resulting in students being unable to truly understand their own learning status of the content that needs to be mastered, affecting the student experience. Technical problems.

[0006] In a first aspect, the present application provides a method for determining a learning path, comprising: Obtain the operational behavior data generated by students in the key learning area of ​​the learning map; Determining a correlation network between a plurality of behavior factors included in the operation behavior data; Performing clustering processing on the operation behavior data based on the association relationship network to cluster the operation behavior data into cluster categories corresponding to the behavior factors that have the greatest impact on the operation behavior data; Extracting characteristic information of students according to the operation behavior data in a plurality of clustering categories, and determining the number of target students corresponding to each learning grid in the key learning area based on the characteristic information, wherein the target students are students who have not mastered the knowledge points in the key knowledge point cluster corresponding to the learning grid; The learning grid contained in the key learning area is updated according to the number of the target students, and the learning path of the students in the learning map is determined based on the updated key learning area.

[0007] In a second aspect, the present application provides a device for determining a learning path, comprising: An acquisition module is configured to acquire operation behavior data generated by students in learning in a key learning area in a learning map; A determination module, configured to determine a correlation relationship network between a plurality of behavior factors included in the operation behavior data; A clustering module is configured to perform clustering processing on the operation behavior data based on the association relationship network, so as to cluster the operation behavior data into cluster categories corresponding to the behavior factors that have the greatest impact on the operation behavior data; The determination module is further configured to extract characteristic information of students according to the operation behavior data in the plurality of clustering categories, and determine the number of target students corresponding to each learning grid in the key learning area based on the characteristic information, wherein the target students are students who have not mastered the knowledge points in the key knowledge point cluster corresponding to the learning grid; The updating module is configured to update the learning grid contained in the key learning area according to the number of the target students, and determine the learning path of the students in the learning map based on the updated key learning area.

[0008] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for determining a learning path described in the first aspect is implemented.

[0009] In a fourth aspect, the present application provides an electronic device, 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 for determining the learning path described in the first aspect when executing the computer program.

[0010] By means of the above technical scheme, the present application provides a learning path determination, device, storage medium and electronic device. Compared with the current existing technology, the present application obtains the operation behavior data generated by students learning in the key learning area in the learning map, determines the association network between the multiple behavior factors contained in the operation behavior data, and clusters the operation behavior data based on the association network to cluster the operation behavior data into the clustering category corresponding to the behavior factor that has the greatest impact on it. The behavior factor that has the greatest impact on the student's operation behavior data can be accurately clustered, and then the characteristic information of the students can be extracted from the clustered operation behavior data to accurately analyze the students' learning situation in the learning grid, and the number of students who have not mastered the knowledge point corresponding to the learning grid in each learning grid in the key learning area is determined based on the characteristic information. The overall mastery of the students for the knowledge point cluster corresponding to the learning grid can be judged by the number of students, and a more accurate key learning area can be obtained by the overall mastery, and then a more accurate learning path of the students can be obtained by the updated key learning area, so that students can more clearly understand their own learning situation of the key knowledge points according to the updated learning path, thereby improving students' learning efficiency and experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0012] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0013] Figure 1 A flow chart of a method for determining a learning path provided in an embodiment of the present application is shown; Figure 2 A flow chart of a method for determining a learning path provided in an embodiment of the present application is shown; Figure 3 A flow chart of a method for determining a learning path provided in an embodiment of the present application is shown; Figure 4 A schematic diagram showing an example provided by an embodiment of the present application; Figure 5 A schematic diagram showing an example provided by an embodiment of the present application; Figure 6 A flow chart of a method for determining a learning path provided in an embodiment of the present application is shown; Figure 7 A schematic diagram showing an example provided by an embodiment of the present application; Figure 8 A schematic diagram of the structure of a device for determining a learning path provided in an embodiment of the present application is shown; Fig. 9 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0014] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0015] In order to improve the existing technology, since the knowledge points that students need to learn may include knowledge points that need to be fully mastered and knowledge points that only need to be understood, the learning path obtained by using this learning path determination method contains the entire track of the students' learned knowledge points. In the case of a large number of learned knowledge points, it will not be possible to clearly present the learning path of the content that students actually need to master, resulting in students being unable to truly understand their own learning status of the content that needs to be mastered, affecting the technical problem of student experience. This embodiment provides a method for determining a learning path, such as Figure 1 As shown, the method includes: Step 101: Obtain the operational behavior data generated by students in learning in the key learning area in the learning map.

[0016] The learning map is composed of a plurality of learning grids arranged according to a predetermined relationship, and the key learning area is obtained by marking and circling adjacent learning grids corresponding to the key knowledge point cluster in the learning map.

[0017] Optionally, the execution entity of the embodiment of the present application may be a cloud server.

[0018] In an embodiment of the present application, a learning map may be composed of a plurality of learning grids arranged in a predetermined relationship, wherein each grid corresponds to questions in a target question bank, and the target question bank is generated based on knowledge point clusters extracted from student textbooks. It should be noted that each learning grid corresponds to all questions in a knowledge point cluster, and the grid corresponding to the key knowledge point cluster may be a key learning grid, and a key learning area may be obtained by circling adjacent key learning grids.

[0019] In some examples, mapping questions in the question bank to a learning map allows students to clearly see their learning progress, learning content, and learning path when viewing the learning map, and can also view the knowledge points and importance of the knowledge points in the textbooks corresponding to the current learning content.

[0020] As an optional method, the embodiments of the present application can obtain students' operational behavior data in key learning areas through information interaction between a cloud server and a client. Specifically, the operational behavior data may include but are not limited to students' answering progress data, answering time data, accuracy data, answering content data, number of times questions are skipped data, wrong question situation data, review frequency data, etc. In some examples, the present application can obtain students' operational behavior data by monitoring key learning areas in a learning map. It should be noted that key learning areas are areas where students must study. The embodiments of the present application obtain students' operational behavior data by monitoring key learning areas. Compared with obtaining students' learning trajectories in real time, this can save the amount of information that the cloud server needs to process and save computing power to a greater extent.

[0021] Step 102: Determine a correlation network between multiple behavior factors included in the operation behavior data.

[0022] In an embodiment of the present application, the multiple behavioral factors contained in the operation behavior data may be specific operation behaviors corresponding to the operation behavior data. For example, if the operation behavior data is question answering progress data, then the behavioral factor is the question answering progress. Correspondingly, if the operation behavior data is question answering time data, then the behavioral factor is the question answering time, and so on. Examples are not given one by one here.

[0023] In some examples, the association network may be a network composed of all associations between multiple behavioral factors. For example, if the multiple behavioral factors include answering progress, answering time, accuracy and answering content, then all the associations between the multiple behavioral factors may correspond to the association relationship of answering progress affecting answering time, the association relationship of answering time affecting answering progress, the association relationship of answering time affecting accuracy, the association relationship of accuracy affecting answering content, the association relationship of answering content affecting answering progress, and so on. All the associations form an association network.

[0024] It should be noted that the association relationship network in the embodiment of the present application may also include the association relationship between operation behavior data and different types of parameters, the relationship between operation behavior data information and learning paths, the adjacency relationship of surrounding user terminal positioning, the preset relationship of surrounding key monitoring areas, etc.

[0025] Step 103: cluster the operation behavior data based on the association relationship network to cluster the operation behavior data into cluster categories corresponding to the behavior factors that have the greatest impact on the operation behavior data.

[0026] In the embodiment of the present application, clustering the data is an unsupervised learning method for finding natural groupings of objects in a data set. The goal of clustering is to classify similar objects into the same cluster while ensuring that objects in different clusters are as dissimilar as possible.

[0027] For this embodiment, the user's operation behavior data corresponds to multiple behavior factors, each of which will have an impact on the overall operation behavior data. The present application can cluster the operation behavior data through an association relationship network to determine the behavior factor that has the greatest impact on each operation behavior data, and cluster the operation behavior data into different clustering categories according to the different behavior factors that have the greatest impact.

[0028] For example, if there are operation behavior data 1, operation behavior data 2, operation behavior data 3 and operation behavior data 4, it is necessary to match operation behavior data 1 based on the association relationship network, determine the behavior factor A that has the greatest impact on operation behavior data 1, and then match operation behavior data 2 with operation behavior data 1. If the match is successful, it is determined that the behavior factor that has the greatest impact on operation behavior data 2 is also behavior factor A, and then match operation behavior data 3 with operation behavior data 2, and so on.

[0029] If the operation behavior data 2 fails to match the operation behavior data 1, the operation behavior data 2 is determined as a cluster category alone, and then the operation behavior data 3 is matched with the operation behavior data 1 and the operation behavior data 2 in sequence, and so on.

[0030] Step 104: extracting characteristic information of students according to the operation behavior data in a plurality of clustering categories, and determining the number of target students corresponding to each learning grid in the key learning area based on the characteristic information.

[0031] Among them, the target students are those students who have not mastered the knowledge points in the key knowledge point cluster corresponding to the learning grid.

[0032] As an optional method, student characteristic information may include but is not limited to the student's user ID, learning progress (virtual map location information), notes, learning honors, learning tracks, learning time, etc.

[0033] In an embodiment of the present application, by extracting the student's characteristic information, it is possible to determine whether the student has mastered the knowledge point cluster corresponding to the learning grid, and then determine the student's overall mastery of the corresponding learning grid, and determine the number of target students who have not mastered the knowledge points in the key knowledge point cluster corresponding to the learning grid.

[0034] It should be noted that the number of terminals can reflect the students' mastery of the knowledge points at that location. If the number of people who have mastered the knowledge points is large, the number of student terminals in this area will be small. It is believed that most students can master the knowledge points, which can be used as the basis for setting key points, so that new key monitoring area information can be constructed or added.

[0035] Step 105: Update the learning grid contained in the key learning area according to the number of target students, and determine the learning path of the students in the learning map based on the updated key learning area.

[0036] It should be noted that, in the learning map of the embodiment of the present application, the learning grid corresponding to the basic knowledge point cluster can be an unfilled blank white block, and the learning grid corresponding to the key knowledge point cluster can be filled with a first color block; the blank white blocks that the current user has learned can also be filled with a second color block in the learning map, and the second color blocks can be connected in sequence to obtain the user's learning path; by distinguishing the colors of the learning grids in the learning map, it can be used to distinguish the source of the learning grids, and the current student's learning progress can also be visualized for parental supervision.

[0037] In some examples, an updated learning path may be obtained by updating the learning grid corresponding to the key learning area in the learning map, that is, re-marking the color blocks of the learning grid.

[0038] Furthermore, the learning map can also be presented on the user terminal, so that students' parents can clearly and intuitively understand the students' learning progress and learning status, etc. through the learning map. Therefore, the learning map of this application can also enable parents to understand the students' learning process more accurately and comprehensively.

[0039] Compared with the current existing technology, this embodiment obtains the operational behavior data generated by students learning in the key learning area in the learning map, determines the association relationship network between multiple behavioral factors contained in the operational behavior data, and clusters the operational behavior data based on the association relationship network to cluster the operational behavior data into the clustering category corresponding to the behavioral factor that has the greatest impact on it. The behavioral factors that have the greatest impact on the students' operational behavior data can be accurately clustered, and then the students' learning situation in the learning grid can be accurately analyzed by extracting the students' characteristic information from the clustered operational behavior data, and the number of students who have not mastered the knowledge points corresponding to the learning grid in each learning grid in the key learning area can be determined based on the characteristic information. The number of students can be used to judge the students' overall mastery of the knowledge point cluster corresponding to the learning grid. A more accurate key learning area can be obtained through the overall mastery, and then a more accurate student learning path can be obtained through the updated key learning area, so that students can more clearly understand their own learning situation of key knowledge points according to the updated learning path, thereby improving students' learning efficiency and experience.

[0040] As a refinement and extension of the above embodiment, when clustering the operation behavior data based on the association relationship network to cluster the operation behavior data into cluster categories corresponding to the behavior factors that have the greatest impact on it, the following methods may be used but are not limited to: Figure 2 As shown, the method includes: Step 201: clean the operation behavior data to obtain an operation behavior data sample set.

[0041] In the embodiment of the present application, data cleaning is a crucial step before cluster analysis. It ensures the quality of the data, thereby improving the accuracy and reliability of the clustering results. Specifically, the process of cleaning the data may include but is not limited to missing value processing, outlier processing, standardization and normalization processing, etc. Specifically, the embodiment of the present application may organize the collected user operation behavior data including answering progress, accuracy, time spent, etc., and obtain a sample set of operation behavior data.

[0042] It should be noted that the operation behavior data sample set may include operation behavior data of multiple users, wherein each user in the operation behavior sample data set may correspond to multiple operation behavior data.

[0043] Step 202: Match the first sample data in the operation behavior data sample set with the association relationship network to match the target behavior factor that has the greatest impact on the first sample data.

[0044] The first sample data is any sample data in the operation behavior data sample set.

[0045] In an embodiment of the present application, any sample data in the operation behavior data sample set can be matched with the association relationship network, and then the behavior factor that has the greatest impact on the sample data can be matched. For example, sample data 1 in the operation behavior data sample set can be matched with the association relationship network, and then the behavior factor that has the greatest impact on the sample data can be matched as behavior factor 1.

[0046] Step 203: determine the first sample data as the cluster center sample of the target cluster.

[0047] The cluster center of the target cluster is the target behavior factor, and the cluster center of each cluster is used to indicate the operation behavior that has the greatest impact on the sample data of the cluster.

[0048] In the embodiment of the present application, when the target behavior factor corresponding to the first sample data is determined, the first sample data can be determined as the cluster center sample of the cluster corresponding to the target behavior factor.

[0049] Exemplarily, based on step 202 , when behavior factor 1 corresponding to sample data 1 is determined, sample data 1 may be determined as a cluster center sample of a cluster corresponding to behavior factor 1 .

[0050] Step 204: perform similarity matching on the other sample data in the operation behavior data sample set except the first sample data with the cluster center samples of the target cluster in turn, so as to match multiple clusters except the target cluster, and determine the multiple clusters as multiple cluster categories.

[0051] The cluster center of each cluster among the multiple clusters corresponds to one behavioral factor among the multiple behavioral factors.

[0052] Optionally, in the process of executing step 204, the following steps may be adopted but are not limited to: Step 11: perform similarity matching on the other sample data in the operation behavior data sample set except the first sample data with the cluster center samples of the target cluster in sequence.

[0053] In the embodiment of the present application, based on the example in step 203, a sample data 2 can be arbitrarily selected from other sample data except the first sample data in the operation behavior data sample set, and the sample data 2 is matched with the sample data 1 through formula 1 (the distance formula based on the cosine angle) to obtain the similarity matching result between the sample data 1 and the sample data 2. The formula 1 is specifically as follows: (Formula 1) In formula 1, X i and X j are the vector representations corresponding to sample i and sample j respectively.

[0054] Step 12: When it is determined that the similarity value obtained by matching is greater than or equal to a predetermined similarity threshold, the second sample data currently matching the first sample data is updated as the cluster center sample of the target cluster.

[0055] The second sample data is any sample data among the other sample data.

[0056] For this embodiment, based on the example in step 11, the similarity matching results of sample data 1 and sample data 2 can be compared with a predetermined similarity threshold, and when the similarity matching result is greater than or equal to the predetermined similarity threshold, sample data 2 is determined as the cluster center sample of the cluster where sample data 1 is located.

[0057] Step 13: When it is determined that the similarity value obtained by matching is less than a predetermined similarity threshold, the second sample data is matched with other clusters for similarity, and based on the matching result, the second sample data is used as the cluster center sample of the cluster with the highest similarity.

[0058] In some examples, based on the example in step 12, if the similarity matching result is less than a predetermined similarity threshold, it is necessary to match sample data 2 with the cluster center samples of the determined clusters respectively. If there is a cluster center sample whose similarity with sample data 2 is greater than or equal to the predetermined similarity threshold, the cluster center sample of the cluster is updated to sample data 2.

[0059] Step 14: If it is determined based on the matching result that there is currently no cluster whose similarity with the second sample data is greater than or equal to a predetermined similarity threshold, a cluster is constructed using the second sample data as a cluster center sample.

[0060] As an optional method, based on the example in step 13, if among all the determined clusters, there is no cluster core sample whose similarity with sample data 2 is greater than or equal to a predetermined similarity threshold, then it is necessary to reconstruct a cluster using sample data 2 as the cluster core sample.

[0061] Repeat steps 11 to 14 until all sample data of the operation behavior data sample set are matched to obtain several clusters, and select the sample corresponding to the cluster center of each cluster as the cluster center sample; through sample purification, the samples with large similarity within the category are reduced, and the accuracy of subsequent clustering features is improved.

[0062] Further, when extracting characteristic information of students based on the operation behavior data in multiple cluster categories and determining the number of target students corresponding to each learning grid in the key learning area based on the characteristic information, the following steps may be adopted but are not limited to: Figure 3 As shown, including: Step 301: Based on multiple clustering categories and cluster centers corresponding to the multiple clustering categories, a label portrait feature database corresponding to the operation behavior data sample set is constructed, and label information of students corresponding to the operation behavior data is extracted from the label portrait database.

[0063] In the embodiment of the present application, the tag image feature database may be as shown in Table 1 below: Table 1

[0064] In Table 1, the first tag (X) may include: recording the effective duration of the question-answer interaction, and finally obtaining X1: that is, the online learning duration in Table 1; obtaining the student's operation behavior record, and counting X2, that is, the number of requests for help in Table 1; according to the student's dialogue feedback result that the problem has been solved, counting X3 of the helped object, that is, the number of effective helps in Table 1; counting the response time and total duration of the dialogue of the helped object, and obtaining X4, that is, the response efficiency in Table 1.

[0065] Correspondingly, in Table 1, the second label (Y) may include: Y1: accuracy: statistics of the first answer to the same question; Y2: learning progress; Y3: answering time: statistics of the first answer to the same question; Y4: proficiency of a single knowledge point: users may have multiple questions on the same knowledge point, and some users have only done some of the questions but have mastered the knowledge point. Some users have made mistakes in the question, and then read the answer, exit the software, and answer the question again to refresh the data. Therefore, it is necessary to accurately judge whether the user has mastered the knowledge point.

[0066] Furthermore, the third label (Z) may include: Z1: balanced development type; Z2: basic cultivation type; Z3: advanced challenge type; the third label mainly analyzes the user's learning style according to the user's learning path. For the top students, they may be more inclined to challenge high difficulty; for the balanced development type, they may be more inclined to combine basic knowledge points with key knowledge points; for those with weak foundation, they are more inclined to consolidate basic knowledge; based on the user's answering performance on basic knowledge points (K_B) and important knowledge points (K_I), they are divided into three categories: (1) Basic type: need to strengthen basic knowledge point training; (2) Solid type: both basic and important knowledge points meet the standards; (3) Challenge type: have the ability to expand high-difficulty content.

[0067] In an embodiment of the present application, label information corresponding to each student can be extracted based on the label portrait feature database. For example, if the operation behavior data set includes the operation behavior data of student 1, that is, after determining the greatest influencing factor affecting the operation behavior data of student 1, the label information of student 1 can be extracted based on the label portrait feature database, for example, it may include X1 learning time, Y2 learning progress, Z2 basic type, etc.

[0068] Step 302: determine the students to be analyzed who have learned and generated operational learning data in the target learning grid, and analyze the students' mastery of knowledge points in the key knowledge point cluster corresponding to the target learning grid based on the label information.

[0069] The target learning grid is any one of the learning grids included in the key learning area.

[0070] In some examples, the operation behavior data in the key monitoring area will be obtained, and the operation behavior data will be cleaned, classified, clustered, and a multi-category operation behavior database will be established; based on the deep integration and machine learning of the operation behavior data in the multi-category operation behavior database, a user label portrait feature database will be established. The user label portrait feature is the user feature information of the user terminal when entering the key monitoring area. The machine learning algorithm can be based on the knowledge graph or the k-means clustering algorithm. The portrait features of users in the key monitoring area are obtained by deep mining and analysis of the user label portrait feature database, and the user's portrait features are perceived and identified.

[0071] In an embodiment of the present application, the learning situation of the students in the learning grid of the key learning area can be analyzed based on the students' label information, and then the students' mastery of the key knowledge point clusters corresponding to the learning grid can be accurately judged.

[0072] It should be noted that the learning map of the present application includes grids corresponding to basic knowledge point clusters and grids corresponding to key knowledge point clusters, wherein the process of extracting basic knowledge point clusters based on student textbooks may include: uploading textbook content to a first knowledge point analysis module to obtain basic knowledge point clusters; extracting knowledge points from the text and performing hierarchical identification by using text mining, natural language, semantic analysis and other technologies through text analysis of textbook content, encoding the identification to obtain identification coding values, and constructing an identification vector set Uori (basic knowledge point clusters) corresponding to the knowledge point identification according to the identification coding values ​​to generate a knowledge point network; the hierarchical identification is divided into n levels, for example, n=3, then U=[An, Bm, Ck], general expression: U=(k, β), k is the knowledge point set, β is the edge set, such as Figure 4 As shown: the first-level classification An is the main node, which can be classified by chapter, and we can get An=[A1, A2...An]; the second-level classification Bm is the slave node of An, which can be classified by section, and we can get Bm=[A1b1, A1b2, A2b1..Anbm]; the third-level classification Ck is the slave node of Bm, which can be classified by unit results, and we can get Ck=[A1b1c1, A1b2c2, A1b2c3..Anbmck].

[0073] Because knowledge points appear in the form of a single unit or a combination of multiple units depending on the degree of difficulty and the depth of learning, when constructing a knowledge point network, it is determined that the first-level classification An exists in the form of a single-unit identification vector, and the second and third levels exist in the form of a combination of multiple units, with identification vectors superimposed in sequence; multiple knowledge point networks form a knowledge point cluster; this way of constructing a knowledge point network coverage can cover all combinations of knowledge points. In the actual question push, if the difficulty is simple, the first level can be pushed, if the difficulty is medium, the second level can be pushed, and if the difficulty is difficult, the third level can be pushed.

[0074] The process of extracting key knowledge point clusters based on student textbooks may include: obtaining important knowledge point clusters through a knowledge point extraction module according to the distribution of knowledge point clusters U; Figure 5 As shown, the number of first-level branch identification vectors Z, the number of second-level branch identification vectors X, and the number of third-level branch identification vectors V are counted and input into the pre-trained scoring model G to calculate the knowledge point cluster score, as shown in the following formula 2: , , ,

[0075] (Formula 2) Formula 2 represents the weights of the importance of the corresponding indicators Z, X, and V, and β1<β2<β3, because as the degree of refinement of the knowledge points increases, the importance accounts for a larger proportion; set the screening threshold T1, when G>T1, the screened knowledge point identification vector is used as the first important knowledge point cluster Unew; the threshold T can be set by custom, average, median, etc.

[0076] Furthermore, a new important knowledge point cluster is generated by screening through a pre-trained key knowledge point evaluation model, including: obtaining a history question bank, constructing a set of identification vectors G corresponding to the knowledge point identification by the same steps as the process of extracting the basic knowledge point cluster based on the student textbook, and generating a knowledge point network; taking the same method of hierarchical identification in step S1 to obtain a set of Wn, Rm, and Hk vectors, Zori=[Wn, Rm, Hk]; screening through a pre-trained key knowledge point evaluation model to obtain a key knowledge point evaluation model, as shown in the following formula 3: (Formula 3) In Formula 3, G j Indicates the number of times the knowledge point appears in the test; G q : The frequency of errors in the test knowledge points; G r : The number of branches of test points; α is the weight score of the corresponding indicator; set the scoring threshold T2, when y GWhen >T2, the key knowledge point combination is selected to form the second important knowledge point cluster Znew.

[0077] Step 303: Based on the analysis results, students who have not mastered the knowledge points in the key knowledge point cluster corresponding to the target learning grid among the students to be analyzed are determined as target students.

[0078] In an embodiment of the present application, based on the mastery of key knowledge point clusters by students in a key learning area, students who have not mastered the key knowledge points are identified as target students, and the number of target students is counted.

[0079] Optionally, when updating the learning grids included in the key learning area according to the target number of students, the following methods may be used but are not limited to: Figure 6 As shown, the method includes: Step 401: Evaluate the overall mastery of knowledge points in the key knowledge point cluster corresponding to the target learning grid based on the number of target students.

[0080] In an embodiment of the present application, if the acquired operation behavior data indicates that there are 10 students studying in the learning grid 1 within the key learning area 1, it is necessary to determine the number of students among the 10 who have not mastered the knowledge points in the key knowledge point cluster corresponding to the learning grid 1, and judge the students' overall mastery of the knowledge points in the key knowledge point cluster corresponding to the learning grid 1 based on the number of students who have not mastered and the total number of students.

[0081] Step 402: When the number of target students is greater than or equal to a predetermined number threshold, it is determined that the overall mastery status does not meet the standard, and a second mark for the target learning grid is retained in the learning map.

[0082] The second identifier is used to indicate a learning grid corresponding to a key knowledge point cluster in the learning map.

[0083] Exemplarily, based on the example in step 401, if the number of people who have not mastered the content is 5, and the predetermined number threshold is 3 people who have not mastered the content, then it can be determined that the current overall mastery situation does not meet the standard, which means that the students as a whole have not mastered the knowledge points in the key knowledge point cluster corresponding to learning grid 1, and the second identifier for marking the target learning grid can be retained in the learning map.

[0084] In the embodiment of the present application, the first identification and the second identification can be distinguished by color, and can also be set differently by other factors. The specific method of distinguishing the first identification and the second identification is not limited here.

[0085] Step 403: When the number of target students is less than a predetermined number threshold, it is determined that the overall mastery is up to standard, and the second mark of the target learning grid mark in the learning map is replaced with the first mark.

[0086] The first identifier is used to indicate a learning grid corresponding to a basic knowledge point cluster in the learning map.

[0087] In some examples, based on the example in step 402, if the number of people who have not mastered the content is 1, and the predetermined number threshold is 3 people who have not mastered the content, then it can be judged that the current overall mastery situation meets the standard, which means that the students have generally mastered the knowledge points in the key knowledge point cluster corresponding to learning grid 1. In this case, the second identifier marking the target learning grid can be replaced with the first identifier in the learning map, that is, the target learning grid will no longer be included in the key learning area.

[0088] Step 404: re-mark and circle the learning grid marked with the second identifier in the learning map to obtain an updated key learning area.

[0089] Optionally, before executing step 404, the method of this embodiment also includes: collecting the number of requests for learning collaboration requests triggered by students in the learning grid marked with a first identifier in the learning map, and comparing the number of requests with a predetermined request threshold; marking the learning grid with a second identifier whose number of requests in the learning grid marked with the first identifier is greater than the predetermined request threshold.

[0090] In an embodiment of the present application, a student can trigger a learning assistance request during the learning process, so that other students can respond to the learning assistance request. Specifically, the embodiment of the present application can count the number of learning assistance requests triggered by students in a learning grid outside a key learning area. When the number of learning assistance requests reaches a certain threshold, it is judged that the student's overall mastery of the learning grid is poor, and the learning grid can be marked with a second identifier.

[0091] Further, when determining the learning path of the student in the learning map based on the updated key learning area, the following steps may be adopted but are not limited to: Step 21, determine the first number of learning grids marked with the first identifier in the student's learned content, the second number of learning grids marked with the second identifier, and determine the third number of learning grids marked with the second identifier in the learned content based on the updated key learning area.

[0092] Step 22: Perform weighted scoring using Formula 4 based on the first quantity, the second quantity, and the third quantity to update the learning path for the key knowledge point cluster in the student's current learning path to obtain an updated learning path.

[0093] (Formula 4) Wherein, C represents the first quantity, V represents the second quantity, and V new represents the third quantity, αβ represents the corresponding weight score, and Z base Indicates the total progress score of the learning path.

[0094] It should be noted that if Figure 7 As shown, in the learning map of the embodiment of the present application, the learning grid corresponding to the basic knowledge point cluster can be an unfilled blank white block, and the learning grid corresponding to the key knowledge point cluster can be filled with a first color block; the blank white blocks that the current user has learned can also be filled with a second color block in the learning map, and the user's learning path can be obtained by connecting the second color blocks in sequence; by distinguishing the colors of the learning grids in the learning map, it is possible to distinguish the sources of the learning grids, and the current student's learning progress can also be visualized for parental supervision.

[0095] Furthermore, the learning map can also be presented on the user terminal, so that students' parents can clearly and intuitively understand the students' learning progress and learning status, etc. through the learning map. Therefore, the learning map of this application can also enable parents to understand the students' learning process more accurately and comprehensively.

[0096] Compared with the current existing technology, this embodiment obtains the operational behavior data generated by students learning in the key learning area in the learning map, determines the association relationship network between multiple behavioral factors contained in the operational behavior data, and clusters the operational behavior data based on the association relationship network to cluster the operational behavior data into the clustering category corresponding to the behavioral factor that has the greatest impact on it. The behavioral factors that have the greatest impact on the students' operational behavior data can be accurately clustered, and then the students' learning situation in the learning grid can be accurately analyzed by extracting the students' characteristic information from the clustered operational behavior data, and the number of students who have not mastered the knowledge points corresponding to the learning grid in each learning grid in the key learning area can be determined based on the characteristic information. The number of students can be used to judge the students' overall mastery of the knowledge point cluster corresponding to the learning grid. A more accurate key learning area can be obtained through the overall mastery, and then a more accurate student learning path can be obtained through the updated key learning area, so that students can more clearly understand their own learning situation of key knowledge points according to the updated learning path, thereby improving students' learning efficiency and experience.

[0097] Further, as Figures 1 to 6 The specific implementation of the method shown in the embodiment provides a device for determining a learning path, such as Figure 8As shown, the device includes: an acquisition module 51, a determination module 52, a cluster matching module 53, and an update module 54.

[0098] An acquisition module 51 is configured to acquire operation behavior data generated by students in the key learning area in the learning map; A determination module 52, configured to determine a correlation network between a plurality of behavior factors included in the operation behavior data; A clustering module 53 is configured to perform clustering processing on the operation behavior data based on the association relationship network, so as to cluster the operation behavior data into cluster categories corresponding to the behavior factors that have the greatest impact on the operation behavior data; The determination module 52 is further configured to extract characteristic information of students according to the operation behavior data in the plurality of clustering categories, and determine the number of target students corresponding to each learning grid in the key learning area based on the characteristic information, wherein the target students are students who have not mastered the knowledge points in the key knowledge point cluster corresponding to the learning grid; The updating module 54 is configured to update the learning grid contained in the key learning area according to the number of the target students, and determine the learning path of the students in the learning map based on the updated key learning area.

[0099] In some examples of this embodiment, the clustering module 53 is specifically configured to clean the operation behavior data to obtain an operation behavior data sample set; match the first sample data in the operation behavior data sample set with the association relationship network to match the target behavior factor that has the greatest impact on the first sample data, wherein the first sample data is any sample data in the operation behavior data sample set; determine the first sample data as the cluster center sample of the target cluster, and the cluster center of the target cluster is the target behavior factor, wherein the cluster center of each cluster is used to indicate the operation behavior that has the greatest impact on the cluster sample data; perform similarity matching on the other sample data in the operation behavior data sample set except the first sample data with the cluster center sample of the target cluster in turn to match multiple clusters except the target cluster, and determine the multiple clusters as the multiple clustering categories, wherein the cluster center of each cluster in the multiple clusters corresponds to one of the multiple behavior factors.

[0100] In some examples of the present embodiment, the clustering module 53 is further configured to perform similarity matching on the other sample data in the operation behavior data sample set except the first sample data in turn with the cluster core sample of the target cluster; when it is determined that the similarity value obtained by matching is greater than or equal to a predetermined similarity threshold, the second sample data currently matched with the first sample data is updated to the cluster core sample of the target cluster, and the second sample data is any one of the other sample data; when it is determined that the similarity value obtained by matching is less than the predetermined similarity threshold, the second sample data is respectively matched with other clusters for similarity, and based on the matching result, the second sample data is used as the cluster core sample of the cluster with the highest similarity thereto; if it is determined based on the matching result that there is currently no cluster whose similarity with the second sample data is greater than or equal to the predetermined similarity threshold, a cluster is constructed using the second sample data as the cluster core sample.

[0101] In some examples of the present embodiment, the determination module 52 is specifically configured to construct a label portrait feature database corresponding to the operation behavior data sample set based on the multiple clustering categories and the cluster centers corresponding to the multiple clustering categories, and extract label information of students corresponding to the operation behavior data from the label portrait database; determine the students to be analyzed who learn and generate operation learning data in the target learning grid, and analyze the mastery of the knowledge points in the key knowledge point cluster corresponding to the target learning grid by the students to be analyzed based on the label information, and the target learning grid is any one of the learning grids included in the key learning area; based on the analysis results, the students among the students to be analyzed who have not mastered the knowledge points in the key knowledge point cluster corresponding to the target learning grid are determined as the target students.

[0102] In some examples of this embodiment, the updating module 54 is specifically configured to evaluate the overall mastery of the knowledge points in the key knowledge point cluster corresponding to the target learning grid according to the number of the target students; When the target number of students is greater than or equal to the predetermined threshold number, it is determined that the overall mastery situation does not meet the standard, and the second identifier marking the target learning grid is retained in the learning map, and the second identifier is used to indicate the learning grid corresponding to the key knowledge point cluster in the learning map; when the target number of students is greater than or equal to the predetermined threshold number, it is determined that the overall mastery situation meets the standard, and the second identifier marking the target learning grid is replaced with the first identifier in the learning map, and the first identifier is used to indicate the learning grid corresponding to the basic knowledge point cluster in the learning map; the learning grid marked with the second identifier is re-marked and circled in the learning map to obtain an updated key learning area.

[0103] In some examples of this embodiment, the update module 54 is further configured to collect the number of requests for learning collaboration requests triggered by students marking the learning grid with the first identifier in the learning map, and compare the number of requests with a predetermined request threshold; mark the learning grid with the first identifier whose number of requests is greater than the predetermined request threshold with the second identifier.

[0104] In some examples of this embodiment, the update module 54 is further specifically configured to determine a first number of learning grids marked with the first identifier in the student's learned content, a second number of learning grids marked with the second identifier, and a third number of learning grids marked with the second identifier in the learned content based on the updated key learning area; weighted scoring is performed using formula 1 based on the first number, the second number, and the third number to update the learning path for the key knowledge point cluster in the student's current learning path to obtain an updated learning path.

[0105] Formula 1 Wherein, C represents the first quantity, V represents the second quantity, and V new represents the third quantity, αβ represents the corresponding weight score, and Z base Indicates the total progress score of the learning path.

[0106] It should be noted that for other corresponding descriptions of the functional units involved in the device for determining a learning path provided in this embodiment, reference can be made to Figures 1 to 6 The corresponding description in will not be repeated here.

[0107] Based on the above Figures 1 to 6 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 6 The method shown.

[0108] 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.

[0109] like Fig. 9 The figure shows a hardware structure diagram of an electronic device of the present invention, including: at least one processor 501; and, A memory 502 is communicatively connected to at least one of the processors 501; wherein, The memory 502 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 of the processors can perform the method for determining the learning path as described above.

[0110] Fig. 9 A processor 501 is taken as an example.

[0111] The electronic device may further include: an input device 503 and a display device 504 .

[0112] The processor 501, the memory 502, the input device 503 and the display device 504 may be connected via a bus or other means. Fig. 9 The example of connecting through bus is taken in the following.

[0113] The memory 502 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 method for determining a learning path in the embodiment of the present application, for example, Figures 1 to 6 The processor 501 executes various functional applications and data processing by running the non-volatile software programs, instructions and modules stored in the memory 502, that is, the method for determining the learning path in the above embodiment is implemented.

[0114] The memory 502 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required by at least one function; the data storage area may store data created according to the use of the method for determining the learning path, etc. In addition, the memory 502 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 device. In some embodiments, the memory 502 may optionally include a memory remotely arranged relative to the processor 501, and these remote memories may be connected to the device for executing the method for determining the learning path 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.

[0115] The input device 503 can receive user clicks and generate signal inputs related to user settings of the learning path determination method and function control. The display device 504 can include display devices such as a display screen.

[0116] The one or more modules are stored in the memory 502 and when executed by the one or more processors 501 , the method for determining the learning path in any of the above method embodiments is executed.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 the operation behavior data generated by the students in the key learning area in the learning map, determines the association network between the multiple behavior factors contained in the operation behavior data, and clusters the operation behavior data based on the association network, so as to cluster the operation behavior data into the cluster category corresponding to the behavior factor that has the greatest impact on it, and can accurately cluster the behavior factors that have the greatest impact on the student's operation behavior data, and then extract the student's characteristic information through the clustered operation behavior data to accurately analyze the student's learning situation in the learning grid, and determine the number of students who have not mastered the knowledge point corresponding to the learning grid in each learning grid in the key learning area based on the characteristic information, and the number of students can be used to judge the overall mastery of the knowledge point cluster corresponding to the learning grid by the number of students, and a more accurate key learning area can be obtained through the overall mastery, and then a more accurate student learning path can be obtained through the updated key learning area, so that students can more clearly understand their own learning situation for key knowledge points according to the updated learning path, and improve students' learning efficiency and experience.

[0121] 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.

[0122] 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 method for determining a learning path, characterized in that: include: Obtain the operational behavior data generated by students in the key learning areas in the learning map; Determining a correlation network between a plurality of behavior factors included in the operation behavior data; Performing clustering processing on the operation behavior data based on the association relationship network to cluster the operation behavior data into cluster categories corresponding to the behavior factors that have the greatest impact on the operation behavior data; Extracting characteristic information of students according to the operation behavior data in a plurality of clustering categories, and determining the number of target students corresponding to each learning grid in the key learning area based on the characteristic information, wherein the target students are students who have not mastered the knowledge points in the key knowledge point cluster corresponding to the learning grid; The learning grid contained in the key learning area is updated according to the number of the target students, and the learning path of the students in the learning map is determined based on the updated key learning area.

2. The method according to claim 1, characterized in that: The clustering process of the operation behavior data based on the association relationship network to cluster the operation behavior data into cluster categories corresponding to the behavior factors that have the greatest impact on the operation behavior data includes: Cleaning the operation behavior data to obtain an operation behavior data sample set; Matching the first sample data in the operation behavior data sample set with the association relationship network to match the target behavior factor that has the greatest impact on the first sample data, the first sample data being any sample data in the operation behavior data sample set; Determine the first sample data as a cluster center sample of a target cluster, the cluster center of the target cluster is the target behavior factor, wherein the cluster center of each cluster is used to indicate the operation behavior that has the greatest impact on the cluster sample data; Similarity matching is performed on other sample data in the operation behavior data sample set except the first sample data with the cluster center samples of the target cluster in turn to match multiple clusters except the target cluster, and the multiple clusters are determined as the multiple clustering categories, wherein the cluster center of each cluster in the multiple clusters corresponds to one of the multiple behavior factors.

3. The method according to claim 2, characterized in that The similarity matching of other sample data in the operation behavior data sample set except the first sample data with the cluster center samples of the target cluster in sequence to match multiple clusters except the target cluster includes: Performing similarity matching on other sample data in the operation behavior data sample set except the first sample data and the cluster center samples of the target cluster in sequence; When it is determined that the similarity value obtained by matching is greater than or equal to a predetermined similarity threshold, updating the second sample data currently matching the first sample data as the cluster center sample of the target cluster, wherein the second sample data is any one of the other sample data; When it is determined that the similarity value obtained by matching is less than the predetermined similarity threshold, the second sample data is matched with other clusters respectively for similarity, and based on the matching result, the second sample data is used as the cluster center sample of the cluster with the highest similarity; If it is determined based on the matching result that there is currently no cluster whose similarity with the second sample data is greater than or equal to the predetermined similarity threshold, a cluster is constructed by taking the second sample data as a cluster core sample.

4. The method according to claim 3, characterized in that Extracting characteristic information of students according to the operation behavior data in a plurality of cluster categories, and determining the number of target students corresponding to each learning grid in the key learning area based on the characteristic information, including: Based on the multiple clustering categories and the cluster centers corresponding to the multiple clustering categories, a label portrait feature database corresponding to the operation behavior data sample set is constructed, and label information of students corresponding to the operation behavior data is extracted from the label portrait database; Determine a student to be analyzed who has learned and generated operational learning data in a target learning grid, and analyze the mastery of the knowledge points in the key knowledge point cluster corresponding to the target learning grid by the student to be analyzed based on the label information, wherein the target learning grid is any one of the learning grids included in the key learning area; Based on the analysis results, students among the students to be analyzed who have not mastered the knowledge points in the key knowledge point cluster corresponding to the target learning grid are determined as the target students.

5. The method according to claim 4, characterized in that The updating of the learning grid contained in the key learning area according to the target number of students includes: Evaluate the overall mastery of the knowledge points in the key knowledge point cluster corresponding to the target learning grid according to the number of the target students; When the number of target students is greater than or equal to a predetermined number threshold, it is determined that the overall mastery is not up to standard, and a second identifier for marking the target learning grid is retained in the learning map, the second identifier being used to indicate a learning grid corresponding to a cluster of key knowledge points in the learning map; When the number of target students is greater than or equal to the predetermined number threshold, it is determined that the overall mastery is up to standard, and the second identifier marking the target learning grid in the learning map is replaced with the first identifier, where the first identifier is used to indicate the learning grid corresponding to the basic knowledge point cluster in the learning map; In the learning map, the learning grid marked with the second identifier is re-marked and circled to obtain an updated key learning area.

6. The method according to claim 5, characterized in that Before re-marking and circling the learning grid marked with the second identifier in the learning map to obtain an updated key learning area, the method further includes: Collecting the number of requests for learning collaboration triggered by the student marking the learning grid with the first identifier in the learning map, and comparing the number of requests with a predetermined request threshold; The learning grids with the first identification and the number of requests of which is greater than the predetermined request threshold are marked with the second identification.

7. The method according to claim 5, characterized in that Determine the learning path of students in the learning map based on the updated key learning areas, including: Determine a first number of learning grids marked with the first identifier in the student's learned content, a second number of learning grids marked with the second identifier, and determine a third number of learning grids marked with the second identifier in the learned content based on the updated key learning area; Based on the first number, the second number, and the third number, weighted scoring is performed using the following formula to update the learning path for the key knowledge point cluster in the student's current learning path to obtain an updated learning path; Wherein, C represents the first quantity, V represents the second quantity, and V new represents the third quantity, αβ represents the corresponding weight score, and Z base Indicates the total progress score of the learning path.

8. A device for determining a learning path, characterized in that: include: An acquisition module is configured to acquire operation behavior data generated by students in learning in a key learning area in a learning map; A determination module, configured to determine a correlation relationship network between a plurality of behavior factors included in the operation behavior data; A clustering module is configured to perform clustering processing on the operation behavior data based on the association relationship network, so as to cluster the operation behavior data into cluster categories corresponding to the behavior factors that have the greatest impact on the operation behavior data; The determination module is further configured to extract characteristic information of students according to the operation behavior data in the plurality of clustering categories, and determine the number of target students corresponding to each learning grid in the key learning area based on the characteristic information, wherein the target students are students who have not mastered the knowledge points in the key knowledge point cluster corresponding to the learning grid; The updating module is configured to update the learning grid contained in the key learning area according to the number of the target students, and determine the learning path of the students in the learning map based on the updated key learning area.

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 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 method according to any one of claims 1 to 7 is implemented.

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