Student score analysis method and system based on multi-knowledge graph

Abstract

The invention discloses a student score analysis method and system based on a multi-knowledge graph. The method comprises the following steps: acquiring examination answer data and student parameter data of a to-be-analyzed student; screening out a plurality of associated subjects according to the examination answer data and the student parameter data; inputting the examination answer data into a knowledge graph model corresponding to each associated subject to obtain a plurality of model analysis results; and determining an examination score analysis result of the to-be-analyzed student according to the plurality of model analysis results and a preset advantage and disadvantage association rule between subjects. Visibly, the knowledge graph models of multiple subjects can be fully utilized to realize student score analysis, so that more effective and deep student score analysis results are obtained, and a more accurate data basis is provided for subsequent teaching.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a student performance analysis method and system based on multiple knowledge graphs. Background Art

[0002] When existing technologies are used to analyze students' grades using big data scenarios, they generally only make simple data judgments based on students' test answer data, without combining knowledge graph models of multiple subjects to comprehensively analyze students' grades. Therefore, they cannot obtain effective and in-depth student grade analysis results, and the teaching assistance effect is poor. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a student performance analysis method and system based on multiple knowledge graphs, which can make full use of the knowledge graph models of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a student performance analysis method based on multiple knowledge graphs, the method comprising: Obtain the test answer data and student parameter data of the students to be analyzed; Filtering out a plurality of related subjects according to the test answer data and the student parameter data; Inputting the test answer data into the knowledge graph model corresponding to each of the related subjects to obtain multiple model analysis results; According to the analysis results of the multiple models and the advantages and disadvantages association rules between the preset subjects, the test score analysis results of the students to be analyzed are determined.

[0005] As an optional embodiment, in the first aspect of the present invention, the student parameter data includes at least one of student grade, student age, student class and student gender.

[0006] As an optional implementation, in the first aspect of the present invention, the test answer data includes test answer data of multiple subjects of the student to be analyzed in the same learning stage.

[0007] As an optional implementation, in the first aspect of the present invention, the screening out of a plurality of related subjects according to the test answer data and the student parameter data includes: Based on the preset correspondence between subjects and data matching keywords, a plurality of subject keywords are matched in the test answer data; For each candidate subject, obtain the history teaching record and history examination record corresponding to the candidate subject; Calculating the matching degree between the candidate subject and the student parameter data according to the historical teaching records and the historical examination records; Determine the candidate subjects whose matching degree is greater than a first matching degree threshold as candidate associated subjects; The intersections between all the candidate associated subjects and all the subjects corresponding to the subject keywords are calculated to obtain a plurality of associated subjects.

[0008] As an optional implementation, in the first aspect of the present invention, the calculating the matching degree between the candidate subject and the student parameter data according to the historical teaching records and the historical examination records includes: Calculating an average value of similarities between a student parameter of each teaching student in the historical teaching record and the student parameter data to obtain a first similarity parameter; Calculating an average of similarities between the student parameter of each test student in the historical test record and the student parameter data to obtain a second similarity parameter; Calculate the weighted average of the first similarity parameter and the second similarity parameter to obtain the matching degree between the candidate subject and the student parameter data; wherein the weighted calculation weight corresponding to the first similarity parameter or the second similarity parameter is proportional to the total number of the corresponding teaching students or the examination students.

[0009] As an optional implementation, in the first aspect of the present invention, the test answer data is input into the knowledge graph model corresponding to each of the associated subjects to obtain multiple model analysis results, including: Determine multiple candidate knowledge graph models in a preset knowledge graph library; For each of the associated subjects, the similarity between the subject parameters in the knowledge base data of each of the candidate knowledge graph models and the associated subject is calculated to obtain the subject matching degree corresponding to each of the candidate knowledge graph models; Determine the candidate knowledge graph model with the highest subject matching degree as the knowledge graph model corresponding to the associated subject; The test answer data is input into the knowledge graph model corresponding to each of the related subjects to obtain the model analysis results output by the knowledge graph model corresponding to each of the related subjects.

[0010] As an optional embodiment, in the first aspect of the present invention, the knowledge graph model is trained by a training data set including a plurality of training students' answer data and corresponding knowledge point mastery annotations; the model analysis results include mastered knowledge points, weak knowledge points, learning advantages and learning disadvantages.

[0011] As an optional implementation, in the first aspect of the present invention, determining the test score analysis results of the students to be analyzed based on the analysis results of the multiple models and the advantages and disadvantages association rules between the preset subjects includes: Calculate the intersection of the analysis conclusions in all the model analysis results to obtain a set of analysis conclusions; the analysis conclusions are mastered knowledge points, weak knowledge points, learning advantages or learning disadvantages; For each analysis conclusion in the analysis conclusion set, calculating the number of occurrences of the analysis conclusion in all the model analysis results; Determine the subject corresponding to the model analysis result in which the analysis conclusion appears, and obtain multiple corresponding subjects corresponding to the analysis conclusion; According to the preset common teaching point rules between subjects, all common teaching points corresponding to the multiple corresponding subjects are determined to obtain a common teaching point set; the common teaching point rules are used to define the common teaching points in the teaching tasks of any two subjects; Calculating a matching degree parameter between the analysis conclusion and the common teaching point set; Calculate the number of occurrences and the scores of the matching degree parameters to obtain the credibility corresponding to the analysis conclusion; Deleting the analysis conclusions whose credibility is lower than a preset credibility threshold in the analysis conclusion set; All the remaining analysis conclusions and the corresponding credibility in the analysis conclusion set are determined as the test score analysis result of the student to be analyzed.

[0012] A second aspect of an embodiment of the present invention discloses a student performance analysis system based on multiple knowledge graphs, the system comprising: An acquisition module is used to acquire the test answer data and student parameter data of the students to be analyzed; A screening module, used for screening out a plurality of related subjects according to the test answer data and the student parameter data; An analysis module, used for inputting the test answer data into the knowledge graph model corresponding to each of the related subjects to obtain multiple model analysis results; The determination module is used to determine the test score analysis results of the students to be analyzed based on the analysis results of the multiple models and the advantages and disadvantages association rules between preset subjects.

[0013] As an optional implementation, in the second aspect of the present invention, the student parameter data includes at least one of student grade, student age, student class and student gender.

[0014] As an optional implementation, in the second aspect of the present invention, the test answer data includes test answer data of multiple subjects of the student to be analyzed in the same learning stage.

[0015] As an optional implementation, in the second aspect of the present invention, the specific manner in which the screening module screens out multiple related subjects according to the test answer data and the student parameter data includes: Based on the preset correspondence between subjects and data matching keywords, a plurality of subject keywords are matched in the test answer data; For each candidate subject, obtain the history teaching record and history examination record corresponding to the candidate subject; Calculating the matching degree between the candidate subject and the student parameter data according to the historical teaching records and the historical examination records; Determine the candidate subjects whose matching degree is greater than a first matching degree threshold as candidate associated subjects; The intersections between all the candidate associated subjects and all the subjects corresponding to the subject keywords are calculated to obtain a plurality of associated subjects.

[0016] As an optional implementation, in the second aspect of the present invention, the specific manner in which the screening module calculates the degree of matching between the candidate subject and the student parameter data according to the historical teaching records and the historical examination records includes: Calculating an average value of similarities between a student parameter of each teaching student in the historical teaching record and the student parameter data to obtain a first similarity parameter; Calculating an average of similarities between the student parameter of each test student in the historical test record and the student parameter data to obtain a second similarity parameter; Calculate the weighted average of the first similarity parameter and the second similarity parameter to obtain the matching degree between the candidate subject and the student parameter data; wherein the weighted calculation weight corresponding to the first similarity parameter or the second similarity parameter is proportional to the total number of the corresponding teaching students or the examination students.

[0017] As an optional implementation, in the second aspect of the present invention, the analysis module inputs the test answer data into the knowledge graph model corresponding to each of the associated subjects to obtain a specific method of multiple model analysis results, including: Determine multiple candidate knowledge graph models in a preset knowledge graph library; For each of the associated subjects, the similarity between the subject parameters in the knowledge base data of each of the candidate knowledge graph models and the associated subject is calculated to obtain the subject matching degree corresponding to each of the candidate knowledge graph models; Determine the candidate knowledge graph model with the highest subject matching degree as the knowledge graph model corresponding to the associated subject; The test answer data is input into the knowledge graph model corresponding to each of the related subjects to obtain the model analysis results output by the knowledge graph model corresponding to each of the related subjects.

[0018] As an optional embodiment, in the second aspect of the present invention, the knowledge graph model is trained by a training data set including a plurality of training students' answer data and corresponding knowledge point mastery annotations; the model analysis results include mastered knowledge points, weak knowledge points, learning advantages and learning disadvantages.

[0019] As an optional implementation, in the second aspect of the present invention, the determination module determines the specific method of the test score analysis result of the student to be analyzed according to the multiple model analysis results and the advantages and disadvantages association rules between the preset subjects, including: Calculate the intersection of the analysis conclusions in all the model analysis results to obtain a set of analysis conclusions; the analysis conclusions are mastered knowledge points, weak knowledge points, learning advantages or learning disadvantages; For each analysis conclusion in the analysis conclusion set, calculating the number of occurrences of the analysis conclusion in all the model analysis results; Determine the subject corresponding to the model analysis result in which the analysis conclusion appears, and obtain multiple corresponding subjects corresponding to the analysis conclusion; According to the preset common teaching point rules between subjects, all common teaching points corresponding to the multiple corresponding subjects are determined to obtain a common teaching point set; the common teaching point rules are used to define the common teaching points in the teaching tasks of any two subjects; Calculating a matching degree parameter between the analysis conclusion and the common teaching point set; Calculate the number of occurrences and the scores of the matching degree parameters to obtain the credibility corresponding to the analysis conclusion; Deleting the analysis conclusions whose credibility is lower than a preset credibility threshold in the analysis conclusion set; All the remaining analysis conclusions and the corresponding credibility in the analysis conclusion set are determined as the test score analysis result of the student to be analyzed.

[0020] The third aspect of the present invention discloses another student performance analysis system based on multiple knowledge graphs, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the student performance analysis method based on multiple knowledge graphs disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the student performance analysis method based on multiple knowledge graphs disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can determine multiple related subjects based on test answer data and student parameter data, and then analyze the student's test answer data according to the knowledge graph model corresponding to each related subject to obtain multiple model analysis results, and finally determine the student's test score analysis results based on the preset advantages and disadvantages association rules between subjects, thereby making full use of the knowledge graph models of multiple subjects to realize student score analysis, so as to obtain more effective and in-depth student score analysis results, and provide a more accurate data basis for subsequent teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flowchart of a student performance analysis method based on multiple knowledge graphs disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural diagram of a student performance analysis system based on multiple knowledge graphs disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural diagram of another student performance analysis system based on multiple knowledge graphs disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a student performance analysis method and system based on multiple knowledge graphs, which can determine multiple related subjects based on test answer data and student parameter data, and then analyze the student's test answer data according to the knowledge graph model corresponding to each related subject to obtain multiple model analysis results, and finally determine the student's test performance analysis results based on the advantages and disadvantages association rules between preset subjects, so as to fully utilize the knowledge graph models of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 : is a flow chart of a method for analyzing student performance based on multiple knowledge graphs disclosed in an embodiment of the present invention. Figure 1 The described method for analyzing student performance based on multiple knowledge graphs can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1As shown, the student performance analysis method based on multiple knowledge graphs may include the following operations: 101. Obtain the test answer data and student parameter data of the students to be analyzed.

[0032] 102. Filter out multiple related subjects based on test answer data and student parameter data. 103. Input the test answer data into the knowledge graph model corresponding to each related subject to obtain multiple model analysis results. 104. According to the analysis results of multiple models and the advantages and disadvantages association rules between preset subjects, the test score analysis results of the students to be analyzed are determined.

[0033] It can be seen that the above-mentioned embodiments of the invention can determine multiple related subjects based on the test answer data and student parameter data, and then analyze the student's test answer data according to the knowledge graph model corresponding to each related subject to obtain multiple model analysis results, and finally determine the student's test score analysis results based on the preset advantages and disadvantages association rules between subjects, thereby making full use of the knowledge graph models of multiple subjects to realize student score analysis, so as to obtain more effective and in-depth student score analysis results, and provide a more accurate data basis for subsequent teaching.

[0034] As an optional embodiment, in the above steps, the student parameter data includes at least one of student grade, student age, student class and student gender.

[0035] It can be seen that through the above optional embodiments, the content of student parameter data is limited to comprehensively characterize the characteristics of the students to be analyzed, and to assist in making full use of the knowledge graph models of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0036] As an optional embodiment, in the above steps, the test answer data includes test answer data of multiple subjects of the students to be analyzed in the same learning stage.

[0037] It can be seen that through the above optional embodiments, the content of the test answer data is limited to comprehensively characterize the answers of the students to be analyzed in multiple subjects in the same learning stage, and assist in making full use of the knowledge graph model of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0038] As an optional embodiment, in the above step, multiple related subjects are screened out according to the test answer data and the student parameter data, including: Based on the correspondence between preset subjects and data matching keywords, multiple subject keywords are matched in the test answer data; For each candidate subject, obtain the history teaching record and history examination record corresponding to the candidate subject; According to the historical teaching records and historical examination records, the matching degree between the candidate subject and the student parameter data is calculated; Determine the candidate subjects whose matching degree is greater than the first matching degree threshold as candidate associated subjects; The intersections between all candidate associated subjects and subjects corresponding to all subject keywords are calculated to obtain multiple associated subjects.

[0039] It can be seen that through the above-mentioned optional embodiments, the most reasonable multiple related subjects can be determined by calculating the intersection between the results of keyword matching and the screening results of historical record matching, which can be used for subsequent model analysis and comprehensive performance analysis, and assist in making full use of the knowledge graph model of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0040] As an optional embodiment, in the above step, calculating the matching degree between the candidate subject and the student parameter data according to the historical teaching records and the historical examination records includes: Calculate the average of the similarities between the student parameter and the student parameter data of each teaching student in the historical teaching record to obtain a first similarity parameter; Calculate the average of the similarities between the student parameter and the student parameter data of each test student in the historical test records to obtain a second similarity parameter; The weighted average of the first similarity parameter and the second similarity parameter is calculated to obtain the matching degree between the candidate subject and the student parameter data; wherein the weighted calculation weight corresponding to the first similarity parameter or the second similarity parameter is proportional to the total number of the corresponding teaching students or examination students.

[0041] It can be seen that through the above-mentioned optional embodiments, the matching degree of candidate subjects can be accurately calculated by calculating the similarity between students in historical teaching records and examination records and current students, so as to facilitate the subsequent determination of the most reasonable multiple related subjects for model analysis and comprehensive performance analysis, and assist in making full use of the knowledge graph model of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0042] As an optional embodiment, in the above steps, the test answer data is input into the knowledge graph model corresponding to each associated subject to obtain multiple model analysis results, including: Determine multiple candidate knowledge graph models in a preset knowledge graph library; For each associated subject, calculate the similarity between the subject parameters in the knowledge base data of each candidate knowledge graph model and the associated subject, and obtain the subject matching degree corresponding to each candidate knowledge graph model; Determine the candidate knowledge graph model with the highest subject matching degree as the knowledge graph model corresponding to the associated subject; The test answer data is input into the knowledge graph model corresponding to each related subject to obtain the model analysis results output by the knowledge graph model corresponding to each related subject.

[0043] It can be seen that through the above-mentioned optional embodiments, the knowledge graph model corresponding to the related subjects can be determined based on similarity calculation and screening to predict more accurate model analysis results, and assist in making full use of the knowledge graph models of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0044] As an optional embodiment, in the above steps, the knowledge graph model is trained by a training data set including multiple training students' answer data and corresponding knowledge point mastery annotations; the model analysis results include mastered knowledge points, weak knowledge points, learning advantages and learning disadvantages.

[0045] It can be seen that through the above optional embodiments, the training details of the knowledge graph model and the content of the analysis results are limited, which can effectively analyze and predict the students' learning situation, so as to facilitate the subsequent implementation of a more comprehensive performance analysis, and assist in making full use of the knowledge graph models of multiple subjects to realize the student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0046] As an optional embodiment, in the above steps, determining the test score analysis results of the students to be analyzed based on the analysis results of multiple models and the advantages and disadvantages association rules between preset subjects includes: Calculate the intersection of the analysis conclusions in all model analysis results to obtain a set of analysis conclusions; optionally, the analysis conclusions are mastered knowledge points, weak knowledge points, learning advantages or learning disadvantages; For each analysis conclusion in the analysis conclusion set, calculate the number of occurrences of the analysis conclusion in all model analysis results; Determine the subject corresponding to the model analysis result in which the analysis conclusion appears, and obtain multiple corresponding subjects corresponding to the analysis conclusion; According to the preset common teaching point rules between subjects, all common teaching points corresponding to multiple corresponding subjects are determined to obtain a common teaching point set; optionally, the common teaching point rules are used to define the common teaching points in the teaching tasks of any two subjects; Calculate the matching degree parameter between the analysis conclusion and the common teaching point set; Calculate the scores of the number of occurrences and the matching degree parameters to obtain the corresponding credibility of the analysis conclusion; Deleting analysis conclusions whose credibility is lower than a preset credibility threshold in the analysis conclusion set; All remaining analysis conclusions and corresponding credibility in the analysis conclusion set are determined as the test score analysis results of the students to be analyzed.

[0047] It can be seen that through the above-mentioned optional embodiments, the common conclusions of multiple model analysis results can be screened and the credibility calculated according to the common teaching point rules between preset subjects, so as to obtain more comprehensive analysis results of students' learning advantages and disadvantages, and make full use of the knowledge graph models of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0048] Embodiment 2 See also Figure 2 , Figure 2 Schematic diagram of a student performance analysis system based on multiple knowledge graphs disclosed in an embodiment of the present invention. Figure 2 The described student performance analysis system based on multiple knowledge graphs can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the student performance analysis system based on multiple knowledge graphs may include: The acquisition module 201 is used to acquire the test answer data and student parameter data of the students to be analyzed.

[0049] The screening module 202 is used to screen out a plurality of related subjects according to the test answer data and the student parameter data. The analysis module 203 is used to input the test answer data into the knowledge graph model corresponding to each related subject to obtain multiple model analysis results. The determination module 204 is used to determine the test score analysis results of the students to be analyzed based on the analysis results of multiple models and the advantages and disadvantages association rules between preset subjects.

[0050] It can be seen that the above-mentioned embodiments of the invention can determine multiple related subjects based on the test answer data and student parameter data, and then analyze the student's test answer data according to the knowledge graph model corresponding to each related subject to obtain multiple model analysis results, and finally determine the student's test score analysis results based on the preset advantages and disadvantages association rules between subjects, thereby making full use of the knowledge graph models of multiple subjects to realize student score analysis, so as to obtain more effective and in-depth student score analysis results, and provide a more accurate data basis for subsequent teaching.

[0051] As an optional embodiment, the student parameter data includes at least one of student grade, student age, student class and student gender.

[0052] It can be seen that through the above optional embodiments, the content of student parameter data is limited to comprehensively characterize the characteristics of the students to be analyzed, and to assist in making full use of the knowledge graph models of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0053] As an optional embodiment, the test answer data includes test answer data of multiple subjects of the students to be analyzed in the same learning stage.

[0054] It can be seen that through the above optional embodiments, the content of the test answer data is limited to comprehensively characterize the answers of the students to be analyzed in multiple subjects in the same learning stage, and assist in making full use of the knowledge graph model of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0055] As an optional embodiment, the screening module screens out a plurality of related subjects according to the test answer data and the student parameter data, including: Based on the correspondence between preset subjects and data matching keywords, multiple subject keywords are matched in the test answer data; For each candidate subject, obtain the history teaching record and history examination record corresponding to the candidate subject; According to the historical teaching records and historical examination records, the matching degree between the candidate subject and the student parameter data is calculated; Determine the candidate subjects whose matching degree is greater than the first matching degree threshold as candidate associated subjects; The intersections between all candidate associated subjects and subjects corresponding to all subject keywords are calculated to obtain multiple associated subjects.

[0056] It can be seen that through the above-mentioned optional embodiments, the most reasonable multiple related subjects can be determined by calculating the intersection between the results of keyword matching and the screening results of historical record matching, which can be used for subsequent model analysis and comprehensive performance analysis, and assist in making full use of the knowledge graph model of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0057] As an optional embodiment, the specific manner in which the screening module calculates the matching degree between the candidate subject and the student parameter data according to the historical teaching records and the historical examination records includes: Calculate the average of the similarities between the student parameter and the student parameter data of each teaching student in the historical teaching record to obtain a first similarity parameter; Calculate the average of the similarities between the student parameter and the student parameter data of each test student in the historical test records to obtain a second similarity parameter; The weighted average of the first similarity parameter and the second similarity parameter is calculated to obtain the matching degree between the candidate subject and the student parameter data; wherein the weighted calculation weight corresponding to the first similarity parameter or the second similarity parameter is proportional to the total number of the corresponding teaching students or examination students.

[0058] It can be seen that through the above-mentioned optional embodiments, the matching degree of candidate subjects can be accurately calculated by calculating the similarity between students in historical teaching records and examination records and current students, so as to facilitate the subsequent determination of the most reasonable multiple related subjects for model analysis and comprehensive performance analysis, and assist in making full use of the knowledge graph model of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0059] As an optional embodiment, the analysis module inputs the test answer data into the knowledge graph model corresponding to each associated subject to obtain a plurality of model analysis results, including: Determine multiple candidate knowledge graph models in a preset knowledge graph library; For each associated subject, calculate the similarity between the subject parameters in the knowledge base data of each candidate knowledge graph model and the associated subject, and obtain the subject matching degree corresponding to each candidate knowledge graph model; Determine the candidate knowledge graph model with the highest subject matching degree as the knowledge graph model corresponding to the associated subject; The test answer data is input into the knowledge graph model corresponding to each related subject to obtain the model analysis results output by the knowledge graph model corresponding to each related subject.

[0060] It can be seen that through the above-mentioned optional embodiments, the knowledge graph model corresponding to the related subjects can be determined based on similarity calculation and screening to predict more accurate model analysis results, and assist in making full use of the knowledge graph models of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0061] As an optional embodiment, the knowledge graph model is trained by a training data set including multiple training students' answer data and corresponding knowledge point mastery annotations; the model analysis results include mastered knowledge points, weak knowledge points, learning advantages and learning disadvantages.

[0062] It can be seen that through the above optional embodiments, the training details of the knowledge graph model and the content of the analysis results are limited, which can effectively analyze and predict the students' learning situation, so as to facilitate the subsequent implementation of a more comprehensive performance analysis, and assist in making full use of the knowledge graph models of multiple subjects to realize the student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0063] As an optional embodiment, the determination module determines the specific method of analyzing the test scores of the students to be analyzed based on the analysis results of multiple models and the advantages and disadvantages association rules between preset subjects, including: Calculate the intersection of the analysis conclusions in all model analysis results to obtain a set of analysis conclusions; optionally, the analysis conclusions are mastered knowledge points, weak knowledge points, learning advantages or learning disadvantages; For each analysis conclusion in the analysis conclusion set, calculate the number of occurrences of the analysis conclusion in all model analysis results; Determine the subject corresponding to the model analysis result in which the analysis conclusion appears, and obtain multiple corresponding subjects corresponding to the analysis conclusion; According to the preset common teaching point rules between subjects, all common teaching points corresponding to multiple corresponding subjects are determined to obtain a common teaching point set; optionally, the common teaching point rules are used to define the common teaching points in the teaching tasks of any two subjects; Calculate the matching degree parameter between the analysis conclusion and the common teaching point set; Calculate the scores of the number of occurrences and the matching degree parameters to obtain the corresponding credibility of the analysis conclusion; Deleting analysis conclusions whose credibility is lower than a preset credibility threshold in the analysis conclusion set; All remaining analysis conclusions and corresponding credibility in the analysis conclusion set are determined as the test score analysis results of the students to be analyzed.

[0064] It can be seen that through the above-mentioned optional embodiments, the common conclusions of multiple model analysis results can be screened and the credibility calculated according to the common teaching point rules between preset subjects, so as to obtain more comprehensive analysis results of students' learning advantages and disadvantages, and make full use of the knowledge graph models of multiple subjects to realize student performance analysis, so as to obtain more effective and in-depth student performance analysis results, and provide a more accurate data basis for subsequent teaching.

[0065] Embodiment 3 See also Figure 3 , Figure 3 It is another student performance analysis system based on multiple knowledge graphs disclosed in an embodiment of the present invention. Figure 3 The described student performance analysis system based on multiple knowledge graphs is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the student performance analysis system based on multiple knowledge graphs may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; Among them, the processor 302 calls the executable program code stored in the memory 301 to execute the steps of the student performance analysis method based on multiple knowledge graphs described in Example 1.

[0066] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium that stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the student performance analysis method based on multiple knowledge graphs described in Embodiment 1.

[0067] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the student performance analysis method based on multiple knowledge graphs described in Example 1.

[0068] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0070] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0077] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0079] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0080] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0081] Finally, it should be noted that the student performance analysis method and system based on multiple knowledge graphs disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. A student performance analysis method based on multiple knowledge graphs, characterized in that: The method comprises: Obtain the test answer data and student parameter data of the students to be analyzed; Filtering out a plurality of related subjects according to the test answer data and the student parameter data; Inputting the test answer data into the knowledge graph model corresponding to each of the related subjects to obtain multiple model analysis results; According to the analysis results of the multiple models and the advantages and disadvantages association rules between the preset subjects, the test score analysis results of the students to be analyzed are determined.

2. The student performance analysis method based on multiple knowledge graphs according to claim 1 is characterized in that: The student parameter data includes at least one of student grade, student age, student class and student gender.

3. The student performance analysis method based on multiple knowledge graphs according to claim 1 is characterized in that: The test answer data includes test answer data of multiple subjects of the student to be analyzed in the same learning stage.

4. The method for analyzing student performance based on multiple knowledge graphs according to claim 1, characterized in that: The method of screening out a plurality of related subjects according to the test answer data and the student parameter data includes: Based on the preset correspondence between subjects and data matching keywords, a plurality of subject keywords are matched in the test answer data; For each candidate subject, obtain the history teaching record and history examination record corresponding to the candidate subject; Calculating the matching degree between the candidate subject and the student parameter data according to the historical teaching records and the historical examination records; Determine the candidate subjects whose matching degree is greater than a first matching degree threshold as candidate associated subjects; The intersections between all the candidate associated subjects and all the subjects corresponding to the subject keywords are calculated to obtain a plurality of associated subjects.

5. The method for analyzing student performance based on multiple knowledge graphs according to claim 4 is characterized in that: The calculating the matching degree between the candidate subject and the student parameter data according to the historical teaching records and the historical examination records includes: Calculating an average value of similarities between a student parameter of each teaching student in the historical teaching record and the student parameter data to obtain a first similarity parameter; Calculating an average of similarities between the student parameter of each test student in the historical test record and the student parameter data to obtain a second similarity parameter; Calculate the weighted average of the first similarity parameter and the second similarity parameter to obtain the matching degree between the candidate subject and the student parameter data; wherein the weighted calculation weight corresponding to the first similarity parameter or the second similarity parameter is proportional to the total number of the corresponding teaching students or the examination students.

6. The student performance analysis method based on multiple knowledge graphs according to claim 1 is characterized in that: The test answer data is input into the knowledge graph model corresponding to each of the related subjects to obtain multiple model analysis results, including: Determine multiple candidate knowledge graph models in a preset knowledge graph library; For each of the associated subjects, the similarity between the subject parameters in the knowledge base data of each of the candidate knowledge graph models and the associated subject is calculated to obtain the subject matching degree corresponding to each of the candidate knowledge graph models; Determine the candidate knowledge graph model with the highest subject matching degree as the knowledge graph model corresponding to the associated subject; The test answer data is input into the knowledge graph model corresponding to each of the related subjects to obtain the model analysis results output by the knowledge graph model corresponding to each of the related subjects.

7. The student performance analysis method based on multiple knowledge graphs according to claim 1 is characterized in that: The knowledge graph model is trained by a training data set including a plurality of training students' answer data and corresponding knowledge point mastery status annotations; the model analysis results include mastered knowledge points, weak knowledge points, learning advantages and learning disadvantages.

8. The method for analyzing student performance based on multiple knowledge graphs according to claim 7, characterized in that: Determining the test score analysis result of the student to be analyzed based on the analysis results of the multiple models and the advantages and disadvantages association rules between the preset subjects includes: Calculate the intersection of the analysis conclusions in all the model analysis results to obtain a set of analysis conclusions; the analysis conclusions are mastered knowledge points, weak knowledge points, learning advantages or learning disadvantages; For each analysis conclusion in the analysis conclusion set, calculating the number of occurrences of the analysis conclusion in all the model analysis results; Determine the subject corresponding to the model analysis result in which the analysis conclusion appears, and obtain multiple corresponding subjects corresponding to the analysis conclusion; According to the preset common teaching point rules between subjects, all common teaching points corresponding to the multiple corresponding subjects are determined to obtain a common teaching point set; the common teaching point rules are used to define the common teaching points in the teaching tasks of any two subjects; Calculating a matching degree parameter between the analysis conclusion and the common teaching point set; Calculate the number of occurrences and the scores of the matching degree parameters to obtain the credibility corresponding to the analysis conclusion; Deleting the analysis conclusions whose credibility is lower than a preset credibility threshold in the analysis conclusion set; All the remaining analysis conclusions and the corresponding credibility in the analysis conclusion set are determined as the test score analysis result of the student to be analyzed.

9. A student performance analysis system based on multiple knowledge graphs, characterized in that: The system comprises: An acquisition module is used to acquire the test answer data and student parameter data of the students to be analyzed; A screening module, used for screening out a plurality of related subjects according to the test answer data and the student parameter data; An analysis module, used for inputting the test answer data into the knowledge graph model corresponding to each of the related subjects to obtain multiple model analysis results; The determination module is used to determine the test score analysis results of the students to be analyzed based on the analysis results of the multiple models and the advantages and disadvantages association rules between preset subjects.

10. A student performance analysis system based on multiple knowledge graphs, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the student performance analysis method based on multiple knowledge graphs as described in any one of claims 1-8.

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