Interactive content generation method and system for ar intelligent teaching

Abstract

The application provides an interactive content generation method and system for AR intelligent teaching, and relates to the technical field of AR intelligent teaching. The method comprises the following steps: acquiring AR intelligent teaching content data, and establishing an AR intelligent teaching content database; performing knowledge point-based clustering and connection analysis on the teaching content in the AR intelligent teaching content database to form teaching content clusters; collecting historical teaching result data of the teaching content clusters, and performing hierarchical mapping analysis based on the teaching results to form interactive content hierarchical mapping data; acquiring real-time teaching result information, and performing interactive content generation guidance processing according to the interactive content hierarchical mapping data to form real-time interactive teaching content. The method realizes the interactive real-time generation of AR intelligent teaching content based on historical teaching data, further improves the personalized function of AR intelligent teaching, and greatly improves the teaching quality and teaching effect, and has stronger adaptability to meet the needs of different teaching objects.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of AR intelligent teaching, in particular to an interactive content generation method and system for AR intelligent teaching. BACKGROUND

[0002] With the progress of society and science, the emergence and application of new technologies such as intelligence and virtual reality in various industries have effectively improved production efficiency. In the field of teaching, applying AR intelligent teaching can bring a sense of being on the scene to the teaching objects to absorb and understand the knowledge in a more intuitive and efficient way, further improving the quality and effect of teaching.

[0003] At present, AR intelligent teaching basically uses a pre-stored teaching database to arrange a step-by-step teaching course according to a set teaching order, which although strengthens the understanding and absorption effect of knowledge points, cannot completely adapt to all teaching objects due to the unevenness of basic knowledge of different teaching objects. If the teaching content can be generated and interacted individually, the learning effect and efficiency of the teaching objects can be certainly improved more effectively.

[0004] Therefore, a kind of interactive content generation method and system for AR intelligent teaching are designed, which realizes the interactive real-time generation of AR intelligent teaching content based on historical teaching data, further improves the individualization function of AR intelligent teaching, makes the teaching quality and teaching effect greatly improved, meets the needs of different teaching objects and has stronger adaptability, which is the problem to be solved at present. SUMMARY

[0005] The purpose of the present application is to provide an interactive content generation method for AR intelligent teaching, which clusters different teaching contents based on the connection relationship of knowledge points by establishing a database of AR intelligent teaching content, forms a teaching content cluster that can guide the teaching order, and on this basis, analyzes the adaptability of different knowledge point teaching contents according to the score of different teaching contents in the historical teaching process by the teaching objects, i.e. the determination of the teaching relationship of the teaching effect of the adjacent knowledge points, and then provides reasonable and accurate data reference for the interactive generation of the content of the real-time teaching by selecting the teaching content that adapts to the teaching objects, greatly improves the individualization level of the teaching content, and improves the overall teaching effect and teaching quality.

[0006] The application also aims to provide an interactive content generation system for AR intelligent teaching, which forms a complete interactive content generation system through a teaching data acquisition unit, a teaching database, a feature analysis unit and an interactive content generation unit.

[0007] In the first aspect, the application provides an interactive content generation method for AR intelligent teaching, which includes: obtaining AR intelligent teaching content data and establishing an AR intelligent teaching content database; performing knowledge point-based clustering and connection analysis on the teaching content in the AR intelligent teaching content database to form teaching content clusters; collecting historical teaching result data of the teaching content clusters and performing hierarchical mapping analysis based on the teaching results to form interactive content hierarchical mapping data; obtaining real-time teaching result information and performing interactive content generation guidance processing according to the interactive content hierarchical mapping data to form real-time interactive teaching content.

[0008] In the application, the method performs clustering processing on the connection relationship of different teaching content based on knowledge points by establishing a database of AR intelligent teaching content to form teaching content clusters that can guide the teaching sequence, and on this basis, the adaptability of different knowledge point teaching content is analyzed in combination with the score of the teaching object in the historical teaching process, that is, the teaching content of adjacent knowledge points is determined in the teaching relationship of the teaching effect with the score as the reference, thereby providing reasonable and accurate data reference for the interactive generation of the content of the teaching content that is suitable for the teaching object in real-time teaching, greatly improving the individualization level of the teaching content and improving the overall teaching effect and teaching quality.

[0009] As a possible implementation manner, the knowledge point-based clustering and connection analysis on the teaching content in the AR intelligent teaching content database to form the teaching content clusters includes: marking the teaching knowledge points and related knowledge points of different teaching content in the AR intelligent teaching content database; clustering the different teaching content in the AR intelligent teaching content database according to the corresponding teaching knowledge points to form different knowledge point teaching content sets; and performing connection analysis on all related knowledge points corresponding to all teaching content in the different knowledge point teaching content sets to establish the teaching content clusters.

[0010] In the present application, in order to carry out the interactive generation of AR intelligent teaching content, it is necessary to first determine the reasonable order of the teaching content. After all, for a course or professional knowledge, the knowledge points contained are in a system, and different knowledge points have mutual connection relationship. Essentially, only after completing the learning and absorption of the corresponding knowledge points can the next knowledge point related to the application of this knowledge point be effectively learned. The present application completes the reasonable clustering based on knowledge points by marking the relevant knowledge points of different teaching content, and forms a teaching content cluster with a teaching logical order. Here, since different teaching content has pertinence in knowledge points, the teaching knowledge points and related knowledge points are distinguished. For related knowledge points, other knowledge points that are necessarily involved when the teaching knowledge points are the main teaching of the teaching content are distinguished.

[0011] As a possible implementation manner, according to different knowledge points, all related knowledge points corresponding to all teaching content are analyzed, and a teaching content cluster is established, including: determining any knowledge point teaching content set as an analysis object set, and performing the following manner of contact analysis: for the analysis object set, determining the teaching knowledge points of other knowledge point teaching content set as the knowledge point teaching content set of the related knowledge points of the analysis object set, and forming the contact direction from the teaching knowledge points to the related knowledge points on different knowledge point teaching content sets; for each knowledge point teaching content set determined to be in contact with the analysis object set, continue to perform contact analysis for all related knowledge points, determine the corresponding all knowledge point teaching content sets in contact, and until all knowledge point teaching content sets are contacted, a teaching content cluster is formed.

[0012] In the present application, it can be understood that for a course or professional knowledge, the amount of involvement of related knowledge points in the formed teaching content with order has a certain regularity to a certain extent, especially in the initial teaching, the single teaching knowledge point is used as the basic knowledge point of the corresponding course or professional knowledge at the beginning. This kind of knowledge point basically does not involve other related knowledge points, so the number can be used for judgment. But according to the distinction between the teaching knowledge points and the related knowledge points of the teaching content, the present application can determine the teaching order of the knowledge system for any teaching content, that is, according to the related knowledge points of the teaching content, the related knowledge points are traced as the teaching knowledge points, and finally the contact of all teaching content is realized to form a teaching content cluster. Of course, since the related knowledge points are indispensable supplementary knowledge around the teaching knowledge points of the teaching content, for the teaching object, it is necessary to master the related knowledge points before learning and absorbing the teaching knowledge points. Based on such logical order, the teaching order relationship between different teaching contents in the teaching content cluster can be determined.

[0013] As a possible implementation manner, the historical teaching result data of the teaching content cluster is collected, and the teaching result-based hierarchical mapping analysis is performed to form the interactive content hierarchical mapping data, including: determining the historical teaching score data of different teaching contents in different knowledge point teaching content sets according to the historical teaching result data; performing difficulty division based on the historical teaching score on different teaching contents in different knowledge point teaching content sets to form corresponding teaching content difficulty division data; performing qualified screening based on the historical teaching score on different teaching content difficulty division data to form corresponding historical teaching qualified score data; performing score mapping analysis based on the teaching object according to the historical teaching qualified score data corresponding to the different teaching content difficulty division data to form the interactive content hierarchical mapping data.

[0014] In the present application, for different teaching objects, the learning effect on different teaching knowledge points is different, and the difference in learning effect will affect the mastery of the current learning teaching knowledge point and the learning of other teaching knowledge points in the future, and the involvement of this related knowledge point will also affect the mastery of the corresponding teaching knowledge point. The mastery of the knowledge point is usually intuitively reflected in the form of exercises or practice tests, and the mastery of the teaching content is also affected to some extent by the difficulty of the teaching content. Therefore, the score of the historical teaching object on the corresponding teaching content can be used as the basis for dividing the difficulty of the teaching content, especially the total score under big data, which can better eliminate the influence of score changes caused by individual learning conditions on teaching difficulty determination, making the division of teaching content more accurate. Of course, there is a benchmark for judging whether the teaching object has learned through the teaching content of different difficulty. The hierarchical mapping of the teaching content is preferably based on the teaching effect, and the data of unqualified learning results has no practical reference value. After all, these data are largely related to the seriousness of individual learning or the randomness of testing.

[0015] As a possible implementation manner, the difficulty division based on the historical teaching score is performed on different teaching contents in different knowledge point teaching content sets to form corresponding teaching content difficulty division data, including: extracting the historical scores of all teaching objects corresponding to different teaching contents from different knowledge point teaching content sets; performing normalization processing on the historical scores of all teaching objects to form historical normalized scores, and determining the historical total score of the teaching content; according to the historical total score of the teaching content, the different teaching contents are sorted in descending order to form the teaching content difficulty division data corresponding to the knowledge point teaching content set.

[0016] In the present application, when the difficulty of the same teaching content is divided, the learning score of the historical teaching object is used to determine the big data, it can be understood that different teaching contents may have different total score setting conditions, in order to ensure the uniformity and rationality of the analysis, the normalization processing of the score is necessary, which can make the difficulty division result more accurate and reasonable.

[0017] As a possible implementation, the different teaching content difficulty division data is screened based on the historical teaching score to form corresponding historical teaching qualified score data, including: setting corresponding teaching qualified score for different teaching content difficulty division data; for different teaching content in the teaching content difficulty division data, all historical normalized scores reaching the teaching qualified score are determined as historical normalized qualified scores, and the teaching object is labeled; all historical normalized qualified scores under different teaching contents in the teaching content difficulty division data are collected to form the historical teaching qualified score data.

[0018] In the present application, different teaching objects need to be screened according to individual conditions, learning attitude and learning test contingency, and the data that does not reach the learning satisfactory effect, it can be understood that the teaching qualified score can be set according to the actual evaluation of the absorption effect of the teaching knowledge points, or the reasonable score value can be determined based on big data. After excluding the data that does not reach the qualified score, the remaining data can be used as the basis data for subsequent learning content interaction correlation analysis of different teaching knowledge points, so that the analysis is more reasonable and accurate.

[0019] As a possible implementation, according to the historical teaching qualified score data corresponding to the different teaching content difficulty division data, the score mapping analysis based on the teaching object is performed to form the interactive content hierarchical mapping data, including: for different historical teaching qualified score data, the following score mapping analysis is performed according to the teaching object as the analysis standard: according to the order relationship of different knowledge point teaching content sets in the teaching content cluster, the corresponding teaching content and the historical normalized qualified score of the teaching object under any historical teaching qualified score data are determined for different teaching objects; for different teaching objects, the historical normalized qualified score of the same teaching object in the next historical teaching qualified score data is determined according to the connection relationship of the teaching content cluster, and the historical normalized qualified score of the same teaching object in the next historical teaching qualified score data is continued to be obtained until no historical normalized qualified score of the same teaching object is obtained, and all historical normalized qualified scores of the teaching object are labeled according to the corresponding teaching content and the order connection based on the teaching content cluster to form the object score mapping relationship; for different teaching objects, the different historical normalized qualified scores of different objects on different teaching contents in the next historical teaching qualified score data are determined on any two adjacent knowledge point teaching content sets according to the order connection relationship of the teaching content cluster, and the teaching content mapping score range of each last knowledge point teaching content set is formed m represents the number of different knowledge point teaching content sets, m+1 represents the next knowledge point teaching content set adjacent to the knowledge point teaching content set numbered m in the connection relationship of the teaching content cluster, represents the qualified score value corresponding to the teaching content numbered n in the knowledge point teaching content set numbered m, n represents the number of different teaching contents corresponding to the knowledge point teaching content set numbered m, k represents the number of different teaching contents in the next knowledge point teaching content set adjacent to the knowledge point teaching content set numbered m in the connection relationship of the teaching content cluster, represents the qualified score value determined for the teaching content numbered n in the knowledge point teaching content set numbered m the qualified score value range formed by the teaching content numbered k in the adjacent next knowledge point teaching content set; the teaching content mapping score range corresponding to the different scores of different teaching contents in each knowledge point teaching content set is formed to form the interactive content hierarchical mapping data.

[0020] In the present application, the hierarchical mapping data to be obtained is mainly to determine how to select the subsequent teaching content after the score obtained on the previous teaching content is determined for the teaching object in the teaching sequence defined by the teaching content cluster. After all, different teaching objects based on the mastery of the previous teaching content and the individual's own situation of knowledge learning will affect the selection of the difficulty level of the subsequent learning content. By taking the teaching object as the analysis reference target, the determination of the score that can be obtained by the same object in the subsequent teaching content set after selecting a certain teaching content corresponding to the score determined on the previous teaching content is performed. In this way, the next teaching content difficulty and the teaching content that can be qualified and the corresponding score prediction can be interactively selected according to the score of the current teaching object on the teaching content. Here, for different teaching objects, since the learning ability of the teaching object is unknown during subsequent analysis, if different teaching objects obtain the same score in the current teaching content set, the value range of the score that can be obtained by the teaching content in the next teaching content set can be determined. This can predict the teaching score obtained by teaching objects with different learning abilities, making the subsequent interactive generation of teaching content more flexible and the selection range larger.

[0021] As a possible implementation manner, real-time teaching result information is obtained, and interactive content generation guidance processing is performed according to the interactive content hierarchical mapping data to form real-time interactive teaching content, including: taking the connection relationship of the teaching content cluster as the generation guidance sequence of the teaching content, starting from the first knowledge point teaching content set, generating any teaching content located in the middle level to form an initial sequence teaching content; obtaining the real-time teaching normalized score of the initial sequence teaching content; if the real-time teaching normalized score corresponding to the initial sequence teaching content reaches the corresponding teaching qualified score, the interactive content generation analysis of the next knowledge point teaching content set is performed; if the real-time teaching normalized score corresponding to the initial sequence teaching content does not reach the corresponding teaching qualified score, the difficulty level of the current knowledge point teaching content set and the teaching content adjacent to the initial sequence teaching content and lower in difficulty are taken as new sequence teaching content to continue the score qualification analysis until the real-time teaching normalized score reaches the position of the corresponding teaching qualified score, and then the interactive content generation analysis of the next knowledge point teaching content set is performed.

[0022] In the present application, of course, when generating real-time interactive content, first, teaching is performed in the order of the teaching content cluster, the initial sequential teaching content is determined and score acquisition is performed on the initial sequential teaching content, and the next teaching content is analyzed based on the score of the initial sequential teaching content. For the initial teaching content, since the arbitrary teaching content of the intermediate level is started at the beginning, the intermediate arbitrary teaching is only to select one of the two difficulty levels in the middle if the number of levels of the teaching content is even, or to take the teaching content in the middle as the initial sequential teaching content if it is odd. If the score reaches the teaching passing score value, it can be considered that the teaching object has completed the learning of the teaching content, and the learning of the next teaching content on the teaching content cluster can be performed, and if the teaching passing score value is not reached, the learning is continued in the teaching content cluster with a lower difficulty level until the result of reaching the teaching passing score value is obtained in the selection of the next teaching content set.

[0023] As a possible implementation, the interactive content generation analysis of the next knowledge point teaching content set includes: according to the interactive content grading mapping data, the most difficult teaching content existing in the mapping relationship in the adjacent next knowledge point teaching content set is taken as the interactive generated content to perform the following mapping score range analysis: if the real-time teaching normalized score formed belongs to the mapping score range, then the real-time teaching normalized score is taken as a reference to perform the interactive content generation analysis of the next knowledge point teaching content set; if the real-time teaching normalized score formed does not belong to the mapping score range, the difficulty level of the current knowledge point teaching content set and the teaching content adjacent to the current content and lower in difficulty are taken as new sequential teaching content to continue the score eligibility analysis until the real-time teaching normalized score formed belongs to the corresponding mapping score range, and then the real-time teaching normalized score is taken as a reference to perform the interactive content generation analysis of the next knowledge point teaching content set.

[0024] In the present application, for the selection of the teaching content in the next teaching content set, since the score obtained in the initial sequential teaching content has a mapping relationship in the interactive content grading mapping data, the teaching content suitable for the different difficulty of the teaching object on the next teaching content set and the corresponding score range can be determined, and usually the teaching content with the greatest difficulty is taken as the first choice. If the score after learning is in the corresponding score range, it is considered that the learning state of the teaching object is in a reasonable state, and the learning of the teaching content is passed, and the teaching content selection of the next teaching content set can be performed based on the score reference interactive content grading mapping data, and if the score is not in the corresponding range, it is considered that the corresponding teaching effect is not reached or exceeded. The expected teaching effect is not reached in the usual case, so the learning is performed again by obtaining the teaching content with lower difficulty until the score is in the corresponding range in the selection of the teaching content of the next teaching content set.

[0025] In a second aspect, the present application provides an interactive content generation system for AR intelligent teaching, comprising: a teaching data acquisition unit configured to acquire AR intelligent teaching content data and real-time teaching result information; a teaching database configured to store the AR intelligent teaching content data acquired by the teaching data acquisition unit; a feature analysis unit configured to perform clustering and connection analysis on the AR intelligent teaching content data in the teaching database to form teaching content clusters, and perform hierarchical mapping analysis based on teaching results to form interactive content hierarchical mapping data; and an interactive content generation unit configured to acquire the real-time teaching result information acquired by the teaching data acquisition unit, and perform interactive content generation guidance processing based on the teaching content clusters and the interactive content hierarchical mapping data formed by the feature analysis unit to form real-time interactive teaching content.

[0026] In the present application, the system forms a complete interactive content generation system through the teaching data acquisition unit, the teaching database, the feature analysis unit, and the interactive content generation unit. The different units are different in function and work closely together to complete different content processing work, effectively ensuring the rationality and accuracy of the interactive content generation, and providing an important material basis for realizing the interactive content generation.

[0027] The interactive content generation method and system for AR intelligent teaching provided by the present application have the following beneficial effects:

[0028] The method performs clustering processing on the connection relationship between different teaching content based on knowledge points by establishing a database of AR intelligent teaching content to form teaching content clusters that can guide the teaching order. On this basis, the adaptability of different knowledge point teaching content is analyzed by combining the score of the teaching object in the historical teaching process, i.e., the determination of the teaching relationship of the teaching content of adjacent knowledge points based on the score as a reference, thereby providing reasonable and accurate data reference for the interactive generation of content by selecting teaching content that adapts to the teaching object for real-time teaching, greatly improving the personalization level of the teaching content, and improving the overall teaching effect and teaching quality.

[0029] The system forms a complete interactive content generation system through the teaching data acquisition unit, the teaching database, the feature analysis unit, and the interactive content generation unit. The different units are different in function and work closely together to complete different content processing work, effectively ensuring the rationality and accuracy of the interactive content generation, and providing an important material basis for realizing the interactive content generation. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation to the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0031] Fig. 1 The step diagram of the interactive content generation method for AR intelligent teaching provided by the embodiments of the present application is provided.

[0032] Fig. 2 The structural schematic diagram of the interactive content generation system for AR intelligent teaching provided by the embodiments of the present application is provided. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.

[0034] With the progress of society and science, the emergence and application of new technologies such as intelligence and virtual reality in various industries effectively promote production efficiency. In the field of teaching, applying AR intelligent teaching can bring a sense of being on the scene to the teaching objects to more intuitively and efficiently absorb and understand the knowledge, and further improve the quality and effect of teaching.

[0035] At present, AR intelligent teaching basically uses a pre-stored teaching database to arrange a step-by-step teaching course according to a set teaching order, which although strengthens the understanding and absorption effect of knowledge points, but cannot completely adapt to all teaching objects due to the unevenness of basic knowledge of different teaching objects. If the teaching content can be generated and interacted individually, the learning effect and efficiency of the teaching objects can be definitely improved more effectively.

[0036] Reference Figs. 1-2 The embodiments of the present application provide an interactive content generation method for AR intelligent teaching. The method clusters different teaching contents based on the connection relationship of knowledge points by establishing a database of AR intelligent teaching content, forms a teaching content cluster that can guide the teaching order, and on this basis, analyzes the adaptability of different knowledge point teaching contents in combination with the score of different teaching contents in the historical teaching process by the teaching objects, i.e. determines the teaching relationship of the teaching effect of the adjacent knowledge point teaching contents with the score as a reference, and further provides reasonable and accurate data reference for the interactive generation of the content of the real-time teaching by selecting the teaching content suitable for the teaching objects, greatly improves the individualization level of the teaching content, and improves the overall teaching effect and teaching quality.

[0037] The interactive content generation method for AR intelligent teaching specifically comprises the following steps:

[0038] S1: Obtain AR intelligent teaching content data, and establish an AR intelligent teaching content database.

[0039] For AR intelligent teaching, there is already a complete teaching content, so before the production and analysis of interactive content, the content data needs to be collected to form a data basis.

[0040] S2: Perform knowledge point-based clustering and connection analysis on the teaching content in the AR intelligent teaching content database to form a teaching content cluster.

[0041] The knowledge point-based clustering and connection analysis on the teaching content in the AR intelligent teaching content database to form a teaching content cluster comprises: marking the teaching knowledge points and related knowledge points of different teaching content in the AR intelligent teaching content database; clustering different teaching content in the AR intelligent teaching content database according to the corresponding teaching knowledge points to form different knowledge point teaching content sets; and performing connection analysis on all related knowledge points corresponding to all teaching content in different knowledge point teaching content sets to establish a teaching content cluster.

[0042] To generate interactive AR intelligent teaching content, the teaching content needs to be reasonably sequenced first. After all, for a course or professional knowledge, the knowledge points it contains are in a system, and different knowledge points have mutual connection relationships. Essentially, only after the corresponding knowledge points are learned and absorbed can the next knowledge point associated with the need to apply this knowledge point be effectively learned. The present application completes reasonable knowledge point-based clustering by marking the involved knowledge points of different teaching content to form a teaching content cluster with a teaching logic sequence. Here, because different teaching content has pertinence in knowledge points, the teaching knowledge points and related knowledge points are distinguished. The related knowledge points are other knowledge points that are necessarily involved when the teaching knowledge points are the main teaching knowledge points of the teaching content.

[0043] According to different knowledge points, the teaching content set is analyzed to contact all related knowledge points, and a teaching content cluster is established, including: determining the knowledge point teaching content set as the analysis object set, and performing the following contact analysis: for the analysis object set, determining the teaching knowledge point of the other knowledge point teaching content set as the knowledge point teaching content set of the related knowledge point of the analysis object set, and forming the contact direction from the teaching knowledge point to the related knowledge point on different knowledge point teaching content sets; for each knowledge point teaching content set determined to be in contact with the analysis object set, continue to perform contact analysis for all related knowledge points to determine the corresponding all knowledge point teaching content sets in contact, until all knowledge point teaching content sets are contacted to form a teaching content cluster.

[0044] It can be understood that for a course or professional knowledge, the formed teaching content with sequence has a certain regularity in the amount of related knowledge points involved to a certain extent, especially in the initial teaching, the first single teaching knowledge point is used as the basic knowledge point of the corresponding course or professional knowledge, which basically does not involve other related knowledge points, so the number can be used for judgment. But according to the present application, the teaching sequence of the knowledge system can be determined for any teaching content after the teaching knowledge points and related knowledge points of the teaching content are distinguished, that is, the related knowledge points are traced as teaching knowledge points, and finally the contact of all teaching contents is formed to form a teaching content cluster. Of course, since the related knowledge points are indispensable supplementary knowledge around the teaching knowledge points of the teaching content, the related knowledge points must be mastered by the teaching object before learning and absorbing the teaching knowledge points, and based on such logical sequence, the teaching sequence relationship between different teaching contents in the teaching content cluster can be determined.

[0045] S3: Collect historical teaching result data of the teaching content cluster, and perform hierarchical mapping analysis based on the teaching result to form interactive content hierarchical mapping data.

[0046] Collecting historical teaching result data of the teaching content cluster, and performing hierarchical mapping analysis based on the teaching result to form interactive content hierarchical mapping data, including: determining the historical teaching score data of different teaching contents in different knowledge point teaching content sets according to the historical teaching result data; performing difficulty division based on the historical teaching score for different teaching contents in different knowledge point teaching content sets to form corresponding teaching content difficulty division data; performing qualified screening based on the historical teaching score for different teaching content difficulty division data to form corresponding historical teaching qualified score data; performing score mapping analysis based on the teaching object according to the historical teaching qualified score data corresponding to the different teaching content difficulty division data to form interactive content hierarchical mapping data.

[0047] For different teaching objects, the learning effect on different teaching knowledge points is different, and the difference in learning effect will affect the mastery of the current learning teaching knowledge point and the learning of other teaching knowledge points in the future, and the related knowledge points will also affect the mastery of the corresponding teaching knowledge points. The mastery of the knowledge points is usually directly reflected in the form of exercises or practice tests, and the mastery of the teaching content is also affected by the degree of difficulty of the teaching content to some extent. Therefore, the scores of historical teaching objects on the corresponding teaching content can be used as the basis for the difficulty division of the teaching content, especially the total score under big data, which can better eliminate the influence of individual learning conditions on the accuracy of teaching difficulty determination, making the division of teaching content more accurate. Of course, there are also criteria for judging whether the teaching objects have learned through the teaching content of different difficulty. The grading mapping of teaching content is best based on the teaching effect. The data of unqualified learning results has no practical reference value, because these data are largely related to the seriousness of individual learning or the randomness of testing.

[0048] The difficulty of different teaching contents in different knowledge point teaching content sets is divided based on historical teaching scores to form corresponding teaching content difficulty division data, including: for different knowledge point teaching content sets, extracting the historical scores of all teaching objects corresponding to different teaching contents; normalizing the historical scores of all teaching objects to form historical normalized scores, and determining the teaching content historical total score; according to the teaching content historical total score, the different teaching contents are sorted in descending order to form the teaching content difficulty division data corresponding to the knowledge point teaching content set.

[0049] When the difficulty of the same teaching content is divided, the learning scores of historical teaching objects are used to determine the big data. It can be understood that different teaching contents may have different total score settings, so in order to ensure the unity and rationality of the analysis, it is necessary to normalize the scores to make the difficulty division result more accurate and reasonable.

[0050] The qualified screening of different teaching content difficulty division data is based on historical teaching scores to form corresponding historical teaching qualified score data, including: setting the corresponding teaching qualified score value for different teaching content difficulty division data; for different teaching contents in the teaching content difficulty division data, all historical normalized scores reaching the teaching qualified score value are determined as historical normalized qualified scores, and the teaching objects are marked; all historical normalized qualified scores under different teaching contents in the teaching content difficulty division data are collected to form the historical teaching qualified score data.

[0051] According to the individual situation and the learning attitude and the learning test contingency of different teaching objects, the data that does not reach the learning satisfaction effect needs to be screened. It can be understood that the teaching qualified score can be set according to the actual judgment of the absorption effect of the teaching knowledge points, or the reasonable score value can be determined based on big data. After excluding the data that does not reach the qualified score, the remaining data can be used as the basis data for subsequent learning content interaction analysis of different teaching knowledge points, so that the analysis is more reasonable and accurate.

[0052] According to the historical teaching qualified score data corresponding to the different teaching content difficulty division data, the score mapping analysis based on the teaching object is carried out to form the interactive content classification mapping data, including: for different historical teaching qualified score data, the following score mapping analysis is carried out according to the analysis standard of the teaching object: according to the order relationship of different knowledge point teaching content sets in the teaching content cluster, the corresponding teaching content and historical normalized qualified score of the teaching object under any historical teaching qualified score data are determined; according to the contact relationship of the teaching content cluster, the historical normalized qualified score of the same teaching object in the next historical teaching qualified score data is determined, and the historical normalized qualified score of the next historical teaching qualified score data is continuously obtained until the historical normalized qualified score of the same teaching object is no longer obtained. The historical normalized qualified score of the teaching object is labeled and mapped according to the teaching content and the order contact based on the teaching content cluster to form the object score mapping relationship; for different teaching objects, on any two adjacent knowledge point teaching content sets, the different historical normalized qualified scores of different objects on different teaching contents in the next historical teaching qualified score data are determined according to the order contact relationship of the teaching content cluster, which have the same historical normalized qualified score on the same teaching content in the last historical teaching qualified score data, and then the teaching content mapping score range of different teaching contents in the next knowledge point teaching content set corresponding to the determined score value of the teaching content of each last knowledge point teaching content set is formed m represents the number of different knowledge point teaching content sets, m+1 represents the next knowledge point teaching content set adjacent to the knowledge point teaching content set numbered m in the contact relationship of the teaching content cluster, represents the qualified score value corresponding to the teaching content numbered n in the knowledge point teaching content set numbered m, n represents the number of different teaching contents corresponding to the knowledge point teaching content set numbered m, k represents the number of different teaching contents in the next knowledge point teaching content set adjacent to the knowledge point teaching content set numbered m in the contact relationship of the teaching content cluster, represents the qualified score value determined for the teaching content numbered n in the knowledge point teaching content set numbered m A qualified score value range formed by the teaching content numbered k in the adjacent next knowledge point teaching content set; and a teaching content mapping score range corresponding to different scores of different teaching contents in each knowledge point teaching content set, forming interactive content classification mapping data.

[0053] The classification mapping data to be obtained mainly determines how to select the subsequent teaching content after the score obtained on the previous teaching content is determined for the teaching object in the teaching sequence defined by the teaching content cluster. After all, the mastery of the previous teaching content and the individual's knowledge learning situation will affect the selection of the difficulty level of the next learning content. By taking the teaching object as the analysis reference target, the score that can be obtained by the selected teaching content in the next teaching content set after the score of the same object on the previous teaching content is determined for the adjacent teaching content set. In this way, the difficulty and the teaching content that can be qualified and the corresponding score prediction of the next teaching content can be interactively selected according to the score of the current teaching object on the teaching content. Here, for different teaching objects, since the learning ability of the teaching object is unknown during subsequent analysis, if different teaching objects obtain the same score in the current teaching content set, the value range of the possible score of the next teaching content set can be determined. This can predict the teaching score obtained by teaching objects with different learning abilities, making the subsequent interactive generation of teaching content more flexible and the selection range larger.

[0054] S4: Obtain real-time teaching result information, and perform interactive content generation guidance processing according to the interactive content classification mapping data to form real-time interactive teaching content.

[0055] Obtaining real-time teaching result information and performing interactive content generation guidance processing according to the interactive content classification mapping data to form real-time interactive teaching content, comprising: taking the connection relationship of the teaching content cluster as the generation guidance sequence of the teaching content, starting from the first knowledge point teaching content set, generating any teaching content located in the middle level to form an initial sequence teaching content; obtaining the real-time teaching normalized score of the initial sequence teaching content; if the real-time teaching normalized score corresponding to the initial sequence teaching content reaches the corresponding teaching qualified score, performing interactive content generation analysis of the next knowledge point teaching content set; if the real-time teaching normalized score corresponding to the initial sequence teaching content does not reach the corresponding teaching qualified score, taking the teaching content adjacent to the initial sequence teaching content and having a lower difficulty level in the current knowledge point teaching content set as a new sequence teaching content to continue the score qualification analysis, until the real-time teaching normalized score reaches the position of the corresponding teaching qualified score, and then performing interactive content generation analysis of the next knowledge point teaching content set.

[0056] Of course, in the generation of real-time interactive content, first, the teaching is carried out in the order of the teaching content cluster, the initial order teaching content is determined and the score of the initial order teaching content is obtained, and the next teaching content is analyzed based on the score of the initial order teaching content. For the initial teaching content, since the initial teaching content is started with an arbitrary teaching content of the intermediate level, the intermediate arbitrary teaching is only to select one of the two difficulty levels in the middle if the number of levels of the teaching content is even, and to select the teaching content in the middle as the initial order teaching content if it is odd. If the score reaches the teaching passing score, it can be considered that the teaching object has completed the learning of the teaching content, and the learning of the next teaching content on the teaching content cluster can be carried out, and if the teaching passing score is not reached, the learning is continued in the teaching content cluster with a lower difficulty level until the result of reaching the teaching passing score is obtained in the selection of the next teaching content set.

[0057] The interactive content generation analysis of the next knowledge point teaching content set is carried out, including: according to the interactive content grading mapping data, the most difficult teaching content in the next knowledge point teaching content set which has a mapping relationship is selected as the interactive generated content for the following mapping score range analysis: if the real-time teaching normalized score formed belongs to the mapping score range, then the real-time teaching normalized score is taken as a reference for the interactive content generation analysis of the next knowledge point teaching content set; if the real-time teaching normalized score formed does not belong to the mapping score range, the difficulty level of the current knowledge point teaching content set and the teaching content adjacent to the current knowledge point teaching content set and lower in difficulty are selected as new order teaching content for further score eligibility analysis until the real-time teaching normalized score formed belongs to the corresponding mapping score range, and then the real-time teaching normalized score is taken as a reference for the interactive content generation analysis of the next knowledge point teaching content set.

[0058] For the selection of the teaching content in the next teaching content set, since the score obtained in the initial order teaching content has a mapping relationship in the interactive content grading mapping data, the teaching content suitable for the different difficulty of the teaching object in the next teaching content set and the corresponding score range can be determined, and the teaching content with the highest difficulty is usually selected as the first choice. If the score after learning is in the corresponding score range, it is considered that the learning state of the teaching object is in a reasonable state, and the learning of the teaching content is passed, and the teaching content selection of the next teaching content set can be carried out based on the score reference interactive content grading mapping data, and if the score is not in the corresponding range, it is considered that the corresponding teaching effect is not reached or exceeded. The expected teaching effect is not reached in general, so the teaching content with lower difficulty is obtained again for learning until the score is in the corresponding range for the selection of the teaching content of the next teaching content set.

[0059] The application further provides an interactive content generation system for AR intelligent teaching, which comprises: a teaching data acquisition unit, configured to acquire AR intelligent teaching content data and real-time teaching result information; a teaching database, configured to store the AR intelligent teaching content data acquired by the teaching data acquisition unit; a feature analysis unit, configured to perform clustering and connection analysis on the AR intelligent teaching content data in the teaching database to form teaching content clusters, and perform hierarchical mapping analysis based on teaching results to form interactive content hierarchical mapping data; and an interactive content generation unit, configured to acquire the real-time teaching result information acquired by the teaching data acquisition unit, and perform interactive content generation guidance processing according to the teaching content clusters and the interactive content hierarchical mapping data formed by the feature analysis unit to form real-time interactive teaching content.

[0060] The system forms a complete interactive content generation system through the teaching data acquisition unit, the teaching database, the feature analysis unit and the interactive content generation unit, and different units are different in function and closely cooperate to complete different content processing work, thereby effectively ensuring the rationality and accuracy of interactive content generation and providing an important material basis for realizing interactive content generation.

[0061] In summary, the AR intelligent teaching-oriented interactive content generation method and system provided by the embodiments of the application have the following advantages:

[0062] The method performs clustering processing on the connection relationship between different teaching contents based on knowledge points by establishing a database of AR intelligent teaching content to form teaching content clusters that can guide the teaching sequence, and then performs adaptability analysis on different knowledge point teaching contents by combining the score of the teaching object in the historical teaching process, that is, determines the teaching relationship of the teaching content of adjacent knowledge points in the teaching effect with the score as a reference, thereby providing reasonable and accurate data reference for the interactive generation of content by selecting the teaching content suitable for the teaching object for real-time teaching, greatly improving the individualization level of the teaching content and improving the overall teaching effect and teaching quality.

[0063] The system forms a complete interactive content generation system through the teaching data acquisition unit, the teaching database, the feature analysis unit and the interactive content generation unit, and different units are different in function and closely cooperate to complete different content processing work, thereby effectively ensuring the rationality and accuracy of interactive content generation and providing an important material basis for realizing interactive content generation.

[0064] In the embodiments of the present application, the indication can include direct indication and indirect indication, and can also include explicit indication and implicit indication. The information indicated by certain information is referred to as to-be-indicated information. In the implementation process, there are many ways to indicate the to-be-indicated information, for example, but not limited to, the to-be-indicated information can be directly indicated, such as the to-be-indicated information itself or an index of the to-be-indicated information. The to-be-indicated information can also be indirectly indicated by indicating other information, where the other information and the to-be-indicated information have an association relationship. The to-be-indicated information can also be indicated only by a part of the to-be-indicated information, and the other part of the to-be-indicated information is known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each information agreed in advance (for example, a protocol). Thus, the indication overhead is reduced to a certain extent. Meanwhile, the common part of each information can be identified and uniformly indicated, so as to reduce the indication overhead caused by separately indicating the same information.

[0065] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0066] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0067] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An interactive content generation method for AR intelligent teaching, characterized in that, The application comprises the following steps: Acquiring AR intelligent teaching content data and establishing an AR intelligent teaching content database; Analyzing the teaching content in the AR intelligent teaching content database based on knowledge points to form teaching content clusters; Collecting historical teaching result data of the teaching content clusters and performing hierarchical mapping analysis based on teaching results to form interactive content hierarchical mapping data; Acquiring real-time teaching result information and generating interactive content based on the interactive content hierarchical mapping data to form real-time interactive teaching content; The analysis of the teaching content in the AR intelligent teaching content database based on knowledge points to form teaching content clusters comprises the following steps: Calibrating the teaching knowledge points and related knowledge points of different teaching content in the AR intelligent teaching content database; Clustering different teaching content in the AR intelligent teaching content database based on corresponding teaching knowledge points to form different knowledge point teaching content sets; Analyzing the connection of all related knowledge points corresponding to all teaching content in different knowledge point teaching content sets to establish the teaching content clusters: Determining any knowledge point teaching content set as an analysis object set and performing the following connection analysis: For the analysis object set, determining the teaching knowledge points of other knowledge point teaching content sets as the knowledge point teaching content sets of the related knowledge points of the analysis object set, and forming the connection direction from the teaching knowledge points to the related knowledge points in different knowledge point teaching content sets; For each knowledge point teaching content set connected with the analysis object set, continue to perform connection analysis for all related knowledge points to determine corresponding connected knowledge point teaching content sets, until all knowledge point teaching content sets are connected to form the teaching content clusters; Collecting historical teaching result data of the teaching content clusters and performing hierarchical mapping analysis based on teaching results to form interactive content hierarchical mapping data comprises the following steps: According to the historical teaching result data, determining the historical teaching score data of different teaching content in different knowledge point teaching content sets; Performing difficulty division based on historical teaching scores for different teaching content in different knowledge point teaching content sets to form corresponding teaching content difficulty division data; Performing qualified screening based on historical teaching scores for different teaching content difficulty division data to form corresponding historical teaching qualified score data; Performing score mapping analysis based on teaching objects for different historical teaching qualified score data corresponding to different teaching content difficulty division data to form the interactive content hierarchical mapping data; The difficulty division based on historical teaching scores for different teaching content in different knowledge point teaching content sets to form corresponding teaching content difficulty division data comprises the following steps: Extracting the historical scores of all teaching objects corresponding to different teaching content for different knowledge point teaching content sets; normalizing the history scores of all teaching objects to form history normalized scores, and determining a history total score of teaching content; According to the history total score of teaching content, different teaching contents are sorted in descending order to form the teaching content difficulty division data corresponding to the knowledge point teaching content set; The different teaching content difficulty division data is screened based on the history teaching score to form the corresponding history teaching passing score data, including: For different teaching content difficulty division data, set the corresponding teaching passing score; For different teaching contents in the teaching content difficulty division data, all the history normalized scores reaching the teaching passing score are determined as the history normalized passing scores, and the teaching objects are calibrated; All the history normalized passing scores of different teaching contents in the teaching content difficulty division data are collected to form the history teaching passing score data; According to the history teaching passing score data corresponding to different teaching content difficulty division data, the score mapping analysis based on the teaching object is carried out to form the interactive content classification mapping data, including: For different history teaching passing score data, the score mapping analysis is carried out in the following way with the teaching object as the analysis standard: According to the order relationship of different knowledge point teaching content sets in the teaching content cluster, for different teaching objects, the corresponding teaching content and the history normalized passing score under any history teaching passing score data are determined; For different teaching objects, according to the connection relationship of the teaching content cluster, the history normalized passing score of the same teaching object in the next history teaching passing score data is determined, and the acquisition of the history normalized passing score of the next history teaching passing score data is continued until no history normalized passing score of the same teaching object is acquired. The history normalized passing scores of all teaching objects are calibrated and mapped according to the order and connection of the teaching content cluster to form the object score mapping relationship; For different teaching objects, the different historical normalized passing scores of different teaching contents in the next historical teaching passing score data of the same teaching content with the same historical normalized passing score in the last historical teaching passing score data are determined on the basis of the order of the contact relationship of the teaching content cluster on any two adjacent knowledge point teaching content sets, and then the teaching content mapping score range of different teaching contents in the next knowledge point teaching content set corresponding to the determination score of the teaching content of each last knowledge point teaching content set is formed , m represents the number of different knowledge point teaching content sets, m+1 represents the next knowledge point teaching content set adjacent to the knowledge point teaching content set numbered m in the contact relationship of the teaching content cluster, represents the corresponding passing score value under the teaching content numbered n in the knowledge point teaching content set numbered m, n represents the number of different teaching contents corresponding to the knowledge point teaching content set numbered m, k represents the number of different teaching contents in the next knowledge point teaching content set adjacent to the knowledge point teaching content set numbered m in the contact relationship of the teaching content cluster, represents the passing score value determined under the teaching content numbered n in the knowledge point teaching content set numbered m The passing score value range formed by the teaching content numbered k in the adjacent next knowledge point teaching content set. The teaching content mapping score range corresponding to different scores of different teaching contents in each knowledge point teaching content set is collected to form the interactive content classification mapping data. 2.The AR-intelligent-teaching-oriented interactive content generation method according to claim 1, wherein, The real-time teaching result information is acquired, and the interactive content generation guidance processing is carried out according to the interactive content classification mapping data to form real-time interactive teaching content, including: Taking the connection relationship of the teaching content cluster as the generation guidance order of the teaching content, starting from the first knowledge point teaching content set, any teaching content located in the middle grade is generated to form the initial order teaching content; The real-time teaching normalized score of the initial order teaching content is acquired: If the real-time teaching normalized score corresponding to the initial order teaching content reaches the corresponding teaching passing score, the interactive content generation analysis of the next knowledge point teaching content set is carried out; If the real-time teaching normalized score corresponding to the initial sequential teaching content does not reach the corresponding teaching passing score, the difficulty level of the current knowledge point teaching content set and the teaching content adjacent to the initial sequential teaching content and lower in difficulty are taken as new sequential teaching content for further score eligibility analysis, until the real-time teaching normalized score reaches the corresponding teaching passing score position, and the interactive content generation analysis of the next knowledge point teaching content set is performed. 3.The AR-intelligent-teaching-oriented interactive content generation method according to claim 2, characterized in that, The interactive content generation analysis of the next knowledge point teaching content set includes: According to the interactive content hierarchical mapping data, the teaching content with the highest difficulty in the adjacent next knowledge point teaching content set is taken as interactive generated content for the following mapping score range analysis: If the real-time teaching normalized score belongs to the mapping score range, the interactive content generation analysis of the next knowledge point teaching content set is performed again with the real-time teaching normalized score as a reference; If the real-time teaching normalized score does not belong to the mapping score range, the difficulty level of the current knowledge point teaching content set and the teaching content adjacent to the current knowledge content and lower in difficulty are taken as new sequential teaching content for further score eligibility analysis, until the real-time teaching normalized score belongs to the corresponding mapping score range, and the interactive content generation analysis of the next knowledge point teaching content set is performed again with the real-time teaching normalized score as a reference.

4. The interactive content generation system for AR intelligent teaching, which adopts the interactive content generation method for AR intelligent teaching according to any one of claims 1-3, characterized in that, It includes: A teaching data acquisition unit for acquiring AR intelligent teaching content data and real-time teaching result information; A teaching database for storing the AR intelligent teaching content data acquired by the teaching data acquisition unit; A feature analysis unit for performing clustering analysis on the AR intelligent teaching content data in the teaching database to form teaching content clusters, and performing hierarchical mapping analysis based on teaching results to form interactive content hierarchical mapping data; An interactive content generation unit for acquiring real-time teaching result information acquired by the teaching data acquisition unit, and performing interactive content generation guidance processing according to the teaching content clusters and interactive content hierarchical mapping data formed by the feature analysis unit to form real-time interactive teaching content.

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