Online course recommendation method and device for real-time teaching

Abstract

The invention provides an online course recommendation method and device for real-time teaching, and the method comprises the steps: obtaining real-time teaching information which is used for reflecting an online course obtaining demand in a real-time teaching process; based on the real-time teaching information, determining a first course feature of a target online course corresponding to the online course acquisition demand, the first course feature being used for reflecting course content of the target online course; and determining a preset online course with a second course feature matched with the first course feature in a preset online course set, and taking the preset online course as the target online course. According to the technical scheme, the manual operation in the real-time teaching process is simplified, the convenience of electronic teaching is improved, and the course quality can be improved.

Description

[Technical field]

[0001] The present application relates to the field of education and teaching technology, and in particular to an online course recommendation method and device for real-time teaching. [Background technology]

[0002] In the current electronic teaching process of primary and secondary schools, teachers often need to expand the knowledge related to the course content for students during the lecture. In related technologies, in order to improve the convenience of real-time teaching, teachers can add file links, picture links, etc. of knowledge related to the course content to the page of the courseware during the courseware production process so that they can be opened and displayed in real time. However, this course expansion method requires the courseware maker to manually edit in advance, and the editing process is cumbersome and labor-intensive. In addition, teachers can also search for course content that needs to be expanded in real time online, or search for course content that needs to be expanded in existing databases. However, manual search is often limited by personal search capabilities, and its search results may not meet the needs of real-time teaching. In addition, manual search takes a long time, which will also have a negative impact on on-site real-time teaching.

[0003] Therefore, how to quickly and efficiently obtain course content related to teaching content during real-time teaching has become a technical problem that needs to be solved urgently. [Summary of the invention]

[0004] The embodiments of the present application provide an online course recommendation method and device for real-time teaching, aiming to solve the technical problem of the time-consuming and labor-intensive way of expanding course content in the real-time teaching process in the related art.

[0005] In a first aspect, an embodiment of the present application provides an online course recommendation method for real-time teaching, comprising:

[0006] Acquiring real-time teaching information, wherein the real-time teaching information is used to reflect the demand for online course acquisition in the real-time teaching process;

[0007] Based on the real-time teaching information, determining a first course feature of a target online course corresponding to the online course acquisition requirement, wherein the first course feature is used to reflect the course content of the target online course;

[0008] A preset online course having a second course characteristic matching the first course characteristic is determined in the preset online course set as the target online course.

[0009] In one embodiment of the present application, optionally, the acquiring of real-time teaching information includes:

[0010] Setting the current course content to the real-time teaching information; or

[0011] The real-time voice information of a designated object in the teaching space is acquired at predetermined intervals, and the real-time voice information is recognized as text information as the real-time teaching information.

[0012] In one embodiment of the present application, optionally, determining the first course feature of the target online course corresponding to the online course acquisition requirement based on the real-time teaching information includes:

[0013] Extracting a first entity from the real-time teaching information;

[0014] Determining the relevance of the first entity to the current course content based on a third course feature of the current course content;

[0015] If the correlation is greater than or equal to a first threshold, it is determined that the first entity is a first course feature of the target online course.

[0016] In one embodiment of the present application, optionally, the third course feature of the current course content includes: course type, course extension type, course quality level, course difficulty level, course duration, and course explanation depth level;

[0017] The determining, based on the third course feature of the current course content, the relevance of the first entity to the current course content includes:

[0018] Based on the preset inter-entity relationship corresponding to the first entity, determining a second entity having a preset inter-entity relationship with the first entity;

[0019] constructing a relevance matrix based on the respective weights of the first entity and the second entity, and the first feature value of each of the third course features;

[0020] Based on the relevance matrix, the relevance between the first entity and the current course content is determined.

[0021] In one embodiment of the present application, optionally, constructing a relevance matrix based on the respective weights of the first entity and the second entity, and the first feature value of each of the third course features, includes:

[0022] Determine the product of the first characteristic values ​​of each of the plurality of third course characteristics as the second characteristic value;

[0023] Determine the product of the weights of each of the plurality of third course features as the third feature value corresponding to the second feature value;

[0024] Sort the second eigenvalues ​​corresponding to the third eigenvalues ​​in ascending order of the third eigenvalues ​​to obtain a second eigenvalue sequence;

[0025] Arrange the weights of the first entity and the second entity in ascending order to obtain an entity weight sequence;

[0026] The square root of the product of the mth second eigenvalue in the second eigenvalue sequence and the nth entity weight in the entity weight sequence is determined as the element in the mth row and nth column of the correlation matrix.

[0027] In one embodiment of the present application, optionally, determining a preset online course having a second course feature matching the first course feature in the preset online course set includes:

[0028] For each entity in the first course feature, searching the preset online course set for a preset online course having the entity;

[0029] If the course duration of the preset online course with the entity is less than or equal to the current extendable duration, and the course quality level and the course difficulty level are each different from the current course content within a preset difference range, the preset online course with the entity is set as the target online course.

[0030] In a second aspect, an embodiment of the present application provides an online course recommendation device for real-time teaching, comprising:

[0031] A real-time teaching information acquisition module is used to acquire real-time teaching information, wherein the real-time teaching information is used to reflect the demand for online course acquisition in the real-time teaching process;

[0032] A first course feature acquisition module, used to determine a first course feature of a target online course corresponding to the online course acquisition requirement based on the real-time teaching information, wherein the first course feature is used to reflect the course content of the target online course;

[0033] The second course feature matching module is used to determine a preset online course with a second course feature matching the first course feature in the preset online course set as the target online course.

[0034] In one embodiment of the present application, optionally, the real-time teaching information acquisition module is used to: set the current course content as the real-time teaching information; or obtain the real-time voice information of a specified object in the teaching space at predetermined intervals, and recognize the real-time voice information as text information as the real-time teaching information.

[0035] In one embodiment of the present application, optionally, the first course feature acquisition module is used to: extract a first entity from the real-time teaching information; determine the relevance of the first entity to the current course content based on the third course feature of the current course content; if the relevance is greater than or equal to a first threshold, determine that the first entity is the first course feature of the target online course.

[0036] In one embodiment of the present application, optionally, the third course feature of the current course content includes: course type, course extension type, course quality level, course difficulty level, course duration, and course explanation depth level; the first course feature acquisition module is used to: determine a second entity having a preset entity relationship with the first entity based on a preset entity relationship corresponding to the first entity; construct a correlation matrix based on the respective weights of the first entity and the second entity, and the first eigenvalue of each of the third course features; and determine the correlation between the first entity and the current course content based on the correlation matrix.

[0037] In one embodiment of the present application, optionally, the first course feature acquisition module is used to: determine the product of the first eigenvalues ​​of each of the multiple third course features as the second eigenvalue; determine the product of the weights of each of the multiple third course features as the third eigenvalue corresponding to the second eigenvalue; sort the second eigenvalues ​​corresponding to the third eigenvalues ​​in order from small to large to obtain a second eigenvalue sequence; arrange the weights of the first entity and the second entity in order from small to large to obtain an entity weight sequence; determine the square root of the product of the mth second eigenvalue in the second eigenvalue sequence and the nth entity weight in the entity weight sequence as the element of the mth row and nth column of the correlation matrix.

[0038] In one embodiment of the present application, optionally, the second course feature matching module is used to: for each entity within the first course feature, retrieve a preset online course with the entity in the preset online course set; if the course duration of the preset online course with the entity is less than or equal to the current extendable duration, and the course quality level and the course difficulty level are each different from the current course content within a preset difference range, set the preset online course with the entity as the target online course.

[0039] In a third aspect, an embodiment of the present application provides a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the method described in the first aspect above.

[0040] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method described in the first aspect above.

[0041] The above technical solution addresses the time-consuming and labor-intensive technical problem of expanding course content in the real-time teaching process in related technologies. It does not require teachers to manually edit courseware before teaching or manually search for relevant course content in real-time teaching. It can automatically recommend relevant online courses on the teaching page based on the current teaching content during real-time teaching, which simplifies manual operations in the real-time teaching process, improves the convenience of e-teaching, and helps improve the quality of courses.

Brief Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A flow chart of an online course recommendation method for real-time teaching according to an embodiment of the present application is shown;

[0044] Figure 2 A block diagram of a computer device according to an embodiment of the present application is shown;

[0045] Figure 3 A block diagram of a computer device according to another embodiment of the present application is shown. [Specific implementation method]

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0047] Figure 1 A flow chart of an online course recommendation method for real-time teaching according to an embodiment of the present application is shown.

[0048] like Figure 1 As shown, the method includes:

[0049] Step s1, obtaining real-time teaching information.

[0050] The real-time teaching information is used to reflect the demand for online course acquisition in the real-time teaching process. The online courses mentioned here include but are not limited to multimedia information in any format.

[0051] In one embodiment of the present application, step s1 includes: setting the current course content as the real-time teaching information. That is, the real-time teaching information can be the current course content in the current courseware, and the current course content, as the basis for extended teaching, can effectively indicate the direction of the course to be extended.

[0052] In another embodiment of the present application, step s1 includes: acquiring real-time voice information of a designated object in the teaching space at predetermined intervals, and identifying the real-time voice information as text information as the real-time teaching information. That is to say, the audio information in the teaching space can be periodically collected, where the teaching space refers to any space for real-time teaching, such as a classroom, and the designated object refers to any object such as a teacher, whose sound has been pre-recorded. The system can identify an object that has recorded sound, and the real-time voice information of the object is related to the current course content or the current course content to be expanded. Therefore, the text information obtained by the recognition of the real-time voice information is actually text content related to the current course content or the current course content to be expanded. As the basis for extended teaching, it can effectively indicate the direction of the course that needs to be expanded.

[0053] In another embodiment of the present application, step s1 includes: obtaining text information input by the user in real time as the real-time teaching information. Of course, the teacher can also directly indicate the required teaching content expansion direction in the system through text input, voice input, function item selection, etc.

[0054] Step s2: determining a first course feature of a target online course corresponding to the online course acquisition requirement based on the real-time teaching information.

[0055] Specifically, the real-time teaching information reflects the demand for online course acquisition in the real-time teaching process, and this demand itself also indicates what the required course is and what characteristics it has. It can be said that the first course feature is used to reflect the course content of the target online course, and can be used as a basis for retrieving the target online course in subsequent steps.

[0056] Step s3: determining a preset online course having a second course feature matching the first course feature in the preset online course set as the target online course.

[0057] A preset online course set can be set in the system, which is a database of courses accessible to real-time teaching. The preset online courses are searched in this data block. If the second course feature of a preset online course matches the first course feature mentioned above, it means that the preset online course has a certain degree of overlap with the course content of the target online course. Therefore, the preset online course can be set as the target online course and recommended on the current real-time teaching page.

[0058] The above technical solution does not require teachers to manually edit courseware before teaching or manually search for relevant course content during real-time teaching. It can automatically recommend relevant online courses on the teaching page based on the current teaching content during real-time teaching, which simplifies manual operations in the real-time teaching process, improves the convenience of e-teaching, and helps improve the quality of courses.

[0059] In one embodiment of the present application, step s2 specifically includes: extracting a first entity from the real-time teaching information; determining the relevance of the first entity to the current course content based on the third course feature of the current course content; if the relevance is greater than or equal to a first threshold, determining that the first entity is the first course feature of the target online course.

[0060] Among them, the first entity refers to an independent object in the real-time teaching information, including but not limited to any object with conceptual meaning such as people, objects, places, behaviors, etc. mentioned in the real-time teaching information. The first entity is the embodiment of the specific content of the real-time teaching information.

[0061] Among them, the third course characteristics of the current course content include: course type, course extension type, course quality level, course difficulty level, course duration, and course depth level to be explained. It can be said that the third course characteristics reflect the performance level of the current course content in multiple dimensions. Among them, the course type itself refers to the subject range described in the current course content itself, the course extension type refers to the subject range that is related to the current course content and can be expanded when teaching the current course content, the course quality level refers to the evaluation level of the current course content itself, the course difficulty level reflects the learning difficulty of the current course content, the course duration reflects the time cost required to learn the current course content, and the course depth level to be explained reflects the depth level of the connotation that the current course content needs to be learned and excavated by the listeners. These third course characteristics can be coded, and then the coded values ​​are normalized so that the first characteristic values ​​of each third course characteristic are at the same level, reducing the calculation difficulty and error level in subsequent steps.

[0062] Further, the relevance of the first entity to the current course content is equivalent to the degree of necessity of the first entity as an extended explanation content of the current course content. For this, a first threshold can be set, which represents the relevance of the first entity to the current course content when there is sufficient explanation demand for the first entity during the real-time teaching process of the current course content. Optionally, the first threshold is the minimum relevance of the first entity to the current course content when there is sufficient explanation demand for the first entity during the real-time teaching process of the current course content.

[0063] On this basis, if the relevance is greater than or equal to the first threshold, it means that the real-time teaching process of the current course content has the necessity to explain the first entity, so at this time, the first entity can be determined as the first course feature of the target online course as the basis for selecting the target online course. On the contrary, if the relevance is less than the first threshold, it means that it is not a necessary condition to explain the relevant content of the first entity in the real-time teaching process of the current course content, that is, not explaining the relevant content of the first entity does not affect the teaching of the current course content. Therefore, it can be determined that the first entity is not a necessary feature of the target online course.

[0064] In the process of determining the relevance, optionally, a second entity having a preset inter-entity relationship with the first entity can be determined based on the preset inter-entity relationship corresponding to the first entity. The second entity refers to any object with conceptual meaning, such as a person, object, place, behavior, etc., that is associated with the first entity in the real-time teaching information. It can be said that the course feature represented by the second entity is the associated information of the course feature represented by the first entity, and the two have the possibility of being required to be explained together.

[0065] On this basis, a relevance matrix may be constructed based on the respective weights of the first entity and the second entity, and the first feature value of each of the third course features.

[0066] The weights of the first entity and the second entity represent the necessary degree to which the entity needs to be extended in real time. Of course, the first entity and the second entity can be one or more.

[0067] Specifically, the product of the first eigenvalues ​​of each of the multiple third course characteristics can be determined as the second eigenvalue. It can be understood that each second eigenvalue represents a comprehensive characteristic obtained by combining at least two third course characteristics, or in other words, represents the degree to which the necessity of the current course content for explaining the first entity and the second entity is jointly influenced by at least two third course characteristics.

[0068] Next, the product of the weights of each of the third course features can be determined as the third feature value corresponding to the second feature value. The third course features of the current course content include: the type of course itself, the type of course extension, the course quality level, the course difficulty level, the course duration, and the depth level of the course to be explained. The weight of the third course feature reflects the influence of the third course feature on the explanation effect of the current course content. Any third feature value, that is, the product of the weights of multiple third course features, reflects the comprehensive influence of multiple dimensions of multiple third course features on the explanation effect of the current course content.

[0069] At this point, the second eigenvalues ​​corresponding to the third eigenvalues ​​can be sorted in order from small to large according to the third eigenvalues ​​to obtain a second eigenvalue sequence, and the weights of the first entity and the second entity can be arranged in order from small to large to obtain an entity weight sequence. The second eigenvalue sequence reflects the ranking of the comprehensive influence on the explanation effect of the current course content when different dimensions are superimposed, and the entity weight sequence reflects the ranking of the necessity of real-time extended explanation of each entity. Furthermore, the square root of the product of the mth second eigenvalue in the second eigenvalue sequence and the nth entity weight in the entity weight sequence can be determined as the element of the mth row and nth column of the correlation matrix, and the value of the element can reflect the explanation effect of the entity ranked nth in the necessity of real-time extended explanation under the superposition condition of multiple course feature dimensions represented by the mth second eigenvalue.

[0070] Then, based on the correlation matrix, the process of determining the correlation between the first entity and the current course content includes: setting the rank of the correlation matrix to the correlation, or calculating the variance of the elements in each row and column of the correlation matrix, and setting the mean of all variances to the correlation.

[0071] It can be said that the correlation matrix integrates multiple dimensions of course characteristics with entities that may need to be explained in the course content and objects that can be expanded from the entities as factors affecting the effect of explanation. The correlation matrix as a whole reflects the degree of necessity for explaining entities that may need to be explained in the course content, that is, the relevance.

[0072] Based on the above steps, in one embodiment, step s3 includes: for each entity in the first course feature, searching the preset online course set for a preset online course having the entity as the target online course.

[0073] That is to say, for each entity with sufficiently high relevance, the course with the entity is retrieved from the preset online course set, and is displayed on the page of the current course content as an expandable course of the current course content for real-time teaching.

[0074] Based on the aforementioned steps, in another embodiment, step s3 includes: for each entity within the first course feature, retrieving a preset online course with the entity in the preset online course set; if the course duration of the preset online course with the entity is less than or equal to the current extendable duration, and the course quality level and the course difficulty level are each different from the current course content within a preset difference range, setting the preset online course with the entity as the target online course.

[0075] For each entity with sufficiently high relevance, preset online courses with the entity are retrieved from the preset online course set, and then these preset online courses with the entity are further screened.

[0076] Specifically, the course duration, course quality level and course difficulty level of the preset online course with the entity may be evaluated separately.

[0077] The current extendable duration refers to the maximum time that the preset online course to be extended can be explained without affecting the teaching of the current course content. If the course duration of the preset online course with the entity is less than or equal to the current extendable duration, it means that the extended explanation will not affect the teaching of the current course content itself. At the same time, the course quality level and course difficulty level of the preset online course with the entity should also be within a reasonable range compared with the current course content. If the course quality level and course difficulty level differ too much, it means that the teaching objects adapted by the preset online course with the entity do not match the teaching objects of the current course content, and blindly extending the explanation will affect the listening quality of the teaching objects of the current course content.

[0078] Finally, if the course duration, course quality level and course difficulty level of the preset online course with the entity are reasonable, the preset online course with the entity can be set as the target online course and displayed on the display page of the current course content, or directly jump to the explanation page of the target online course to achieve real-time seamless explanation. On the contrary, if any of the course duration, course quality level and course difficulty level is unreasonable, it means that the preset online course with the entity does not match the learning needs of the teaching object of the current course content, and it is prohibited to use it as the target online course for extended explanation and display.

[0079] The embodiment of the present application provides an online course recommendation device for real-time teaching, comprising:

[0080] A real-time teaching information acquisition module is used to acquire real-time teaching information, wherein the real-time teaching information is used to reflect the demand for online course acquisition in the real-time teaching process;

[0081] A first course feature acquisition module, used to determine a first course feature of a target online course corresponding to the online course acquisition requirement based on the real-time teaching information, wherein the first course feature is used to reflect the course content of the target online course;

[0082] The second course feature matching module is used to determine a preset online course with a second course feature matching the first course feature in the preset online course set as the target online course.

[0083] In one embodiment of the present application, optionally, the real-time teaching information acquisition module is used to: set the current course content as the real-time teaching information; or obtain the real-time voice information of a specified object in the teaching space at predetermined intervals, and recognize the real-time voice information as text information as the real-time teaching information.

[0084] In one embodiment of the present application, optionally, the first course feature acquisition module is used to: extract a first entity from the real-time teaching information; determine the relevance of the first entity to the current course content based on the third course feature of the current course content; if the relevance is greater than or equal to a first threshold, determine that the first entity is the first course feature of the target online course.

[0085] In one embodiment of the present application, optionally, the third course feature of the current course content includes: course type, course extension type, course quality level, course difficulty level, course duration, and course explanation depth level; the first course feature acquisition module is used to: determine a second entity having a preset entity relationship with the first entity based on a preset entity relationship corresponding to the first entity; construct a correlation matrix based on the respective weights of the first entity and the second entity, and the first eigenvalue of each of the third course features; and determine the correlation between the first entity and the current course content based on the correlation matrix.

[0086] In one embodiment of the present application, optionally, the first course feature acquisition module is used to: determine the product of the first eigenvalues ​​of each of the multiple third course features as the second eigenvalue; determine the product of the weights of each of the multiple third course features as the third eigenvalue corresponding to the second eigenvalue; sort the second eigenvalues ​​corresponding to the third eigenvalues ​​in order from small to large to obtain a second eigenvalue sequence; arrange the weights of the first entity and the second entity in order from small to large to obtain an entity weight sequence; determine the square root of the product of the mth second eigenvalue in the second eigenvalue sequence and the nth entity weight in the entity weight sequence as the element of the mth row and nth column of the correlation matrix.

[0087] In one embodiment of the present application, optionally, the second course feature matching module is used to: for each entity within the first course feature, retrieve a preset online course with the entity in the preset online course set; if the course duration of the preset online course with the entity is less than or equal to the current extendable duration, and the course quality level and the course difficulty level are each different from the current course content within a preset difference range, set the preset online course with the entity as the target online course.

[0088] The device uses any one of the solutions described in the above embodiments, and therefore has all the above technical effects, which will not be repeated here.

[0089] In addition, in one embodiment, the present application provides a computer device, which may be a server, and its internal structure diagram may be as follows: Figure 2 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, the method described in any of the above embodiments can be implemented.

[0090] In one embodiment, the present application further provides a computer device, which may be a client, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, the method described in any of the above embodiments can be implemented.

[0091] An embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to perform the following steps: obtaining real-time teaching information, wherein the real-time teaching information is used to reflect the demand for online course acquisition in the real-time teaching process; based on the real-time teaching information, determining a first course feature of a target online course corresponding to the online course acquisition demand, wherein the first course feature is used to reflect the course content of the target online course; determining a preset online course having a second course feature matching the first course feature in a preset online course set as the target online course. It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant description in the aforementioned method embodiment, and will not be described one by one here to avoid repetition.

[0092] The above, in combination with the accompanying drawings, describes in detail the technical solution of the present application. Through the technical solution of the present application, there is no need for teachers to manually edit courseware before teaching or manually search for relevant course content during real-time teaching. Relevant online courses can be automatically recommended on the teaching page based on the current teaching content during real-time teaching, which simplifies manual operations in the real-time teaching process, improves the convenience of electronic teaching, and helps improve the quality of courses.

[0093] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0094] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0095] In addition, each functional module in each embodiment of the present application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional modules.

Claims

1. An online course recommendation method for real-time teaching, characterized in that: include: Acquiring real-time teaching information, wherein the real-time teaching information is used to reflect the demand for online course acquisition in the real-time teaching process; Based on the real-time teaching information, determining a first course feature of a target online course corresponding to the online course acquisition requirement, wherein the first course feature is used to reflect the course content of the target online course; A preset online course having a second course characteristic matching the first course characteristic is determined in the preset online course set as the target online course.

2. The method according to claim 1, characterized in that The obtaining of real-time teaching information includes: Setting the current course content to the real-time teaching information; or The real-time voice information of a designated object in the teaching space is acquired at predetermined intervals, and the real-time voice information is recognized as text information as the real-time teaching information.

3. The method according to claim 1 or 2, characterized in that: The determining, based on the real-time teaching information, a first course feature of a target online course corresponding to the online course acquisition requirement comprises: Extracting a first entity from the real-time teaching information; Determining the relevance of the first entity to the current course content based on a third course feature of the current course content; If the correlation is greater than or equal to a first threshold, it is determined that the first entity is a first course feature of the target online course.

4. The method according to claim 3, characterized in that The third course characteristics of the current course content include: course type, course extension type, course quality level, course difficulty level, course duration, and course explanation depth level; The determining, based on the third course feature of the current course content, the relevance of the first entity to the current course content includes: Based on the preset inter-entity relationship corresponding to the first entity, determining a second entity having a preset inter-entity relationship with the first entity; constructing a relevance matrix based on the respective weights of the first entity and the second entity, and the first feature value of each of the third course features; Based on the relevance matrix, the relevance between the first entity and the current course content is determined.

5. The method according to claim 4, characterized in that The constructing of a relevance matrix based on the respective weights of the first entity and the second entity and the first feature value of each of the third course features comprises: Determine the product of the first characteristic values ​​of each of the plurality of third course characteristics as the second characteristic value; Determine the product of the weights of each of the plurality of third course features as the third feature value corresponding to the second feature value; Sort the second eigenvalues ​​corresponding to the third eigenvalues ​​in ascending order of the third eigenvalues ​​to obtain a second eigenvalue sequence; Arrange the weights of the first entity and the second entity in ascending order to obtain an entity weight sequence; The square root of the product of the mth second eigenvalue in the second eigenvalue sequence and the nth entity weight in the entity weight sequence is determined as the element in the mth row and nth column of the correlation matrix.

6. The method according to claim 3, characterized in that The step of determining, from the set of preset online courses, a preset online course having a second course characteristic that matches the first course characteristic comprises: For each entity in the first course feature, searching the preset online course set for a preset online course having the entity; If the course duration of the preset online course with the entity is less than or equal to the current extendable duration, and the course quality level and the course difficulty level are each different from the current course content within a preset difference range, the preset online course with the entity is set as the target online course.

7. An online course recommendation device for real-time teaching, characterized in that: include: A real-time teaching information acquisition module is used to acquire real-time teaching information, wherein the real-time teaching information is used to reflect the demand for online course acquisition in the real-time teaching process; A first course feature acquisition module, used to determine a first course feature of a target online course corresponding to the online course acquisition requirement based on the real-time teaching information, wherein the first course feature is used to reflect the course content of the target online course; The second course feature matching module is used to determine a preset online course with a second course feature matching the first course feature in the preset online course set as the target online course.

8. A computer device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, wherein the instructions are configured to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: Computer executable instructions are stored, and the computer executable instructions are used to execute the method according to any one of claims 1 to 6.

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