CN110647613A - Courseware construction method, courseware construction device, courseware construction server and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a courseware construction method, a courseware construction device, a server and a storage medium, wherein the method comprises the following steps: identifying the target audio to obtain target keywords; matching at least two target knowledge points according to the target keywords; and constructing a target courseware according to the dependency relationship between the target knowledge points and the target knowledge points. The embodiment of the invention can improve the individuation degree and quality of courseware because the courseware is constructed in real time according to the problems of different students.
Description
Technical Field
The embodiment of the invention relates to the technical field of information, in particular to a courseware construction method, a courseware construction device, a courseware construction server and a storage medium.
Background
With the popularization and commercialization of internet technology, more and more people start to learn on the internet. Due to the demands of lifelong learning and online teaching, more and more educational resource courseware appears on the internet.
At present, courseware in the internet is made in advance, and a user can retrieve courseware of related education resources through keywords. Teaching contents in courseware are the same for all users, but even the keywords of different users are the same, learning requirements can also be different, the existing courseware can not meet the requirement of differential learning caused by different learning abilities of the users, and the courseware quality is low.
Disclosure of Invention
The embodiment of the invention provides a courseware construction method, a courseware construction device, a server and a storage medium, and the individuation degree and quality of courseware can be improved.
In a first aspect, an embodiment of the present invention provides a courseware construction method, where the method includes:
identifying the target audio to obtain target keywords;
matching at least two target knowledge points according to the target keywords;
and constructing a target courseware according to the dependency relationship between the target knowledge points and the target knowledge points.
In a second aspect, an embodiment of the present invention further provides a courseware construction apparatus, where the apparatus includes:
the recognition module is used for recognizing the target audio to obtain a target keyword;
the matching module is used for matching at least two target knowledge points according to the target keywords;
and the building module is used for building the target courseware according to the dependency relationship between the target knowledge points and the target knowledge points.
In a third aspect, an embodiment of the present invention further provides a server, where the server includes:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a courseware construction method as described above.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the courseware construction method as described above.
The embodiment of the invention identifies the target audio to obtain the target keywords, matches at least two target knowledge points according to the target keywords, and constructs the target courseware according to the dependency relationship between the target knowledge points and the target knowledge points. Because courseware is constructed according to the keywords of different users, the personalization degree and quality of courseware can be improved.
Drawings
FIG. 1 is a flowchart of a courseware construction method according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a courseware construction method according to a second embodiment of the present invention;
FIG. 3 is another flowchart of a courseware construction method according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of a courseware construction device in the third embodiment of the present invention;
fig. 5 is a schematic structural diagram of a server in the fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a courseware construction method in a first embodiment of the present invention, which is applicable to a courseware construction situation, and the method may be executed by a courseware construction device, and the device may be implemented in a software and/or hardware manner, for example, the device may be configured in a server. As shown in fig. 1, the method may specifically include:
and 110, identifying the target audio to obtain a target keyword.
The target audio can be any audio which can reflect the current learning problem of the student, such as a section of recording of the student on the description of the learning problem or communication audio of the student and a teacher on the learning problem.
After the target audio is acquired, optionally, identifying the target audio to obtain a target keyword may include: and carrying out continuous voice recognition on the target audio to obtain text information corresponding to the target audio, and taking words with the word frequency larger than a preset word frequency threshold value as target keywords through word segmentation and word frequency calculation. Target keywords of the text information can also be extracted through a term Frequency-Inverse Document Frequency (TF-IDF) algorithm.
In this embodiment, the keywords in the target audio may also be directly extracted through the acoustic model. Specifically, the target audio is subjected to extraction of acoustic features, and the acoustic features may include Linear Prediction parameters (LPC), Linear Prediction Cepstrum Coefficient (LPCC), Mel Cepstrum Coefficient (MFCC), and the like. And establishing an acoustic Model based on a Hidden Markov Model (HMM) and training, and inputting the acoustic features of the target audio into the trained acoustic Model to obtain the target keywords.
And step 120, matching at least two target knowledge points according to the target keywords.
Optionally, matching at least two target knowledge points according to the target keyword includes: matching the target keywords with predetermined knowledge point categories; and respectively calculating the word frequency of each knowledge point to which the successfully matched knowledge point category belongs, and taking the knowledge points with the word frequency larger than a preset word frequency threshold value as target knowledge points.
The knowledge point categories can be obtained by classifying all locally labeled knowledge points through a classification model in advance, and the number of the knowledge point categories is at least two. Matching the target keywords with predetermined knowledge point categories to obtain the knowledge point categories successfully matched with the target keywords; and respectively calculating the word frequency of each knowledge point belonging to the successfully matched knowledge point category, wherein the knowledge points with the word frequency larger than a preset word frequency threshold are target knowledge points, the number of the target knowledge points is at least two, and the word frequency threshold can be set as required.
For example, if the target keyword obtained by target audio recognition is an integral, the matched knowledge point category may be a calculus category, and the determined target knowledge point may be a fixed integral, an indeterminate integral, a curve area score, and the like.
And 130, constructing a target courseware according to the dependency relationship between the target knowledge points and the target knowledge points.
After the target knowledge point is determined, knowledge content or teaching resources corresponding to the target knowledge point can be acquired through a database in a network access server, and a target courseware is edited and constructed according to the dependency relationship between the knowledge content corresponding to the target knowledge point and the target knowledge point through a courseware editor built in a terminal such as a computer, a mobile phone, a tablet computer and the like.
The dependency relationship between the target knowledge points may be a front-back dependency relationship determined by the target knowledge points according to the difficulty of learning. In this embodiment, a plurality of target courseware can be constructed, the knowledge content and the sequencing of the knowledge content corresponding to each target courseware are different, the server can store the constructed target courseware, and can sequence and screen the target courseware through experts, and can sequence and screen the target courseware according to experience through a current student or a teacher of the current student, so that the student can obtain the target courseware more in line with the requirements.
According to the technical scheme, the target audio is identified to obtain the target keywords, at least two target knowledge points are matched according to the target keywords, and the target courseware is constructed according to the dependency relationship between the target knowledge points and the target knowledge points. Because courseware is constructed in real time according to the problem keywords of different students, the individuation degree and quality of courseware can be improved.
On the basis of the above technical solution, optionally, matching at least two target knowledge points according to the target keyword includes: respectively calculating cosine similarity of the word frequency vector of the target keyword and the word frequency vector of each knowledge point; and taking the knowledge points with the cosine similarity of the word frequency vector of the target keyword larger than a preset similarity threshold as target knowledge points.
Optionally, constructing a target courseware according to the dependency relationship between the target knowledge point and the target knowledge point, including: and determining a target knowledge graph of the target knowledge point, and constructing a target courseware according to the target knowledge graph and the content information of the target knowledge point.
Optionally, determining a target knowledge-graph of the target knowledge point includes: and extracting a knowledge graph corresponding to the target knowledge graph in a preset knowledge graph, and taking the knowledge graph as the target knowledge graph, or establishing a relation between the target knowledge points according to the weight values of the target knowledge points to generate the target knowledge graph.
Example two
Fig. 2 is a flowchart of a courseware construction method in the second embodiment of the present invention. On the basis of the above embodiments, the present embodiment further optimizes the courseware construction method. Correspondingly, the method of the embodiment specifically includes:
and step 210, identifying the target audio to obtain a target keyword.
After the target audio is obtained, the target audio is identified to obtain target keywords, continuous voice identification can be carried out on the target audio to obtain text information corresponding to the target audio, and keywords in the text information are extracted to serve as the target keywords; keywords in the target audio can also be directly extracted through the acoustic model as target keywords.
And step 220, matching at least two target knowledge points according to the target keywords.
The process of matching at least two target knowledge points according to the target keyword may include: matching the target keywords with predetermined knowledge point categories; and respectively calculating the word frequency of each knowledge point to which the successfully matched knowledge point category belongs, and taking the knowledge points with the word frequency larger than a preset word frequency threshold value as target knowledge points.
If the identification of the target keyword is to obtain text information corresponding to the target audio by performing continuous speech recognition on the target audio, and extract keywords in the text information as the target keyword, the process of matching at least two target knowledge points according to the target keyword may further be: respectively calculating cosine similarity of the word frequency vector of the target keyword and the word frequency vector of each knowledge content; and taking the knowledge points in the knowledge content with the cosine similarity of the word frequency vector of the target keyword larger than a preset similarity threshold value as target knowledge points. Wherein the number of target knowledge points is at least two.
Obtaining corresponding text information by carrying out continuous voice recognition on target audio, wherein a plurality of keywords, namely target keywords, in the extracted text information can be extracted, and generating word frequency vectors corresponding to the text information based on the target keywords; for a knowledge content, extracting keywords through a Term Frequency-Inverse document Frequency (TF-IDF) algorithm, and generating a Term Frequency vector of the knowledge content based on the extracted keywords, or directly generating the Term Frequency vector of the knowledge content based on at least two knowledge points belonging to the knowledge content to generate a Term Frequency vector corresponding to a plurality of knowledge contents; respectively calculating cosine similarity of word frequency vectors corresponding to the text information and word frequency vectors corresponding to a plurality of knowledge contents; and taking the knowledge points in the knowledge content with the cosine similarity larger than the preset similarity threshold value as target knowledge points.
And step 230, determining a target knowledge graph of the target knowledge point.
The knowledge map is also called scientific knowledge map, is called knowledge domain visualization or knowledge domain mapping map in the book information field, is a series of different graphs for displaying the relation between the knowledge development process and the structure, describes knowledge resources and carriers thereof by using visualization technology, and excavates, analyzes, constructs, draws and displays the mutual relation between knowledge. And determining the target knowledge graph of the target knowledge points can accurately determine the mutual relation between the target knowledge points.
Optionally, determining a target knowledge-graph of the target knowledge point includes: and extracting a knowledge graph corresponding to the target knowledge graph in a preset knowledge graph, and taking the knowledge graph as the target knowledge graph, or establishing a relation between the target knowledge points according to the weight values of the target knowledge points to generate the target knowledge graph.
The preset knowledge graph is stored in a database in the server, corresponding knowledge points are obtained in advance through Natural Language Processing (NLP) according to resources in the database, the relation between the knowledge points is established according to the lesson schema requirement, the preset knowledge graph is established, and after the preset knowledge graph is successfully established, each node in the preset knowledge graph has corresponding content information. In this embodiment, the knowledge graph corresponding to the target knowledge point in the preset knowledge graph may be extracted and used as the target knowledge graph.
The target knowledge graph can be constructed in real time according to a plurality of matched target knowledge points, the weight values of the target knowledge points are defined according to the rundown requirements, the relation before the target knowledge points is established according to the weight values, and the target knowledge graph is generated.
And step 240, constructing a target courseware according to the target knowledge graph and the content information of the target knowledge points.
After the target knowledge graph is determined, a courseware editor is built in a terminal, such as a computer, a mobile phone, a tablet computer and the like, and the target courseware is edited and constructed according to the knowledge content corresponding to the target knowledge graph and the target knowledge graph.
In this embodiment, a plurality of target courseware can be constructed, the knowledge content corresponding to each target courseware and the sequencing of the knowledge content are different, the server can store the constructed target courseware, the target courseware can be sequenced and screened by experts, and the target courseware can also be sequenced and screened according to experience by current students or teachers of the current students, so that the students can obtain the target courseware which more meets the requirements.
In addition, fig. 3 is another flowchart of the courseware construction method in the second embodiment of the present invention, and a specific process of courseware construction is further described in the flowchart, as shown in fig. 3, the courseware construction method includes: step 310, acquiring a target audio; step 320, performing voice recognition on the target audio to obtain target keywords; step 330, obtaining target knowledge points according to the target keyword matching; step 340, judging whether the voice recognition is finished, if so, entering step 350, otherwise, returning to step 320; and 350, constructing courseware according to the target knowledge points and the dependency relationship among the target knowledge points.
According to the technical scheme, target keywords are obtained by identifying target audio, at least two target knowledge points are matched according to the target keywords, a target knowledge graph of the target knowledge points is determined, and target courseware is constructed according to the target knowledge graph and content information of the target knowledge points. Because courseware is constructed in real time according to the problem keywords of different users, the personalized degree and quality of courseware can be improved.
EXAMPLE III
Fig. 4 is a schematic structural diagram of a courseware construction device in the third embodiment of the present invention, which is applicable to a courseware construction situation. The courseware building device provided by the embodiment of the invention can execute the courseware building method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. As shown in fig. 4, the apparatus specifically includes an identifying module 410, a matching module 420, and a constructing module 430, where:
the identification module 410 is used for identifying the target audio to obtain a target keyword;
a matching module 420, configured to match at least two target knowledge points according to the target keyword;
and the constructing module 430 is configured to construct the target courseware according to the dependency relationship between the target knowledge points and the target knowledge points.
Optionally, the matching module 420 includes a similarity unit, and the similarity unit is specifically configured to:
respectively calculating cosine similarity of the word frequency vector of the target keyword and the word frequency vector of each knowledge content;
and taking the knowledge points in the knowledge content with the cosine similarity of the word frequency vector of the target keyword larger than a preset similarity threshold value as target knowledge points.
Optionally, the matching module 420 includes a word frequency unit, where the word frequency unit is specifically configured to:
matching the target keywords with predetermined knowledge point categories;
and respectively calculating the word frequency of each knowledge point to which the successfully matched knowledge point category belongs, and taking the knowledge points with the word frequency larger than a preset word frequency threshold value as target knowledge points.
Optionally, the building module 430 includes:
the knowledge graph unit is used for determining a target knowledge graph of the target knowledge point;
and the courseware unit is used for constructing the target courseware according to the target knowledge graph and the content information of the target knowledge points.
Optionally, the knowledge-graph unit is configured to:
extracting a knowledge graph corresponding to the target knowledge graph in a preset knowledge graph, and using the knowledge graph as the target knowledge graph, or,
and establishing the relation between the target knowledge points according to the weight values of the target knowledge points to generate the target knowledge graph.
According to the technical scheme, target keywords are obtained by identifying target audio, at least two target knowledge points are matched according to the target keywords, a target knowledge graph of the target knowledge points is determined, and target courseware is constructed according to the target knowledge graph and content information of the target knowledge points. Because courseware is constructed according to the keywords of different users, the personalization degree and quality of courseware can be improved.
Example four
Fig. 5 is a schematic structural diagram of a server in the fourth embodiment of the present invention. FIG. 5 illustrates a block diagram of an exemplary server 512 suitable for use in implementing embodiments of the present invention. The server 512 shown in fig. 5 is only an example and should not bring any limitations to the function and scope of the use of the embodiments of the present invention.
As shown in FIG. 5, the server 512 is in the form of a general purpose server. Components of server 512 may include, but are not limited to: one or more processors 516, a storage device 528, and a bus 518 that couples the various system components including the storage device 528 and the processors 516.
The server 512 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by server 512 and includes both volatile and nonvolatile media, removable and non-removable media.
A program/utility 540 having a set (at least one) of program modules 542 may be stored, for example, in storage 528, such program modules 542 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. The program modules 542 generally perform the functions and/or methods of the described embodiments of the invention.
The server 512 may also communicate with one or more external devices 514 (e.g., keyboard, pointing terminal, display 524, etc.), with one or more terminals that enable a user to interact with the server 512, and/or with any terminals (e.g., network card, modem, etc.) that enable the server 512 to communicate with one or more other computing terminals. Such communication may occur via input/output (I/O) interfaces 522. Further, server 512 may communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN), and/or a public Network such as the internet) via Network adapter 520. As shown in FIG. 5, the network adapter 520 communicates with the other modules of the server 512 via the bus 518. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the server 512, including but not limited to: microcode, end drives, Redundant processors, external disk drive Arrays, RAID (Redundant Arrays of Independent Disks) systems, tape drives, and data backup storage systems, among others.
The processor 516 executes various functional applications and data processing by running programs stored in the storage device 528, for example, implementing a courseware construction method provided by the embodiment of the present invention, the method includes:
identifying the target audio to obtain target keywords;
matching at least two target knowledge points according to the target keywords;
and constructing a target courseware according to the dependency relationship between the target knowledge points and the target knowledge points.
EXAMPLE five
The fifth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the courseware construction method provided in the fifth embodiment of the present invention, where the method includes:
identifying the target audio to obtain target keywords;
matching at least two target knowledge points according to the target keywords;
and constructing a target courseware according to the dependency relationship between the target knowledge points and the target knowledge points. Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A courseware construction method is characterized by comprising the following steps:
identifying the target audio to obtain target keywords;
matching at least two target knowledge points according to the target keywords;
and constructing a target courseware according to the dependency relationship between the target knowledge points and the target knowledge points.
2. The method of claim 1, wherein matching at least two target knowledge points based on the target keyword comprises:
respectively calculating cosine similarity of the word frequency vector of the target keyword and the word frequency vector of each knowledge content;
and taking the knowledge points in the knowledge content with the cosine similarity of the word frequency vector of the target keyword larger than a preset similarity threshold value as target knowledge points.
3. The method of claim 1, wherein matching at least two target knowledge points based on the target keyword comprises:
matching the target keywords with predetermined knowledge point categories;
and respectively calculating the word frequency of each knowledge point to which the successfully matched knowledge point category belongs, and taking the knowledge points with the word frequency larger than a preset word frequency threshold value as target knowledge points.
4. The method of claim 1, wherein constructing a target courseware according to dependencies between the target knowledge points and the target knowledge points comprises:
determining a target knowledge graph of the target knowledge points;
and constructing a target courseware according to the target knowledge graph and the content information of the target knowledge points.
5. The method of claim 4, wherein determining the target knowledge-graph of the target knowledge-point comprises:
extracting a knowledge graph corresponding to the target knowledge graph in a preset knowledge graph, and using the knowledge graph as the target knowledge graph, or,
and establishing the relation between the target knowledge points according to the weight values of the target knowledge points to generate the target knowledge graph.
6. A courseware construction apparatus comprising:
the recognition module is used for recognizing the target audio to obtain a target keyword;
the matching module is used for matching at least two target knowledge points according to the target keywords;
and the building module is used for building the target courseware according to the dependency relationship between the target knowledge points and the target knowledge points.
7. The apparatus of claim 6, wherein the building module comprises:
the knowledge graph unit is used for determining a target knowledge graph of the target knowledge point;
and the courseware unit is used for constructing the target courseware according to the target knowledge graph and the content information of the target knowledge points.
8. The apparatus of claim 7, wherein the knowledge-graph unit is configured to:
extracting a knowledge graph corresponding to the target knowledge graph in a preset knowledge graph, and using the knowledge graph as the target knowledge graph, or,
and establishing the relation between the target knowledge points according to the weight values of the target knowledge points to generate the target knowledge graph.
9. A server, characterized in that the server comprises:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a courseware construction method according to any one of claims 1 to 5.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out a courseware construction method according to any one of claims 1-5.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112052652A (en) * | 2020-09-08 | 2020-12-08 | 国家电网有限公司技术学院分公司 | Automatic generation method and device for electronic courseware script |
| CN113077670A (en) * | 2021-03-30 | 2021-07-06 | 上海知到知识数字科技有限公司 | Interactive online training method |
| CN114125537A (en) * | 2021-11-29 | 2022-03-01 | Oook(北京)教育科技有限责任公司 | Discussion method, device, medium and electronic equipment for live broadcast teaching |
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