Video playing method, device and system for teaching
阅读说明：本技术 一种用于教学的视频播放方法、装置和系统 (Video playing method, device and system for teaching ) 是由 崔宝成 于 2020-08-05 设计创作，主要内容包括：本发明公开了一种用于教学的视频播放方法、装置、系统和存储介质,方法包括：获取老师的教学视频,对所述教学视频进行划分,形成多个教学主题及对应的教学主题视频；获取用户输入的学习问题；将所述学习问题输入主题匹配模型,与所述教学主题进行匹配；播放与所述学习问题匹配的教学主题对应的教学主题视频。采用该技术方案,通过主题匹配模型,能够很好的将学生提出的问题与老师教学视频相匹配,更加准确地给学生播放相关视频,提高学生的学习效率。(The invention discloses a video playing method, a device, a system and a storage medium for teaching, wherein the method comprises the following steps: the method comprises the steps of obtaining a teaching video of a teacher, dividing the teaching video, and forming a plurality of teaching themes and corresponding teaching theme videos; acquiring a learning problem input by a user; inputting the learning question into a theme matching model, and matching the learning question with the teaching theme; and playing a teaching theme video corresponding to the teaching theme matched with the learning question. By adopting the technical scheme, through the theme matching model, the problem brought forward by the student can be well matched with the teacher teaching video, the relevant video can be more accurately played for the student, and the learning efficiency of the student is improved.)
1. A video playing method for teaching, comprising:
the method comprises the steps of obtaining a teaching video of a teacher, dividing the teaching video, and forming a plurality of teaching themes and corresponding teaching theme videos;
acquiring a learning problem input by a user;
inputting the learning question into a theme matching model, and matching the learning question with the teaching theme;
and playing a teaching theme video corresponding to the teaching theme matched with the learning question.
2. The video playing method according to claim 1, wherein the dividing the teaching video to form a plurality of teaching subjects and corresponding teaching subject videos specifically comprises:
extracting teaching audio synchronized with the teaching video from the teaching video;
converting the teaching audio into teaching text data;
performing cluster analysis on the teaching text data to form a plurality of teaching subjects;
and dividing the teaching video according to the teaching theme to form a corresponding teaching theme video.
3. The video playback method of claim 1, wherein the topic matching model comprises an encoding layer and a matching layer, the encoding layer is used for converting input words into sentence vectors, and the matching layer is used for matching calculation between the sentence vectors.
4. The video playback method of claim 3, wherein the coding layer has a plurality of input interfaces, one of which is used for inputting a user's learning question and the other of which is used for inputting the teaching theme.
5. A video playback device for teaching, comprising:
the video acquisition module is used for acquiring a teaching video of a teacher, dividing the teaching video and forming a plurality of teaching themes and corresponding teaching theme videos;
the problem acquisition module is used for acquiring a learning problem input by a user;
the question matching module is used for inputting the learning question into a theme matching model and matching the learning question with the teaching theme;
and the video playing module is used for playing the teaching theme video corresponding to the teaching theme matched with the learning problem.
6. The video playback device of claim 5, wherein the dividing the instructional video to form a plurality of instructional themes and corresponding instructional theme videos specifically comprises:
extracting teaching audio synchronized with the teaching video from the teaching video;
converting the teaching audio into teaching text data;
performing cluster analysis on the teaching text data to form a plurality of teaching subjects;
and dividing the teaching video according to the teaching theme to form a corresponding teaching theme video.
7. The video playback device of claim 5, wherein the topic matching model comprises an encoding layer and a matching layer, the encoding layer is used for converting input words into sentence vectors, and the matching layer is used for matching calculation between the sentence vectors.
8. The video playback device of claim 7, wherein the coding layer comprises a plurality of input interfaces, one of the input interfaces is used for inputting a user's learning question, and the other input interfaces are used for inputting the teaching theme.
9. A video playback system for teaching, comprising:
a storage unit for storing a computer executable program;
a processing unit for reading the computer executable program in the storage unit to execute the video playing method for teaching of any one of claims 1 to 4.
10. A computer-readable medium storing a computer-readable program for executing the video playback method for teaching of any one of claims 1 to 4.
Technical Field
The invention relates to the technical field of multimedia, in particular to a video playing method, device and system for teaching.
Background
With the development of network technology, more and more students still learn through network courses. The current network course learning is mainly that students acquire teaching videos uploaded by teachers through a network and learn knowledge by watching the teaching videos.
However, the current online course teaching video is generally a video of a whole course, and when a student is not familiar with the content of a certain measure in the course, the student needs to manually adjust the playing degree and adjust the related content for watching when the student wants to watch and learn repeatedly. However, it is difficult to accurately control the playing progress, and it is difficult to play the contents of other knowledge points in advance, or to adjust the contents again, which often takes a lot of time to adjust the progress of the video, and thus the learning efficiency of the students is reduced.
Disclosure of Invention
The invention aims to solve the problem that the existing teaching video cannot be well matched with the problems provided by students and played.
In order to solve the above technical problem, a first aspect of the present invention provides a video playing method for teaching, including:
the method comprises the steps of obtaining a teaching video of a teacher, dividing the teaching video, and forming a plurality of teaching themes and corresponding teaching theme videos;
acquiring a learning problem input by a user;
inputting the learning question into a theme matching model, and matching the learning question with the teaching theme;
and playing a teaching theme video corresponding to the teaching theme matched with the learning question.
According to a preferred embodiment of the present invention, the dividing the teaching video to form a plurality of teaching subjects and corresponding teaching subject videos specifically includes:
extracting teaching audio synchronized with the teaching video from the teaching video;
converting the teaching audio into teaching text data;
performing cluster analysis on the teaching text data to form a plurality of teaching subjects;
and dividing the teaching video according to the teaching theme to form a corresponding teaching theme video.
According to a preferred embodiment of the present invention, the topic matching model includes an encoding layer and a matching layer, the encoding layer is configured to convert an input text into a sentence vector, and the matching layer is configured to perform matching calculation between the sentence vectors.
According to a preferred embodiment of the present invention, the input interface of the coding layer is plural, one of the input interfaces is used for inputting the learning question of the user, and the other input interface is used for inputting the teaching theme.
A second aspect of the present invention provides a video playing apparatus for teaching, including:
the video acquisition module is used for acquiring a teaching video of a teacher, dividing the teaching video and forming a plurality of teaching themes and corresponding teaching theme videos;
the problem acquisition module is used for acquiring a learning problem input by a user;
the question matching module is used for inputting the learning question into a theme matching model and matching the learning question with the teaching theme;
and the video playing module is used for playing the teaching theme video corresponding to the teaching theme matched with the learning problem.
According to a preferred embodiment of the present invention, the dividing the teaching video to form a plurality of teaching subjects and corresponding teaching subject videos specifically includes:
extracting teaching audio synchronized with the teaching video from the teaching video;
converting the teaching audio into teaching text data;
performing cluster analysis on the teaching text data to form a plurality of teaching subjects;
and dividing the teaching video according to the teaching theme to form a corresponding teaching theme video.
According to a preferred embodiment of the present invention, the topic matching model includes an encoding layer and a matching layer, the encoding layer is configured to convert an input text into a sentence vector, and the matching layer is configured to perform matching calculation between the sentence vectors.
According to a preferred embodiment of the present invention, the input interface of the coding layer is plural, one of the input interfaces is used for inputting the learning question of the user, and the other input interface is used for inputting the teaching theme.
The third aspect of the present invention provides a video playing system for teaching, including:
a storage unit for storing a computer executable program;
and the processing unit is used for reading the computer executable program in the storage unit so as to execute the video playing method for teaching.
A fourth aspect of the present invention is directed to a computer-readable medium storing a computer-readable program, wherein the computer-readable program is configured to execute the video playback method for teaching.
By adopting the technical scheme, through the theme matching model, the problems brought forward by the students are matched with the teacher teaching video, the relevant videos are more accurately played for the students, and the learning efficiency of the students is improved.
Drawings
In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects obtained more clear, the following will describe in detail the embodiments of the present invention with reference to the accompanying drawings. It should be noted, however, that the drawings described below are only illustrations of exemplary embodiments of the invention, from which other embodiments can be derived by those skilled in the art without inventive step.
FIG. 1 is a flow chart of a video playing method for teaching according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a topic matching model in an embodiment of the invention;
fig. 3 is a schematic structural diagram of a video playing apparatus for teaching in the embodiment of the present invention.
FIG. 4 is a schematic diagram of a video playback computer system for teaching in an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer-readable storage medium in an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention may be embodied in many specific forms, and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art.
The structures, properties, effects or other characteristics described in a certain embodiment may be combined in any suitable manner in one or more other embodiments, while still complying with the technical idea of the invention.
In describing particular embodiments, specific details of structures, properties, effects, or other features are set forth in order to provide a thorough understanding of the embodiments by one skilled in the art. However, it is not excluded that one skilled in the art may, in certain cases, practice the present invention in a manner that does not include the structures, properties, effects or other features described above.
The flow chart in the drawings is only an exemplary flow demonstration, and does not represent that all the contents, operations and steps in the flow chart are necessarily included in the scheme of the invention, nor does it represent that the execution is necessarily performed in the order shown in the drawings. For example, operations/steps in the flowcharts may be divided, operations/steps may be combined or partially combined, and the like, and the execution sequence shown in the flowcharts may be changed according to actual situations without departing from the gist of the present invention.
Fig. 1 is a schematic flow chart of a video playing method for teaching according to the present invention, as shown in fig. 1, the present invention has the following steps:
s101, obtaining a teaching video of a teacher, dividing the teaching video, and forming a plurality of teaching themes and corresponding teaching theme videos.
In this embodiment, a video recording device records a teaching video of a teacher, and the teaching video further includes audio data synchronized with the video.
On the basis of the above technical solution, further, the dividing the teaching video to form a plurality of teaching subjects and corresponding teaching subject videos specifically includes:
extracting teaching audio synchronized with the teaching video from the teaching video;
converting the teaching audio into teaching text data;
performing cluster analysis on the teaching text data to form a plurality of teaching subjects;
and dividing the teaching video according to the teaching theme to form a corresponding teaching theme video.
In the present embodiment, a teaching audio synchronized with a teaching video is extracted. Through speech recognition, the teaching audio is converted into teaching text data, and the teaching text data comprises a timestamp for corresponding to the teaching video.
The teaching text data after word segmentation processing is converted into vectors by performing word segmentation on the teaching text data. And carrying out cluster analysis on the vectors to form a plurality of teaching subjects. For example, a teacher may explain a calculation method of a cylinder volume, a calculation method of a cylinder side area, a calculation method of a cone volume, a calculation method of a cone side area, a calculation method of a circular truncated cone volume, and a calculation method of a circular truncated cone side area in the whole teaching video, and classify the teaching subjects through cluster analysis.
Vectorization of text, i.e., representing text using numerical features, because computers do not have direct understanding of human-created languages and words. In order to make a computer understand text, we need to map text information into a numerical semantic space, which we can refer to as word vector space. There are many algorithms for converting text into vectors, such as TF-IDF, BOW, One-Hot, word2vec, etc. In the embodiment, vectorization of the text adopts a word2vec algorithm, the word2vec model is an unsupervised learning model, and the mapping of the text information to the semantic space can be realized by training of an unmarked corpus. Teachers accumulate a large amount of historical teaching text data in long-term teaching activities, and the historical teaching text data can be used as corpora for word2vec model training.
After the vectorization of the text is completed, the vectors need to be subjected to cluster analysis, and there are many algorithm models for performing the cluster analysis, such as: LDA, LSI, SVM, Chameleon algorithm model. In the present embodiment, a TextCNN model based on deep learning is used. The TextCNN model includes an input layer, a convolutional layer, a pooling layer, and a fully-connected layer. The clustering analysis work is completed through the textCNN model based on deep learning, a teacher does not need to classify the teaching text data, the work of the teacher is relieved, and the working pressure of the teacher is relieved.
And S102, acquiring the learning problem input by the user.
In the embodiment, the user can input the problems to be known and learned in the webpage client, the PC client and the client of the mobile terminal, and because the ages of students are small, sentences input when the problems are input are not very standard, and the users are likely to directly copy the contents in the questions encountered in the direct learning to inquire. If the workload is too large manually, the computer can not directly obtain the subject of the problem that the student wants to know, such as the user inputting "radius 5 above, radius 8 below, height 10, side area".
S103, inputting the learning question into a theme matching model and matching the learning question with the teaching theme.
On the basis of the technical scheme, the topic matching model further comprises a coding layer and a matching layer, wherein the coding layer is used for converting input characters into sentence vectors, and the matching layer is used for matching calculation among the sentence vectors.
On the basis of the technical scheme, the number of the input interfaces of the coding layer is further multiple, wherein one of the input interfaces is used for inputting the learning problem of the user, and the other input interfaces are used for inputting the teaching theme.
Fig. 2 is a schematic structural diagram of a topic matching model, and as shown in fig. 2, the coding layer adopts a bidirectional long-term and short-term memory network model.
The bidirectional long-short term memory network model is one of the recurrent neural networks, and can better consider words before and after a sentence, such as 'I do not feel the clothes look good', wherein 'not' is to limit the following 'clothes look good' and represents the negation of the clothes look good ', the bidirectional long-short term memory network model can better capture the dependency relationship of a longer distance, and can also consider the limitation of the following words to the preceding words, such as' the cup looks beautiful 'and' is to modify and limit 'impersonal'. In the present embodiment, the bidirectional long and short term memory network model is trained by using a history dialogue record as training data in a supervised learning manner.
In this embodiment, the number of the input interfaces of the coding layer is four, the first of the four input interfaces is a question for inputting a user, and the last three input interfaces are standard question sentences selected from teaching subjects obtained by cluster analysis. Through the operation of the bidirectional long-term and short-term memory network model, the coding layer outputs sentence vectors corresponding to the four question sentences. In other embodiments, more input interfaces may be provided, or a traversal manner may be adopted to traverse the standard question sentences in the standard question sentence set to match the closest standard question sentence.
On the basis of the technical scheme, further, the matching layer calculates the matching degree between the sentence vectors of the question sentences of the user and the sentence vectors of the standard question sentences by adopting a cosine algorithm. The problem raised by the user is calculated to be closest to the calculation method of the circular truncated cone side area, so that the calculation method of the circular truncated cone side area is determined as the content which the user wants to learn.
And S104, playing a teaching theme video corresponding to the teaching theme matched with the learning question.
On the basis of the above technical solution, further, playing a teaching topic video corresponding to the teaching topic matched with the learning question specifically includes:
and the client acquires the teaching theme video, loads a video renderer locally, performs video rendering on the teaching theme video, and plays the teaching theme video subjected to video rendering.
In this embodiment, because the network rates of each student family are different, in order to ensure that most students can watch the teaching video well, the teaching video transmitted through the network is a streaming media through video compression, and the effect may be fuzzy when the client displays, which may affect the watching of the students. Therefore, when the client acquires the corresponding teaching theme video, the video renderer is loaded locally, in the embodiment, the video renderer is a madVR (madhVideorderer) video renderer, and the video is rendered through the video renderer, so that the definition of the teaching theme video is improved, and the use experience of watching by students is improved.
Fig. 3 is a schematic structural diagram of a video playback device for teaching according to an embodiment of the present invention, and as shown in fig. 3, a
the
In this embodiment, a video recording device records a teaching video of a teacher, and the teaching video further includes audio data synchronized with the video.
On the basis of the above technical solution, further, the dividing the teaching video to form a plurality of teaching subjects and corresponding teaching subject videos specifically includes:
extracting teaching audio synchronized with the teaching video from the teaching video;
converting the teaching audio into teaching text data;
performing cluster analysis on the teaching text data to form a plurality of teaching subjects;
and dividing the teaching video according to the teaching theme to form a corresponding teaching theme video.
In the present embodiment, a teaching audio synchronized with a teaching video is extracted. Through speech recognition, the teaching audio is converted into teaching text data, and the teaching text data comprises a timestamp for corresponding to the teaching video.
The teaching text data after word segmentation processing is converted into vectors by performing word segmentation on the teaching text data. And carrying out cluster analysis on the vectors to form a plurality of teaching subjects. For example, a teacher may explain a calculation method of a cylinder volume, a calculation method of a cylinder side area, a calculation method of a cone volume, a calculation method of a cone side area, a calculation method of a circular truncated cone volume, and a calculation method of a circular truncated cone side area in the whole teaching video, and classify the teaching subjects through cluster analysis.
Vectorization of text, i.e., representing text using numerical features, because computers do not have direct understanding of human-created languages and words. In order to make a computer understand text, we need to map text information into a numerical semantic space, which we can refer to as word vector space. There are many algorithms for converting text into vectors, such as TF-IDF, BOW, One-Hot, word2vec, etc. In the embodiment, vectorization of the text adopts a word2vec algorithm, the word2vec model is an unsupervised learning model, and the mapping of the text information to the semantic space can be realized by training of an unmarked corpus. Teachers accumulate a large amount of historical teaching text data in long-term teaching activities, and the historical teaching text data can be used as corpora for word2vec model training.
After the vectorization of the text is completed, the vectors need to be subjected to cluster analysis, and there are many algorithm models for performing the cluster analysis, such as: LDA, LSI, SVM, Chameleon algorithm model. In the present embodiment, a TextCNN model based on deep learning is used. The TextCNN model includes an input layer, a convolutional layer, a pooling layer, and a fully-connected layer. The clustering analysis work is completed through the textCNN model based on deep learning, a teacher does not need to classify the teaching text data, the work of the teacher is relieved, and the working pressure of the teacher is relieved.
The
In the embodiment, the user can input the problems to be known and learned in the webpage client, the PC client and the client of the mobile terminal, and because the ages of students are small, sentences input when the problems are input are not very standard, and the users are likely to directly copy the contents in the questions encountered in the direct learning to inquire. If the workload is too large manually, the computer can not directly obtain the subject of the problem that the student wants to know, such as the user inputting "radius 5 above, radius 8 below, height 10, side area".
And the
On the basis of the technical scheme, the topic matching model further comprises a coding layer and a matching layer, wherein the coding layer is used for converting input characters into sentence vectors, and the matching layer is used for matching calculation among the sentence vectors.
On the basis of the technical scheme, the number of the input interfaces of the coding layer is further multiple, wherein one of the input interfaces is used for inputting the learning problem of the user, and the other input interfaces are used for inputting the teaching theme.
The bidirectional long-short term memory network model is one of the recurrent neural networks, and can better consider words before and after a sentence, such as 'I do not feel the clothes look good', wherein 'not' is to limit the following 'clothes look good' and represents the negation of the clothes look good ', the bidirectional long-short term memory network model can better capture the dependency relationship of a longer distance, and can also consider the limitation of the following words to the preceding words, such as' the cup looks beautiful 'and' is to modify and limit 'impersonal'. In the present embodiment, the bidirectional long and short term memory network model is trained by using a history dialogue record as training data in a supervised learning manner.
In this embodiment, the number of the input interfaces of the coding layer is four, the first of the four input interfaces is a question for inputting a user, and the last three input interfaces are standard question sentences selected from teaching subjects obtained by cluster analysis. Through the operation of the bidirectional long-term and short-term memory network model, the coding layer outputs sentence vectors corresponding to the four question sentences. In other embodiments, more input interfaces may be provided, or a traversal manner may be adopted to traverse the standard question sentences in the standard question sentence set to match the closest standard question sentence.
On the basis of the technical scheme, further, the matching layer calculates the matching degree between the sentence vectors of the question sentences of the user and the sentence vectors of the standard question sentences by adopting a cosine algorithm. The problem raised by the user is calculated to be closest to the calculation method of the circular truncated cone side area, so that the calculation method of the circular truncated cone side area is determined as the content which the user wants to learn.
And the video playing module 304 is configured to play a teaching theme video corresponding to the teaching theme matched with the learning question.
On the basis of the above technical solution, further, playing a teaching topic video corresponding to the teaching topic matched with the learning question specifically includes:
and the client acquires the teaching theme video, loads a video renderer locally, performs video rendering on the teaching theme video, and plays the teaching theme video subjected to video rendering.
In this embodiment, because the network rates of each student family are different, in order to ensure that most students can watch the teaching video well, the teaching video transmitted through the network is a streaming media through video compression, and the effect may be fuzzy when the client displays, which may affect the watching of the students. Therefore, when the client acquires the corresponding teaching theme video, the video renderer is loaded locally, in the embodiment, the video renderer is a madVR (madhVideorderer) video renderer, and the video is rendered through the video renderer, so that the definition of the teaching theme video is improved, and the use experience of watching by students is improved.
The invention also provides a video playing system for teaching, comprising:
a storage unit for storing a computer executable program;
and the processing unit is used for reading the computer executable program in the storage unit so as to execute the video playing method for teaching.
The present invention also provides a computer-readable medium storing a computer-readable program, characterized in that the computer-readable program is for executing a video playback method for teaching.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described above can be implemented by software, and can also be implemented by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a data processing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention.
The computer readable storage medium may include a data signal propagating in baseband or as part of a carrier wave, which carries readable program code. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a 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 readable storage 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.
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, 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 computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the present invention can be implemented as a method, an apparatus, an electronic device, or a computer-readable medium executing a computer program. Some or all of the functions of the present invention may be implemented in practice using general purpose data processing equipment such as a micro-processing unit or a digital signal processing unit (DSP).
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not limited to the specific embodiments, but rather should be construed as broadly within the spirit and scope of the invention as defined in the appended claims.
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