CN116596071A - VR live-action teaching model big data teaching knowledge mining method and system - Google Patents
VR live-action teaching model big data teaching knowledge mining method and system Download PDFInfo
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Abstract
The invention belongs to the technical field of VR live-action teaching, and discloses a method and a system for mining big data teaching knowledge of a VR live-action teaching model, wherein the method comprises the following steps: the system comprises a teaching scene design module, a virtual model construction module, a central control module, a teaching knowledge acquisition module, a knowledge map construction module, a knowledge mining module, a teaching quality report generation module and a VR demonstration module. According to the invention, a knowledge graph construction module is used for constructing a multi-mode VR live-action teaching model big data teaching knowledge graph, so that the relation between knowledge points of VR live-action teaching materials is shown in a graph form, and the multi-mode is integrated into the knowledge graph, so that knowledge is enriched from the showing form, and the class interest is enhanced; meanwhile, a teacher teaching quality report which is more in line with the actual situation of a classroom is generated through the teaching quality report generation module according to the multi-layer feedforward characteristic code converter and the second characteristic matrix, namely the accuracy of a teacher teaching evaluation result is effectively improved.
Description
Technical Field
The invention belongs to the technical field of VR live-action teaching, and particularly relates to a method and a system for mining large data teaching knowledge of a VR live-action teaching model.
Background
Teaching is a human-specific talent training activity consisting of teacher's teaching and student's learning. Through the activities, teachers purposefully, planarly and organically guide students to learn and master cultural scientific knowledge and skills, promote the improvement of the quality of the students, and enable the students to be people required by society; the teaching is to complete the tasks with high quality and high efficiency, and a critical aspect is to follow the teaching rule, process the relationship of combining indirect experience and direct experience, teach the relationship of knowledge and improving thought and insight, teach the relationship of knowledge and developing intelligence, and exert the dominant effect of teachers and the relationship of enthusiasm and consciousness of students; however, the knowledge graph adopted by the conventional VR live-action teaching model big data teaching knowledge mining system only completes the collection and integration of text knowledge to a great extent, and for students, no multimedia resources such as pictures, audios, videos and the like are matched, and boring characters can not fully mobilize the learning interests of the students; meanwhile, the teaching quality cannot be accurately evaluated.
Through the above analysis, the problems and defects existing in the prior art are as follows:
(1) The knowledge graph adopted by the conventional VR live-action teaching model big data teaching knowledge mining system only completes the collection and integration of text knowledge to a great extent, and for students, no multimedia resources such as pictures, audios and videos are matched, and boring texts can not fully mobilize the learning interests of the students.
(2) The teaching quality cannot be accurately evaluated.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a method and a system for mining the big data teaching knowledge of a VR live-action teaching model.
The invention is realized in such a way that a VR live-action teaching model big data teaching knowledge mining system comprises:
the system comprises a teaching scene design module, a virtual model construction module, a central control module, a teaching knowledge acquisition module, a knowledge map construction module, a knowledge mining module, a teaching quality report generation module and a VR demonstration module;
the teaching scene design module is connected with the virtual model construction module and is used for designing a teaching virtual scene;
the teaching scene design module design method comprises the following steps:
acquiring teaching scene requirements, and patterning the teaching scene according to the teaching scene requirements through a scene design program;
then, adjusting the light shadow and the color of the teaching scene image;
finally, the designed teaching scene image is stored;
the virtual model construction module is connected with the teaching scene design module and the central control module and is used for constructing a teaching virtual scene model;
the virtual model building module building method comprises the following steps:
importing the designed teaching scene image into modeling software;
constructing a teaching virtual scene model according to the teaching scene image through modeling software;
carrying out surface reduction, UV spreading, baking, rendering and the like on the teaching virtual scene model;
the central control module is connected with the virtual model construction module, the teaching knowledge acquisition module, the knowledge map construction module, the knowledge mining module, the teaching quality report generation module and the VR demonstration module and used for controlling the modules to work normally;
the teaching knowledge acquisition module is connected with the central control module and used for acquiring teaching knowledge;
the knowledge graph construction module is connected with the central control module and used for constructing a teaching knowledge graph;
the knowledge graph construction module construction method comprises the following steps:
processing the acquired data by taking a teaching outline of the VR live-action teaching material as a theme to generate a VR live-action teaching material big data teaching knowledge graph;
the knowledge mining module is connected with the central control module and used for mining teaching knowledge;
the teaching quality report generation module is connected with the central control module and used for generating a teaching quality report;
and the VR demonstration module is connected with the central control module and used for demonstrating the big data teaching of the VR live-action teaching model through VR equipment.
A VR live-action teaching model big data teaching knowledge mining method comprises the following steps:
step one, designing a teaching virtual scene through a teaching scene design module; constructing a teaching virtual scene model through a virtual model construction module;
step two, the central control module collects teaching knowledge through the teaching knowledge collection module; constructing a teaching knowledge graph through a knowledge graph construction module; excavating teaching knowledge through a knowledge excavating module;
generating a teaching quality report through a teaching quality report generating module;
and step four, utilizing VR equipment to demonstrate the large data teaching of the VR live-action teaching model through a VR demonstration module.
Further, the knowledge graph construction module construction method comprises the following steps:
(1) Constructing knowledge points of a VR live-action teaching model big data teaching material and attributes corresponding to the knowledge points; acquiring a plurality of original data from a plurality of data sources according to the knowledge points and the attributes of the knowledge points, wherein the original data comprise pictures, audio or video resources;
(2) And processing the acquired data by taking the teaching outline of the VR real-scene teaching material as a theme to generate a VR real-scene teaching model big-data teaching knowledge graph.
Further, the construction of the knowledge points of the VR real scene teaching model big data teaching materials and the corresponding attributes of the knowledge points specifically includes:
acquiring text resources of a VR live-action teaching model big data teaching material;
preprocessing the text resource;
adopting TF-IDF to complete knowledge point extraction;
after knowledge points are extracted, teaching material course standards and teaching outline input attributes are taught according to the VR live-action teaching model big data.
Further, the preprocessing includes text format conversion, word segmentation and new word merging.
Further, before the obtaining of the plurality of original data from the plurality of data sources according to the knowledge points and the attributes of the knowledge points, creating a knowledge extraction strategy is further included.
Further, the knowledge extraction strategy includes: and creating the knowledge extraction strategy according to the interest requirement of the preset VR live-action teaching model big data teaching knowledge graph.
Further, the method takes a teaching outline of the VR live-action teaching model big data teaching material as a theme, processes the acquired data, and creates a knowledge graph construction strategy before generating the VR live-action teaching model big data teaching knowledge graph;
the map construction strategy at least comprises a knowledge point attribute mapping strategy: the knowledge point attribute mapping strategy is obtained by using the directivity, the interactivity and the transitivity of the knowledge points based on discipline teaching rules, teaching outlines and culture targets.
Further, the teaching quality report generating module generates the following steps:
1) Constructing a VR real-scene teaching model big data teaching knowledge graph, wherein the VR real-scene teaching model big data teaching knowledge graph comprises teacher behavior data and student behavior data; inputting the VR real-scene teaching model big data teaching knowledge graph to a teacher teaching quality generation model to obtain a teacher teaching quality report;
the teacher teaching quality generation model comprises a multilayer cyclic filtering graph feature extractor, an extreme deep layer coding and decoding network and a multilayer feedforward feature code converter;
the multi-layer cyclic filtering graph feature extractor is used for extracting classroom characterization information of the VR real scene teaching model big data teaching knowledge graph as a first feature matrix;
the extreme deep layer encoding and decoding network is used for carrying out data refining and concentration on the first feature matrix to obtain a second feature matrix;
the multi-layer feedforward characteristic code converter is used for generating a teacher teaching quality report according to the second characteristic matrix.
Further, the construction of the VR real scene teaching model big data teaching knowledge graph includes:
taking a class main body as a node, taking the relation between the class main body and the class main body attribute as an edge, and connecting the nodes through the edge, wherein the class main body comprises the teaching organizer and the learner;
taking the classroom behavior as a node, and connecting the classroom behavior with the classroom main body to obtain a VR live-action teaching model big data teaching knowledge graph;
the multi-layer cyclic filtering graph feature extractor comprises a multi-layer graph convolution network, a filter and a feature encoder;
the multi-layer graph convolution network is used for combining the integrated feature coding information output by the feature encoder and extracting a plurality of classroom characterization information of the VR real scene teaching model big data teaching knowledge graph as feature coding information;
the filter is used for filtering the characteristic coding information generated by the multi-layer graph rolling network;
the feature encoder is used for integrating the filtered feature encoding information to obtain integrated feature encoding information; after the fact that the extraction times of the multi-layer graph convolutional network meet preset requirements is determined, taking the currently integrated feature coding information as a first feature matrix;
the extreme deep codec network comprises at least a 1000 layer codec network and a normalization function;
generating a teacher teaching quality report according to the second feature matrix, including:
converting the second feature matrix into text information through a coding dictionary, wherein the teacher teaching quality report comprises the text information;
before the teacher teaching quality generation model generates the teacher teaching quality report, the method further includes the following steps:
initializing the teacher teaching quality generation model parameters;
inputting training data into the teacher teaching quality generation model to obtain a teacher teaching quality report corresponding to the training data;
calculating a cross entropy loss value of a teacher teaching quality report and a target report corresponding to the training data;
when the cross entropy loss value is larger than or equal to a preset loss value, adjusting the teacher teaching quality generation model parameters;
and when the cross entropy loss value is smaller than a preset loss value, determining that the training of the teacher teaching quality generation model is completed.
In combination with the technical scheme and the technical problems to be solved, the technical scheme to be protected has the following advantages and positive effects:
firstly, constructing a multi-mode VR live-action teaching model big data teaching knowledge graph through a knowledge graph construction module, displaying the relation between knowledge points of a VR live-action teaching model big data teaching material in a graph form, integrating multiple modes into the knowledge graph, enriching the knowledge from the display form, and enhancing the class interest; meanwhile, a multi-layer cyclic filtering graph feature extractor, an extreme deep layer coding and decoding network and a multi-layer feedforward feature code converter are arranged in a teacher teaching quality generation model through a teaching quality report generation module, after a VR real scene teaching model big data teaching knowledge graph comprising teacher behavior data and student behavior data is built, the VR real scene teaching model big data teaching knowledge graph is input into the teacher teaching quality generation model, classroom characterization information of the VR real scene teaching model big data teaching knowledge graph is extracted through the multi-layer cyclic filtering graph feature extractor to serve as a first feature matrix, the first feature matrix is subjected to data refining and concentration through the extreme deep layer coding and decoding network to obtain a second feature matrix, and a teacher teaching quality report which is more in line with actual situations of a classroom is generated through the multi-layer feedforward feature code converter according to the second feature matrix, namely the accuracy of a teacher teaching evaluation result is effectively improved.
Secondly, constructing a multi-mode VR live-action teaching model big data teaching knowledge graph through a knowledge graph construction module, displaying the relation between knowledge points of the VR live-action teaching model big data teaching materials in a graph mode, integrating multiple modes into the knowledge graph, enriching the knowledge from the display mode, and enhancing the class interest; meanwhile, a multi-layer cyclic filtering graph feature extractor, an extreme deep layer coding and decoding network and a multi-layer feedforward feature code converter are arranged in a teacher teaching quality generation model through a teaching quality report generation module, after a VR real scene teaching model big data teaching knowledge graph comprising teacher behavior data and student behavior data is built, the VR real scene teaching model big data teaching knowledge graph is input into the teacher teaching quality generation model, classroom characterization information of the VR real scene teaching model big data teaching knowledge graph is extracted through the multi-layer cyclic filtering graph feature extractor to serve as a first feature matrix, the first feature matrix is subjected to data refining and concentration through the extreme deep layer coding and decoding network to obtain a second feature matrix, and a teacher teaching quality report which is more in line with actual situations of a classroom is generated through the multi-layer feedforward feature code converter according to the second feature matrix, namely the accuracy of a teacher teaching evaluation result is effectively improved.
In addition, the VR live-action teaching model big data teaching knowledge mining system provided by the invention has the following advantages and positive effects:
1. the teaching interactivity and the interest are increased. Through realizing virtual reality technology, put the student in real teaching scene, improve student's participation degree and interest, increase the interactivity and the interest of teaching.
2. Improving the teaching effect and quality. Through big data analysis and excavation, the system can rapidly acquire, integrate and recommend teaching resources, and improve teaching effects and quality. Meanwhile, the teaching quality report generating module can evaluate and feed back the teaching process, and provides a basis for improving teaching methods and strategies for teachers.
3. The teaching resources and the cost are saved. Through virtual model construction module and teaching resource integration, the system can save teaching resource and cost, realizes the multi-angle, all-round and dynamic presentation to the teaching content simultaneously.
4. Convenient teaching management and monitoring function. Through the central control module, the system can realize real-time monitoring and management of teaching process and student learning condition, and provides convenient teaching management tool for teachers.
5. Sustainability development. The system can continuously accumulate and update teaching resources and knowledge maps along with the continuous promotion of teaching processes, and realize sustainable development.
Drawings
Fig. 1 is a flowchart of a VR real-scene teaching model big data teaching knowledge mining method provided by an embodiment of the present invention.
Fig. 2 is a block diagram of a VR real-scene teaching model big data teaching knowledge mining system according to an embodiment of the present invention.
Fig. 3 is a flowchart of a knowledge graph construction module construction method according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for generating a teaching quality report generating module according to an embodiment of the present invention.
In fig. 2: 1. a teaching scene design module; 2. a virtual model building module; 3. a central control module; 4. a teaching knowledge acquisition module; 5. a knowledge graph construction module; 6. a knowledge mining module; 7. a teaching quality report generating module; 8. and VR demonstration module.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the VR real scene teaching model big data teaching knowledge mining method provided by the invention comprises the following steps:
s101, designing a teaching virtual scene through a teaching scene design module; constructing a teaching virtual scene model through a virtual model construction module;
s102, a central control module collects teaching knowledge through a teaching knowledge collection module; constructing a teaching knowledge graph through a knowledge graph construction module; excavating teaching knowledge through a knowledge excavating module;
s103, generating a teaching quality report through a teaching quality report generation module;
s104, utilizing VR equipment to demonstrate the big data teaching of the VR live-action teaching model through the VR demonstration module.
As shown in fig. 2, the VR real scene teaching model big data teaching knowledge mining system provided by the embodiment of the present invention includes: the teaching scene design module 1, the virtual model construction module 2, the central control module 3, the teaching knowledge acquisition module 4, the knowledge graph construction module 5, the knowledge mining module 6, the teaching quality report generation module 7 and the VR demonstration module 8.
The teaching scene design module 1 is connected with the virtual model construction module 2 and is used for designing a teaching virtual scene;
the teaching scene design module design method comprises the following steps:
acquiring teaching scene requirements, and patterning the teaching scene according to the teaching scene requirements through a scene design program;
then, adjusting the light shadow and the color of the teaching scene image;
finally, the designed teaching scene image is stored;
the virtual model construction module 2 is connected with the teaching scene design module 1 and the central control module 3 and is used for constructing a teaching virtual scene model;
the virtual model building module building method comprises the following steps:
importing the designed teaching scene image into modeling software;
constructing a teaching virtual scene model according to the teaching scene image through modeling software;
carrying out surface reduction, UV spreading, baking, rendering and the like on the teaching virtual scene model;
the central control module 3 is connected with the virtual model construction module 2, the teaching knowledge acquisition module 4, the knowledge graph construction module 5, the knowledge mining module 6, the teaching quality report generation module 7 and the VR demonstration module 8 and is used for controlling the normal work of each module;
the teaching knowledge acquisition module 4 is connected with the central control module 3 and is used for acquiring teaching knowledge;
the knowledge graph construction module 5 is connected with the central control module 3 and is used for constructing a teaching knowledge graph;
the knowledge graph construction module construction method comprises the following steps:
processing the acquired data by taking a teaching outline of the VR live-action teaching material as a theme to generate a VR live-action teaching material big data teaching knowledge graph;
the knowledge mining module 6 is connected with the central control module 3 and is used for mining teaching knowledge;
the teaching quality report generation module 7 is connected with the central control module 3 and is used for generating a teaching quality report;
and the VR demonstration module 8 is connected with the central control module 3 and used for demonstrating the big data teaching of the VR live-action teaching model through VR equipment.
As an embodiment of the present invention, the following is a specific scheme:
teaching scene design module 1: the working principle of the module is that the teaching scene requirement is acquired, the scene design program is utilized for composition, then the teaching scene image is subjected to light and shadow adjustment, color treatment and the like, and finally the designed teaching scene image is stored. The main realization process comprises the steps of scene requirement acquisition, scene composition, image processing, storage and the like.
Virtual model construction module 2: the working principle of the module is that a designed teaching scene image is imported into modeling software, a teaching virtual scene model is constructed according to teaching scene requirements, and then the model is subjected to surface reduction, UV spreading, baking, rendering and other treatments. The method mainly comprises the steps of image importing, model building, model processing, rendering and the like.
Central control module 3: the working principle of the module is to connect other modules, control the normal work of each module and realize the unified coordination and management of the system.
Teaching knowledge acquisition module 4: the working principle of the module is that teaching resources are connected through a central control module, and teaching knowledge is collected. The method mainly comprises the steps of teaching resource acquisition, information extraction, knowledge acquisition and the like.
Knowledge graph construction module 5: the working principle of the module is to process acquired data by taking a teaching outline of a VR live-action teaching model big data teaching material as a theme, and generate a VR live-action teaching model big data teaching knowledge graph. The main implementation process comprises the steps of data processing, knowledge classification, knowledge graph construction and the like.
Knowledge mining module 6: the working principle of the module is that the knowledge graph construction module is connected through the central control module to excavate teaching knowledge. The main implementation process comprises the steps of knowledge graph analysis, knowledge association mining, knowledge recommendation and the like.
Teaching quality report generation module 7: the working principle of the module is that a central control module is connected with a teaching resource and knowledge graph construction module to generate a teaching quality report. The method mainly comprises the steps of teaching data analysis, teaching quality assessment, report generation and the like.
VR demonstration module 8: the working principle of the module is that the virtual model construction module and teaching resources are connected through the central control module, and the VR equipment is utilized to demonstrate the large data teaching of the VR live-action teaching model. The method mainly comprises the steps of virtual model import, resource integration, VR demonstration and the like.
As shown in fig. 3, the knowledge graph construction module construction method provided by the invention comprises the following steps:
s201, constructing knowledge points of a VR live-action teaching model big data teaching material and attributes corresponding to the knowledge points; acquiring a plurality of original data from a plurality of data sources according to the knowledge points and the attributes of the knowledge points, wherein the original data comprise pictures, audio or video resources;
s202, processing the acquired data by taking a teaching outline of the VR live-action teaching model big data teaching material as a theme, and generating a VR live-action teaching model big data teaching knowledge graph.
The invention provides a knowledge point for constructing a VR live-action teaching model big data teaching material, which specifically comprises the following attributes corresponding to the knowledge point:
acquiring text resources of a VR live-action teaching model big data teaching material;
preprocessing the text resource;
adopting TF-IDF to complete knowledge point extraction;
after knowledge points are extracted, teaching material course standards and teaching outline input attributes are taught according to the VR live-action teaching model big data.
The preprocessing provided by the invention comprises text format conversion, word segmentation and new word merging.
The method further comprises creating a knowledge extraction strategy before acquiring a plurality of original data from a plurality of data sources according to the knowledge points and the attributes of the knowledge points.
The knowledge extraction strategy provided by the invention comprises the following steps: and creating the knowledge extraction strategy according to the interest requirement of the preset VR live-action teaching model big data teaching knowledge graph.
The method provided by the invention takes the teaching outline of the VR live-action teaching model big data teaching material as a theme, processes the acquired data, and also comprises the step of creating a knowledge graph construction strategy before generating the VR live-action teaching model big data teaching knowledge graph;
the map construction strategy at least comprises a knowledge point attribute mapping strategy: the knowledge point attribute mapping strategy is obtained by using the directivity, the interactivity and the transitivity of the knowledge points based on discipline teaching rules, teaching outlines and culture targets.
As shown in fig. 4, the method for generating the teaching quality report provided by the invention comprises the following steps:
s301, constructing a VR real scene teaching model big data teaching knowledge graph which comprises teacher behavior data and student behavior data; inputting the VR real-scene teaching model big data teaching knowledge graph to a teacher teaching quality generation model to obtain a teacher teaching quality report;
the teacher teaching quality generation model comprises a multilayer cyclic filtering graph feature extractor, an extreme deep layer coding and decoding network and a multilayer feedforward feature code converter;
the multi-layer cyclic filtering graph feature extractor is used for extracting classroom characterization information of the VR real scene teaching model big data teaching knowledge graph as a first feature matrix;
the extreme deep layer encoding and decoding network is used for carrying out data refining and concentration on the first feature matrix to obtain a second feature matrix;
the multi-layer feedforward characteristic code converter is used for generating a teacher teaching quality report according to the second characteristic matrix.
The invention provides a method for constructing a VR live-action teaching model big data teaching knowledge graph, which comprises the following steps:
taking a class main body as a node, taking the relation between the class main body and the class main body attribute as an edge, and connecting the nodes through the edge, wherein the class main body comprises the teaching organizer and the learner;
taking the classroom behavior as a node, and connecting the classroom behavior with the classroom main body to obtain a VR live-action teaching model big data teaching knowledge graph;
the multi-layer cyclic filtering graph feature extractor comprises a multi-layer graph convolution network, a filter and a feature encoder;
the multi-layer graph convolution network is used for combining the integrated feature coding information output by the feature encoder and extracting a plurality of classroom characterization information of the VR real scene teaching model big data teaching knowledge graph as feature coding information;
the filter is used for filtering the characteristic coding information generated by the multi-layer graph rolling network;
the feature encoder is used for integrating the filtered feature encoding information to obtain integrated feature encoding information; after the fact that the extraction times of the multi-layer graph convolutional network meet preset requirements is determined, taking the currently integrated feature coding information as a first feature matrix;
the extreme deep codec network comprises at least a 1000 layer codec network and a normalization function;
generating a teacher teaching quality report according to the second feature matrix, including:
converting the second feature matrix into text information through a coding dictionary, wherein the teacher teaching quality report comprises the text information;
before the teacher teaching quality generation model generates the teacher teaching quality report, the method further includes the following steps:
initializing the teacher teaching quality generation model parameters;
inputting training data into the teacher teaching quality generation model to obtain a teacher teaching quality report corresponding to the training data;
calculating a cross entropy loss value of a teacher teaching quality report and a target report corresponding to the training data;
when the cross entropy loss value is larger than or equal to a preset loss value, adjusting the teacher teaching quality generation model parameters;
and when the cross entropy loss value is smaller than a preset loss value, determining that the training of the teacher teaching quality generation model is completed.
According to the invention, a knowledge graph construction module is used for constructing a multi-mode VR live-action teaching model big data teaching knowledge graph, so that the relation between knowledge points of VR live-action teaching materials is shown in a graph form, and the multi-mode is integrated into the knowledge graph, so that knowledge is enriched from the showing form, and the class interest is enhanced; meanwhile, a multi-layer cyclic filtering graph feature extractor, an extreme deep layer coding and decoding network and a multi-layer feedforward feature code converter are arranged in a teacher teaching quality generation model through a teaching quality report generation module, after a VR real scene teaching model big data teaching knowledge graph comprising teacher behavior data and student behavior data is built, the VR real scene teaching model big data teaching knowledge graph is input into the teacher teaching quality generation model, classroom characterization information of the VR real scene teaching model big data teaching knowledge graph is extracted through the multi-layer cyclic filtering graph feature extractor to serve as a first feature matrix, the first feature matrix is subjected to data refining and concentration through the extreme deep layer coding and decoding network to obtain a second feature matrix, and a teacher teaching quality report which is more in line with actual situations of a classroom is generated through the multi-layer feedforward feature code converter according to the second feature matrix, namely the accuracy of a teacher teaching evaluation result is effectively improved.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
According to the invention, a knowledge graph construction module is used for constructing a multi-mode VR live-action teaching model big data teaching knowledge graph, so that the relation between knowledge points of VR live-action teaching materials is shown in a graph form, and the multi-mode is integrated into the knowledge graph, so that knowledge is enriched from the showing form, and the class interest is enhanced; meanwhile, a multi-layer cyclic filtering graph feature extractor, an extreme deep layer coding and decoding network and a multi-layer feedforward feature code converter are arranged in a teacher teaching quality generation model through a teaching quality report generation module, after a VR real scene teaching model big data teaching knowledge graph comprising teacher behavior data and student behavior data is built, the VR real scene teaching model big data teaching knowledge graph is input into the teacher teaching quality generation model, classroom characterization information of the VR real scene teaching model big data teaching knowledge graph is extracted through the multi-layer cyclic filtering graph feature extractor to serve as a first feature matrix, the first feature matrix is subjected to data refining and concentration through the extreme deep layer coding and decoding network to obtain a second feature matrix, and a teacher teaching quality report which is more in line with actual situations of a classroom is generated through the multi-layer feedforward feature code converter according to the second feature matrix, namely the accuracy of a teacher teaching evaluation result is effectively improved.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (10)
1. The utility model provides a VR live-action teaching mode big data teaching knowledge excavation system which characterized in that, VR live-action teaching mode big data teaching knowledge excavation system includes:
the teaching scene design module is connected with the virtual model construction module and is used for designing a teaching virtual scene;
the teaching scene design module design method comprises the following steps: acquiring teaching scene requirements, and patterning the teaching scene according to the teaching scene requirements through a scene design program; then, adjusting the light shadow and the color of the teaching scene image; finally, the designed teaching scene image is stored;
the virtual model construction module is connected with the teaching scene design module and the central control module and is used for constructing a teaching virtual scene model; the virtual model building module building method comprises the following steps: importing the designed teaching scene image into modeling software; constructing a teaching virtual scene model according to the teaching scene image through modeling software; carrying out surface reduction, UV spreading, baking and rendering treatment on the teaching virtual scene model;
the central control module is connected with the virtual model construction module, the teaching knowledge acquisition module, the knowledge map construction module, the knowledge mining module, the teaching quality report generation module and the VR demonstration module and used for controlling the modules to work normally;
the teaching knowledge acquisition module is connected with the central control module and used for acquiring teaching knowledge;
the knowledge graph construction module is connected with the central control module and used for constructing a teaching knowledge graph; the knowledge graph construction module construction method comprises the following steps: processing the acquired data by taking a teaching outline of the VR live-action teaching material as a theme to generate a VR live-action teaching material big data teaching knowledge graph;
the knowledge mining module is connected with the central control module and used for mining teaching knowledge;
the teaching quality report generation module is connected with the central control module and used for generating a teaching quality report;
and the VR demonstration module is connected with the central control module and used for demonstrating the big data teaching of the VR live-action teaching model through VR equipment.
2. The VR real-scene teaching model big data teaching knowledge mining method as set forth in claim 1, wherein the VR real-scene teaching model big data teaching knowledge mining method comprises the following steps:
step one, designing a teaching virtual scene through a teaching scene design module; constructing a teaching virtual scene model through a virtual model construction module;
step two, the central control module collects teaching knowledge through the teaching knowledge collection module; constructing a teaching knowledge graph through a knowledge graph construction module; excavating teaching knowledge through a knowledge excavating module;
generating a teaching quality report through a teaching quality report generating module;
and step four, utilizing VR equipment to demonstrate the large data teaching of the VR live-action teaching model through a VR demonstration module.
3. The VR real-scene teaching model big data teaching knowledge mining system of claim 1, wherein the knowledge graph construction module construction method comprises the following steps:
(1) Constructing knowledge points of a VR live-action teaching model big data teaching material and attributes corresponding to the knowledge points; acquiring a plurality of original data from a plurality of data sources according to the knowledge points and the attributes of the knowledge points, wherein the original data comprise pictures, audio or video resources;
(2) And processing the acquired data by taking the teaching outline of the VR real-scene teaching material as a theme to generate a VR real-scene teaching model big-data teaching knowledge graph.
4. The VR real scene teaching model big data teaching knowledge mining system as set forth in claim 3, wherein the constructing knowledge points of VR real scene teaching model big data teaching materials and the attributes corresponding to the knowledge points specifically includes:
acquiring text resources of a VR live-action teaching model big data teaching material;
preprocessing the text resource;
adopting TF-IDF to complete knowledge point extraction;
after knowledge points are extracted, teaching material course standards and teaching outline input attributes are taught according to the VR live-action teaching model big data.
5. The VR real teaching model big data teaching knowledge mining system as set forth in claim 3, wherein said preprocessing includes text format conversion, word segmentation and new word merging.
6. The VR real teaching model big data teaching knowledge mining system as set forth in claim 3, further comprising creating a knowledge extraction strategy before said obtaining a plurality of raw data from a plurality of data sources based on said knowledge points and attributes of said knowledge points.
7. The VR real teaching model big data teaching knowledge mining system as set forth in claim 3, wherein said knowledge extraction strategy comprises: and creating the knowledge extraction strategy according to the interest requirement of the preset VR live-action teaching model big data teaching knowledge graph.
8. The VR real scene teaching model big data teaching knowledge mining system as set forth in claim 3, wherein the processing the obtained data with the teaching outline of VR real scene teaching model big data teaching materials as the subject, before generating VR real scene teaching model big data teaching knowledge graph, further includes creating a knowledge graph construction strategy;
the map construction strategy at least comprises a knowledge point attribute mapping strategy: the knowledge point attribute mapping strategy is obtained by using the directivity, the interactivity and the transitivity of the knowledge points based on discipline teaching rules, teaching outlines and culture targets.
9. The VR real-scene teaching model big data teaching knowledge mining system according to claim 1, wherein the teaching quality report generating module generates the following steps:
1) Constructing a VR real-scene teaching model big data teaching knowledge graph, wherein the VR real-scene teaching model big data teaching knowledge graph comprises teacher behavior data and student behavior data; inputting the VR real-scene teaching model big data teaching knowledge graph to a teacher teaching quality generation model to obtain a teacher teaching quality report;
the teacher teaching quality generation model comprises a multilayer cyclic filtering graph feature extractor, an extreme deep layer coding and decoding network and a multilayer feedforward feature code converter;
the multi-layer cyclic filtering graph feature extractor is used for extracting classroom characterization information of the VR real scene teaching model big data teaching knowledge graph as a first feature matrix;
the extreme deep layer encoding and decoding network is used for carrying out data refining and concentration on the first feature matrix to obtain a second feature matrix;
the multi-layer feedforward characteristic code converter is used for generating a teacher teaching quality report according to the second characteristic matrix.
10. The VR real-scene teaching model big data teaching knowledge mining system of claim 9, wherein the constructing the VR real-scene teaching model big data teaching knowledge graph comprises:
taking a class main body as a node, taking the relation between the class main body and the class main body attribute as an edge, and connecting the nodes through the edge, wherein the class main body comprises the teaching organizer and the learner;
taking the classroom behavior as a node, and connecting the classroom behavior with the classroom main body to obtain a VR live-action teaching model big data teaching knowledge graph;
the multi-layer cyclic filtering graph feature extractor comprises a multi-layer graph convolution network, a filter and a feature encoder;
the multi-layer graph convolution network is used for combining the integrated feature coding information output by the feature encoder and extracting a plurality of classroom characterization information of the VR real scene teaching model big data teaching knowledge graph as feature coding information;
the filter is used for filtering the characteristic coding information generated by the multi-layer graph rolling network;
the feature encoder is used for integrating the filtered feature encoding information to obtain integrated feature encoding information; after the fact that the extraction times of the multi-layer graph convolutional network meet preset requirements is determined, taking the currently integrated feature coding information as a first feature matrix;
the extreme deep codec network comprises at least a 1000 layer codec network and a normalization function;
generating a teacher teaching quality report according to the second feature matrix, including:
converting the second feature matrix into text information through a coding dictionary, wherein the teacher teaching quality report comprises the text information;
before the teacher teaching quality generation model generates the teacher teaching quality report, the method further includes the following steps:
initializing the teacher teaching quality generation model parameters;
inputting training data into the teacher teaching quality generation model to obtain a teacher teaching quality report corresponding to the training data;
calculating a cross entropy loss value of a teacher teaching quality report and a target report corresponding to the training data;
when the cross entropy loss value is larger than or equal to a preset loss value, adjusting the teacher teaching quality generation model parameters;
and when the cross entropy loss value is smaller than a preset loss value, determining that the training of the teacher teaching quality generation model is completed.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117672027A (en) * | 2024-02-01 | 2024-03-08 | 青岛培诺教育科技股份有限公司 | VR teaching method, device, equipment and medium |
| CN118138794A (en) * | 2024-05-08 | 2024-06-04 | 深圳市科路教育科技有限公司 | Mobile network-based teaching video live broadcast control method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117672027A (en) * | 2024-02-01 | 2024-03-08 | 青岛培诺教育科技股份有限公司 | VR teaching method, device, equipment and medium |
| CN117672027B (en) * | 2024-02-01 | 2024-04-30 | 青岛培诺教育科技股份有限公司 | VR teaching method, device, equipment and medium |
| CN118138794A (en) * | 2024-05-08 | 2024-06-04 | 深圳市科路教育科技有限公司 | Mobile network-based teaching video live broadcast control method |
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