Multi-mode intelligent teaching digital management platform

Abstract

The invention relates to the technical field of intelligent teaching management, in particular to a multi-mode intelligent teaching digital management platform which comprises a management center, and an infrastructure layer, a core technology layer, a business application layer, a user interaction layer and a storage array which are connected with the management center. The infrastructure layer provides resource support, the core technology layer is used for receiving a function calling instruction of the application layer, the business application layer is used for receiving a user operation instruction, the user interaction layer is provided with three types of exclusive interfaces, and the storage array realizes full-process data storage and update pushing. The platform completes multi-mode textbook structured analysis, teaching content automatic generation, full-question intelligent correction, personalized learning plan pushing and class learning situation report generation through cooperation of all levels, solves the problems that textbook analysis is single, content generation depends on manpower, homework correction is limited, and plans lack pertinence, realizes teaching full-process digital closed loop, and improves teaching efficiency. And the practical value is remarkable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent teaching management, and particularly relates to a multi-modal intelligent teaching digital management platform. BACKGROUND

[0002] In the current field of education informatization, a teaching management platform has become an auxiliary teaching management platform for various educational institutions such as higher vocational education, schools, training institutions and online education platforms. In the prior art, when a teacher prepares lessons on the platform, the content of the teaching material still needs to be manually uploaded and associated with a preset tag, and only the existing content can be filtered based on the tag selected by the teacher, new knowledge points cannot be automatically extracted from the teaching material, new questions cannot be generated, and the platform is limited to automatic correction of objective questions and manual assisted correction of subjective questions for the homework submitted by students, and cannot generate learning plans for different students according to the teaching content and the homework correction results. The data of each functional module is fragmented and cannot be closed, and it is difficult to meet the teaching needs.

[0003] Therefore, it is necessary to propose a multi-modal intelligent teaching digital management platform to solve the above problems. SUMMARY

[0004] The purpose of the present application is to provide a multi-modal intelligent teaching digital management platform to solve the problem that the content of the teaching material still needs to be manually uploaded and associated with a preset tag, and only the existing content can be filtered based on the tag selected by the teacher, new knowledge points cannot be automatically extracted from the teaching material, new questions cannot be generated, and the platform is limited to automatic correction of objective questions and manual assisted correction of subjective questions for the homework submitted by students, and cannot generate learning plans for different students according to the teaching content and the homework correction results.

[0005] To achieve the above purpose, the present application provides the following technical scheme: The multi-modal intelligent teaching digital management platform comprises a management center, wherein the management center is connected with an infrastructure layer, a core technology layer, a business application layer, a user interaction layer and a storage array; The infrastructure layer and the core technology layer are connected, and are used for responding to a resource request of the core technology layer and providing calculation, storage and network support; The core technology layer is connected with the business application layer, and comprises a multi-modal analysis engine, an NLP module, an intelligent algorithm recommendation library, an OCR module and a semantic analysis module, and is used for receiving a function calling instruction of the business application layer and outputting a processing result; The business application layer comprises a multi-modal teaching material analysis module, an intelligent content generation module, a homework submission unit, an intelligent homework correction module, a personalized learning module and a management module, and is used for receiving a user operation instruction and calling the core technology layer to output business data; The user interaction layer is connected with the business application layer, and the user interaction layer includes a teacher interaction interface, a student interaction interface and a management interaction interface, which are used to receive user operations and issue instructions to the business application layer. The storage array is connected with the business application layer, and is used to receive a data storage request of the business application layer, store data information of the business application layer, and push a data update notification to the business application layer. The multi-modal teaching material analysis module is used to upload a teaching material file, analyze the teaching material file, obtain a structured knowledge graph and analysis data, and store the structured knowledge graph and the analysis data in the storage array. The intelligent content generation module is used to call the structured knowledge graph and the analysis data stored in the storage array, generate corresponding teaching content, and store the teaching content in the storage array. The homework submission unit is used to submit homework and send the homework to the intelligent homework correction module. The intelligent homework correction module is used to identify the homework, output correction results and learning situation data, and store the correction results and the learning situation data in the storage array. The personalized learning module is used to output a learning plan according to the correction data and push the learning plan. The management module is used to generate a class learning situation report according to full-amount business data of the business application layer in the storage array.

[0006] Preferably, the process of obtaining the structured knowledge graph and the analysis data and storing the structured knowledge graph and the analysis data in the storage array includes: In the teacher interaction interface of the user interaction layer, a teacher uploads a teaching material file operation is triggered, and a file upload instruction is issued to the multi-modal teaching material analysis module; The multi-modal teaching material analysis module triggers a parsing instruction issuing operation according to the teaching material file, and calls a parsing capability of the multi-modal parsing engine; The multi-modal parsing engine triggers a text, picture, audio and video content input operation, distributes teaching content of the teaching material file to an NLP module for semantic visual analysis, the NLP module triggers a semantic visual analysis completion operation, and outputs a structured knowledge graph and analysis data; The multi-modal teaching material analysis module triggers a data storage request operation, and writes the structured knowledge graph and the analysis data into the storage array.

[0007] Preferably, the process of generating corresponding teaching content and storing the teaching content in the storage array includes: The intelligent content generation module triggers a call analysis data operation to obtain the structured knowledge graph and the analysis data from the storage array; The intelligent content generation module triggers the teaching standard matching operation, calls the semantic matching capability of the NLP module, and obtains the teaching standards for the corresponding grade level and subject. The NLP module triggers a content generation rule matching operation, outputting teaching content that includes lesson plans, knowledge point tags, and questions; The intelligent content generation module triggers the content storage operation, writing the teaching content into the storage array.

[0008] Preferably, the process of sending the assignment to the intelligent assignment grading module includes: In the student interaction interface of the user interaction layer, the student is triggered to submit homework data, and the homework upload instruction is sent to the homework submission unit. The assignment submission unit triggers the assignment data upload operation, sending the assignment data to the intelligent assignment grading module.

[0009] Preferably, the process of outputting the grading results and learning data, and storing the grading results and learning data in the storage array includes: The intelligent homework grading module triggers the answer recognition command, which calls upon the recognition capabilities of the OCR module and the semantic analysis module; After completing the recognition, the intelligent homework grading module triggers the answer content recognition completion operation and outputs the grading results and learning data; The intelligent homework grading module triggers the data entry operation for learning progress, writing the grading results and learning progress data into the storage array.

[0010] Preferably, the process of outputting the learning plan and pushing the learning plan includes: The personalized learning module triggers the operation of calling learning data, and retrieves the grading results and learning data from the storage array; The personalized learning module triggers the recommendation algorithm calculation operation, calls the recommendation capabilities of the intelligent recommendation algorithm library, and the intelligent recommendation algorithm library triggers the learning plan generation operation, outputting a personalized learning plan. The personalized learning module triggers a plan push request, which pushes the personalized learning plan to the student's interactive interface.

[0011] Preferably, the process of generating the class learning report includes: The management module triggers a data update notification operation to retrieve all business data from the storage array; The management module triggers management analysis requests, generating class learning reports by calling the core technology layer; The management module triggers a report display command, pushing the class learning progress report to the management interface.

[0012] The technical effects and advantages of the present invention in the above technical solution are as follows: This invention, through a management center linking the infrastructure layer, core technology layer, business application layer, user interaction layer, and storage array, enables structured analysis of multimodal teaching materials, automated generation of teaching content, intelligent grading of all types of assignments, personalized learning plan delivery, and generation of class learning reports. It effectively solves the problems of existing technologies, such as singular teaching material analysis, reliance on manual generation of teaching content, assignment grading limited to objective questions, lack of targeted learning plans, and fragmented data across functional modules. Suitable for the teaching needs of educational institutions across all grade levels and of various types, it supports multi-terminal access and flexible deployment, reduces teachers' burden of lesson preparation and grading, improves the accuracy and efficiency of teaching management, and achieves a digital closed loop for the entire teaching process. Attached Figure Description

[0013] Figure 1 This is a flowchart of the multimodal intelligent teaching digital management platform of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] like Figure 1 As shown, this embodiment provides a multimodal smart teaching digital management platform, including a management center, which is connected to an infrastructure layer, a core technology layer, a business application layer, a user interaction layer, and a storage array; The infrastructure layer is connected to the core technology layer and is used to respond to resource requests from the core technology layer, providing computing, storage, and network support. The core technology layer is connected to the business application layer. The core technology layer includes a multimodal parsing engine, an NLP module, an intelligent algorithm recommendation library, an OCR module, and a semantic analysis module, which are used to receive function call instructions from the business application layer and output processing results. The business application layer includes a multimodal textbook parsing module, an intelligent content generation module, an assignment submission unit, an intelligent assignment grading module, a personalized learning module, and a management module, which are used to receive user operation instructions and call the core technology layer to output business data. The user interaction layer is connected to the business application layer. The user interaction layer includes a teacher interaction interface, a student interaction interface, and a management interaction interface, which are used to receive user operations and send instructions to the business application layer. The storage array is connected to the business application layer and is used to receive data storage requests from the business application layer, store data information from the business application layer, and push data update notifications to the business application layer. The multimodal textbook parsing module is used to upload textbook files, parse the textbook files, obtain structured knowledge graphs and parsed data, and store the structured knowledge graphs and parsed data in a storage array; The intelligent content generation module is used to retrieve the structured knowledge graph and parsed data stored in the storage array, generate corresponding teaching content, and store the teaching content in the storage array. The job submission unit is used to submit jobs and send them to the intelligent job grading module; The intelligent homework grading module is used to identify homework, output grading results and learning data, and store the grading results and learning data in the storage array; The personalized learning module is used to output a learning plan based on the graded data and push the learning plan. The management module is used to generate a class learning report based on all the business data of the business application layer in the storage array; In this embodiment of the invention, the platform system supports cloud + terminal devices. Cloud devices include cloud server clusters (such as Tencent Cloud), which are connected to the core technology layer and used to run algorithm components such as multimodal parsing engines and NLP / CV modules. Terminal devices include teacher terminals, student terminals, and management terminals. Teacher terminals include PCs with Windows 10 or later systems, connected to the teacher's interactive interface, used by teachers to upload teaching materials and view teaching content. Student terminals include mobile devices such as Android phones, connected to the student's interactive interface, used by students to submit assignments and view learning plans. Management terminals include any device with a built-in browser, connected to the management interactive interface, used by administrators to view teaching progress.

[0016] In one embodiment of the present invention, the process of obtaining the structured knowledge graph and parsed data, and storing the structured knowledge graph and parsed data in a storage array includes: In the teacher's interaction interface of the user interaction layer, the teacher is triggered to upload the textbook file, and a file upload instruction is sent to the multimodal textbook parsing module; The multimodal textbook parsing module triggers a parsing instruction issuance operation based on the textbook file, and calls the parsing capability of the multimodal parsing engine; The multimodal parsing engine triggers input operations for text, images, audio and video content, distributes the textbook content of the textbook file to the NLP module for semantic visual analysis, and the NLP module triggers semantic visual analysis to complete the operation, outputting a structured knowledge graph and parsed data; The multimodal textbook parsing module triggers a data storage request operation, writing the structured knowledge graph and parsed data into the storage array; In this embodiment of the invention, when teachers need to parse the textbook content, a self-developed multimodal parsing engine is used to integrate NLP and CV technologies to parse the textbook content. NLP is used to parse the text content of the textbook, and the CV module is used to parse the image and audio-visual content. Finally, the parsed textbook content is stored in the storage array. The parsing of textbook text content can also be achieved by using a combination of third-party APIs, such as calling Baidu AI Cloud's text semantic analysis API and Tencent Cloud's image and audio / video recognition API, to replace the platform's multimodal parsing engine. The specific implementation method is as follows: after the teacher uploads the textbook file, the system calls the third-party API according to the file type, obtains the parsing results, and integrates them into structured data. The parsing accuracy is reduced by 5%-8% compared to the original platform solution, but the development cost is reduced by 40%, which can meet the basic parsing needs of primary and secondary educational institutions.

[0017] In one embodiment of the present invention, the process of generating corresponding teaching content and storing the teaching content in a storage array includes: The intelligent content generation module triggers the data parsing operation to retrieve structured knowledge graphs and parsed data from the storage array; The intelligent content generation module triggers the teaching standard matching operation, calls the semantic matching capability of the NLP module, and obtains the teaching standards for the corresponding grade level and subject. The NLP module triggers a content generation rule matching operation, outputting teaching content that includes lesson plans, knowledge point tags, and questions; The intelligent content generation module triggers the content storage operation, writing the teaching content into the storage array; In this embodiment of the invention, based on the structured knowledge graph and parsing data stored in the storage array by the multimodal textbook parsing module, the NLP module is used to perform semantic matching on the structured knowledge graph and parsing data to obtain the corresponding academic stage and subject, and output the teaching content.

[0018] In one embodiment of the present invention, the process of sending the job to the intelligent job grading module includes: In the student interaction interface of the user interaction layer, the student is triggered to submit homework data, and the homework upload instruction is sent to the homework submission unit. The assignment submission unit triggers the assignment data upload operation, sending the assignment data to the intelligent assignment grading module; In one embodiment of the present invention, the process of outputting grading results and learning data, and storing the grading results and learning data in a storage array includes: The intelligent homework grading module triggers the answer recognition command, which calls upon the recognition capabilities of the OCR module and the semantic analysis module; After completing the recognition, the intelligent homework grading module triggers the answer content recognition completion operation and outputs the grading results and learning data; The intelligent homework grading module triggers the data entry operation for learning information, writing the grading results and learning information data into the storage array; In this embodiment of the invention, the student triggers the homework data submission operation on the student interaction interface of the user interaction layer, and sends a homework upload instruction to the homework submission unit. The homework submission unit then triggers the homework data upload operation and sends the homework data to the intelligent homework grading module. After receiving the homework data, the intelligent homework grading module triggers the answer recognition instruction operation, calls the recognition capabilities of the OCR module and the semantic analysis module, and after completing the recognition, triggers the answer content recognition completion operation, outputs the grading result and learning data, and finally triggers the learning data storage operation to write the grading result and learning data into the storage array. Students can also upload their homework data by batch scanning paper assignments. For example, teachers can use a scanner to scan all the class's assignments in batches, replacing individual student submissions. The specific implementation method is as follows: the scanner converts the batch assignments into image files, and the system automatically associates student information (identified by the name / student ID on the assignment cover) before grading. Using this method improves homework submission efficiency by 80%, but additional scanning equipment is required to adapt to scenarios where class assignments are submitted in a concentrated manner.

[0019] In one embodiment of the present invention, the process of outputting a learning plan and pushing the learning plan includes: The personalized learning module triggers the operation of calling learning data, and retrieves the grading results and learning data from the storage array; The personalized learning module triggers the recommendation algorithm calculation operation, calls the recommendation capabilities of the intelligent recommendation algorithm library, and the intelligent recommendation algorithm library triggers the learning plan generation operation, outputting a personalized learning plan. The personalized learning module triggers a plan push request, which pushes the personalized learning plan to the student's interactive interface. In this embodiment of the invention, the personalized learning module first triggers the operation of calling learning data, obtains the grading results and learning data from the storage array, performs a recommendation algorithm calculation operation, calls the recommendation capability of the intelligent recommendation algorithm library, the intelligent recommendation algorithm library triggers the learning plan generation operation and outputs the personalized learning plan, and the personalized learning module triggers the plan push request operation to push the generated personalized learning plan to the student interaction interface of the user interaction layer.

[0020] In one embodiment of the present invention, the process of generating a class learning report includes: The management module triggers a data update notification operation to retrieve all business data from the storage array; The management module triggers management analysis requests, generating class learning reports by calling the core technology layer; The management module triggers a report display command, pushing the class learning report to the management interface; In this embodiment of the invention, by linking with the storage array, the core technology layer and the management interface, and by triggering a data update notification operation, the full amount of business data covering textbook analysis, teaching content, homework correction and learning feedback is obtained from the storage array. Then, the statistical analysis capabilities of the core technology layer are called to process the data and generate a class learning report that includes teaching progress, class learning situation and knowledge point mastery. The report is pushed to the management interface of the user interaction layer to provide the administrator with full-process teaching data.

[0021] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A multimodal intelligent teaching digital management platform, including a management center, characterized in that: The management center is connected to an infrastructure layer, a core technology layer, a business application layer, a user interaction layer, and a storage array; The infrastructure layer is connected to the core technology layer and is used to respond to resource requests from the core technology layer, providing computing, storage, and network support. The core technology layer is connected to the business application layer. The core technology layer includes a multimodal parsing engine, an NLP module, an intelligent algorithm recommendation library, an OCR module, and a semantic analysis module, which are used to receive function call instructions from the business application layer and output processing results. The business application layer includes a multimodal textbook parsing module, an intelligent content generation module, an assignment submission unit, an intelligent assignment grading module, a personalized learning module, and a management module, which are used to receive user operation instructions and call the core technology layer to output business data. The user interaction layer is connected to the business application layer. The user interaction layer includes a teacher interaction interface, a student interaction interface, and a management interaction interface, which are used to receive user operations and send instructions to the business application layer. The storage array is connected to the business application layer and is used to receive data storage requests from the business application layer, store data information from the business application layer, and push data update notifications to the business application layer. The multimodal textbook parsing module is used to upload textbook files, parse the textbook files, obtain structured knowledge graphs and parsed data, and store the structured knowledge graphs and parsed data in a storage array; The intelligent content generation module is used to retrieve the structured knowledge graph and parsed data stored in the storage array, generate corresponding teaching content, and store the teaching content in the storage array. The job submission unit is used to submit jobs and send them to the intelligent job grading module; The intelligent homework grading module is used to identify homework, output grading results and learning data, and store the grading results and learning data in the storage array; The personalized learning module is used to output a learning plan based on the graded data and push the learning plan. The management module is used to generate class learning reports based on all business data from the business application layer in the storage array.

2. The multimodal intelligent teaching digital management platform according to claim 1, characterized in that: The process of obtaining the structured knowledge graph and parsed data, and storing the structured knowledge graph and parsed data in the storage array includes: In the teacher's interaction interface of the user interaction layer, the teacher is triggered to upload the textbook file, and a file upload instruction is sent to the multimodal textbook parsing module; The multimodal textbook parsing module triggers a parsing instruction issuance operation based on the textbook file, and calls the parsing capability of the multimodal parsing engine; The multimodal parsing engine triggers input operations for text, images, audio and video content, distributes the textbook content of the textbook file to the NLP module for semantic visual analysis, and the NLP module triggers semantic visual analysis to complete the operation, outputting a structured knowledge graph and parsed data; The multimodal textbook parsing module triggers a data storage request operation, writing the structured knowledge graph and parsed data into the storage array.

3. The multimodal intelligent teaching digital management platform according to claim 1, characterized in that, The process of generating corresponding teaching content and storing the teaching content in the storage array includes: The intelligent content generation module triggers the data parsing operation to retrieve structured knowledge graphs and parsed data from the storage array; The intelligent content generation module triggers the teaching standard matching operation, calls the semantic matching capability of the NLP module, and obtains the teaching standards for the corresponding grade level and subject. The NLP module triggers a content generation rule matching operation, outputting teaching content that includes lesson plans, knowledge point tags, and questions; The intelligent content generation module triggers the content storage operation, writing the teaching content into the storage array.

4. The multimodal intelligent teaching digital management platform according to claim 1, characterized in that, The process of sending the assignment to the intelligent assignment grading module includes: In the student interaction interface of the user interaction layer, the student is triggered to submit homework data, and the homework upload instruction is sent to the homework submission unit. The assignment submission unit triggers the assignment data upload operation, sending the assignment data to the intelligent assignment grading module.

5. The multimodal intelligent teaching digital management platform according to claim 1, characterized in that, The process of outputting the grading results and learning data, and storing the grading results and learning data in the storage array includes: The intelligent homework grading module triggers the answer recognition command, which calls upon the recognition capabilities of the OCR module and the semantic analysis module; After completing the recognition, the intelligent homework grading module triggers the answer content recognition completion operation and outputs the grading results and learning data; The intelligent homework grading module triggers the data entry operation for learning progress, writing the grading results and learning progress data into the storage array.

6. The multimodal intelligent teaching digital management platform according to claim 1, characterized in that, The process of outputting the learning plan and pushing the learning plan includes: The personalized learning module triggers the operation of accessing learning data, retrieving grading results and learning data from the storage array; The personalized learning module triggers the recommendation algorithm calculation operation, calls the recommendation capabilities of the intelligent recommendation algorithm library, and the intelligent recommendation algorithm library triggers the learning plan generation operation, outputting a personalized learning plan. The personalized learning module triggers a plan push request, which pushes the personalized learning plan to the student's interactive interface.

7. The multimodal intelligent teaching digital management platform according to claim 1, characterized in that, The process of generating the class learning report includes: The management module triggers a data update notification operation to retrieve all business data from the storage array; The management module triggers management analysis requests, generating class learning reports by calling the core technology layer; The management module triggers a report display command, pushing the class learning progress report to the management interface.

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