Teacher group teaching system and method based on AI intelligent recommendation

Abstract

The invention relates to the technical field of teaching, in particular to a teacher group teaching system and method based on AI intelligent recommendation, and the method comprises the steps: carrying out the matching analysis of multi-dimensional data, matching data and a teaching resource library of a preset intelligent distribution model, so as to obtain a course distribution result; when entering a teaching stage according to a course allocation result, acquiring teaching data of the teaching stage in real time; evaluating and analyzing the teaching data by adopting an effect evaluation model constructed by an analytic hierarchy process to obtain an effect evaluation result; and the preset intelligent distribution model is optimized and adjusted according to the effect evaluation result, so that the distribution result of the preset intelligent distribution model better meets the requirements of the students. According to the technical scheme, the problems that the matching accuracy of students and teachers is low and the utilization rate of teaching resources is low due to the fact that a traditional group teaching mode is difficult to meet the learning requirements of different students are solved.

Description

Technical Field

[0001] The present invention relates to the field of cognitive teaching technology, and in particular to a teacher group teaching system and method based on AI intelligent recommendation. Background Art

[0002] At present, the traditional way of teaching and grouping courses is often to assign courses to teachers according to the teaching plan and submit the course list to the course grouping system; then students select the corresponding courses from the course grouping system according to the training plan formulated by the school. When students select courses, due to limited understanding of the course content and the teacher's teaching methods, when students enter the class after selecting the course, the teaching methods of the teachers they encounter do not meet their own needs, resulting in reduced student enthusiasm for learning, and then reduced teacher teaching efficiency. In short, the traditional way of grouping courses is difficult to meet the learning needs of different students, resulting in low matching accuracy between students and teachers, and then low utilization of teaching resources.

[0003] Therefore, the present invention provides a teacher group teaching system and method based on AI intelligent recommendation to solve the above problems. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a teacher group teaching system and method based on AI intelligent recommendation, so as to solve the problem that the above-mentioned traditional group teaching method is difficult to meet the learning needs of different students, resulting in low matching accuracy between students and teachers, and further leading to low utilization rate of teaching resources.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] In a first aspect, the present invention provides a method for teacher group teaching based on AI intelligent recommendation, comprising: obtaining multi-dimensional data of students and matching data related to teaching, wherein the multi-dimensional data at least includes students' learning habits, learning needs, learning performance, and interests, and the matching data at least includes teachers' teaching style, teaching plan, and teaching type; pre-processing the multi-dimensional data and matching data to obtain corresponding first pre-processed data and second pre-processed data, respectively; using a preset intelligent allocation model to match and analyze the first pre-processed data, the second pre-processed data, and a teaching resource library to obtain course allocation results for students and teachers; teaching according to the course allocation results, and obtaining teaching data of the teaching stage, wherein the teaching data at least includes students' various grade data, student evaluation data, student classroom participation data, teacher evaluation data, and resource utilization; using an effect evaluation model constructed by a hierarchical analysis method to evaluate and analyze the teaching data to obtain an effect evaluation result of the course allocation result; adjusting the preset intelligent allocation model according to the effect evaluation result to optimize the preset intelligent allocation model.

[0007] Preferably, the processing process of the preset intelligent allocation model includes: extracting features of learning habits, learning needs and interests in the first preprocessed data based on the first preset vocabulary to obtain first feature information; evaluating the learning performance in the first preprocessed data based on the preset performance grading standard to obtain second feature information of the students' good and bad courses; fusing and analyzing the first feature information and the second feature information to obtain comprehensive feature information of the students; adjusting the teaching resource library based on the second preprocessed data to update the teaching resource library; matching analysis of the comprehensive feature information and the updated teaching resource library to generate a list of teaching resources whose matching degree meets the preset display rules; randomly selecting matching courses from the teaching resource list according to the preset course design rules to obtain the course allocation results of students and teachers; or, obtaining the course data selected by the students from the teaching resource list to obtain the course allocation results of students and teachers.

[0008] Preferably, the matching analysis of the comprehensive feature information and the updated teaching resource library to generate a list of teaching resources whose matching degree meets the preset display rules includes: selecting a first teaching resource that meets any feature information from the teaching resource library according to the comprehensive feature information; Perform weighted average calculation based on preset weights of the comprehensive feature information and feature information included in each first teaching resource to obtain the matching degree of each first teaching resource; obtain a list of teaching resources whose matching degrees meet preset display rules based on the matching degrees of each first teaching resource; or, The comprehensive feature information is matched according to the preset priority order of each feature information to obtain the comprehensive priority order corresponding to the comprehensive feature information, and a second preset weight is assigned to the comprehensive feature information according to the comprehensive priority order; a weighted average calculation is performed based on the second preset weight of the comprehensive feature information and the feature information included in each first teaching resource to obtain the matching degree of each first teaching resource; based on the matching degree of each first teaching resource, a list of teaching resources whose matching degrees meet the preset display rules is obtained.

[0009] Preferably, the processing process of the effect evaluation model includes three analysis layers, wherein the first analysis layer is used to classify the teaching data to obtain the factor data of each preset evaluation indicator; the second analysis layer is used to query and obtain the weight corresponding to each factor data according to the weight database established according to expert experience, and adopt the weighted average method to obtain the evaluation indicator data of each preset evaluation indicator according to the factor data and the corresponding weight; the third analysis layer is used to obtain the evaluation indicator data for evaluating the comprehensive score of students' grades, and calculate according to the evaluation indicator data and the corresponding third preset weight to obtain the comprehensive score of students' grades; the fourth analysis layer is used to perform weighted analysis on each evaluation indicator data and the corresponding fourth preset weight to obtain the overall score of intelligent allocation.

[0010] Preferably, the evaluation indicators of the third analysis layer include at least performance indicators, interaction indicators, performance change indicators, learning progress indicators, and participation indicators.

[0011] Preferably, the calculation process of the interaction index data includes: obtaining video data during the teaching process; using an image analysis model to extract interactive video data and audio data from the video data; analyzing the interactive video data and audio data, calculating the ratio of student interaction time to total teacher interaction time, and determining first interaction data based on the ratio; converting the audio data into text data, performing correlation analysis on the text data, and determining the correlation between student and teacher interaction information to obtain second interaction data; determining the interaction index data based on the first interaction data and the second interaction data and the corresponding preset weights.

[0012] Preferably, the preset intelligent allocation model is adjusted according to the effect evaluation results to optimize the preset intelligent allocation model, including: obtaining abnormal evaluation indicators in the effect evaluation results, and obtaining corresponding recorded data according to the abnormal evaluation indicators; obtaining adjustment strategies formulated by experts according to the abnormal evaluation indicators and recorded data; and adjusting the preset intelligent allocation model according to the adjustment strategies.

[0013] Preferably, a teacher class grouping and teaching method based on AI intelligent recommendation also includes: obtaining class grouping information input by students, analyzing the class grouping information, extracting key features in the class grouping information according to a preset class grouping vocabulary; and adjusting the comprehensive priority order of the corresponding comprehensive feature information in the preset intelligent allocation model according to the key features.

[0014] Preferably, a teacher group teaching method based on AI intelligent recommendation also includes: using an encryption algorithm to encrypt the recorded student data and teacher data; and using a biometric method and setting access rights to verify the visitor.

[0015] In a second aspect, the present invention provides a teacher-class group teaching system based on AI intelligent recommendation, comprising: a data acquisition module, for acquiring multi-dimensional data of students and matching data related to teaching, wherein the multi-dimensional data at least includes the students' learning habits, learning needs, learning performance, and interests, and the matching data at least includes the teacher's teaching style, teaching plan, and teaching type; A data preprocessing module, used to preprocess the multi-dimensional data and the matching data to obtain corresponding first preprocessed data and second preprocessed data respectively; The course allocation module uses a preset intelligent allocation model to match and analyze the first preprocessed data, the second preprocessed data, and the teaching resource library to obtain the course allocation results of students and teachers; A data acquisition module, which teaches according to the course allocation results and acquires the teaching data of the teaching stage, wherein the teaching data at least includes students' various grade data, student evaluation data, student class participation data, teacher evaluation data, and resource utilization rate; The effect evaluation module uses the effect evaluation model constructed by the hierarchical analysis method to evaluate and analyze the teaching data to obtain the effect evaluation results of the course allocation results; The model optimization module adjusts the preset intelligent allocation model according to the effect evaluation results to optimize the preset intelligent allocation model.

[0016] The beneficial effects of the present invention are:

[0017] 1. The present invention uses a preset intelligent allocation model to optimize and match the multi-dimensional data of students, the corresponding matching data, and the teaching resource library to obtain course allocation results that meet the diverse needs of students; when entering the teaching stage according to the course allocation results, the teaching data of the teaching stage will be collected in real time; and then the effect evaluation model constructed by the hierarchical analysis method is used to evaluate and analyze the teaching data to obtain the effect evaluation results of the course allocation results; and the preset intelligent allocation model is optimized and adjusted according to the effect evaluation results, so that the allocation results of the preset intelligent allocation model are more in line with the needs of students. The present invention optimizes and matches the multi-dimensional data of students, the corresponding matching data, and the teaching resource library through a preset intelligent allocation model to obtain course allocation results that meet the diverse needs of students, solving the problem that the traditional group teaching method is difficult to meet the learning needs of different students, resulting in low matching accuracy between students and teachers, and then resulting in low utilization of teaching resources.

[0018] 2. In the preset intelligent allocation model, the present invention uses a feature extraction method to process and analyze the multi-dimensional data of students to obtain the comprehensive feature data of the students' comprehensive performance; then, feature matching is performed based on the comprehensive feature information and the resource feature labels in the teaching resource library to calculate the matching degree of each teaching resource; according to the matching degree of each first teaching resource, a list of teaching resources whose matching degree meets the preset display rules is obtained; and then the course allocation results that meet the needs of students are screened out from the teaching resource list. And when matching, the comprehensive feature information is analyzed according to the preset priority order of each feature information to obtain the comprehensive priority order corresponding to the comprehensive feature information, and the second preset weight is assigned to the comprehensive feature information according to the comprehensive priority order; then, a weighted average calculation is performed based on the second preset weight of the comprehensive feature information and the feature information contained in each first teaching resource to obtain the matching degree of each first teaching resource. The present invention can make the calculated matching degree more accurate through the above technical solution, so that the obtained course allocation results are more in line with the needs of students.

[0019] 3. The present invention also adopts the method of obtaining the course grouping information required by students, analyzing the course grouping information, extracting the key features in the course grouping information according to the preset course grouping vocabulary; adjusting the comprehensive priority order of the corresponding comprehensive feature information in the preset intelligent allocation model according to the key features; and then assigning a second preset weight to the comprehensive feature information according to the comprehensive priority order; and then performing a weighted average calculation based on the second preset weight of the comprehensive feature information and the feature information contained in each first teaching resource to obtain the matching degree of each first teaching resource. The present invention provides an input window for students through the above technical solution, allowing students to input the course grouping information they need, so that the final course allocation result is more in line with the personalized needs of students. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of a method for teacher group teaching based on AI intelligent recommendation in the present invention;

[0021] Figure 2 This is a schematic module diagram of a teacher class group teaching system based on AI intelligent recommendation in the present invention. DETAILED DESCRIPTION

[0022] The following will refer to the attached Figure 1 To Attachment Figure 2 The embodiments of the present invention are described in detail. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0023] A teacher-led teaching method based on AI intelligent recommendation, as shown in the attached Figure 1 As shown, the following steps are included:

[0024] Step S11: Acquire multi-dimensional data of students and matching data related to teaching, wherein the multi-dimensional data at least includes the students' learning habits, learning needs, learning performance, and interests, and the matching data at least includes the teacher's teaching style, teaching plan, and teaching type.

[0025] Step S12: pre-process the multi-dimensional data and the matching data to obtain corresponding first pre-processed data and second pre-processed data respectively.

[0026] Preferably, the first preprocessed data is preprocessed multi-dimensional data, and the second preprocessed data is preprocessed matching data.

[0027] Step S13: using a preset intelligent allocation model to perform matching analysis on the first preprocessed data, the second preprocessed data, and the teaching resource library to obtain course allocation results for students and teachers.

[0028] Step S14: Conduct teaching according to the course allocation result and obtain teaching data of the teaching stage, wherein the teaching data at least includes students' performance data, student evaluation data, student class participation data, teacher evaluation data, and resource utilization rate.

[0029] Preferably, the teaching data also includes video data, which includes video data and audio data during the class; statistical methods for resource utilization include but are not limited to the proportion of selected courses in the teaching plan to the total planned courses or the frequency of interaction between teachers and students.

[0030] Step S15: Use the effect evaluation model constructed by the hierarchical analysis method to evaluate and analyze the teaching data to obtain the effect evaluation results of the course allocation results.

[0031] Preferably, the effect evaluation results include a comprehensive score of student performance that evaluates student performance from multiple perspectives, and an overall score of intelligent allocation that comprehensively evaluates the class situation based on the course allocation results.

[0032] Step S16: adjusting the preset intelligent allocation model according to the effect evaluation result to optimize the intelligent allocation model.

[0033] Preferably, Artificial Intelligence (AI) is abbreviated as AI.

[0034] Specifically, the present invention uses a preset intelligent allocation model to optimize and match the multi-dimensional data of students, the corresponding matching data, and the teaching resource library to obtain a course allocation result that meets the diverse needs of students; when entering the teaching stage according to the course allocation result, the teaching data of the teaching stage will be collected in real time; and then the effect evaluation model constructed by the hierarchical analysis method is used to evaluate and analyze the teaching data to obtain the effect evaluation result of the course allocation result; and the preset intelligent allocation model is optimized and adjusted according to the effect evaluation result, so that the allocation result of the preset intelligent allocation model is more in line with the needs of students. The present invention optimizes and matches the multi-dimensional data of students, the corresponding matching data, and the teaching resource library through a preset intelligent allocation model to obtain a course allocation result that meets the diverse needs of students, solving the problem that the traditional group teaching method is difficult to meet the learning needs of different students, resulting in low matching accuracy between students and teachers, and then resulting in low utilization of teaching resources.

[0035] In one embodiment of the present invention, the processing process of the preset intelligent allocation model includes:

[0036] Based on the first preset vocabulary, feature extraction is performed on the learning habits, learning needs and interests in the first preprocessed data to obtain first feature information; based on the preset score grading standard, the learning performance in the first preprocessed data is evaluated to obtain second feature information of the courses with good or bad performance of the students; the first feature information and the second feature information are fused and analyzed to obtain comprehensive feature information of the students; based on the second preprocessed data, the teaching resource library is adjusted to update the teaching resource library; matching analysis is performed on the comprehensive feature information and the updated teaching resource library to generate a teaching resource list whose matching degree meets the preset display rules; matching courses are randomly selected from the teaching resource list according to the preset course design rules to obtain the course allocation results of students and teachers; or, the course data selected by the students from the teaching resource list is obtained to obtain the course allocation results of students and teachers.

[0037] Preferably, the preset display rules include but are not limited to displaying the top five or top three teaching resources ranked by matching degree.

[0038] Preferably, in the teaching resource library, each teaching resource is provided with a plurality of characteristic information tags to indicate the characteristics contained in the teaching resource, and the specific setting method is not limited, for example, the setting method is Arabic numerals, special symbols, colors, and English letters. Teaching resources include but are not limited to online courses, offline courses, engineering experience courses, ppt courseware, question banks, foreign language pronunciation data, foreign language training data, etc.

[0039] Through the setting method of this embodiment, the present invention uses a feature extraction method in the preset intelligent allocation model to process and analyze the multi-dimensional data of students to obtain comprehensive feature data of students' comprehensive performance; then feature matching is performed based on the comprehensive feature information and the resource feature labels in the teaching resource library to calculate the matching degree of each teaching resource; according to the matching degree of each first teaching resource, a list of teaching resources whose matching degree meets the preset display rules is obtained; and then the course allocation results that meet the needs of students are screened out from the teaching resource list. The present invention solves the problem that the traditional group teaching method is difficult to meet the learning needs of different students, resulting in low matching accuracy between students and teachers, and then resulting in low utilization of teaching resources.

[0040] In one embodiment of the present invention, the matching analysis of the comprehensive feature information and the updated teaching resource library to generate a list of teaching resources whose matching degree meets the preset display rules includes:

[0041] The first implementation method is: based on the comprehensive feature information, select the first teaching resources that meet any feature information from the teaching resource library; perform weighted average calculation based on the preset weight of the comprehensive feature information and the feature information contained in each first teaching resource to obtain the matching degree of each first teaching resource; based on the matching degree of each first teaching resource, obtain a list of teaching resources whose matching degree meets the preset display rules.

[0042] The second implementation method is: based on the comprehensive feature information, select the first teaching resources that meet any feature information from the teaching resource library; match the comprehensive feature information according to the preset priority order of each feature information to obtain the comprehensive priority order corresponding to the comprehensive feature information, and assign a second preset weight to the comprehensive feature information according to the comprehensive priority order; perform weighted average calculation based on the second preset weight of the comprehensive feature information and the feature information contained in each first teaching resource to obtain the matching degree of each first teaching resource; based on the matching degree of each first teaching resource, obtain a list of teaching resources whose matching degree meets the preset display rules.

[0043] Through the configuration of this embodiment, the present invention can select one of the above two implementation modes according to the actual data processing amount or actual needs. For example, when the data processing amount exceeds a certain set value, the first implementation mode is used to calculate the matching degree of teaching resources to improve the data processing speed; when the data processing amount is less than the set value, the second implementation mode is used to calculate the matching degree of teaching resources to better meet the personalized needs of students.

[0044] Specifically, in the preset intelligent allocation model, the present invention uses a feature extraction method to process and analyze the multi-dimensional data of students to obtain the comprehensive feature data of the students' comprehensive performance; then, feature matching is performed according to the comprehensive feature information and the resource feature labels in the teaching resource library to calculate the matching degree of each teaching resource; according to the matching degree of each first teaching resource, a list of teaching resources whose matching degree meets the preset display rules is obtained; and then the course allocation results that meet the needs of students are screened out from the teaching resource list. And when matching, the comprehensive feature information is analyzed according to the preset priority order of each feature information to obtain the comprehensive priority order corresponding to the comprehensive feature information, and the second preset weight is assigned to the comprehensive feature information according to the comprehensive priority order; then, a weighted average calculation is performed according to the second preset weight of the comprehensive feature information and the feature information contained in each first teaching resource to obtain the matching degree of each first teaching resource respectively. The present invention can make the calculated matching degree more accurate through the above technical solution, so that the obtained course allocation results are more in line with the needs of students.

[0045] In one embodiment of the present invention, the processing process of the effect evaluation model includes three analysis layers, wherein the first analysis layer is used to classify the teaching data to obtain the factor data of each preset evaluation indicator; the second analysis layer is used to query and obtain the weight corresponding to each factor data according to the weight database established according to expert experience, and obtain the evaluation indicator data of each preset evaluation indicator by weighted average method according to the factor data and the corresponding weight; the third analysis layer is used to obtain the evaluation indicator data for evaluating the comprehensive score of student performance, and calculate according to the evaluation indicator data and the corresponding third preset weight to obtain the comprehensive score of student performance; the fourth analysis layer is used to perform weighted analysis on each evaluation indicator data and the corresponding fourth preset weight to obtain the overall score of intelligent allocation.

[0046] Preferably, the evaluation indicators of the fourth analysis layer include at least performance indicators, interaction indicators, performance change indicators, learning progress indicators, participation indicators, resource utilization indicators, and teacher work efficiency indicators.

[0047] Furthermore, the evaluation indicators of the third analysis layer include at least performance indicators, interaction indicators, performance change indicators, learning progress indicators, and participation indicators.

[0048] Through the facilities of this embodiment, the present invention evaluates the teaching effect of the group course allocation result of the preset intelligent allocation model through four analysis layers, wherein the third analysis layer is used to evaluate the comprehensive scoring of student scores from multiple angles, avoiding determining the student's learning effect based on the assessment score alone, making the evaluation of the student's learning results throughout the entire stage more objective. The fourth analysis layer is used to perform weighted analysis on each evaluation indicator data and the corresponding fourth preset weight, comprehensively considering various influencing factors, and objectively evaluating the teaching effect of the group course allocation result, making the evaluation result more comprehensive and accurate.

[0049] In one embodiment of the present invention, a calculation process of interaction index data is: obtaining video data during the teaching process; using an image analysis model to extract interactive video data and audio data from the video data; analyzing the interactive video data and audio data, calculating the ratio of student interaction time to the total teacher interaction time, and determining first interaction data based on the ratio; converting the audio data into text data, performing correlation analysis on the text data, and determining the correlation between student and teacher interaction information to obtain second interaction data; determining the interaction index data based on the first interaction data and the second interaction data and the corresponding preset weights.

[0050] Another calculation process for the interactive indicator data is: after the teacher finishes each class, the student performance data is promptly evaluated and counted, and recorded in the teaching data record database. The record database automatically records the relevant data in the student's personal learning data list based on the teacher's statistical results, so as to determine the interactive indicator data based on the personal learning data and the corresponding preset weight value.

[0051] Through the setting method of this embodiment, the present invention can select the corresponding calculation process to calculate the interaction index data according to the actual situation, so as to calculate the comprehensive score of the students' grades based on the interaction index data and the students' various performance data, making the evaluation of students' grades more objective and avoiding the error of the comprehensive score of students due to a single score.

[0052] In one embodiment of the present invention, the preset intelligent allocation model is adjusted according to the effect evaluation results to optimize the preset intelligent allocation model, including: obtaining abnormal evaluation indicators in the effect evaluation results, and obtaining corresponding recorded data according to the abnormal evaluation indicators; obtaining adjustment strategies formulated by experts according to the abnormal evaluation indicators and recorded data; and adjusting the preset intelligent allocation model according to the adjustment strategies.

[0053] In this embodiment, the present invention sets up a preset intelligent allocation model optimization method, in which experts analyze the abnormal evaluation indicators and corresponding record data in the effect evaluation results, and then formulate targeted adjustment strategies. According to the adjustment strategy, relevant record data is collected to train the preset intelligent allocation model, so that the course allocation results output by the preset intelligent allocation model are more in line with the needs of students.

[0054] In one embodiment of the present invention, a teacher class grouping and teaching method based on AI intelligent recommendation also includes: obtaining class grouping information input by students, analyzing the class grouping information, extracting key features in the class grouping information based on a preset class grouping vocabulary; and adjusting the comprehensive priority order of the corresponding comprehensive feature information in the preset intelligent allocation model based on the key features.

[0055] In this embodiment, the present invention also adopts the method of obtaining the course grouping information required by students, analyzing the course grouping information, extracting the key features in the course grouping information according to the preset course grouping vocabulary; adjusting the comprehensive priority order of the corresponding comprehensive feature information in the preset intelligent allocation model according to the key features; and then assigning a second preset weight to the comprehensive feature information according to the comprehensive priority order; and then performing a weighted average calculation based on the second preset weight of the comprehensive feature information and the feature information contained in each first teaching resource to obtain the matching degree of each first teaching resource. The present invention provides an input window for students through the above technical solution, allowing students to input the course grouping information they need, so that the final course allocation result is more in line with the personalized needs of students.

[0056] In one embodiment of the present invention, a teacher group teaching method based on AI intelligent recommendation also includes: using an encryption algorithm to encrypt recorded student data and teacher data; and using a biometric method and setting access rights to verify the visitor.

[0057] In this embodiment, the present invention also uses an encryption algorithm to encrypt the student data and teacher data recorded in the entire stage, and uses a biometric identification method and sets access rights to verify the access personnel, so as to improve the security and confidentiality of the student data and teacher data. In this embodiment, the price list algorithm, biometric identification method and permission access setting method are not limited.

[0058] The present invention also provides a teacher group teaching system based on AI intelligent recommendation, as shown in the attached Figure 2 As shown, it includes a data collection module, a data preprocessing module, a course allocation module, a data acquisition module, an effect evaluation module, a model optimization module and a data input module.

[0059] Among them, the data acquisition module is used to obtain students' multi-dimensional data and matching data related to teaching. The multi-dimensional data at least includes students' learning habits, learning needs, academic performance, and interests. The matching data at least includes teachers' teaching style, teaching plan, and teaching type.

[0060] The data preprocessing module is used to preprocess the multi-dimensional data and the matching data to obtain corresponding first preprocessed data and second preprocessed data respectively.

[0061] The course allocation module uses a preset intelligent allocation model to match and analyze the first preprocessed data, the second preprocessed data and the teaching resource library to obtain the course allocation results for students and teachers.

[0062] The data acquisition module teaches according to the course allocation results and obtains the teaching data of the teaching stage. The teaching data at least includes students' performance data, student evaluation data, student class participation data, teacher evaluation data, and resource utilization rate.

[0063] The effect evaluation module uses the effect evaluation model constructed by the hierarchical analysis method to evaluate and analyze the teaching data to obtain the effect evaluation results of the course allocation results.

[0064] The model optimization module adjusts the preset intelligent allocation model according to the effect evaluation results to optimize the preset intelligent allocation model.

[0065] Data input module, obtains the course group information entered by students.

[0066] The present invention realizes the processing process of any embodiment of a teacher class group teaching method based on AI intelligent recommendation through the mutual cooperation between the above modules. The system solves the problem that the traditional class group teaching method is difficult to meet the learning needs of different students, resulting in low matching accuracy between students and teachers, and thus low utilization rate of teaching resources through the mutual cooperation between the above modules.

[0067] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0068] It should be noted that in the description of the present invention, the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings, which are only for the convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0069] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0070] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0071] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0072] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0073] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0074] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0075] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A teacher-led teaching method based on AI intelligent recommendation, characterized in that: include: Acquire multi-dimensional data of students and matching data related to teaching, wherein the multi-dimensional data at least includes the students' learning habits, learning needs, academic performance, and interests, and the matching data at least includes the teacher's teaching style, teaching plan, and teaching type; Preprocessing the multi-dimensional data and the matching data to obtain corresponding first preprocessed data and second preprocessed data respectively; Using a preset intelligent allocation model to match and analyze the first preprocessed data, the second preprocessed data, and the teaching resource library to obtain course allocation results for students and teachers; Teaching according to the course allocation results, obtaining teaching data during the teaching stage, wherein the teaching data at least includes students' performance data, student evaluation data, student class participation data, teacher evaluation data, and resource utilization rate; The effect evaluation model constructed by the hierarchical analysis method is used to evaluate and analyze the teaching data to obtain the effect evaluation results of the course allocation results; The preset intelligent allocation model is adjusted according to the effect evaluation results to optimize the preset intelligent allocation model.

2. The teacher-led teaching method according to claim 1, characterized in that: The processing process of the preset intelligent allocation model includes: Based on the first preset vocabulary, feature extraction is performed on the learning habits, learning needs and interests in the first preprocessed data to obtain first feature information; based on the preset score grading standard, the learning performance in the first preprocessed data is evaluated to obtain second feature information of the courses with good or bad performance of the students; the first feature information and the second feature information are fused and analyzed to obtain comprehensive feature information of the students; based on the second preprocessed data, the teaching resource library is adjusted to update the teaching resource library; matching analysis is performed on the comprehensive feature information and the updated teaching resource library to generate a teaching resource list whose matching degree meets the preset display rules; matching courses are randomly selected from the teaching resource list according to the preset course design rules to obtain the course allocation results of students and teachers; or, the course data selected by the students from the teaching resource list is obtained to obtain the course allocation results of students and teachers.

3. The teacher-led teaching method according to claim 2, characterized in that: The matching analysis of the comprehensive feature information and the updated teaching resource library to generate a list of teaching resources whose matching degree meets the preset display rules includes: selecting a first teaching resource that meets any feature information from the teaching resource library according to the comprehensive feature information; Perform weighted average calculation based on preset weights of the comprehensive feature information and feature information included in each first teaching resource to obtain the matching degree of each first teaching resource; obtain a list of teaching resources whose matching degrees meet preset display rules based on the matching degrees of each first teaching resource; or, The comprehensive feature information is matched according to the preset priority order of each feature information to obtain the comprehensive priority order corresponding to the comprehensive feature information, and a second preset weight is assigned to the comprehensive feature information according to the comprehensive priority order; a weighted average calculation is performed based on the second preset weight of the comprehensive feature information and the feature information included in each first teaching resource to obtain the matching degree of each first teaching resource; based on the matching degree of each first teaching resource, a list of teaching resources whose matching degrees meet the preset display rules is obtained.

4. The teacher-led teaching method according to claim 1, characterized in that: The processing process of the effect evaluation model includes four analysis layers, wherein the first analysis layer is used to classify the teaching data to obtain the factor data of each preset evaluation indicator; the second analysis layer is used to query and obtain the weight corresponding to each factor data according to the weight database established according to expert experience, and adopt the weighted average method to obtain the evaluation indicator data of each preset evaluation indicator according to the factor data and the corresponding weight; the third analysis layer is used to obtain the evaluation indicator data for evaluating the comprehensive score of students' grades, and calculate according to the evaluation indicator data and the corresponding third preset weight to obtain the comprehensive score of students' grades; the fourth analysis layer is used to perform weighted analysis on each evaluation indicator data and the corresponding fourth preset weight to obtain the overall score of intelligent allocation.

5. The teacher-led teaching method according to claim 4, characterized in that: The evaluation indicators of the third analysis layer include at least performance indicators, interaction indicators, performance change indicators, learning progress indicators, and participation indicators.

6. The teacher-led teaching method according to claim 4, characterized in that: The calculation process of the interaction index data includes: obtaining video data during the teaching process; using an image analysis model to extract interactive video data and audio data from the video data; analyzing the interactive video data and audio data, calculating the ratio of student interaction time to the total teacher interaction time, and determining first interaction data based on the ratio; converting the audio data into text data, performing a correlation analysis on the text data, and determining the correlation between student and teacher interaction information to obtain second interaction data; and determining the interaction index data based on the first interaction data and the second interaction data and the corresponding preset weights.

7. The teacher-led teaching method according to claim 6, characterized in that: The method of adjusting the preset intelligent allocation model according to the effect evaluation results to optimize the preset intelligent allocation model includes: obtaining abnormal evaluation indicators in the effect evaluation results, and obtaining corresponding recorded data according to the abnormal evaluation indicators; obtaining adjustment strategies formulated by experts according to the abnormal evaluation indicators and recorded data; and adjusting the preset intelligent allocation model according to the adjustment strategies.

8. The teacher-led teaching method according to claim 3, characterized in that: Also includes: Obtain the course group information input by the students, analyze the course group information, and extract key features in the course group information according to the preset course group vocabulary; The comprehensive priority order of the corresponding comprehensive feature information in the preset intelligent allocation model is adjusted according to the key features.

9. The teacher-led teaching method according to claim 1, characterized in that: Also includes: The recorded student data and teacher data are encrypted using encryption algorithms; and the access personnel are verified using biometric methods and setting access rights.

10. A teacher-led teaching system based on AI intelligent recommendation, characterized in that: include: A data collection module is used to obtain multi-dimensional data of students and matching data related to teaching, wherein the multi-dimensional data at least includes the students' learning habits, learning needs, academic performance, and interests, and the matching data at least includes the teacher's teaching style, teaching plan, and teaching type; A data preprocessing module, used to preprocess the multi-dimensional data and the matching data to obtain corresponding first preprocessed data and second preprocessed data respectively; The course allocation module uses a preset intelligent allocation model to match and analyze the first preprocessed data, the second preprocessed data, and the teaching resource library to obtain the course allocation results of students and teachers; A data acquisition module, which teaches according to the course allocation results and acquires the teaching data of the teaching stage, wherein the teaching data at least includes students' various grade data, student evaluation data, student class participation data, teacher evaluation data, and resource utilization rate; The effect evaluation module uses the effect evaluation model constructed by the hierarchical analysis method to evaluate and analyze the teaching data to obtain the effect evaluation results of the course allocation results; The model optimization module adjusts the preset intelligent allocation model according to the effect evaluation results to optimize the preset intelligent allocation model.

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