Teaching data processing method, device, equipment, medium and product

Abstract

The invention provides a teaching data processing method and apparatus, a device, a medium and a product. The method comprises the steps of obtaining learning feedback data of each student in a class for a first teaching design scheme; according to learning feedback data of each student on the first teaching design scheme, determining a learning condition category corresponding to each student; and obtaining second teaching design schemes corresponding to the students of different learning condition categories in the students according to the learning condition categories corresponding to the students and the second teaching design schemes generated in advance for the students of different learning condition categories, and teaching the students according to the second teaching design schemes. The second teaching design scheme generated for the students of different learning condition categories in advance is generated according to the teaching resource data and a teaching method learned based on a preset teaching model. According to the invention, the adaptation degree between the students and the teaching design scheme can be improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a teaching data processing method, device, equipment, medium and product. Background Art

[0002] With the continuous advancement of educational technology, the teaching model has gradually transitioned from traditional textbook teaching to multimedia courseware teaching. In traditional textbook teaching, the teaching content mainly relies on paper teaching materials, and students' understanding of knowledge points is often limited by the teacher's language explanation and writing on the blackboard. This method is highly dependent on students' personal imagination to supplement learning, which increases the difficulty of knowledge absorption to a certain extent.

[0003] In recent years, multimedia courseware teaching has significantly improved the classroom atmosphere and stimulated students' learning interest by virtue of its rich and diverse presentation forms and high interactivity, such as through interesting activities such as videos and animations. However, due to the obvious individual differences between different students, the existing courseware design often cannot fully meet the needs of all students, resulting in a low match between the courseware and students, which in turn affects the actual teaching effect. Summary of the invention

[0004] Based on the above-mentioned technical status, the present application provides a teaching data processing method, device, equipment, medium and product, which can generate a teaching design plan that is more suitable for students' situations, thereby improving the compatibility between students and teaching design plans.

[0005] In order to achieve the above technical objectives, this application specifically proposes the following technical solutions:

[0006] According to the first aspect of an embodiment of the present application, a teaching data processing method is provided, including: obtaining learning feedback data of each student in a class on a first teaching design plan; determining the learning situation category corresponding to each of the students based on the learning feedback data of each student on the first teaching design plan; obtaining the second teaching design plan corresponding to students of different learning situation categories among the students based on the learning situation category corresponding to each of the students and a second teaching design plan pre-generated for students of different learning situation categories, and teaching each of the students based on the second teaching design plan, wherein the second teaching design plan pre-generated for students of different learning situation categories is generated based on teaching resource data and a teaching method learned based on a preset teaching model.

[0007] In some implementations, the first teaching design plan is obtained through the following steps: in response to the class chapters and classes selected by the teacher, according to the first teaching design plan pre-generated for each class and each chapter, the first teaching design plan corresponding to the class chapters and the class selected by the teacher is obtained, and the first teaching design plan is generated according to the teaching resource data and the teaching method learned based on a preset teaching model.

[0008] In some implementations, the first teaching design plan includes a universal teaching design plan or a personalized teaching design plan, and the second teaching design plan includes a personalized teaching design plan.

[0009] In some implementations, the first teaching design plan and the second teaching design plan generated in advance are generated by a teaching design plan generation model according to teaching resource data and a teaching method learned based on a preset teaching model, and the preset teaching model is a model that provides teaching methods; wherein the teaching design plan generation model is trained using the following steps: obtaining students' learning situation data, teaching materials and digital textbook content, and associating the digital textbook content with preset knowledge points, examination outlines and examination points to obtain the teaching resource data; generating a predicted teaching design plan according to the teaching resource data and the teaching model through the teaching design plan generation model to be trained; determining the teaching design plan prediction loss based on the feedback data of the teacher and / or the student on the predicted teaching design plan, and converging the teaching design plan generation model to be trained based on the teaching design plan prediction loss to obtain the teaching design plan generation model.

[0010] In some implementations, each of the second teaching design plans includes a teaching process and teaching content, the teaching process includes various teaching steps, the teaching content includes various teaching activities, and each of the teaching steps corresponds to a teaching activity; teaching each of the students according to the second teaching design plan includes: presenting the digital textbook content corresponding to the textbook used by each of the students in class on the student's terminal device; in response to the triggering action of each of the teaching steps, calling the teaching activity data corresponding to each of the teaching steps to teach each of the students.

[0011] In some implementations, the method further includes: obtaining teacher feedback data on the second teaching design plan; and optimizing the second teaching design plan based on the teacher's feedback data on the second teaching design plan.

[0012] In some implementations, the second teaching design plans include multiple ones; the second teaching design plans corresponding to students of different learning situation categories among the students are obtained according to the learning situation categories corresponding to the students and the second teaching design plans pre-generated for students of different learning situation categories, including: generating a new second teaching design plan according to the learning situation categories corresponding to the students and the multiple second teaching design plans pre-generated for students of different learning situation categories, and the new second teaching design plan is obtained by re-combining the multiple second teaching design plans.

[0013] According to the second aspect of an embodiment of the present application, a teaching data processing device is provided, comprising: an acquisition unit for acquiring learning feedback data of each student in a class on a first teaching design plan; a determination unit for determining the learning situation category corresponding to each student based on the learning feedback data of each student on the first teaching design plan; an acquisition and teaching unit for acquiring second teaching design plans corresponding to students of different learning situation categories among the students based on the learning situation category corresponding to each student and a second teaching design plan pre-generated for students of different learning situation categories, and teaching each student according to the second teaching design plan, wherein the second teaching design plan pre-generated for students of different learning situation categories is generated based on teaching resource data and a teaching method learned based on a preset teaching model.

[0014] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory and a processor; the memory is connected to the processor and is used to store programs; the processor is used to implement the teaching data processing method as described in the first aspect by running the program in the memory.

[0015] According to a fourth aspect of an embodiment of the present application, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the teaching data processing method as described in the first aspect is implemented.

[0016] According to a fifth aspect of an embodiment of the present application, a computer program product is provided, comprising computer program instructions, which, when executed by a processor, cause the processor to execute: the teaching data processing method as described in the first aspect.

[0017] The embodiment of the present application provides a teaching data processing method, device, equipment and storage medium, which determines the learning situation category corresponding to each student according to the learning feedback data of each student in the class on the first teaching design plan, obtains the second teaching design plan corresponding to the students of different learning situation categories among each student according to the learning situation category corresponding to each student and the second teaching design plan generated in advance for students of different learning situation categories, and teaches each student according to the second teaching design plan. Among them, the second teaching design plan generated in advance for students of different learning situation categories is generated based on teaching resource data and the teaching method learned based on the preset teaching model, so that a teaching design plan that is more suitable for the student's situation can be generated, the quality of the teaching design plan is improved, and then the adaptability between the student and the teaching design plan is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0019] Figure 1 A flowchart of a teaching data processing method provided in an embodiment of the present application;

[0020] Figure 2 A flowchart of a specific implementation of the teaching data processing method provided in an embodiment of the present application;

[0021] Figure 3 A flowchart of another specific implementation of the teaching data processing method provided in the embodiment of the present application;

[0022] Figure 4 A flowchart of the training process of the teaching design solution generation model provided in the embodiment of the present application;

[0023] Figure 5 A schematic diagram of the training principle of the teaching design solution generation model provided in the embodiment of the present application;

[0024] Figure 6 A framework diagram of the construction process of the teaching data processing method provided in the embodiment of the present application;

[0025] Figure 7 A structural diagram of a teaching data processing device provided in an embodiment of the present application;

[0026] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solution proposed in the embodiment of the present application is applicable to the field of teaching. By adopting the technical solution in the embodiment of the present application, a teaching design plan that is more suitable for students' situations can be generated, thereby improving the compatibility between students and the teaching design plan.

[0028] The technical solution provided in the embodiments of the present application can be exemplarily applied to hardware devices such as processors, electronic devices, servers (including cloud servers), or packaged into software programs to be run. When the hardware device executes the processing of the technical solution of the embodiments of the present application, or the above-mentioned software program is run, the automatic splitting of the target task and the automatic calling of the application program interface required for the task can be achieved to complete the purpose of the target task. The embodiments of the present application only exemplarily introduce the specific processing of the technical solution of the present application, and do not limit the specific implementation form of the technical solution of the present application. Any technical implementation form that can execute the processing of the technical solution of the present application can be adopted by the embodiments of the present application.

[0029] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] Before introducing this application solution, the relevant technologies are first introduced:

[0031] Traditional textbook teaching relies on paper teaching materials, and students' understanding of knowledge points mainly depends on teachers' oral explanations and writing on the blackboard. Although this method has a certain stability in information transmission, its inherent limitations cannot be ignored. First, students' understanding of knowledge is highly dependent on their personal imagination, which undoubtedly increases the difficulty of learning for students with weak abstract thinking ability. Secondly, traditional teaching methods lack interactivity and fun, and it is difficult to stimulate students' interest in learning, especially when faced with boring theoretical knowledge, students are prone to develop a dislike for learning. Finally, teachers' explanations in class often fail to take care of the learning needs of every student, causing some students to fall behind because they cannot keep up with the pace.

[0032] In recent years, multimedia courseware teaching has been widely welcomed for its rich forms of expression and high interactivity. Through multimedia elements such as videos, animations, and games, multimedia courseware can significantly enhance the active atmosphere of the classroom and stimulate students' interest in learning. However, this teaching method also faces a series of challenges. First, there are obvious individual differences between different students, including cognitive levels, learning styles, and interest preferences. The existing courseware design often fails to fully meet the needs of all students, resulting in a low match between the courseware and students, which affects the actual teaching effect. Secondly, the inconsistency between the courseware content and the textbook content is also a problem that cannot be ignored. In order to pursue fun and interactivity, many multimedia courseware often deviate from the core knowledge points of the textbook, making it difficult for students to effectively integrate the courseware content with the textbook content during the learning process, affecting the systematicness and integrity of knowledge.

[0033] In view of this, the embodiments of the present application are committed to providing a teaching data processing method, device, equipment, medium and product, which can generate a teaching design plan that is more suitable for students' situations, thereby improving the compatibility between students and the teaching design plan, which is described in detail one by one in the following embodiments.

[0034] Exemplary Methods

[0035] Figure 1 A flowchart of a teaching data processing method provided in an embodiment of the present application. Figure 1 As shown, the teaching data processing method provided in this embodiment includes step S101, step S102 and step S103:

[0036] S101. Obtain learning feedback data of each student in the class on the first teaching design plan.

[0037] In some embodiments, the first teaching design scheme can be obtained by the following steps: in response to the class chapter and class selected by the teacher, according to the first teaching design scheme generated in advance for each class and each class chapter, the first teaching design scheme corresponding to the class chapter and class selected by the teacher is obtained. The first teaching design scheme is generated according to the teaching resource data and the teaching method learned based on the preset teaching model.

[0038] In the specific implementation process, the teacher can log in to his account in the course management interface and select the course chapter to be taught and the corresponding class. For example, the teacher can select "Senior High School Biology Chapter 2" as the course chapter and select "Senior High School Class 1 (1)" as the class.

[0039] After the teacher selects the teaching chapter and class, he or she can automatically match the first teaching design plan suitable for the learning situation of the students in the class based on the teaching design plans customized in advance for each class and each teaching chapter.

[0040] S102. Determine the learning situation category corresponding to each student based on the learning feedback data of each student on the first teaching design plan.

[0041] The first teaching design usually includes one or more classroom introduction activities, which are designed to assess students' understanding of the content to be learned. These classroom introduction activities can include but are not limited to test questions or interactive dialogues.

[0042] In the process of implementing the above-mentioned classroom introduction activities, by collecting learning feedback data from each student, such as test scores, student participation and other performance indicators, we can comprehensively evaluate the students' adaptability and understanding level to the current teaching design plan.

[0043] Specifically, by analyzing the collected learning feedback data, we can obtain the learning status data of each student, and then identify learners of different levels and classify them into preset learning status categories. For example, students can be divided into three levels: "good learning mastery", "average learning mastery" and "poor learning mastery", so that more personalized teaching strategies can be adopted according to the needs of different students.

[0044] S103. According to the learning situation categories corresponding to each student and the second teaching design plan pre-generated for students with different learning situation categories, obtain the second teaching design plan corresponding to students with different learning situation categories among each student, and teach each student according to the second teaching design plan.

[0045] The pre-generated second teaching design is generated for students with different learning situations, and its generation basis includes teaching resource data and teaching methods learned based on the preset teaching model. The second teaching design is a teaching design that is further refined and optimized based on the first teaching design, combined with students' learning feedback data and learning situation categories, and aims to provide more accurate teaching support for students with different learning situations.

[0046] In this embodiment, the first teaching design scheme may include a universal teaching design scheme or a personalized teaching design scheme, and the second teaching design scheme may include a personalized teaching design scheme. The universal teaching design scheme aims to cover the basic learning needs of most students in the class, while the personalized teaching design scheme focuses more on meeting the learning needs of different student groups.

[0047] On the basis that the first teaching design scheme includes a universal teaching design scheme and the second teaching design scheme includes a personalized teaching design scheme, the implementation methods of step S101, step S102 and step S103 may be specifically as follows:

[0048] In some implementations, such as Figure 2 As shown, step S101 may include step S201, step S102 may include step S202, and step S103 may include step S203. Steps S201, S202, and S203 may be specifically as follows:

[0049] S201. Obtain learning feedback data of each student in the class on the universal teaching design plan.

[0050] S202. Determine the learning situation category corresponding to each student based on the learning feedback data of each student on the universal teaching design plan.

[0051] S203. According to the learning situation categories corresponding to each student and the personalized teaching design plans pre-generated for students with different learning situation categories, the personalized teaching design plans corresponding to students with different learning situation categories are obtained, and each student is taught according to the personalized teaching design plans.

[0052] In this implementation, the aim is to gain a preliminary understanding of the overall learning situation of the class through a universal teaching design plan, and then provide personalized teaching design plans for students with different learning situations based on the overall learning situation of the class, and conduct targeted teaching accordingly.

[0053] In some implementations, both the first teaching design scheme and the second teaching design scheme may be personalized teaching design schemes. To distinguish between the two, the personalized teaching design scheme in the first teaching design scheme is referred to as the first personalized teaching design scheme, and the personalized teaching design scheme in the second teaching design scheme is referred to as the second personalized teaching design scheme. Figure 3 As shown, step S101 may include step S301, step S102 may include step S302, and step S103 may include step S303. Steps S301, S302, and S303 may be specifically as follows:

[0054] S301. Obtain learning feedback data of each student in the class on the first personalized teaching design plan.

[0055] S302. Determine the learning situation category corresponding to each student based on the learning feedback data of each student on the first personalized teaching design plan.

[0056] S303. According to the learning situation categories corresponding to each student and the second personalized teaching design plan pre-generated for students with different learning situation categories, obtain the second personalized teaching design plan corresponding to students with different learning situation categories among each student, and teach each student according to the second personalized teaching design plan.

[0057] In this implementation, as the class progresses, students' learning status will continue to change. Therefore, the learning progress of students can be continuously monitored based on their learning feedback data on the current first personalized teaching design plan, and the personalized teaching design plan corresponding to each student can be dynamically adjusted based on the latest learning feedback data to ensure that each student can obtain the most suitable teaching support for their current learning status.

[0058] In these two implementation methods, whether transitioning from a universal teaching design plan to a personalized teaching design plan, or directly adopting a personalized teaching design plan, the purpose is to improve teaching effectiveness through precise teaching design and flexible teaching adjustments, thereby meeting the learning needs of different students.

[0059] In order to improve classroom efficiency, the first teaching design plan and the second teaching design plan can be generated in advance and called on demand in the actual teaching process. Specifically, the first teaching design plan and the second teaching design plan can be generated in advance through the teaching design plan generation model according to the teaching resource data and the teaching method learned based on the preset teaching model.

[0060] Among them, teaching resource data includes teachers’ teaching feedback data, students’ learning situation data, teaching materials, digital textbook content, knowledge points related to each part of the digital textbook content, examination outlines and examination points. Figure 4 As shown, the teaching design solution generation model can be trained by the following steps S401, S402 and S403:

[0061] Step S401: Obtain student learning data, teaching materials and digital textbook content, and associate the digital textbook content with preset knowledge points, examination outlines and examination points to obtain teaching resource data.

[0062] Figure 5 A schematic diagram of the training principle of the teaching design solution generation model provided in the embodiment of the present application. Figure 5As shown, the student's learning data can be obtained from the teaching system, and the teaching system stores the learning data obtained based on the student's historical learning data. The historical learning data includes the student's pre-class preparation, classroom interaction, after-class homework completion, examination data, learning behavior data and learning effect data during the historical learning process. The learning behavior data includes the student's behavior data during the electronic textbook learning process, such as reading time, knowledge points clicked, questions asked, etc.

[0063] Teaching materials are stored in the resource management system. Teaching materials include lesson plans, courseware and multimedia teaching materials. Lesson plans refer to teaching plans, which can be designed according to curriculum standards, teaching syllabus, textbook content and students' actual situation. Courseware refers to multimedia teaching materials used in the teaching process, usually in the form of electronic documents, which can contain text, pictures, audio, video, animation and other media elements. The main purpose of courseware is to assist teachers in teaching, enhance the intuitiveness and fun of teaching, and improve students' learning interest and learning effect. Courseware can include teaching syllabus, teaching content, example analysis, exercises, extended reading materials, etc. Common courseware formats include PPT (PowerPoint), PDF, HTML pages, video tutorials, etc. Multimedia teaching materials include at least one of PPT materials, text materials, audio materials and video materials.

[0064] Digital textbook content can be obtained by scanning various paper textbooks into textbook images and using OCR (Optical Character Recognition) technology to convert the scanned textbook images into text content that can be recognized by computers. Digital textbook content can provide a standardized teaching material basis for subsequent generation of teaching content.

[0065] After obtaining the digital textbook content and students' learning data, pre-processing operations such as cleaning, removing noise and erroneous data can be performed. After that, the content in the digital textbook content is associated with the subject knowledge system, examination outline and examination points in the basic data system. Specifically, the digital textbook content is annotated, such as the difficulty level, importance and related knowledge points of the knowledge points corresponding to each part of the digital textbook content. In addition, based on the students' historical learning data, the students' mastery of different knowledge points and learning preferences are annotated.

[0066] It should be noted that after the first iteration of training, teachers' teaching feedback data (i.e., teachers' feedback on personalized teaching design plans) can also be obtained, and model training can be performed based on the teachers' teaching feedback data.

[0067] Step S402: Generate a predicted teaching design plan based on the teaching resource data and the preset teaching model through the teaching design plan generation model to be trained.

[0068] In some embodiments, the teaching design solution generation model to be trained may include a pre-trained language model, a scenario activity generation model, a teaching and learning process generation model, and a knowledge link prediction model.

[0069] Pre-trained language models are used to generate question-answering systems and lesson preparation materials based on digital textbook content.

[0070] The scenario activity generation model can be a generative adversarial network (GAN) for generating scenario activities based on digital textbook content.

[0071] The teaching and learning process generation model can be a reinforcement learning model, which is used to generate the teaching process and the learning process.

[0072] The knowledge link prediction model can be a graph neural network (GNN), which is used to generate a knowledge point map based on the content of the digital textbook and predict other knowledge points related to the current knowledge point. For example, when students encounter weak knowledge points during the learning process, other related knowledge points can be predicted through knowledge links and recommended to students.

[0073] Continue reading Figure 5 The preset teaching models can include ADDIE model, ISD model, BOPPS model, O-AMAS model, HPT model, CBET model and DACUM model, etc.

[0074] ADDIE is an acronym for Analysis, Design, Development, Implementation and Evaluation. It is a systematic process model widely used in education and technology. Each stage provides the necessary input for the next stage, forming a continuous process. The model emphasizes a systematic approach from needs analysis to final evaluation in the development of educational projects or training programs.

[0075] ISD (Instructional Systems Design) is an instructional system design. The ADDIE model can be understood as a specific form of ISD. ISD is a broader term that refers to a series of methods and techniques for analyzing learning needs, designing solutions, developing materials, implementing teaching activities, and evaluating results. The ISD model aims to ensure the effectiveness and efficiency of education or training.

[0076] BOPPPS is a micro-teaching design model suitable for designing a single course or lecture. Its components include: Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment, and Summary. BOPPPS emphasizes promoting learning through interaction and participation and ensuring the achievement of learning objectives.

[0077] O-AMAS stands for Objective, Analyze, Method, Apply, Summarize, and is an instructional design model focused on skill acquisition. The model first defines the learning objectives, then analyzes learner needs, selects appropriate learning methods, applies these methods to teach, and finally summarizes the entire process. O-AMAS is applicable to skill training and professional development.

[0078] HPT (Human Performance Technology) is a systematic approach to identifying performance gaps and designing interventions to improve the performance of individuals, teams, and organizations. HPT is often combined with instructional design to solve performance problems in the workplace.

[0079] CBET (Competency-Based Education and Training) is a teaching model that directly links learning objectives to specific work tasks or professional abilities. CBET emphasizes that students can demonstrate that they have mastered specific knowledge, skills and attitudes, rather than just completing courses or exams. This model is widely used in vocational education and technical training.

[0080] DACUM (Developing A Curriculum) is a method used to identify and describe the specific skills and knowledge required for a certain occupation. Through DACUM meetings, industry experts and educators work together to list all the key tasks of a profession and detail the skills and knowledge required to complete these tasks. The DACUM model is mainly used in vocational education and adult education to help ensure that course content is closely related to industry needs.

[0081] The teaching plan design generation model is an artificial intelligence generated content (AIGC) model. By learning the teaching methods of the above-mentioned teaching models, the ability to generate teaching design plans can be acquired. In addition, the teaching plan design generation model itself has the ability to generate content, such as text images, text audio, text video, etc. Combining the two can make the quality of the generated teaching design plan higher.

[0082] Specifically, machine learning or deep learning algorithms can be used to train the teaching design plan generation model, and appropriate loss functions and optimization algorithms can be selected to ensure the accuracy and generalization ability of the model, and the model performance can be optimized through cross-validation and hyperparameter tuning. Among them, the teaching resource data and the preset teaching model are used as training samples and input into the teaching plan design generation model to be trained, so that the teaching plan design generation model generates a preliminary teaching design plan based on the input training samples. Afterwards, manual review and optimization are performed based on the generated teaching plan.

[0083] Step S403: determine the teaching design plan prediction loss based on the feedback data of teachers and / or students on the predicted teaching design plan, and converge the teaching design plan generation model to be trained based on the teaching design plan prediction loss to obtain the teaching design plan generation model.

[0084] Specifically, by implementing the generated teaching design plan and recording the feedback data from teachers and students. The feedback data may include students' academic performance, teachers' evaluations, students' satisfaction surveys, classroom observation records, etc. In addition, the collected feedback data is analyzed to evaluate the effectiveness and applicability of the teaching design plan. Based on the feedback data analysis results, the predicted loss of the teaching design plan is calculated. The predicted loss can be multifaceted, such as a decline in students' academic performance, a decrease in teachers' evaluations, a decline in students' satisfaction, etc.

[0085] By selecting appropriate loss functions, such as mean square error (MSE) and cross entropy loss, the prediction loss is quantified. Based on the prediction loss of the teaching design plan, the parameters of the model are adjusted, and back propagation and gradient descent are performed to optimize the performance of the model. Through multiple iterations and optimizations, the prediction loss is gradually reduced and the generation quality of the model is improved. When the prediction loss reaches the preset threshold or no longer decreases significantly after multiple iterations, the model is considered to have converged. Save the converged teaching design plan generation model for subsequent teaching design tasks.

[0086] Through the above steps, the teaching design plan generation model can generate predictive teaching design plans based on teaching resource data and preset teaching models, and continuously optimize and improve through feedback data from teachers and students, ultimately obtaining a high-quality teaching design plan generation model.

[0087] Continue reading Figure 5 After the teaching design plan generation model is trained, multiple teaching design plans can be generated using the teaching design plan model. Multiple teaching design plans include universal teaching design plans for each class and each chapter, and at least one set of personalized teaching design plans for each class and each chapter. Storing these teaching design plans and calling them directly in class can improve classroom efficiency.

[0088] In some embodiments, the second teaching design includes a teaching process and teaching content. The teaching process includes various teaching steps, and the teaching content includes various teaching activities. Each teaching step corresponds to a teaching activity.

[0089] Specifically, each teaching step in the teaching process is used to guide the class to proceed according to the predetermined steps. Each teaching activity in the teaching content corresponds to each teaching step one by one, and is used to guide what kind of materials to use in the current teaching step, which is the specific implementation form of the teaching step.

[0090] For example, the teaching process includes teaching step 1, teaching step 2, and teaching step 3. The teaching content includes teaching activity 1, teaching activity 2, and teaching activity 3. Teaching step 1 corresponds to teaching activity 1, teaching step 2 corresponds to teaching activity 2, and teaching step 3 corresponds to teaching activity 3.

[0091] For an English class, the teaching process includes: teaching step 1, classroom introduction activities to inspire students to think; teaching step 2, leading the reading of the text; teaching step 3, questioning session.

[0092] For teaching step 1, a model with the ability to generate text videos in the teaching design plan can be used to generate a demonstration video related to the content of the class chapter as teaching activity 1.

[0093] For teaching step 2, in order to help students better understand the content of the text, the scenario activity generation model in the teaching design plan generation model can be used to generate scenario activities for the content of the text as teaching activity 2. These activities can include role-playing, dialogue exercises, etc., so that students can deepen their understanding of the text in actual situations.

[0094] For teaching step 3, a series of questions related to the content of the text can be generated through the pre-trained language model. These questions can cover the main points, detailed information and extended thinking of the text, helping students consolidate what they have learned and conduct in-depth discussions.

[0095] In this way, the teaching design plan generation model can generate corresponding teaching activities and materials according to each teaching step in the teaching process, ensuring that the teaching process is both organized and interesting, thereby improving the teaching effect and students' learning enthusiasm.

[0096] Among them, teaching each student according to the second teaching design plan includes: presenting the textbook content related to the class chapter in the digital textbook content on the terminal device of the student and / or teacher; responding to the trigger action of each teaching step associated with the textbook content related to the class chapter, calling the teaching activities corresponding to each teaching step to teach each student.

[0097] For example, if a teacher chooses the second chapter of high school biology, "Basic Structure and Function of Cells," as the teaching chapter, the teacher will load the digital textbook content of "Basic Structure and Function of Cells" into the teacher's and students' respective terminal devices (personal learning devices), such as tablets or laptops.

[0098] Next, the corresponding teaching activities of each teaching step can be called according to each teaching step in the generated teaching process to teach students. The triggering action of each teaching step can be triggered by the teacher or automatically triggered according to a preset rule, which is not limited in this embodiment.

[0099] Assuming that the teaching process includes the introduction stage, explanation stage, practice stage, discussion stage and evaluation stage, in the introduction stage, the teaching activities can be to introduce the concept of cells and their importance to students through videos, animations, etc., to stimulate students' interest in learning.

[0100] In the explanation phase, teaching activities can be to explain the various parts of the cell and their functions in detail to students using tools such as digital whiteboards. At the same time, interactive diagrams can be provided to allow students to explore the functions of different organelles through touch or mouse clicks to deepen their understanding. This phase can be set to automatically enter after the video in the introduction phase is played.

[0101] In the practical stage, teaching activities can be called online experiments or virtual laboratory activities, allowing students to operate simulation software, observe the cell division process or build cell models. The trigger condition of this link can be set after all students have completed a certain number of reading tasks or quizzes.

[0102] In the discussion phase, teaching activities can create an online discussion area through forums, chat rooms and other functional modules to promote communication between teachers and students and between students. The discussion phase can be triggered after most students have completed the practical activities.

[0103] During the assessment stage, teaching activities can be in the form of online tests or project assignments to assess students' mastery of the knowledge in this chapter.

[0104] In some embodiments, the teaching data processing method also includes: obtaining teacher feedback data on the second teaching design plan; and optimizing the second teaching design plan based on the teacher feedback data on the second teaching design plan.

[0105] Specifically, the second teaching design plan can be presented on the teacher's terminal device to obtain the teacher's feedback data on the second teaching design plan. The teacher's feedback data includes the teacher's supplementation and improvement of the second teaching design plan, as well as the supplementation and guidance of the student's learning feedback data based on the second teaching design plan, so as to obtain more comprehensive learning feedback data. Next, the second teaching design plan can be continuously optimized based on the teacher's feedback data.

[0106] In some embodiments, the second teaching design scheme generated by the teaching design scheme generation model includes multiple. Then, according to the learning situation category corresponding to each student and the second teaching design scheme generated in advance for students of different learning situation categories, the second teaching design scheme corresponding to students of different learning situation categories among each student is obtained, including: according to the learning situation category corresponding to each student and the multiple second teaching design schemes generated in advance for students of different learning situation categories, a new second teaching design scheme is generated, and the new second teaching design scheme is obtained by recombining the multiple second teaching design schemes.

[0107] Specifically, the teaching design generation model generates multiple second teaching designs for the same chapter or topic. The multiple second teaching designs are designed to adapt to the learning needs and preferences of different students. Specifically, based on the learning situation category corresponding to each student (such as visual, auditory, hands-on, etc.), and multiple second teaching designs pre-customized for these different learning situation categories, the system can obtain and match the most suitable teaching design for each student.

[0108] In classroom practice, the teaching steps and teaching activities in multiple pre-generated second teaching design plans can also be reorganized according to students' learning feedback data to obtain a new second teaching design plan.

[0109] For example, the plurality of second teaching design schemes include teaching design scheme 1 and teaching design scheme 2. Teaching design scheme 1 includes teaching step 1-1 and corresponding teaching activity 1-1', teaching step 1-2 and corresponding teaching activity 1-2', teaching step 1-3 and corresponding teaching activity 1-3'. Teaching design scheme 2 includes teaching step 2-1 and corresponding teaching activity 2-1', teaching step 2-2 and corresponding teaching activity 2-2', teaching step 2-3 and corresponding teaching activity 2-3'.

[0110] If it is found through analyzing students' learning feedback data that most students have a good grasp of the concepts in teaching steps 1-1 and 1-2, but have difficulty understanding the content in teaching step 1-3, then you can choose to combine teaching step 1-1 and the corresponding teaching activity 1-1', teaching step 1-2 and the corresponding teaching activity 1-2' in teaching design plan 1 with teaching step 2-3 and the corresponding teaching activity 2-3' in teaching design plan 2 to form a new and more effective teaching design plan. This will not only strengthen the teaching of difficult content in a targeted manner, but also maintain the overall coherence and logic of the course.

[0111] In summary, this scheme aims to solve the problem of low adaptability between students and teaching design schemes, and proposes a framework of a virtual human adaptive "teacher-machine-student" three-in-one teaching method based on AIGC electronic textbooks. Figure 6 As shown in the figure, the framework mainly includes the teacher end, AI agent and student end, where the teacher end refers to the terminal device used by the teacher, the AI ​​agent can be a teaching data processing method system, and the student end refers to the terminal device used by the student:

[0112] (1) Training AI Agents

[0113] The teaching resource data including the digital textbook content is input into the AI ​​agent, so that it can learn in the role of a teacher how to combine the teaching resource data and the teaching methods provided by the preset teaching model to generate a universal teaching design plan suitable for the learning situation of most students in the class and a personalized teaching design plan suitable for students with different learning situations in the class.

[0114] Among them, the model can be trained using supervised learning, unsupervised learning or semi-supervised learning methods during the training process.

[0115] (2) Generate multiple teaching design plans using trained AI agents

[0116] Specifically, the AI ​​agent can generate multiple sets of teaching design plans based on each chapter in the digital textbook content and each learning situation category in each class, and associate them with the digital textbook content so that they can be called when needed. Multiple sets of teaching design plans include universal teaching design plans and personalized teaching design plans.

[0117] Among them, the content difficulty and content form of the personalized teaching design plan generated by the AI ​​agent can be adjusted according to the students' learning mastery and learning style.

[0118] (3) Calling up teaching design plans according to classroom needs

[0119] Specifically, in classroom practice, after the teacher selects the chapter and class, the AI ​​agent will call the universal teaching design plan corresponding to the chapter and class, such as the universal classroom introduction activity.

[0120] Next, the AI ​​agent collects students' feedback data on the universal teaching classroom introduction activities, and based on the students' feedback data, verifies the students' understanding of the course content and calls on personalized teaching design plans to provide personalized classroom supplementary teaching.

[0121] Then, students learn by following the teaching activities presented by the AI ​​agent in each teaching process, and provide learning feedback to generate learning feedback data. Based on the students' learning feedback data, the AI ​​agent can build a student portrait, and based on the student portrait and teaching resource data, call a personalized teaching design plan, which specifically includes personalized teaching mode (teaching process), teaching activities, learning paths, knowledge point explanations, exercises, case analysis, etc.

[0122] Afterwards, the personalized teaching design plan will be presented to students and teachers in combination with the digital textbook content.

[0123] In addition, teachers can supplement, improve and guide the AI ​​agent’s teaching design plan and students’ learning feedback data. The AI ​​agent continuously optimizes the quality of the teaching design plan based on the teacher’s feedback.

[0124] In summary, the teaching data processing method provided in the embodiment of the present application, by adding an artificial intelligence content generation model on the basis of the original textbook, can generate a teaching design plan suitable for students to understand and associate it with the digital textbook based on the understanding of the course content of the original textbook and the learning situation of the students in the class. The teacher opens the digital textbook in the classroom and shows the students the teaching content based on the textbook. For students, by clicking on a certain content in the textbook in class, the corresponding teaching content can be retrieved and displayed on a personal learning device, thereby achieving a textbook-based teaching effect that allows students to view more teaching content outside the textbook, thereby achieving a teaching effect that originates from the textbook but is not limited to the textbook.

[0125] Exemplary Devices

[0126] Corresponding to the above-mentioned teaching data processing method, the embodiment of the present application also provides a teaching data processing device. Figure 7 Schematic diagram of a teaching data processing device provided in an embodiment of the present application. Figure 7 As shown, the teaching data processing device provided by the embodiment of the present application includes: an acquisition unit 71, which is used to acquire learning feedback data of each student in the class on the first teaching design plan; a determination unit 72, which is used to determine the learning situation category corresponding to each of the students based on the learning feedback data of each student on the first teaching design plan; an acquisition and teaching unit 73, which acquires the second teaching design plan corresponding to the students of different learning situation categories among the students based on the learning situation category corresponding to each of the students and the second teaching design plan pre-generated for students of different learning situation categories, and teaches each student according to the second teaching design plan, wherein the second teaching design plan pre-generated for students of different learning situation categories is generated based on teaching resource data and the teaching method learned based on a preset teaching model.

[0127] In some embodiments, the first teaching design plan is obtained through the following steps: in response to the class chapters and classes selected by the teacher, according to the first teaching design plan pre-generated for each class and each chapter, the first teaching design plan corresponding to the class chapters and the class selected by the teacher is obtained, and the first teaching design plan is generated according to the teaching resource data and the teaching method learned based on the preset teaching model.

[0128] In some embodiments, the first teaching design plan includes a universal teaching design plan or a personalized teaching design plan, and the second teaching design plan includes a personalized teaching design plan.

[0129] In some embodiments, the pre-generated first teaching design plan and the second teaching design plan are generated by a teaching design plan generation model according to teaching resource data and a teaching method learned based on a preset teaching model, and the preset teaching model is a model that provides teaching methods; wherein the teaching design plan generation model is trained using the following steps: obtaining students' learning situation data, teaching materials and digital textbook content, and associating the digital textbook content with preset knowledge points, syllabus and test points to obtain the teaching resource data; generating a predicted teaching design plan according to the teaching resource data and the teaching model through the teaching design plan generation model to be trained; determining the teaching design plan prediction loss based on the feedback data of the teacher and / or the student on the predicted teaching design plan, and converging the teaching design plan generation model to be trained based on the teaching design plan prediction loss to obtain the teaching design plan generation model.

[0130] In some embodiments, the second teaching design plan includes a teaching process and teaching content, the teaching process includes various teaching steps, the teaching content includes various teaching activities, and each teaching step corresponds to a teaching activity; the acquisition and teaching unit 73 teaches each student according to the second teaching design plan, including: presenting the textbook content corresponding to the class chapter in the digital textbook content on the student's terminal device; responding to the triggering action of each teaching step associated with the textbook content corresponding to the class chapter, calling the teaching activities corresponding to each teaching step, and teaching each student.

[0131] In some embodiments, the device further includes an optimization unit 74 for obtaining feedback data from the teacher on the second teaching design plan; and optimizing the second teaching design plan according to the feedback data from the teacher on the second teaching design plan.

[0132] In some embodiments, the second teaching design scheme includes multiple ones; the acquisition and teaching unit 73 acquires the second teaching design schemes corresponding to students of different learning situation categories among the students according to the learning situation categories corresponding to the students and the second teaching design schemes pre-generated for students of different learning situation categories, including: generating a new second teaching design scheme according to the learning situation categories corresponding to the students and the multiple second teaching design schemes pre-generated for students of different learning situation categories, and the new second teaching design scheme is obtained by re-combining the multiple second teaching design schemes.

[0133] The teaching data processing device provided in this embodiment belongs to the same application concept as the teaching data processing method provided in the above embodiment of this application, and can execute the teaching data processing method provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects of executing the teaching data processing method. For the technical details not described in detail in this embodiment, please refer to the specific processing content of the teaching data processing method provided in the above embodiment of this application, and will not be repeated here.

[0134] The functions implemented by the above-mentioned acquisition unit 71, determination unit 72 and acquisition and teaching unit 73 may be implemented by the same or different processors respectively, and the embodiments of the present application are not limited thereto.

[0135] It should be understood that the units in the above devices can be implemented in the form of a processor calling software. For example, the device includes a processor, the processor is connected to a memory, and instructions are stored in the memory. The processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of each unit of the device, wherein the processor can be a general-purpose processor, such as a CPU or a microprocessor, etc., and the memory can be a memory in the device or a memory outside the device. Alternatively, the units in the device can be implemented in the form of hardware circuits, and the functions of some or all units can be realized by designing the hardware circuits. The hardware circuit can be understood as one or more processors; for example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units are realized by designing the logical relationship of the components in the circuit; for another example, in another implementation, the hardware circuit can be implemented by PLD, taking FPGA as an example, which can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by the configuration file, so as to realize the functions of some or all of the above units. All units of the above devices can be implemented in the form of a processor calling software, or in the form of hardware circuits, or in part by a processor calling software, and the remaining part is implemented in the form of hardware circuits.

[0136] In an embodiment of the present application, a processor is a circuit with the ability to process signals. In one implementation, the processor may be a circuit with the ability to read and run instructions, such as a CPU, a microprocessor, a GPU, or a DSP; in another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or reconfigurable, such as a hardware circuit implemented by an ASIC or PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, TPU, DPU, etc.

[0137] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0138] In addition, all or part of the units in the above device can be integrated together, or can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a SOC. The SOC may include at least one processor for implementing any of the above methods or implementing the functions of each unit of the device. The type of the at least one processor may be different, for example, including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0139] Exemplary Electronic Devices

[0140] The present application embodiment provides an electronic device, see Figure 8 As shown, the electronic device includes:

[0141] Memory 200 and processor 210;

[0142] The memory 200 is connected to the processor 210 and is used to store programs;

[0143] The processor 210 is used to implement the teaching data processing method disclosed in any of the above embodiments by running the program stored in the memory 200.

[0144] Specifically, the electronic device may further include: a bus, a communication interface 220 , an input device 230 and an output device 240 .

[0145] The processor 210, the memory 200, the communication interface 220, the input device 230 and the output device 240 are connected to each other via a bus.

[0146] A bus may include a pathway that transfers information between components of a computer system.

[0147] The processor 210 may be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the scheme of the present invention. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0148] The processor 210 may include a main processor, and may also include a baseband chip, a modem, and the like.

[0149] The memory 200 stores a program for executing the technical solution of the present invention, and may also store an operating system and other key services. Specifically, the program may include a program code, and the program code includes a computer operation instruction. More specifically, the memory 200 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk storage, a flash, and the like.

[0150] The input device 230 may include a device for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor.

[0151] Output device 240 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.

[0152] The communication interface 220 may include any transceiver or the like to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0153] The processor 210 executes the program stored in the memory 200 and calls other devices, which can be used to implement each step of any teaching data processing method provided in the above embodiments of the present application.

[0154] An embodiment of the present application also proposes a chip, which includes a processor and a data interface. The processor reads and runs the program stored in the memory through the data interface to execute the teaching data processing method introduced in any of the above embodiments. The specific processing process and its beneficial effects can be found in the introduction to the embodiment of the above-mentioned teaching data processing method.

[0155] Exemplary computer program products and storage media

[0156] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the teaching data processing method according to various embodiments of the present application described in any of the above embodiments of this specification.

[0157] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0158] In addition, an embodiment of the present application may also be a storage medium on which a computer program is stored. The computer program is executed by a processor to execute the steps of the teaching data processing method according to various embodiments of the present application described in any of the above embodiments of this specification, and specifically can implement the steps in the above method embodiments.

[0159] For the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0160] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0161] The steps in the methods of each embodiment of the present application can be adjusted in order, combined and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0162] The modules and sub-modules in the devices and terminals of the various embodiments of the present application can be combined, divided and deleted according to actual needs.

[0163] In the several embodiments provided in the present application, it should be understood that the disclosed terminals, devices and methods can be implemented in other ways. For example, the terminal embodiments described above are only schematic, for example, the division of modules or submodules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple submodules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0164] The modules or submodules described as separate components may or may not be physically separated, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place, or they may be distributed on multiple network modules or submodules. Some or all of the modules or submodules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] In addition, each functional module or submodule in each embodiment of the present application may be integrated into one processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into one module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or in the form of software functional modules or submodules.

[0166] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0167] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly by hardware, software units executed by a processor, or a combination of the two. The software units may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0168] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0169] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A teaching data processing method, characterized in that: include: Obtain learning feedback data of each student in the class on the first teaching design plan; Determining the learning situation categories corresponding to the students according to the learning feedback data of the students on the first teaching design scheme; According to the learning situation categories corresponding to each of the students and the second teaching design plan generated in advance for students of different learning situation categories, the second teaching design plan corresponding to the students of different learning situation categories among the students is obtained, and each student is taught according to the second teaching design plan. The second teaching design plan generated in advance for students of different learning situation categories is generated according to teaching resource data and the teaching method learned based on a preset teaching model.

2. The method according to claim 1, characterized in that The first teaching design scheme is obtained through the following steps: In response to the course chapters and classes selected by the teacher, based on the first teaching design plan pre-generated for each class and each course chapter, a first teaching design plan corresponding to the course chapters and classes selected by the teacher is obtained, wherein the first teaching design plan is generated based on teaching resource data and the teaching method learned based on a preset teaching model.

3. The method according to claim 2, characterized in that The first teaching design plan includes a universal teaching design plan or a personalized teaching design plan, and the second teaching design plan includes a personalized teaching design plan.

4. The method according to claim 2, characterized in that: The first teaching design scheme and the second teaching design scheme generated in advance are generated by a teaching design scheme generation model according to teaching resource data and a teaching method learned based on a preset teaching model, and the preset teaching model is a model that provides a teaching method; The teaching design scheme generation model is trained by the following steps: Acquire students' learning data, teaching materials and digital textbook content, and associate the digital textbook content with preset knowledge points, examination outlines and examination points to obtain the teaching resource data; Generate a predicted teaching design plan according to the teaching resource data and the teaching model by using the teaching design plan generation model to be trained; According to the feedback data of the teacher and / or the student on the predicted teaching design plan, the teaching design plan prediction loss is determined, and based on the teaching design plan prediction loss, the teaching design plan generation model to be trained is converged to obtain the teaching design plan generation model.

5. The method according to claim 1, characterized in that The second teaching design scheme includes a teaching process and teaching content, wherein the teaching process includes various teaching steps, the teaching content includes various teaching activities, and each teaching step corresponds to a teaching activity; The teaching of each student according to the second teaching design scheme includes: Present the textbook content corresponding to the class chapter in the digital textbook content on the student's terminal device; In response to the triggering action of each teaching step associated with the textbook content corresponding to the class chapter, the teaching activities corresponding to each teaching step are called to teach the students.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Obtaining teacher feedback data on the second teaching design plan; The second teaching design plan is optimized according to the teacher's feedback data on the second teaching design plan.

7. The method according to any one of claims 1 to 5, characterized in that: The second teaching design scheme includes a plurality of; The obtaining, according to the learning situation categories corresponding to the respective students and the second teaching design schemes pre-generated for the students of different learning situation categories, the second teaching design schemes corresponding to the students of different learning situation categories among the respective students comprises: A new second teaching design plan is generated according to the learning situation categories corresponding to the students and multiple second teaching design plans generated in advance for students with different learning situation categories. The new second teaching design plan is obtained by recombining multiple second teaching design plans.

8. A teaching data processing device, characterized in that: include: An acquisition unit, used for acquiring learning feedback data of each student in the class on the first teaching design plan; A determination unit, configured to determine the learning situation category corresponding to each student according to the learning feedback data of each student on the first teaching design scheme; An acquisition and teaching unit is used to acquire the second teaching design plan corresponding to the students in different learning situation categories among the students according to the learning situation categories corresponding to the students and the second teaching design plan pre-generated for the students in different learning situation categories, and teach the students according to the second teaching design plan. The second teaching design plan pre-generated for the students in different learning situation categories is generated according to the teaching resource data and the teaching method learned based on the preset teaching model.

9. An electronic device, characterized in that: including memory and processor; The memory is connected to the processor and is used to store programs; The processor is configured to implement the method according to any one of claims 1 to 7 by running the program in the memory.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

11. A computer program product, characterized in that The method comprises computer program instructions, which, when executed by a processor, cause the processor to implement the method as claimed in any one of claims 1 to 7.

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