Method, device and equipment for generating AR teaching material and medium
By acquiring teaching content and identifying AR teaching tools, AR teaching materials that meet learning objectives are generated, solving the problem of insufficient professional knowledge among teachers in AR education and training, improving generation efficiency and content quality, and reducing costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUDONG TECH CO LTD
- Filing Date
- 2024-01-23
- Publication Date
- 2026-06-19
AI Technical Summary
The lack of teachers with professional knowledge and skills in existing AR education and training results in insufficiently rich and effective content design, and there are communication and expense costs associated with finding professional teams or relying on third parties.
This invention provides a method for generating AR teaching materials. By acquiring information about the content to be taught and determining the AR teaching tools that can be used, AR teaching materials that meet the learning objectives are generated, including learning resources such as 3D models, videos, pictures and games, as well as interactive elements such as visual interaction, gesture control and haptic feedback.
It improves the efficiency of AR teaching material generation, lowers the technical threshold, enables non-professionals to generate high-quality AR teaching content, and reduces reliance on professional teams and costs.
Smart Images
Figure CN117854339B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of AR teaching, and in particular to a method for generating AR teaching materials, an apparatus for generating AR teaching materials, a device for generating AR teaching materials, and a computer-readable storage medium. Background Technology
[0002] Currently, AR education and training require experienced teachers to guide and mentor students. However, due to the relative newness of AR technology, some teachers may lack the necessary professional knowledge and skills. Furthermore, the design of AR education and training content needs to fully consider the integration of subject knowledge and AR technology. However, some teachers may lack an understanding of AR technology, resulting in insufficiently rich and effective content design. Moreover, seeking professional teams or third parties to create AR teaching materials incurs communication and financial costs. Therefore, relevant content design guidance and resource support are needed to help teachers better utilize AR technology in their teaching. Summary of the Invention
[0003] The main objective of this application is to provide a method, apparatus, device, and computer-readable storage medium for generating AR teaching materials, aiming to improve the efficiency of AR teaching material generation.
[0004] To achieve the above objectives, this application provides a method for generating AR teaching materials, the method comprising:
[0005] Obtain information on the teaching content to be taught, wherein the teaching content includes a description of the learning objectives to be achieved and / or the knowledge and skills to be mastered by the students to be taught;
[0006] Identify usable AR teaching tools;
[0007] Based on the information of the content to be taught and the available AR teaching tools, generate AR teaching materials that meet the learning objectives of the students and can be used for AR teaching with available AR teaching tools.
[0008] For example, the method further includes:
[0009] Identify the types of learning resources to be displayed on available AR teaching tools, including 3D models, videos, images, and games;
[0010] Based on the information of the content to be taught, the available AR teaching tools, and the types of learning resources to be displayed, AR teaching materials are generated that meet the learning objectives of the students, use the available AR teaching tools, and conduct AR teaching through the types of learning resources to be displayed.
[0011] For example, the method further includes:
[0012] Identify interactive elements, wherein the interactive elements include visual interaction, gesture control, sound interaction, and tactile feedback;
[0013] Based on the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and interactive elements, generate AR teaching materials that meet the learning objectives of the students, use available AR teaching tools, utilize the types of learning resources to be displayed, and conduct AR teaching with interactive elements.
[0014] For example, the method further includes:
[0015] Before using the generated AR teaching materials for teaching, the students to be taught are given a knowledge test on the content to be taught and receive the first score.
[0016] After teaching using the generated AR teaching materials, the students to be taught are given a knowledge test on the content to be taught, and a second score is obtained.
[0017] The generative model used to generate AR teaching materials is adjusted based on the first and second scores.
[0018] For example, the step of adjusting the generative model used to generate AR teaching materials based on the first score and the second score includes:
[0019] If the difference between the second score and the first score is greater than a preset difference threshold, then the dataset used to generate AR teaching materials is collected again, and / or the generative model architecture and hyperparameters are adjusted, and / or the data processing method of the generative model is increased, and / or the regularization method of the generative model is increased, and / or the ensemble method of the generative model is increased, and the generative model is retrained and validated until a new generative model is obtained in which the difference between the second score and the first score is less than or equal to the preset difference threshold.
[0020] For example, the step of adjusting the generative model used to generate AR teaching materials based on the first score and the second score includes:
[0021] Based on the difference between the second score and the first score, adjust the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements in the generative model.
[0022] For example, the step of adjusting the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements of the generation model based on the difference between the second score and the first score includes:
[0023] If the difference between the second score and the first score is in the fourth interval, then adjust the relevant generation parameters of one of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the type of learning resources to be displayed, and the interactive elements.
[0024] If the difference between the second score and the first score is in the third interval, then adjust the relevant generation parameters of two of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements.
[0025] If the difference between the second score and the first score is in the second interval, then adjust the relevant generation parameters of three items in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements.
[0026] If the difference between the second score and the first score is within the first interval, then adjust the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements in the generation model.
[0027] Among them, the second score is greater than or equal to the first score, the upper limit difference of the first interval is less than or equal to the lower limit difference of the second interval, the upper limit difference of the second interval is less than or equal to the lower limit difference of the third interval, and the upper limit difference of the third interval is less than or equal to the lower limit difference of the fourth interval.
[0028] This application also provides an apparatus for generating AR teaching materials, the apparatus comprising:
[0029] The acquisition module is used to acquire information about the teaching content, wherein the teaching content includes a description of the learning objectives to be achieved and / or the knowledge and skills to be mastered by the students.
[0030] The module is used to determine the AR teaching tools that can be used.
[0031] The generation module is used to generate AR teaching materials that meet the learning objectives of the students and can be used for AR teaching based on the information of the content to be taught and the available AR teaching tools.
[0032] This application also provides an AR teaching material generation device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the AR teaching material generation method described above.
[0033] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the AR teaching material generation method described above.
[0034] This application provides a method, apparatus, device, and computer-readable storage medium for generating AR teaching materials. The method involves acquiring information about the content to be taught, wherein the content includes a description of the learning objectives and / or knowledge and skills to be mastered by the learner; determining usable AR teaching tools; and generating AR teaching materials that meet the learner's learning objectives and are used for AR teaching with the available AR teaching tools, based on the information about the teaching content and the usable AR teaching tools.
[0035] Currently, there are at least the following technical obstacles in generating AR educational materials: 1. High technical requirements: Generating AR educational materials requires a high level of technical skill and development experience, including skills in 3D modeling, programming, image processing, and other areas. This can be challenging for non-professionals. 2. Complex design: AR educational materials require consideration of multiple design aspects, such as scenes, interactions, and user experience, which demands significant time and resources. 3. Steep learning curve: Generating AR educational materials requires mastering new technologies and tools, which requires time and effort to learn.
[0036] Therefore, this application obtains teaching content information, including descriptions of the learning objectives and / or knowledge and skills to be mastered by the learners, and identifies usable AR teaching tools. Then, using the teaching content information and usable AR teaching tools, AR teaching materials that meet the learners' learning objectives and are used for AR teaching with the available AR teaching tools are generated. In generating AR teaching materials, it is not necessary to master various techniques and experience in generating AR teaching materials; only the teaching content information and usable AR teaching tools need to be determined, and the AR teaching materials are automatically generated, thereby improving the efficiency of AR teaching material generation. Attached Figure Description
[0037] Figure 1 This is a schematic diagram of the structure of the operating device of the hardware operating environment involved in the embodiments of this application;
[0038] Figure 2 This is a flowchart illustrating an embodiment of the method for generating AR teaching materials according to the present application.
[0039] Figure 3 This is a schematic diagram of an AR teaching material generation device involved in the embodiments of this application.
[0040] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0041] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.
[0042] Reference Figure 1 , Figure 1 This is a schematic diagram of the operating device structure of the hardware operating environment involved in the embodiments of this application.
[0043] like Figure 1 As shown, the operating device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.
[0044] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the operating equipment and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0045] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and computer programs.
[0046] exist Figure 1 In the illustrated operating device, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the operating device of this application can be disposed in the operating device, and the operating device calls the computer program stored in the memory 1005 through the processor 1001 and performs the following operations:
[0047] Obtain information on the teaching content to be taught, wherein the teaching content includes a description of the learning objectives to be achieved and / or the knowledge and skills to be mastered by the students to be taught;
[0048] Identify usable AR teaching tools;
[0049] Based on the information of the content to be taught and the available AR teaching tools, generate AR teaching materials that meet the learning objectives of the students and can be used for AR teaching with available AR teaching tools.
[0050] In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 and further perform the following operations:
[0051] The method further includes:
[0052] Identify the types of learning resources to be displayed on available AR teaching tools, including 3D models, videos, images, and games;
[0053] Based on the information of the content to be taught, the available AR teaching tools, and the types of learning resources to be displayed, AR teaching materials are generated that meet the learning objectives of the students, use the available AR teaching tools, and conduct AR teaching through the types of learning resources to be displayed.
[0054] In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 and further perform the following operations:
[0055] The method further includes:
[0056] Identify interactive elements, wherein the interactive elements include visual interaction, gesture control, sound interaction, and tactile feedback;
[0057] Based on the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and interactive elements, generate AR teaching materials that meet the learning objectives of the students, use available AR teaching tools, utilize the types of learning resources to be displayed, and conduct AR teaching with interactive elements.
[0058] In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 and further perform the following operations:
[0059] The method further includes:
[0060] Before using the generated AR teaching materials for teaching, the students to be taught are given a knowledge test on the content to be taught and receive the first score.
[0061] After teaching using the generated AR teaching materials, the students to be taught are given a knowledge test on the content to be taught, and a second score is obtained.
[0062] The generative model used to generate AR teaching materials is adjusted based on the first and second scores.
[0063] In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 and further perform the following operations:
[0064] The step of adjusting the generative model used to generate AR teaching materials based on the first score and the second score includes:
[0065] If the difference between the second score and the first score is greater than a preset difference threshold, then the dataset used to generate AR teaching materials is collected again, and / or the generative model architecture and hyperparameters are adjusted, and / or the data processing method of the generative model is increased, and / or the regularization method of the generative model is increased, and / or the ensemble method of the generative model is increased, and the generative model is retrained and validated until a new generative model is obtained in which the difference between the second score and the first score is less than or equal to the preset difference threshold.
[0066] In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 and further perform the following operations:
[0067] The step of adjusting the generative model used to generate AR teaching materials based on the first score and the second score includes:
[0068] Based on the difference between the second score and the first score, adjust the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements in the generative model.
[0069] In one embodiment, the processor 1001 may invoke a computer program stored in the memory 1005 and further perform the following operations:
[0070] The step of adjusting the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements of the generation model based on the difference between the second score and the first score includes:
[0071] If the difference between the second score and the first score is in the fourth interval, then adjust the relevant generation parameters of one of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the type of learning resources to be displayed, and the interactive elements.
[0072] If the difference between the second score and the first score is in the third interval, then adjust the relevant generation parameters of two of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements.
[0073] If the difference between the second score and the first score is in the second interval, then adjust the relevant generation parameters of three items in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements.
[0074] If the difference between the second score and the first score is within the first interval, then adjust the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements in the generation model.
[0075] Among them, the second score is greater than or equal to the first score, the upper limit difference of the first interval is less than or equal to the lower limit difference of the second interval, the upper limit difference of the second interval is less than or equal to the lower limit difference of the third interval, and the upper limit difference of the third interval is less than or equal to the lower limit difference of the fourth interval.
[0076] This application provides a method for generating AR teaching materials, referring to... Figure 2 In one embodiment of the method for generating AR teaching materials, the method includes:
[0077] Step S10: Obtain the information of the teaching content, wherein the teaching content includes a description of the learning objectives to be achieved and / or the knowledge and skills to be mastered by the students.
[0078] In AR teaching, the learning objectives and / or knowledge and skills that learners are expected to achieve can be determined based on the specific educational field and teaching content, including: 1. Knowledge mastery: Using AR technology for demonstration and interaction to help learners understand and master relevant knowledge points. For example, in medical education, AR models are used to demonstrate human anatomical structures to help learners understand human biology. When the learning objective and / or knowledge and skills to be taught are knowledge mastery, the corresponding information for the teaching content is the relevant descriptive information for knowledge mastery.
[0079] 2. Practical Skills Development: Through AR interactive experiences, students' practical operation and problem-solving abilities are cultivated. For example, in engineering education, AR virtual scenes are used to allow students to perform mechanical repair or assembly operations, learning practical skills and processes. Correspondingly, when the teaching content is a learning objective and / or knowledge and skills for practical skills development, the corresponding teaching content information is the relevant descriptive information for practical skills development.
[0080] 3. Visual Spatial Cognition: AR virtual scenes help learners understand and recognize visual space. For example, AR modeling tools can be used in architectural design education, allowing learners to deepen their understanding of architectural space through virtual architectural design and demonstrations. Correspondingly, when the learning objective and / or knowledge / skills to be taught are visual spatial cognition, the corresponding teaching content information will be descriptive information related to visual spatial cognition.
[0081] 4. Cultivating Creativity and Imagination: Through AR creation and design, students' creativity and imagination are fostered. For example, AR art creation tools can be used in art education, allowing students to create and design virtual artworks or film and animation. Correspondingly, when the learning objective and / or knowledge and skills to be taught are the cultivation of creativity and imagination, the corresponding teaching content information will be descriptive information related to the cultivation of creativity and imagination.
[0082] 5. Teamwork and Communication Skills: AR collaborative projects cultivate students' teamwork and communication skills. For example, AR business simulation scenarios can be used in business education to allow students to improve their collaboration and leadership skills through virtual teamwork and competition. Correspondingly, when the learning objective and / or knowledge / skills to be taught are teamwork and communication skills, the corresponding teaching content information will be descriptive information related to teamwork and communication skills.
[0083] 6. Cultivating Interest and Enhancing Learning Motivation: The fun and innovative nature of AR teaching can stimulate learners' interest and motivation. For example, using AR gamified teaching in language learning allows learners to improve their learning motivation and language proficiency through engaging interactions. Correspondingly, when the teaching content is a learning objective and / or knowledge / skills focused on cultivating interest and enhancing learning motivation, the corresponding teaching content information will include descriptive information related to cultivating interest and enhancing learning motivation.
[0084] Step S20: Determine the available AR teaching tools;
[0085] The available AR teaching tools include one or more of the following: Unity, Unreal Engine, Vuforia, AR Kit, and ARCore. Unreal Engine, a game engine, can also be used for AR teaching. It offers rich AR development features and tools, enabling the creation of realistic virtual scenes, interactive experiences, and effects. Unreal Engine also has a plugin called Unreal Studio, specifically designed for the design and development of virtual reality (VR) and augmented reality (AR) applications. Vuforia is a multi-platform AR development tool that provides image recognition, object tracking, virtual buttons, and other functions. It is compatible with Unity and other major development engines and can be used to create AR applications on mobile devices, including educational content. AR Kit is an AR development framework developed by Apple for iOS devices. It provides a powerful set of tools and functions for creating high-quality AR applications on iPhones and iPads. AR Kit supports features such as environment tracking, facial recognition, and object detection, making it suitable for developing educational AR applications. AR Core is an AR development platform developed by Google for Android devices. It provides similar functionality to AR Kit, allowing developers to create Android applications with virtual and augmented experiences. AR Core supports features such as motion tracking, environment understanding, and lighting estimation.
[0086] Step S30: Based on the information of the content to be taught and the available AR teaching tools, generate AR teaching materials that meet the learning objectives of the students to be taught and are used for AR teaching with available AR teaching tools.
[0087] In this embodiment, an artificial intelligence model for generating AR teaching materials is designed, thus proposing a generative model. In one embodiment, the generative model is obtained through the following steps: 1. Data Collection and Preparation: Collect data related to AR teaching materials, including teaching materials, interaction designs, scene settings, etc. This data can come from existing AR teaching materials or other educational resources. 2. Data Labeling and Annotation: Label and annotate the collected data to specify the input and output of each data sample. For example, use an AR scene image as input and the expected AR effect as output. 3. Model Selection and Architecture Design: Select an appropriate model type, such as a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), or a Generative Adversarial Network (GAN), based on the task requirements and data type. Design a suitable model architecture, including network hierarchy, activation function, loss function, etc. 4. Data Partitioning and Training: Divide the dataset into a training set, a validation set, and a test set, and train the model using the training set. Optimize the model parameters through iterative iteration to improve performance. Use the validation set to evaluate the model's generalization ability and make adjustments. 5. Model Evaluation and Tuning: Evaluate the model using a test set, calculating evaluation metrics such as accuracy, precision, and recall. Based on the evaluation results, adjust the model architecture, hyperparameters, or data processing methods to improve performance. 6. Deployment and Integration: Deploy the trained model to the production environment and integrate it with the AR teaching material generation system. Ensure the model operates smoothly with other components and meets real-time and performance requirements. 7. Continuous Optimization and Updates: Regularly monitor model performance and continuously optimize and update it based on user feedback and needs. This may include adding new teaching materials, improving interaction design, or enhancing AR effects.
[0088] Furthermore, in one embodiment, based on the information of the content to be taught and the available AR teaching tools, the generation model generates AR teaching materials that meet the learning objectives of the students and are used for AR teaching with the available AR teaching tools.
[0089] For example, the method further includes:
[0090] Identify the types of learning resources to be displayed on available AR teaching tools, including 3D models, videos, images, and games;
[0091] Based on the information of the content to be taught, the available AR teaching tools, and the types of learning resources to be displayed, AR teaching materials are generated that meet the learning objectives of the students, use the available AR teaching tools, and conduct AR teaching through the types of learning resources to be displayed.
[0092] In one embodiment, in addition to generating AR teaching materials that meet the learning objectives of the students and are used for AR teaching based on the teaching content information and available AR teaching tools, it is also possible to further determine the types of learning resources to be displayed on the available AR teaching tools. Then, based on the teaching content information, available AR teaching tools, and types of learning resources to be displayed, AR teaching materials that meet the learning objectives of the students, are used with available AR teaching tools, and are used for AR teaching through the types of learning resources to be displayed are generated, thereby generating AR teaching materials that are more accurate and aligned with the teaching tasks and objectives.
[0093] AR teaching can be combined with various types of learning resources to improve student learning outcomes and engagement. Here are some examples of learning resource types that can be showcased on usable AR teaching tools: 1. 3D Models: AR technology can overlay 3D models onto the real-world environment, allowing students to gain a deeper understanding of the structure, appearance, and function of objects. For example, students can explore complex structures such as human organs, astrophysical objects, or mechanical parts through AR applications. 2. Videos and Animations: AR technology can embed videos and animations into the real-world environment to create interactive learning experiences. These videos and animations can be used to demonstrate experimental processes, explain abstract concepts, or recount historical events. 3. Virtual Laboratories: AR technology can create virtual laboratories, allowing students to conduct experiments safely while gaining experiences similar to real experiments. For example, in chemistry classes, students can use AR applications to interact with molecules, look up elements in the periodic table, or simulate chemical reactions. 4. Interactive Games: AR technology can create educational games that stimulate student interest and engagement. For example, students can use AR applications for language learning, geography quizzes, or historical event simulations. 5. Virtual Tours: AR technology can create virtual tours, allowing students to explore geography, culture, and natural landscapes in the classroom. For example, in geography or history classes, students can use AR applications for city tours, national cultural tours, or historical scene simulations. 6. Other Resources: AR technology can also be combined with other types of learning resources, such as audio, images, and comics, to create more diverse learning experiences. These resources can help students better understand and remember subject content.
[0094] For example, the method further includes:
[0095] Identify interactive elements, wherein the interactive elements include visual interaction, gesture control, sound interaction, and tactile feedback;
[0096] Based on the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and interactive elements, generate AR teaching materials that meet the learning objectives of the students, use available AR teaching tools, utilize the types of learning resources to be displayed, and conduct AR teaching with interactive elements.
[0097] In one embodiment, in addition to generating AR teaching materials that meet the learning objectives of the learners and are used for AR teaching based on the teaching content information and available AR teaching tools, and in addition to generating AR teaching materials that meet the learning objectives of the learners, are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools and are used for AR teaching based on available AR teaching tools, ...
[0098] The interactive elements include: 1. Visual Interaction: AR technology provides a novel visual experience, allowing students to interact with the real environment through AR content. For example, in AR applications, students can observe, explore, and manipulate 3D models, virtual laboratories, or historical scenes in a virtual world, interacting with the real environment. 2. Gesture Control: AR applications can utilize gesture recognition technology, allowing students to control the behavior and performance of AR content through gestures. For example, in AR games, students can control the movement and attacks of characters through gestures, or control the movement and rotation of experimental equipment in a virtual laboratory through gestures. 3. Voice Interaction: AR technology supports voice recognition, allowing students to interact with AR content through voice. For example, in AR applications, students can control the behavior and performance of AR content through voice commands, or obtain relevant information and answers through voice interaction. 4. Tactile Feedback: AR technology can also provide tactile feedback, allowing students to feel the texture, shape, and movement of virtual objects. For example, in AR applications, students can touch the screen to feel the surface texture and shape of 3D models, or use gesture controllers to feel the smell and temperature of a virtual laboratory.
[0099] For example, the method further includes:
[0100] Before using the generated AR teaching materials for teaching, the students to be taught are given a knowledge test on the content to be taught and receive the first score.
[0101] After teaching using the generated AR teaching materials, the students to be taught are given a knowledge test on the content to be taught, and a second score is obtained.
[0102] The generative model used to generate AR teaching materials is adjusted based on the first and second scores.
[0103] To ensure the effectiveness of AR teaching materials, the students' knowledge acquisition before and after using AR teaching can be tested. The results of the two tests can be compared to evaluate the effectiveness of AR teaching. The generation model of the generated AR teaching materials can then be adjusted based on the effectiveness of AR teaching.
[0104] When testing students' knowledge of the content to be taught, the following methods can be used, including but not limited to: 1. Multiple-choice questions: Test students' knowledge by adding multiple-choice questions to the AR scene. After viewing AR content, students can answer multiple-choice questions related to that content. Multiple-choice questions can be displayed directly on the screen or presented as virtual objects. 2. Fill-in-the-blank questions: AR applications can provide fill-in-the-blank questions in the AR scene, requiring students to fill in the correct answers in designated places. Students can complete fill-in-the-blank questions by entering answers on the screen or dragging and dropping the correct words or phrases into virtual spaces. 3. Interactive tasks: Design interactive tasks that require active student participation to test their knowledge. For example, in a virtual laboratory, students can perform experiments based on their learned knowledge, complete specific tasks, and observe the results to answer related questions. 4. Scenario simulation: Create scenario simulations in the AR scene, requiring students to make decisions or solve problems based on their learned knowledge. Students can interact with virtual characters, make choices, observe the results, and answer questions. 5. Virtual exams: Design a complete virtual exam containing multiple types of questions, such as multiple-choice, true / false, and fill-in-the-blank questions. Students can complete exams in an AR environment and assess their learning outcomes based on their scores.
[0105] Next, the generation model used to generate the AR teaching materials is adjusted based on the first score obtained by the student on a knowledge test of the content to be taught before teaching using the generated AR teaching materials, and the second score obtained by the student on a knowledge test of the content to be taught after teaching using the generated AR teaching materials.
[0106] For example, the step of adjusting the generative model used to generate AR teaching materials based on the first score and the second score includes:
[0107] If the difference between the second score and the first score is greater than a preset difference threshold, then the dataset used to generate AR teaching materials is collected again, and / or the generative model architecture and hyperparameters are adjusted, and / or the data processing method of the generative model is increased, and / or the regularization method of the generative model is increased, and / or the ensemble method of the generative model is increased, and the generative model is retrained and validated until a new generative model is obtained in which the difference between the second score and the first score is less than or equal to the preset difference threshold.
[0108] When adjusting the generative model used to generate AR teaching materials based on the first and second scores, if the difference between the second score and the first score is greater than a preset difference threshold, the dataset used to generate AR teaching materials is collected again. For example, new teaching materials may be added, scene settings may be improved, and / or the generative model architecture and hyperparameters may be adjusted, such as adding or deleting network hierarchies, modifying activation functions, optimizing loss functions, and / or data processing methods for the generative model may be added, such as data augmentation techniques, denoising techniques, etc., to improve data quality and model generalization ability, and / or regularization methods for the generative model may be added, such as L1, L2 regularization, Dropout, etc., to prevent overfitting and improve model generalization ability, and / or ensemble methods for the generative model may be added, such as model fusion techniques, transfer learning, etc., to improve model performance and generalization ability, and the generative model is retrained and validated, such as through cross-validation, to evaluate the model's performance and generalization ability, until a new generative model is obtained where the difference between the second score and the first score is less than or equal to the preset difference threshold.
[0109] For example, the step of adjusting the generative model used to generate AR teaching materials based on the first score and the second score includes:
[0110] Based on the difference between the second score and the first score, adjust the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements in the generative model.
[0111] When adjusting the generation model used to generate AR teaching materials based on the first score and the second score, the generation parameters of the teaching content information, available AR teaching tools, types of learning resources to be displayed, and interactive elements in the generation model can also be adjusted based on the difference between the second score and the first score.
[0112] For example, the step of adjusting the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements of the generation model based on the difference between the second score and the first score includes:
[0113] If the difference between the second score and the first score is in the fourth interval, then adjust the relevant generation parameters of one of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the type of learning resources to be displayed, and the interactive elements.
[0114] If the difference between the second score and the first score is in the third interval, then adjust the relevant generation parameters of two of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements.
[0115] If the difference between the second score and the first score is in the second interval, then adjust the relevant generation parameters of three items in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements.
[0116] If the difference between the second score and the first score is within the first interval, then adjust the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements in the generation model.
[0117] Among them, the second score is greater than or equal to the first score, the upper limit difference of the first interval is less than or equal to the lower limit difference of the second interval, the upper limit difference of the second interval is less than or equal to the lower limit difference of the third interval, and the upper limit difference of the third interval is less than or equal to the lower limit difference of the fourth interval.
[0118] If the difference between the second score and the first score is in the fourth interval, for example, the fourth interval is [45, 60], it indicates that the student's knowledge test results have improved significantly after learning through the AR teaching materials compared to before learning. This further demonstrates that the AR teaching materials generated by the current generative model are effective enough and have a significant effect on improving the student's learning outcomes. Therefore, at this point, it is only necessary to adjust the relevant generation parameters of one of the following in the generative model: the information of the content to be taught, the available AR teaching tools, the type of learning resources to be displayed, or the interactive elements. Adjusting the relevant generation parameters of the information of the content to be taught, the available AR teaching tools, the type of learning resources to be displayed, or the interactive elements does not require major adjustments to the generative model. Only minor adjustments can further improve the teaching effectiveness of the AR teaching materials generated by the generative model.
[0119] If the difference between the second score and the first score is in the third interval, such as the fourth interval [30, 45), it indicates that the learner's knowledge test results have generally improved after learning through AR teaching materials compared to before learning AR teaching materials. This further indicates that the AR teaching materials generated by the current generative model are generally effective. Therefore, at this time, it is necessary to adjust the relevant generation parameters of two of the following in the generative model: the information of the content to be taught, the available AR teaching tools, the type of learning resources to be displayed, and the interactive elements. Only a major adjustment is needed to the generative model to further improve the teaching effect of the AR teaching materials generated by the generative model.
[0120] If the difference between the second score and the first score is within the second interval (e.g., the fourth interval is [10, 15)), it indicates that the student's knowledge test results have slightly improved after learning through AR teaching materials compared to before learning AR teaching materials. This further suggests that the AR teaching materials generated by the current generative model are unlikely to significantly improve the student's learning outcomes. Therefore, it is necessary to adjust the relevant generation parameters of three items in the generative model: the information on the content to be taught, the available AR teaching tools, the type of learning resources to be displayed, and the interactive elements. This would require a significant adjustment to the generative model to further improve the teaching effectiveness of the AR teaching materials generated by the generative model.
[0121] If the difference between the second score and the first score is within the second interval (e.g., the fourth interval is [15, 30)), it indicates that the student's knowledge test score after learning through AR teaching materials is almost unchanged compared to before learning AR teaching materials. This further indicates that the AR teaching materials generated by the current generation model have almost no effect on the student's learning effect. Therefore, it is necessary to adjust the information of the teaching content, the available AR teaching tools, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements in the generation model to make significant adjustments to the generation model in order to further improve the teaching effect of the AR teaching materials generated by the generation model.
[0122] Therefore, by adjusting the generation parameters of the teaching content information, available AR teaching tools, types of learning resources to be displayed, and interactive elements in the generation model through the difference between the second score and the first score, the teaching effect of the AR teaching materials generated by the generation model can be further improved. This further avoids the generation of inefficient and useless AR teaching materials, which would lead to low efficiency in generating effective AR teaching materials, and improves the generation efficiency of effective AR teaching materials.
[0123] Reference Figure 3 Furthermore, embodiments of this application also provide an AR teaching material generation device, the AR teaching material generation device comprising:
[0124] The acquisition module M1 is used to acquire information about the teaching content, wherein the teaching content includes a description of the learning objectives to be achieved and / or the knowledge and skills to be mastered by the students.
[0125] Module M2 is used to determine the available AR teaching tools;
[0126] The generation module M3 is used to generate AR teaching materials that meet the learning objectives of the students and can be used for AR teaching based on the information of the content to be taught and the available AR teaching tools.
[0127] For example, the AR teaching material generation device further includes an extension module for:
[0128] Identify the types of learning resources to be displayed on available AR teaching tools, including 3D models, videos, images, and games;
[0129] Based on the information of the content to be taught, the available AR teaching tools, and the types of learning resources to be displayed, AR teaching materials are generated that meet the learning objectives of the students, use the available AR teaching tools, and conduct AR teaching through the types of learning resources to be displayed.
[0130] For example, the extension module is also used for:
[0131] Identify interactive elements, wherein the interactive elements include visual interaction, gesture control, sound interaction, and tactile feedback;
[0132] Based on the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and interactive elements, generate AR teaching materials that meet the learning objectives of the students, use available AR teaching tools, utilize the types of learning resources to be displayed, and conduct AR teaching with interactive elements.
[0133] For example, the AR teaching material generation device further includes an adjustment module for:
[0134] Before using the generated AR teaching materials for teaching, the students to be taught are given a knowledge test on the content to be taught and receive the first score.
[0135] After teaching using the generated AR teaching materials, the students to be taught are given a knowledge test on the content to be taught, and a second score is obtained.
[0136] The generative model used to generate AR teaching materials is adjusted based on the first and second scores.
[0137] For example, the adjustment module is further configured to:
[0138] If the difference between the second score and the first score is greater than a preset difference threshold, then the dataset used to generate AR teaching materials is collected again, and / or the generative model architecture and hyperparameters are adjusted, and / or the data processing method of the generative model is increased, and / or the regularization method of the generative model is increased, and / or the ensemble method of the generative model is increased, and the generative model is retrained and validated until a new generative model is obtained in which the difference between the second score and the first score is less than or equal to the preset difference threshold.
[0139] For example, the adjustment module is further configured to:
[0140] Based on the difference between the second score and the first score, adjust the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements in the generative model.
[0141] For example, the adjustment module is also used for
[0142] If the difference between the second score and the first score is in the fourth interval, then adjust the relevant generation parameters of one of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the type of learning resources to be displayed, and the interactive elements.
[0143] If the difference between the second score and the first score is in the third interval, then adjust the relevant generation parameters of two of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements.
[0144] If the difference between the second score and the first score is in the second interval, then adjust the relevant generation parameters of three items in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements.
[0145] If the difference between the second score and the first score is within the first interval, then adjust the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements in the generation model.
[0146] Among them, the second score is greater than or equal to the first score, the upper limit difference of the first interval is less than or equal to the lower limit difference of the second interval, the upper limit difference of the second interval is less than or equal to the lower limit difference of the third interval, and the upper limit difference of the third interval is less than or equal to the lower limit difference of the fourth interval.
[0147] The AR teaching material generation apparatus provided in this application adopts the AR teaching material generation method in the above embodiments, aiming to improve the generation efficiency of AR teaching materials. Compared with conventional technology, the beneficial effects of the AR teaching material generation apparatus provided in this application are the same as the beneficial effects of the AR teaching material generation method provided in the above embodiments, and other technical features in the AR teaching material generation apparatus are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.
[0148] Furthermore, this application embodiment also provides an AR teaching material generation device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the AR teaching material generation method described above.
[0149] Furthermore, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the AR teaching material generation method described above.
[0150] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0151] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to conventional technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0152] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.
Claims
1. A method for generating an AR teaching material, characterized in that, The method includes: Obtain information on the teaching content to be taught, wherein the teaching content includes a description of the learning objectives to be achieved and / or the knowledge and skills to be mastered by the students to be taught; Identify usable AR teaching tools; Based on the information of the content to be taught and the available AR teaching tools, generate AR teaching materials that meet the learning objectives of the students and are used for AR teaching with available AR teaching tools. The method further includes: Before using the generated AR teaching materials for teaching, the students to be taught are given a knowledge test on the content to be taught and receive the first score. After teaching using the generated AR teaching materials, the students to be taught are given a knowledge test on the content to be taught, and a second score is obtained. If the difference between the second score and the first score is greater than a preset difference threshold, then the dataset used to generate AR teaching materials is collected again, and / or the generative model architecture and hyperparameters are adjusted, and / or the data processing method of the generative model is increased, and / or the regularization method of the generative model is increased, and / or the ensemble method of the generative model is increased, and the generative model is retrained and validated until a new generative model is obtained where the difference between the second score and the first score is less than or equal to the preset difference threshold; The generative model is used to generate AR teaching materials; If the difference between the second score and the first score is in the fourth interval, then adjust the relevant generation parameters of one of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the type of learning resources to be displayed, and the interactive elements. If the difference between the second score and the first score is in the third interval, then adjust the relevant generation parameters of two of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements. If the difference between the second score and the first score is in the second interval, then adjust the relevant generation parameters of three items in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements. If the difference between the second score and the first score is within the first interval, then adjust the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements in the generation model. Among them, the second score is greater than or equal to the first score, the upper limit difference of the first interval is less than or equal to the lower limit difference of the second interval, the upper limit difference of the second interval is less than or equal to the lower limit difference of the third interval, and the upper limit difference of the third interval is less than or equal to the lower limit difference of the fourth interval.
2. The method for generating AR teaching materials as described in claim 1, characterized in that, The method further includes: Identify the types of learning resources to be displayed on available AR teaching tools, including 3D models, videos, images, and games; Based on the information of the content to be taught, the available AR teaching tools, and the types of learning resources to be displayed, AR teaching materials are generated that meet the learning objectives of the students, use the available AR teaching tools, and conduct AR teaching through the types of learning resources to be displayed.
3. The method of claim 2, wherein the AR teaching material is generated by a server. The method further includes: Identify interactive elements, wherein the interactive elements include visual interaction, gesture control, sound interaction, and tactile feedback; Based on the information of the content to be taught, the available AR teaching tools, the types of learning resources to be displayed, and interactive elements, generate AR teaching materials that meet the learning objectives of the students, use available AR teaching tools, utilize the types of learning resources to be displayed, and conduct AR teaching with interactive elements.
4. An AR teaching material generation device, characterized by, The device for generating AR teaching materials includes: The acquisition module is used to acquire information about the teaching content, wherein the teaching content includes a description of the learning objectives to be achieved and / or the knowledge and skills to be mastered by the students. The module is used to determine the AR teaching tools that can be used; The generation module is used to generate AR teaching materials that meet the learning objectives of the students and can be used for AR teaching based on the information of the content to be taught and the available AR teaching tools. The AR teaching material generation device also includes an adjustment module, used for: Before using the generated AR teaching materials for teaching, the students to be taught are given a knowledge test on the content to be taught and receive the first score. After teaching using the generated AR teaching materials, the students to be taught are given a knowledge test on the content to be taught, and a second score is obtained. If the difference between the second score and the first score is greater than a preset difference threshold, then the dataset used to generate AR teaching materials is collected again, and / or the generative model architecture and hyperparameters are adjusted, and / or the data processing method of the generative model is increased, and / or the regularization method of the generative model is increased, and / or the ensemble method of the generative model is increased, and the generative model is retrained and validated until a new generative model is obtained where the difference between the second score and the first score is less than or equal to the preset difference threshold; The generative model is used to generate AR teaching materials; If the difference between the second score and the first score is in the fourth interval, then adjust the relevant generation parameters of one of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the type of learning resources to be displayed, and the interactive elements. If the difference between the second score and the first score is in the third interval, then adjust the relevant generation parameters of two of the following in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements. If the difference between the second score and the first score is in the second interval, then adjust the relevant generation parameters of three items in the generative model: the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the interactive elements. If the difference between the second score and the first score is within the first interval, then adjust the information of the content to be taught, the AR teaching tools that can be used, the types of learning resources to be displayed, and the relevant generation parameters in the interactive elements in the generation model. Among them, the second score is greater than or equal to the first score, the upper limit difference of the first interval is less than or equal to the lower limit difference of the second interval, the upper limit difference of the second interval is less than or equal to the lower limit difference of the third interval, and the upper limit difference of the third interval is less than or equal to the lower limit difference of the fourth interval.
5. An AR teaching material generation device, characterized by, The AR teaching material generation device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the AR teaching material generation method as described in any one of claims 1 to 3.
6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for generating AR teaching materials as described in any one of claims 1 to 3.