Big data fusion type intelligent teaching method based on VR and holographic projection

Abstract

The invention discloses a big data fusion type intelligent teaching method based on VR and holographic projection. The method comprises the following steps: S1, constructing a VR and holographic projection teaching environment; s2, collecting and preprocessing teaching behavior data; s3, building a big data analysis platform; s4, personalized teaching content is generated; s5, intelligent teaching interaction is implemented; s6, teaching effect evaluation and feedback are carried out; according to the invention, through combination of VR and holographic projection technology, teaching means are enriched, accurate acquisition and analysis of teaching data are realized, and a scientific basis is provided for personalized teaching; in addition, through implementation of intelligent teaching interaction, the learning interest and participation degree of students are effectively improved, and comprehensive improvement of teaching quality is promoted. By collecting and analyzing the learning behavior data of the students, a teacher can customize personalized teaching contents and interaction forms to meet individual requirements of the students, and personalized teaching is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of teaching systems, and specifically relates to a big data fusion intelligent teaching method based on VR and holographic projection. Background Art

[0002] The current education field is undergoing a transformation from a traditional education model to an intelligent and personalized education model. However, although the existing teaching technology has partially introduced advanced technologies such as VR and holographic projection, it still has deficiencies in data fusion and implementation of personalized teaching strategies, and has failed to fully tap the full potential of the technology. Therefore, an innovative teaching method is provided to achieve deep customization and efficient delivery of teaching content through precise data collection and analysis combined with VR and holographic projection technology. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a big data fusion intelligent teaching method based on VR and holographic projection, which effectively solves the problems raised by the background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a big data fusion intelligent teaching method based on VR and holographic projection, comprising the following steps:

[0005] Step S1, constructing a VR and holographic projection teaching environment;

[0006] Step S2, teaching behavior data collection and preprocessing;

[0007] Step S3: Building a big data analysis platform;

[0008] Step S4, generating personalized teaching content;

[0009] Step S5, implementing smart teaching interaction;

[0010] Step S6: Teaching effect evaluation and feedback.

[0011] Preferably, the step S1, constructing a VR and holographic projection teaching environment, comprises the following steps:

[0012] S1.1. Use VR technology to build a virtual classroom, simulate real teaching scenarios, and ensure that students get a similar experience in the virtual environment as in the real classroom;

[0013] S1.2. Introduce holographic projection technology to display 3D models, animations, and video teaching resources in holographic form to enhance visual impact and improve learning interest;

[0014] S1.3. Design an interactive interface that allows students to freely explore and operate in the VR environment and interact directly with virtual objects.

[0015] Preferably, the step S2, collecting and preprocessing teaching behavior data, comprises the following steps:

[0016] S2.1. Deploy a sensor network, including but not limited to motion capture devices, eye trackers, and microphone arrays, to record students’ learning behaviors in the VR environment in real time;

[0017] S2.2. Collect students’ learning progress data, including but not limited to course completion, correct answer rate, number of review times, and real-time feedback data from teachers;

[0018] S2.3. Clean, denoise and standardize the collected data to ensure data quality and provide a reliable basis for subsequent analysis.

[0019] Preferably, the step S3, building a big data analysis platform, includes the following steps:

[0020] S3.1. Build a big data analysis platform that integrates data processing, storage, and analysis functions to support rapid processing and deep mining of large-scale data;

[0021] S3.2, design an algorithm model to analyze the pre-processed teaching behavior data and identify students’ learning patterns, interest preferences, and key information of difficulties;

[0022] S3.3. Use machine learning technology to continuously optimize algorithm models and improve the accuracy and efficiency of data analysis.

[0023] Preferably, the step S4, generating personalized teaching content, comprises the following steps:

[0024] S4.1. Generate a personalized learning plan for each student based on the results of big data analysis, including but not limited to recommended learning resources, learning paths, and practice questions;

[0025] S4.2. Use holographic projection technology to present personalized teaching content in a vivid and intuitive way to enhance the learning experience;

[0026] S4.3. Adjust teaching content and difficulty in real time, and dynamically optimize learning plans based on students' learning progress and feedback.

[0027] Preferably, the step S5, implementing the smart teaching interaction, includes the following steps:

[0028] S5.1. Set up virtual teaching assistants in the VR environment to interact with students through natural language processing technology, answer questions and provide instant feedback;

[0029] S5.2. Organize virtual group discussions and role-playing activities, and use VR immersion to promote communication and collaboration among students;

[0030] S5.3. Introduce gamification learning elements to encourage students to actively participate in learning and improve their learning motivation.

[0031] Preferably, the step S6, teaching effect evaluation and feedback, comprises the following steps:

[0032] S6.1. Regularly collect students’ learning outcomes data and conduct teaching effectiveness evaluation;

[0033] S6.2. Compare student performance under different teaching strategies through big data analysis and evaluate the effectiveness of teaching methods;

[0034] S6.3. Based on the evaluation results, provide teachers with suggestions for teaching improvement, and provide students with feedback on their learning outcomes to guide their subsequent learning direction.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The present invention not only enriches teaching methods through the combination of VR and holographic projection technology, but also realizes the accurate collection and analysis of teaching data, providing a scientific basis for personalized teaching; in addition, through the implementation of intelligent teaching interaction, it effectively improves students' learning interest and participation, and promotes the overall improvement of teaching quality;

[0037] 2. By collecting and analyzing students' learning behavior data, teachers can customize personalized teaching content and interactive forms to meet students' individual needs, thus achieving personalized teaching;

[0038] 3. Through the analysis and mining of linked and interoperable teaching data chains, accurate teaching evaluation results can be obtained, which provides strong support for teaching improvement and improves teaching efficiency and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0040] In the attached picture:

[0041] Figure 1 It is a flow chart of the big data fusion intelligent teaching method based on VR and holographic projection of the present invention; DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0043] Embodiment 1, by Figure 1 The present invention relates to a big data fusion intelligent teaching method based on VR and holographic projection, comprising the following steps:

[0044] Step S1, constructing a VR and holographic projection teaching environment;

[0045] Step S2, teaching behavior data collection and preprocessing;

[0046] Step S3: Building a big data analysis platform;

[0047] Step S4, generating personalized teaching content;

[0048] Step S5, implementing smart teaching interaction;

[0049] Step S6: Teaching effect evaluation and feedback.

[0050] Step S1 of this embodiment, constructing a VR and holographic projection teaching environment, includes the following steps:

[0051] S1.1. Use VR technology to build a virtual classroom and simulate real teaching scenes, including classroom layout, teaching equipment, student seats, etc., to ensure that students can get a similar experience in the virtual environment as in the real classroom;

[0052] S1.2. Introduce holographic projection technology to display 3D models, animations, and video teaching resources in holographic form to enhance visual impact and improve learning interest;

[0053] S1.3. Design an interactive interface that allows students to freely explore and operate in the VR environment and interact directly with virtual objects.

[0054] Step S2 of this embodiment, teaching behavior data collection and preprocessing, includes the following steps:

[0055] S2.1. Deploy a sensor network, including but not limited to motion capture devices, eye trackers, and microphone arrays, to record students’ learning behaviors in the VR environment in real time, such as head rotation, eye movement, and voice communication;

[0056] S2.2. Collect students’ learning progress data, including but not limited to course completion, correct answer rate, number of review times, and real-time feedback data from teachers;

[0057] S2.3. Clean, denoise and standardize the collected data to ensure data quality and provide a reliable basis for subsequent analysis.

[0058] Step S3 of this embodiment, building a big data analysis platform, includes the following steps:

[0059] S3.1. Build a big data analysis platform that integrates data processing, storage, and analysis functions to support rapid processing and deep mining of large-scale data;

[0060] S3.2, design an algorithm model to analyze the pre-processed teaching behavior data and identify students’ learning patterns, interest preferences, and key information of difficulties;

[0061] S3.3. Use machine learning technology to continuously optimize algorithm models and improve the accuracy and efficiency of data analysis.

[0062] Step S4 of this embodiment, generating personalized teaching content, includes the following steps:

[0063] S4.1. Generate a personalized learning plan for each student based on the results of big data analysis, including but not limited to recommended learning resources, learning paths, and practice questions;

[0064] S4.2. Use holographic projection technology to present personalized teaching content in a vivid and intuitive way to enhance the learning experience;

[0065] S4.3. Adjust teaching content and difficulty in real time, and dynamically optimize learning plans based on students' learning progress and feedback.

[0066] Step S5 of this embodiment, smart teaching interaction implementation, includes the following steps:

[0067] S5.1. Set up virtual teaching assistants in the VR environment to interact with students through natural language processing technology, answer questions and provide instant feedback;

[0068] S5.2. Organize virtual group discussions and role-playing activities, and use VR immersion to promote communication and collaboration among students;

[0069] S5.3. Introduce gamification learning elements, such as point rewards and leaderboards, to encourage students to actively participate in learning and improve their learning motivation.

[0070] Step S6 of this embodiment, teaching effect evaluation and feedback, includes the following steps:

[0071] S6.1. Regularly collect students’ learning outcomes data, such as test scores, work presentations, project reports, etc., to evaluate teaching effectiveness;

[0072] S6.2. Compare student performance under different teaching strategies through big data analysis and evaluate the effectiveness of teaching methods;

[0073] S6.3. Based on the evaluation results, provide teachers with suggestions for teaching improvement, and provide students with feedback on their learning outcomes to guide their subsequent learning direction.

[0074] 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.

[0075] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The big data fusion intelligent teaching method based on VR and holographic projection includes the following steps: Step S1, constructing a VR and holographic projection teaching environment; Step S2, teaching behavior data collection and preprocessing; Step S3: Building a big data analysis platform; Step S4, generating personalized teaching content; Step S5, implementing smart teaching interaction; Step S6: Teaching effect evaluation and feedback.

2. The big data fusion intelligent teaching method based on VR and holographic projection according to claim 1 is characterized by: The step S1, constructing a VR and holographic projection teaching environment, comprises the following steps: S1.

1. Use VR technology to build a virtual classroom, simulate real teaching scenarios, and ensure that students get a similar experience in the virtual environment as in the real classroom; S1.

2. Introduce holographic projection technology to display 3D models, animations, and video teaching resources in holographic form to enhance visual impact and improve learning interest; S1.

3. Design an interactive interface that allows students to freely explore and operate in the VR environment and interact directly with virtual objects.

3. The big data fusion intelligent teaching method based on VR and holographic projection according to claim 1 is characterized by: The step S2, teaching behavior data collection and preprocessing, includes the following steps: S2.

1. Deploy a sensor network, including but not limited to motion capture devices, eye trackers, and microphone arrays, to record students’ learning behaviors in the VR environment in real time; S2.

2. Collect students’ learning progress data, including but not limited to course completion, correct answer rate, number of review times, and real-time feedback data from teachers; S2.

3. Clean, denoise and standardize the collected data to ensure data quality and provide a reliable basis for subsequent analysis.

4. The big data fusion intelligent teaching method based on VR and holographic projection according to claim 1 is characterized by: The step S3, building a big data analysis platform, includes the following steps: S3.

1. Build a big data analysis platform that integrates data processing, storage, and analysis functions to support rapid processing and deep mining of large-scale data; S3.2, design an algorithm model to analyze the pre-processed teaching behavior data and identify students’ learning patterns, interest preferences, and key information of difficulties; S3.

3. Use machine learning technology to continuously optimize algorithm models and improve the accuracy and efficiency of data analysis.

5. The big data fusion intelligent teaching method based on VR and holographic projection according to claim 1 is characterized by: The step S4, generating personalized teaching content, comprises the following steps: S4.

1. Generate a personalized learning plan for each student based on the results of big data analysis, including but not limited to recommended learning resources, learning paths, and practice questions; S4.

2. Use holographic projection technology to present personalized teaching content in a vivid and intuitive way to enhance the learning experience; S4.

3. Adjust teaching content and difficulty in real time, and dynamically optimize learning plans based on students' learning progress and feedback.

6. The big data fusion intelligent teaching method based on VR and holographic projection according to claim 1 is characterized by: The step S5, smart teaching interactive implementation, includes the following steps: S5.

1. Set up virtual teaching assistants in the VR environment to interact with students through natural language processing technology, answer questions and provide instant feedback; S5.

2. Organize virtual group discussions and role-playing activities, and use VR immersion to promote communication and collaboration among students; S5.

3. Introduce gamification learning elements to encourage students to actively participate in learning and improve their learning motivation.

7. The big data fusion intelligent teaching method based on VR and holographic projection according to claim 1 is characterized by: The step S6, teaching effect evaluation and feedback, comprises the following steps: S6.

1. Regularly collect students’ learning outcomes data and conduct teaching effectiveness evaluation; S6.

2. Compare student performance under different teaching strategies through big data analysis and evaluate the effectiveness of teaching methods; S6.

3. Provide suggestions for improving teaching based on the evaluation results to teachers, and provide feedback on learning outcomes to students to guide their subsequent learning direction.

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