Teaching method and system based on virtual reality
By carefully analyzing and evaluating students' experimental teaching data, the problem of teachers' difficulty in comprehensively analyzing students' status is solved, and more effective teaching plans and students' learning effects are achieved.
Patent Information
- Application Number
- CN202411672877.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-21
- Publication Date
- 2025-03-14
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In the prior art, it is difficult for teachers to conduct detailed analysis of each student's behavioral data, which leads to insufficient analysis of student status and difficulty in improving teaching plans.
By obtaining the target experimental teaching data and practical training data, decompose it into multiple data subsets, analyzing the duration and qualification of each student's experimental steps, extracting focus feature data, using the pre-trained focus analysis model to generate focus analysis results, sending reminders to students who are not focused and setting teaching courses and training courses.
A comprehensive analysis of student status has been achieved, teaching effectiveness has been improved, and teachers have provided a theoretical basis for improving teaching plans.
Smart Images
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Figure HDA0005146321890000012
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of intelligent teaching, and in particular to a teaching method and system based on virtual reality. Background Art
[0002] With the rapid development of information technology, the traditional teaching model no longer meets the new teaching needs. The traditional teaching model is limited by time and space, and new technologies need to be introduced into the teaching field. Virtual reality technology directly exerts visual, auditory and tactile sensations on participants through computer generation. Applying virtual reality technology to the teaching field can allow students to have an immersive learning experience, better guide students to actively learn knowledge, and enhance teaching effectiveness.
[0003] In the process of teaching based on virtual reality technology, such as experimental teaching, a large amount of students' behavioral data can be recorded. In the existing technology, teachers mostly adjust their teaching plans based on independent analysis of part of the students' behavioral data. In this case, it is difficult for teachers to conduct a detailed analysis of each student's behavioral data. Therefore, the analysis of students' status is not comprehensive enough, and it is difficult to further improve the teaching plan. Summary of the invention
[0004] The present invention provides a teaching method and system based on virtual reality, aiming to solve the technical problem in the prior art that it is difficult for teachers to conduct detailed analysis of the behavior data of each student, so the analysis of the student status is not comprehensive enough and it is difficult to further improve the teaching plan.
[0005] As one aspect of the present invention, a teaching method based on virtual reality is provided, comprising:
[0006] Acquire target experiment teaching data and target experiment training data, wherein the target experiment training data is operation data of a plurality of students on a target experiment in the same time period, and the target experiment includes a plurality of experimental steps; divide the target experiment training data into a plurality of data subsets according to individual students;
[0007] For any data subset, determine the completion time of each experimental step of the target student, and determine the qualification of each experimental step of the target student in the practical training process of the target experiment based on the standard process of the target experiment and the completion time;
[0008] For any unqualified experimental step of the target student, record it as a target experimental step, extract the focus feature data of the target student during the teaching of the target experimental step from the target experimental teaching data, the focus feature data includes the facial expression feature, posture feature and eye feature of the target student, input the focus feature data into the pre-trained focus analysis model, and generate a focus analysis result;
[0009] Determine whether the target student does not meet the preset concentration standard during the teaching of the target experimental step according to the concentration analysis result. If so, send a concentration reminder to the target student and set a teaching course and training course for the target experimental step for the target student.
[0010] Furthermore, it also includes:
[0011] In the process of determining whether the target student does not meet the preset concentration standard during the teaching of the target experimental step according to the concentration analysis result, if the target student meets the preset concentration standard, determining the qualification of the target experimental step as unqualified students during the practical training of the target experiment, and determining the number of students who meet the preset concentration standard among the multiple unqualified students;
[0012] If the number of students who meet the preset focus standards is greater than the preset number, the target experimental step will be marked as a key experimental step; if the number of students who meet the preset focus standards is not greater than the preset number, teaching courses and training courses for the target experimental step will be set for each student who meets the preset focus standards.
[0013] Furthermore, it also includes:
[0014] In the process of determining whether the target student does not meet the preset concentration standard during the teaching period of the target experimental step according to the concentration analysis result, if the target student does not meet the preset concentration standard, obtaining the concentration feature data of each student during the teaching period of the target experimental step, performing concentration analysis on multiple students through the concentration analysis model, and generating the concentration analysis result of each student;
[0015] Counting the number of students who do not meet the preset focus standard, and for multiple students who do not meet the preset focus standard, counting the number of students who are unqualified in the target experimental step, and calculating the proportion of unqualified students;
[0016] If the proportion of unqualified students is greater than a first preset proportion, the target experimental step is marked as an experimental step to be improved.
[0017] Furthermore, for multiple data subsets, it also includes:
[0018] For the multiple target experimental steps in the multiple data subsets, multiple step sets are constructed with each different target experimental step as the core;
[0019] For any step set, students who have the target experimental steps corresponding to the step set are screened out, multiple incorrect operations performed by each student in the process of executing the target experimental steps are extracted according to the target experimental training data of each student, the number of students who have the same incorrect operation for each incorrect operation is determined, incorrect operations whose number of students is greater than a preset number are screened out, and an incorrect operation subset corresponding to the step set is constructed;
[0020] Traverse all step sets and determine the error subset corresponding to each step set;
[0021] Determine the first teaching content of each step set according to the target experimental steps corresponding to each step set, determine the second teaching content of each step set according to the error subset corresponding to each step set, associate the first teaching content and the second teaching content belonging to the same step set, and obtain the target teaching content corresponding to each step set;
[0022] Supplementary teaching is provided for the target experiment based on a plurality of the target teaching contents.
[0023] Furthermore, the calculated percentage of unqualified students includes:
[0024] The proportion of unqualified students is calculated by dividing the number of students who failed the target experimental step among the students who did not meet the preset focus standards by the total number of students.
[0025] As another aspect of the present invention, a teaching system based on virtual reality is provided, wherein the system is applied to any one of the teaching methods based on virtual reality described above, and comprises:
[0026] A data acquisition module is used to acquire target experiment teaching data and target experiment training data, wherein the target experiment training data is operation data of multiple students on the target experiment in the same time period, and the target experiment includes multiple experimental steps;
[0027] A data partitioning module is used to divide the target experimental training data into multiple data subsets according to individual students;
[0028] A qualification analysis module is used to determine the qualification of each experimental step of the target student during the practical training process of the target experiment;
[0029] A focus analysis module is used to analyze the focus characteristic data of target students through a focus analysis model and generate focus analysis results;
[0030] A focus reminder module is used to send focus reminders to target students;
[0031] The course setting module is used to set teaching courses and training courses of the target experimental steps for target students.
[0032] Furthermore, it also includes:
[0033] The experimental step marking module is used to mark the experimental steps, including marking the target experimental steps as key experimental steps and / or experimental steps to be improved.
[0034] The advantages of the present invention are as follows:
[0035] 1. The present invention collects and analyzes target experiment teaching data and target experiment training data, determines the target experiment steps for each student, analyzes the students' concentration during the learning process of the corresponding experiment steps, sends concentration reminders to students with low concentration, and sets teaching courses and training courses for the target experiment steps for the target students, thereby achieving a comprehensive analysis of the students' status and enhancing the students' learning effect.
[0036] 2. The present invention collects and analyzes the target experiment teaching data and the target experiment training data, determines the key experimental steps and the experimental steps to be improved in the target experiment, provides theoretical analysis data and guidance for teachers to formulate teaching plans, and facilitates teachers to modify the teaching plans. BRIEF DESCRIPTION OF THE DRAWINGS
[0037] In order to more clearly illustrate the embodiments of the present invention 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.
[0038] Figure 1 The figure is a flow chart of a teaching method based on virtual reality provided in an embodiment of the present invention.
[0039] Figure 2 The figure is a schematic diagram of the structure of a teaching system based on virtual reality provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0040] In order to make the purpose, technical scheme and advantages of the present invention clearer, some embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. However, it can be understood by those skilled in the art that in the various embodiments of the present invention, many technical details are proposed in order to enable the reader to better understand the present invention. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present invention can also be implemented.
[0041] See also Figure 1 , is a flow chart of a teaching method based on virtual reality provided in an embodiment of the present invention. The teaching method based on virtual reality provided in an embodiment of the present invention specifically includes:
[0042] S10, obtaining target experiment teaching data and target experiment training data, and dividing the target experiment training data into multiple data subsets according to individual students;
[0043] It is worth mentioning that the target experiment is specifically taught based on virtual reality technology. In particular, during the experimental teaching process, one of the experiments taught can be a physics experiment, a chemistry experiment, etc. The teacher teaches through a pre-built virtual laboratory. During the experimental teaching process, the teacher usually first explains the theoretical knowledge and experimental demonstrations of the target experiment, and then the students conduct independent simulation training. After becoming familiar with the experiment, they conduct experimental training and verify their learning outcomes through the training results. The target experiment teaching data is specifically the data collected during the target experiment explanation process, such as students' physiological characteristics data, expression characteristics data, posture characteristics data, eye characteristics data, etc. The target experiment training data is the operation data of multiple students on the target experiment in the same time period. The target experiment consists of multiple experimental steps. The target experiment training data is classified based on each student as the main body to obtain multiple data subsets, and each data subset includes relevant data of a student in the training process of the target experiment.
[0044] S20, for any data subset, determining the completion time of each experimental step of the target student, and determining the qualification of each experimental step of the target student in the practical training process of the target experiment based on the standard process and completion time of the target experiment;
[0045] It is worth noting that, taking any data subset as an example, the students corresponding to the data subset are recorded as target students. The time spent on each experimental step completed by the target students, that is, the completion time, can be determined through the data in the data subset. The target experiment standard process specifically includes the standard operating steps of the target experiment and the standard completion time of each step, wherein the standard completion time can be set based on the teacher's experience data, or based on the average completion time of multiple students who successfully complete the training objectives of the target experiment in the experiment. The specific setting method is not limited here. The passability of each experimental step of the target student can be determined through the target experiment standard process. Any experimental step completed within the corresponding standard completion time is recorded as qualified, otherwise it is recorded as unqualified.
[0046] S30, for any unqualified experimental step of the target student, record it as the target experimental step, extract the focus feature data of the target student during the teaching of the target experimental step from the target experimental teaching data, input the focus feature data into the pre-trained focus analysis model, and generate a focus analysis result;
[0047] It is worth noting that the concentration feature data can be the facial feature data and body feature data of the target students, such as the expression features, posture features and eye features of the target students. The concentration analysis model is a pre-trained model used to analyze the physiological feature data of an individual and thus measure the individual's concentration status. The output is the concentration value corresponding to the physiological feature data. The concentration analysis model can directly adopt the existing model, which will not be elaborated here. Since the input is the concentration feature data of the target students during the teaching of the target experimental steps, the output concentration analysis result is the concentration data of the target students during that time period.
[0048] S40, determining whether the target student does not meet the preset concentration standard during the teaching of the target experimental step according to the concentration analysis result, and if so, sending a concentration reminder to the target student and setting a teaching course and training course for the target experimental step for the target student;
[0049] It is worth noting that the preset concentration standard is specifically a preset concentration threshold. If during the teaching period of the target experimental step, multiple concentration values of the target student in the concentration analysis results all meet the preset concentration standard, then the target student is considered to be focused during the period; otherwise, the target student is judged to be inattentive and distracted during the period. For target students who are not focused enough and cause unqualified experimental steps, concentration reminders can be sent to them, and for the unqualified experimental steps, that is, the target experimental steps, relevant teaching content of the target experimental steps can be set for them. The teaching courses corresponding to the target experimental steps are extracted from the teaching courses for all students, so that the target students can repeat the study of the relevant content, and training courses are set. The training courses are specifically review courses set by the teacher in advance, that is, learning and inspection courses for the target experiment being taught. Content related to the target experimental steps is extracted to set training courses for the target students.
[0050] For students who are unable to concentrate on learning during the experimental teaching period, the above method is used to determine their learning status, conduct targeted teaching, and enhance their learning effect.
[0051] As an optional implementation, for step S40, in the process of determining whether the target student does not meet the preset concentration standard during the teaching of the target experimental step according to the concentration analysis result, the following is further included:
[0052] If the target student meets the preset concentration standard, determine the students whose qualification of the target experiment steps during the practical training of the target experiment is unqualified, and determine the number of students who meet the preset concentration standard among the multiple unqualified students;
[0053] It is worth noting that the reason for failing the experimental steps is not necessarily because the students did not concentrate on listening and learning, etc. It may also be because the knowledge points involved in the corresponding experimental steps are more complicated and the students have not fully understood them. In this case, after determining the target experimental steps, if the target students meet the preset concentration standards, it means that the target students may have failed the experimental steps due to other reasons. In this case, the completion of the experimental steps by all students during the target experimental teaching process can be analyzed to determine the students who failed the target experimental steps among all students, and count the number of students who focused on listening among these unqualified students, that is, the number of students who met the preset concentration standards.
[0054] If the number of students who meet the preset focus standards is greater than the preset number, the target experimental steps will be marked as key experimental steps; if the number of students who meet the preset focus standards is not greater than the preset number, teaching courses and training courses for the target experimental steps will be set for each student who meets the preset focus standards.
[0055] It is worth noting that the specific value of the preset number can be reasonably set according to the teacher's experience data. The practical significance of the preset number is to measure the difficulty of the target experimental steps to a certain extent. When all students are listening attentively, if there are still more than the preset number of students who do not meet the requirements during the practical training, it means that the knowledge points involved in the experimental step are more difficult to understand. In this case, the corresponding experimental steps will be marked as key experimental steps. For the marked key experimental steps, the teacher can make targeted modifications to the initial teaching plan in the process of formulating the teaching plan; if all students are listening attentively and only a small number of students do not meet the requirements during the practical training, in this case, if the overall teaching plan is modified in a targeted manner, it may require the teacher to spend more energy. Therefore, in this case, relevant teaching courses and training courses can be set up for these students separately, and these students can carry out relevant learning and reinforcement.
[0056] As an optional implementation, for step S40, in the process of determining whether the target student does not meet the preset concentration standard during the teaching of the target experimental step according to the concentration analysis result, the following is further included:
[0057] If the target student does not meet the preset concentration standard, obtain the concentration characteristic data of each student during the teaching period of the target experimental step, perform concentration analysis on multiple students through the concentration analysis model, and generate the concentration analysis result of each student;
[0058] It is worth mentioning that the reason why students are not focused during their studies may be personal factors or other objective factors. For example, the knowledge points involved are too complex and boring and difficult to understand, or the language information in the teaching process is inappropriate, such as the low tone of the voice and the fast speaking speed. In this case, the way to improve students' concentration can be to start with the students themselves, such as reminding students who are not focused, or to start with the teaching content, such as adjusting the voice style of the teaching process, citing some interesting examples, so that students can better understand the relevant knowledge. The specific teaching content can be determined by the relevant teachers after research. Therefore, by conducting concentration analysis on multiple students, the overall state of the students can be determined.
[0059] Count the number of students who do not meet the preset focus standards. For multiple students who do not meet the preset focus standards, count the number of students whose eligibility for the target experimental step is unqualified, and calculate the proportion of unqualified students.
[0060] It is worth mentioning that the reason why students are not focused during the study period may also be that they have already mastered the relevant knowledge. Therefore, some students may not be focused during the explanation of relevant knowledge. Therefore, the number of students who fail to meet the preset concentration standards and have failed the target experimental steps among multiple students who do not meet the preset concentration standards is counted here, and the proportion of these students is calculated. Specifically, the calculated proportion is the proportion of students who do not meet the preset concentration standards and have failed the target experimental steps, to the total number of students studying in the target experimental teaching. Taking a class learning a certain target experiment as an example, the number of students in the class who do not meet the preset concentration standards and have failed the target experimental steps accounts for the above proportion of the total number of students in the class.
[0061] If the proportion of unqualified students is greater than the first preset proportion, the target experimental step is marked as an experimental step to be improved;
[0062] It is worth noting that if we directly believe that the relevant experimental steps need to be improved just because many students are not focused during the study period, there are certain limitations, because the reason for the students' low concentration may be that the knowledge points have been mastered. For students who are not focused because the knowledge has been mastered, it is also necessary to start from the students themselves. Directly improving the relevant experimental steps may not have much effect. Therefore, what needs to be analyzed is the number of students who do not meet the preset concentration standards and whose target experimental steps are unqualified. After calculating the corresponding number of people, if the number of people is greater than the first preset ratio, this may be due to the objective factors of the teaching content corresponding to the target experimental step. Therefore, the target experimental step is marked as an experimental step to be improved. Marking it as an experimental step to be improved means that many students are more distracted during the teaching process of this experimental step. Teachers or relevant technical personnel can combine experience and knowledge to make improvements in the process of formulating teaching plans or building virtual experimental scenes to improve teaching quality.
[0063] As an optional implementation scheme, for multiple data subsets, the method further includes:
[0064] For multiple target experimental steps in multiple data subsets, multiple step sets are constructed with each different target experimental step as the core. Specifically, each step set corresponds to an experimental step that is prone to errors during the practical training of the target experiment.
[0065] For any step set, students who have the target experimental steps corresponding to the step set are screened out, and multiple incorrect operations performed by each student in the process of executing the target experimental steps are extracted according to the target experimental training data of each student;
[0066] It is worth noting that each step set corresponds to an experimental step, and is the experimental step in which a student fails during the experimental training process. Students who fail the experimental step during the training process are screened out. Specifically, the unqualified experimental step takes a long time. During the specific operation process, students may perform multiple incorrect experimental operations, which may lead to the inability to enter the next experimental step due to operational errors, or because of the low level of mastery of knowledge points, or because some knowledge is easily confused. The low level of mastery and confusion of knowledge points lead to incorrect operations. Therefore, each incorrect operation is extracted.
[0067] Determine the number of students who have the same incorrect operation for each incorrect operation, filter out incorrect operations whose number of students is greater than a preset number, and construct an incorrect operation subset corresponding to the step set;
[0068] It is worth mentioning that the significance of counting the number of students who perform incorrect operations is to determine whether each incorrect operation is a common mistake. If many people perform the same operation at the same time, it is important to analyze why most people use the same incorrect operation. It is very likely that it is due to confusion of knowledge points, which leads many students to mistakenly think it is another operation.
[0069] Traverse all step sets and determine the error subset corresponding to each step set;
[0070] Determine the first teaching content of each step set according to the target experimental steps corresponding to each step set, and determine the second teaching content of each step set according to the error subset corresponding to each step set;
[0071] It is worth noting that the first teaching content is specifically the teaching content related to the target experimental steps corresponding to the step set, and the second teaching content is specifically the teaching content of each incorrect operation in the error subset corresponding to the step set. The incorrect operation may be caused by confusion of knowledge points, so it is more likely that there is teaching about the incorrect operation in other experimental teaching contents. Therefore, the corresponding teaching content is extracted as the second teaching content. It can be imagined that the number of incorrect operations in the error subset may be more than one, so the second teaching content may include teaching content corresponding to multiple incorrect operations.
[0072] Associating the first teaching content and the second teaching content belonging to the same step set to obtain the target teaching content corresponding to each step set, and performing supplementary teaching for the target experiment based on the multiple target teaching contents;
[0073] It is worth mentioning that after determining the first teaching content and the second teaching content, the correlation relationship between the first teaching content and the second teaching content is determined, and the first teaching content and the second teaching content with a correlation relationship are regarded as a whole, recorded as the target teaching content. For the experimental steps corresponding to the step set, they can be supplemented with teaching through the target teaching content, so that students can have a deeper understanding of the relevant knowledge points that are easy to confuse, and the target teaching content is specifically determined based on the students' target experimental training data. Therefore, the target teaching content can provide guidance for the improvement of teachers' teaching plans. Using the adjusted teaching plan to conduct experimental teaching for students can deepen students' impressions of relevant knowledge points and improve students' learning efficiency.
[0074] Based on the teaching method based on virtual reality provided in the embodiment of the present invention, the embodiment of the present invention also provides a teaching system based on virtual reality, see Figure 2 , is a structural diagram of a teaching system based on virtual reality provided in an embodiment of the present invention. A teaching system based on virtual reality provided in an embodiment of the present invention includes:
[0075] The data acquisition module is used to acquire the target experiment teaching data and the target experiment training data. The target experiment training data is the operation data of multiple students on the target experiment in the same time period. The target experiment includes multiple experimental steps.
[0076] A data partitioning module is used to divide the target experimental training data into multiple data subsets according to individual students;
[0077] The qualification analysis module is used to determine the qualification of each experimental step of the target student in the practical training process of the target experiment. Specifically, the completion time of each experimental step of the target student is determined, and then the qualification of each experimental step of the target student in the practical training process of the target experiment is determined based on the standard process and completion time of the target experiment;
[0078] A focus analysis module is used to analyze the focus feature data of the target students through a focus analysis model to generate a focus analysis result. Specifically, the focus feature data of the target students during the teaching of the target experimental steps are extracted from the target experimental teaching data. The focus feature data includes the facial expression features, posture features and eye features of the target students. The focus feature data is input into the pre-trained focus analysis model to generate a focus analysis result.
[0079] A concentration reminder module is used to send a concentration reminder to the target student. Specifically, if the target student does not meet the preset concentration standard during the teaching of the target experimental step, a concentration reminder is sent to the target student;
[0080] The course setting module is used to set teaching courses and training courses for target experimental steps for target students. Specifically, for target students who do not meet the preset concentration standards during the teaching period of the target experimental steps, teaching courses and training courses for the target experimental steps are set for the target students.
[0081] As an optional implementation scheme, it also includes:
[0082] An experimental step marking module is used to mark the experimental steps, including marking the target experimental steps as key experimental steps and / or experimental steps to be improved;
[0083] Specifically, in the process of determining whether the target student does not meet the preset focus standard during the teaching of the target experimental step according to the focus analysis result, if the target student meets the preset focus standard, determining the students who are unqualified for the target experimental step during the practical training of the target experiment, and determining the number of students who meet the preset focus standard among the multiple unqualified students;
[0084] If the number of students who meet the preset concentration criteria is greater than the preset number, the target experimental step is marked as a key experimental step;
[0085] In the process of determining whether the target student does not meet the preset concentration standard during the teaching period of the target experimental step according to the concentration analysis result, if the target student does not meet the preset concentration standard, obtaining the concentration feature data of each student during the teaching period of the target experimental step, performing concentration analysis on multiple students through the concentration analysis model, and generating a concentration analysis result for each student;
[0086] Count the number of students who do not meet the preset focus standards. For multiple students who do not meet the preset focus standards, count the number of students whose eligibility for the target experimental step is unqualified, and calculate the proportion of unqualified students.
[0087] If the proportion of unqualified students is greater than the first preset proportion, the target experimental step will be marked as an experimental step to be improved.
[0088] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.
Claims
1. A teaching method based on virtual reality, characterized in that: include: Acquire target experiment teaching data and target experiment training data, wherein the target experiment training data is operation data of a plurality of students on a target experiment in the same time period, and the target experiment includes a plurality of experimental steps; Dividing the target experimental training data into multiple data subsets according to individual students; For any data subset, determine the completion time of each experimental step of the target student, and determine the qualification of each experimental step of the target student in the practical training process of the target experiment based on the standard process of the target experiment and the completion time; For any unqualified experimental step of the target student, record it as a target experimental step, extract the focus feature data of the target student during the teaching of the target experimental step from the target experimental teaching data, the focus feature data includes the facial expression feature, posture feature and eye feature of the target student, input the focus feature data into the pre-trained focus analysis model, and generate a focus analysis result; Determine whether the target student does not meet the preset concentration standard during the teaching of the target experimental step according to the concentration analysis result. If so, send a concentration reminder to the target student and set a teaching course and training course for the target experimental step for the target student.
2. A teaching method based on virtual reality as claimed in claim 1, characterized in that: Also includes: In the process of determining whether the target student does not meet the preset concentration standard during the teaching of the target experimental step according to the concentration analysis result, if the target student meets the preset concentration standard, determining the qualification of the target experimental step as unqualified students during the practical training of the target experiment, and determining the number of students who meet the preset concentration standard among the multiple unqualified students; If the number of students who meet the preset focus standards is greater than the preset number, the target experimental step will be marked as a key experimental step; if the number of students who meet the preset focus standards is not greater than the preset number, teaching courses and training courses for the target experimental step will be set for each student who meets the preset focus standards.
3. A teaching method based on virtual reality as claimed in claim 1, characterized in that: Also includes: In the process of determining whether the target student does not meet the preset concentration standard during the teaching period of the target experimental step according to the concentration analysis result, if the target student does not meet the preset concentration standard, obtaining the concentration feature data of each student during the teaching period of the target experimental step, and generating the concentration analysis result of each student by performing concentration analysis on multiple students; Counting the number of students who do not meet the preset focus standard, and for multiple students who do not meet the preset focus standard, counting the number of students who are unqualified in the target experimental step, and calculating the proportion of unqualified students; If the proportion of unqualified students is greater than a first preset proportion, the target experimental step is marked as an experimental step to be improved.
4. A teaching method based on virtual reality as claimed in claim 1, characterized in that: For multiple data subsets, this also includes: For the multiple target experimental steps in the multiple data subsets, multiple step sets are constructed with each different target experimental step as the core; For any step set, students who have the target experimental steps corresponding to the step set are screened out, multiple incorrect operations performed by each student in the process of executing the target experimental steps are extracted according to the target experimental training data of each student, the number of students who have the same incorrect operation for each incorrect operation is determined, incorrect operations whose number of students is greater than a preset number are screened out, and an incorrect operation subset corresponding to the step set is constructed; Traverse all step sets and determine the error subset corresponding to each step set; Determine the first teaching content of each step set according to the target experimental steps corresponding to each step set, determine the second teaching content of each step set according to the error subset corresponding to each step set, associate the first teaching content and the second teaching content belonging to the same step set, and obtain the target teaching content corresponding to each step set; Supplementary teaching is provided for the target experiment based on a plurality of the target teaching contents.
5. A teaching method based on virtual reality as claimed in claim 3, characterized in that: The calculated percentage of unqualified students includes: The proportion of unqualified students is calculated by dividing the number of students who failed the target experimental step among the students who did not meet the preset focus standards by the total number of students.
6. A teaching system based on virtual reality, the system being applied to a teaching method based on virtual reality as claimed in any one of claims 1 to 5, characterized in that: include: A data acquisition module is used to acquire target experiment teaching data and target experiment training data, wherein the target experiment training data is operation data of multiple students on the target experiment in the same time period, and the target experiment includes multiple experimental steps; A data partitioning module is used to divide the target experimental training data into multiple data subsets according to individual students; A qualification analysis module is used to determine the qualification of each experimental step of the target student during the practical training process of the target experiment; A focus analysis module is used to analyze the focus characteristic data of target students through a focus analysis model and generate focus analysis results; A focus reminder module is used to send focus reminders to target students; The course setting module is used to set teaching courses and training courses of the target experimental steps for target students.
7. A teaching system based on virtual reality as claimed in claim 6, characterized in that: Also includes: The experimental step marking module is used to mark the experimental steps, including marking the target experimental steps as key experimental steps and / or experimental steps to be improved.