Knowledge point proficiency analysis method, system, equipment and medium

Abstract

The invention relates to the technical field of data analysis, in particular to a knowledge point proficiency analysis method, system and device and a medium. The method comprises the steps of obtaining physiological data and environmental data of a target user, determining a current pressure level and a current test taking state of the target user according to the data, and performing fusion analysis to generate a knowledge point proficiency analysis report. The knowledge point proficiency of the target user is accurately evaluated through comprehensive analysis of the multi-dimensional data, and the target user guardian is helped to master the state of the user and the teacher is helped to adjust the teaching strategy through a real-time feedback mechanism.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to an analysis method, system, device and medium for analyzing the proficiency of knowledge points. Background Art

[0002] Currently, smart wearable devices have been widely used for health monitoring, such as the collection of data such as heart rate and sleep quality. In the field of education, smart wearable devices are also used to analyze students' learning and examination status, but they are mainly based on the monitoring of activity and sleep quality, and the analysis of students' mastery of knowledge points is still relatively preliminary.

[0003] In the existing technology, a single physiological parameter monitoring, such as heart rate, is usually used to judge the stress state of students. This method ignores other physiological parameters that may affect students' test performance, such as stress level and sleep quality. In addition, some relatively simple data collection methods are also used in the existing technology, such as pedometers and heart rate monitors, but these methods can only provide basic data information, and do not conduct comprehensive multi-parameter monitoring and data fusion analysis.

[0004] The prior art lacks the ability to analyze the changes in students' heart rates when they are doing questions, and the ability to link such changes with students' knowledge proficiency in different types of questions. In addition, the prior art does not fully utilize the comprehensive monitoring capabilities of multi-functional electronic watches to provide more in-depth and accurate student status analysis reports. Summary of the invention

[0005] The purpose of this application is to overcome the above-mentioned technical problems and provide an analysis method, system, device and medium for analyzing the proficiency of knowledge points.

[0006] A method for analyzing proficiency of knowledge points, the method comprising: obtaining physiological data of a target user, environmental data of an environment in which the target user is located, knowledge points associated with questions answered by the target user, time taken by the target user to answer questions, and historical data of knowledge points answered by the target user; determining the current stress state of the target user based on the physiological data and the environmental data; and determining the current test state of the target user based on the knowledge points associated with questions answered by the target user, time taken by the target user to answer questions, and historical data of knowledge points answered by the target user.

[0007] A fusion analysis is performed on the current stress level and the current stress state, and a knowledge point proficiency analysis report is generated based on the fusion analysis results.

[0008] Preferably, the acquiring of physiological data of the target user comprises: acquiring physiological data collected by a physiological parameter sensor, wherein the physiological data comprises: at least one of heart rate, blood pressure, respiratory rate, sleep quality, exercise steps, menstrual cycle, skin electrical activity and brain waves;

[0009] And / or, the obtaining of the environmental data of the target user's environment includes: obtaining the environmental data of the target user's environment collected by an environmental sensor, the environmental data including: at least one of light intensity, temperature, humidity, time, geographic location, color, smell, crowd density, and noise level;

[0010] And / or, the obtaining of historical data of the target user's answers to knowledge points of the questions includes: obtaining time record information used for historical knowledge points, and the historical time record information includes: the answer time for a single knowledge point and the cumulative answer time for multiple knowledge points.

[0011] Preferably, determining the current stress level of the target user based on the physiological data and the environmental data includes: preprocessing the physiological data and the environmental data; extracting the comprehensive value of the physiological data and the comprehensive value of the environmental data; and using a pre-constructed stress recognition model to determine the current stress level of the target user based on the comprehensive value of the physiological data and the comprehensive value of the environmental data.

[0012] Preferably, the comprehensive values ​​of the physiological data include the ratio of the real-time heart rate, blood pressure, respiratory rate, skin electrodermal activity and brain wave values ​​to the historical values ​​of the same type of data; the ratio to the number of exercise steps and sleep quality within a certain threshold time range; combined with the pre-set values ​​of the female's physiological cycle, a pre-constructed model is used to derive the comprehensive values ​​of the physiological data based on the relevant values ​​and ratios.

[0013] Preferably, the current test status of the target user is determined based on the knowledge points associated with the target user's answer to the question, the time taken by the target user to answer the question, and the historical data of the target user's answer to the knowledge points of the question, and the test status includes poor, moderate, good, and excellent.

[0014] Including the time the target user takes to answer the question, including the end time of the target user completing the previous question and the time difference before answering the next question; including the sum of the difference between the time the target user puts pen to paper to answer the question and the time the target user lifts pen after answering the question.

[0015] Preferably, the current stress level of the target user is determined according to the physiological data and the environmental data, and the stress level includes a mild level, a moderate level, and a severe level.

[0016] Preferably, a fusion analysis is performed on the current stress level and the current test level, and a knowledge point proficiency analysis report is generated based on the fusion analysis results.

[0017] The analysis report provides real-time feedback to the user or teacher through a feedback mechanism.

[0018] Teachers can instantly adjust teaching strategies or provide targeted tutoring to improve students' learning efficiency.

[0019] A knowledge point proficiency analysis system, the system includes: a data acquisition module, used to acquire physiological data of a target user, environmental data of the environment in which the target user is located, knowledge points associated with questions answered by the target user, the time taken by the target user to answer questions, and historical data of knowledge points answered by the target user; a data analysis module, used to determine the current stress level of the target user according to the physiological data and the environmental data; a monitoring module, used to monitor and record students' heart rate, stress level, sleep quality, exercise steps, and menstrual cycle in real time; an image recognition module, used to shoot the expression of the target user, the questions answered, and the environment in which the target user is located; a data processing unit: connected to the monitoring module and the camera module, used to receive and process monitoring data and image data; a machine learning module: used to perform in-depth analysis on the collected multidimensional data, identify the potential correlation between the heart rate change pattern and knowledge proficiency, and continuously optimize the analysis model; a feedback module: based on the real-time analysis results, provide feedback to the user or teacher to adjust the teaching strategy or learning plan. A signal shielding module, used to shield other wireless signals to prevent the target user from cheating.

[0020] A wearable device comprises: a sensor, a processor and a memory, wherein the sensor is used to collect physiological data and environmental data of a target user at a current moment and transmit the data to the processor; the memory stores a computer program; the processor is used to execute the computer program stored in the memory, and when the computer program is run, the processor executes the method for knowledge point proficiency analysis described in any one of claims 1 to 8.

[0021] A computer-readable storage medium stores steps that can be loaded by a processor and executed according to any one of the knowledge point proficiency analysis methods described in claims 1 to 7.

[0022] In summary, this application has the following beneficial effects:

[0023] 1. Through the integration of multi-parameter physiological monitoring and data analysis, accurate assessment of students' stress changes during the exam and their knowledge proficiency in different question types is achieved;

[0024] 2. Real-time feedback is provided to the user's guardian and teacher, helping the guardian to have a more comprehensive understanding of the user and helping the user to adjust his or her physiological state; helping teachers to develop personalized teaching strategies, effectively improving students' learning efficiency and grades, and having important educational application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a knowledge point proficiency analysis method described in a specific embodiment;

[0026] Figure 2 It is a structural schematic diagram of a knowledge point proficiency analysis system described in a specific embodiment. DETAILED DESCRIPTION

[0027] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in the embodiments of the present invention. The described embodiments are only possible technical implementations of the present invention, not all possible implementations. Those skilled in the art can completely combine the embodiments of the present invention to obtain other embodiments without creative work, and these embodiments are also within the scope of protection of the present invention.

[0028] The inventor of the present application has found that in the prior art, a single physiological parameter monitoring, such as heart rate, is usually used to judge the stress state of students. This method ignores other possible physiological parameters, such as stress level and sleep quality. These factors may also affect the analysis of students' test performance and knowledge mastery. For this reason, the present application mainly adopts a knowledge point proficiency analysis method, which integrates multi-parameter physiological monitoring and advanced data analysis technology to achieve a more accurate assessment of students' stress changes during the test and their proficiency in different question types, thereby providing teachers with more personalized teaching suggestions to help students master knowledge more effectively and improve their academic performance. The following is a further detailed description of the present application.

[0029] like Figure 1 As shown, the embodiment of the present application discloses a knowledge point proficiency analysis method, and the specific steps are as follows:

[0030] S1. Acquire physiological data of a target user, environmental data of the target user's environment, knowledge points associated with questions answered by the target user, time taken by the target user to answer questions, and historical data of knowledge points of questions answered by the target user.

[0031] Specifically, the acquiring of physiological data of the target user includes: acquiring physiological data collected by a physiological parameter sensor, wherein the physiological data includes: at least one of heart rate, blood pressure, respiratory rate, sleep quality, exercise steps, physiological cycle, skin electrical activity and brain waves;

[0032] And / or, the obtaining of the environment data of the target user's environment includes:

[0033] Acquire environmental data of the target user's environment collected by an environmental sensor, the environmental data including at least one of light intensity, temperature, humidity, time, geographic location, color, smell, crowd density, and noise level;

[0034] And / or, the obtaining of historical data of knowledge points of questions answered by the target user includes:

[0035] The time record information used for historical knowledge points is obtained, and the historical time record information includes: the answer time of a single knowledge point and the cumulative answer time of multiple knowledge points.

[0036] S2: Determine the current stress level of the target user based on the physiological data and the environmental data.

[0037] Specifically, determining the current stress level of the target user according to the physiological data and the environmental data includes:

[0038] Preprocessing the physiological data and the environmental data; extracting the comprehensive value of the physiological data and the comprehensive value of the environmental data;

[0039] The pre-built stress recognition model is used to determine the current stress level of the target user according to the comprehensive value of the physiological data and the comprehensive value of the environmental data.

[0040] The comprehensive values ​​of the physiological data include the ratio of the real-time heart rate, blood pressure, respiratory rate, skin electrodermal activity and brain wave values ​​to the historical values ​​of the same type of data; the ratio to the number of exercise steps and sleep quality within a certain threshold time range; combined with the values ​​of the pre-set physiological cycle, a pre-built model is used to derive the comprehensive values ​​of the physiological data based on the relevant values ​​and ratios.

[0041] S3: Determine the current test status of the target user based on the knowledge points associated with the target user's answer to the question, the time taken by the target user to answer the question, and the historical data of the target user's answer to the knowledge points.

[0042] Specifically, the current test status of the target user is determined according to the knowledge points associated with the questions answered by the target user, the time taken by the target user to answer the questions, and the historical data of the knowledge points of the questions answered by the target user.

[0043] Including the time the target user takes to answer the question, including the end time of the target user completing the previous question and the time difference before answering the next question; including the sum of the difference between the time the target user puts pen to paper to answer the question and the time the target user lifts pen after answering the question.

[0044] S4: Perform a fusion analysis on the current stress level and the current test state, and generate a knowledge point proficiency analysis report based on the fusion analysis results.

[0045] Specifically, the current stress level and the current test state are integrated and analyzed, and a knowledge point proficiency analysis report is generated according to the integrated analysis result. The analysis report provides real-time feedback to the guardian of the target user or the teacher of the target user through a feedback mechanism.

[0046] A specific embodiment, a method for analyzing the proficiency of knowledge points, includes multiple links such as data acquisition and data analysis. Specifically, the data acquisition module includes multiple parts: physiological data sensors and environmental sensors. Physiological data sensors include but are not limited to heart rate sensors, blood pressure sensors, respiratory rate sensors, skin electricity sensors, brain wave sensors, etc. Environmental sensors include light sensors, temperature and humidity sensors, noise sensors, crowd density sensors, etc. These sensors can collect physiological data and environmental data of the target user in real time, and transmit them to the processor for analysis.

[0047] Heart rate sensors can be implemented using photoelectric or mechanical feedback methods. Photoelectric heart rate sensors use LED light sources and photodetectors to monitor changes caused by blood flow; mechanical feedback heart rate sensors use pressure sensors to detect changes in blood pressure caused by heartbeats. Both methods can accurately capture changes in the target user's heart rate. To further improve accuracy, data from multiple sensors can be combined, such as heart rate and respiratory rate sensors, to enhance the accuracy of data analysis.

[0048] For example, when an increased heart rate is detected, it can be combined with an increase in breathing rate to determine whether the target user is in a state of tension. In addition, the physiological cycle sensor can adjust the analysis model according to the user's physiological cycle. For example, during the menstrual period of female users, the heart rate increase is more obvious than usual.

[0049] Environmental sensors also have multiple implementation methods. For example, a light sensor can be a photoresistor or a photodiode, which is used to detect the intensity of ambient light; a temperature and humidity sensor can be an integrated temperature and humidity sensor, which is used to detect changes in ambient temperature and humidity; a noise sensor can be a sound sensor, which is used to monitor the ambient noise level; and a crowd density sensor can be an infrared sensor, which is used to detect the density of the surrounding crowd. These sensors work together to provide a variety of information sources for data acquisition, thereby improving the comprehensiveness of the analysis.

[0050] For example, when the crowd density is detected to be increased, combined with the increase in noise level, it can be judged that the target user is in a noisy and dense environment. This causes the user's heart rate to be higher than before, and the user is judged to be in a poor state for the test.

[0051] Preprocess the physiological data and environmental data, extract the comprehensive values ​​of the physiological data and the comprehensive values ​​of the environmental data, and use the pre-built stress recognition model to determine the current stress level of the target user based on the comprehensive values ​​of the physiological data and the comprehensive values ​​of the environmental data. The comprehensive values ​​of the physiological data include the ratio of the heart rate, blood pressure, respiratory rate, skin electrodermal activity and brain wave values ​​obtained in real time to the historical values ​​of the same type of data; the ratio to the number of exercise steps and sleep quality within a certain threshold time range; combined with the values ​​of the pre-set physiological cycle, the pre-built model is used to obtain the comprehensive values ​​of the physiological data based on the relevant values ​​and ratios.

[0052] For example, when the real-time heart rate is significantly higher than the historical heart rate value, combined with the physiological cycle, it is judged that the user is in a high stress state. In addition, the user's exercise steps and sleep quality can be combined to further judge the user's physiological condition. For example, when the number of exercise steps is small and the sleep quality is low, the user's physiological state is poor.

[0053] The time it takes for the target user to answer a question includes the time difference between the time the target user finishes the previous question and the time before answering the next question; it also includes the sum of the difference between the time the target user puts pen down to answer the question and the time the target user lifts pen after answering the question. By analyzing these time periods, we can understand the user's reaction speed and proficiency to different question types, thereby providing a more accurate assessment of the test status.

[0054] For example, if the user takes a shorter time on algebra problems but a longer time on geometry problems, it can be judged that the user is more proficient in algebra problems. In addition, comprehensive analysis can be performed in combination with the user's physiological data. For example, when answering a certain type of question, if the heart rate increases significantly, it can be judged that this type of question is more challenging for the user and causes greater psychological pressure.

[0055] For example, for the same knowledge point, when the time left to the end of the exam is less than a certain threshold, the heart rate increases and the expression becomes tense. It can be judged that the psychological pressure is relatively high at this time. This is just an example and is not limited to this.

[0056] The current stress level and the current test status are integrated and analyzed, and a knowledge point proficiency analysis report is generated based on the integrated analysis results. The analysis report provides real-time feedback to the guardian of the target user or the target user's teacher through a feedback mechanism.

[0057] For example, if the analysis results show that the user has a high stress level and poor test performance during a certain period of time, timely feedback can be given to the guardian or teacher so that psychological counseling and teaching and learning adjustments can be made in a timely manner. This feedback mechanism not only improves the teaching effect of teachers, but also improves the learning experience of students.

[0058] The implementation principle of this embodiment is: by integrating multi-parameter physiological monitoring and advanced data analysis technology, this technical solution can more accurately evaluate students' stress changes during the examination and their proficiency in different question types, thereby providing teachers with more personalized teaching suggestions to help students master knowledge more effectively and improve their academic performance.

[0059] like Figure 2 As shown, an embodiment of the present application provides a knowledge point proficiency analysis system, which specifically includes: a data acquisition module 201, a data analysis module 202, a monitoring module 203, an image recognition module 204, a data processing unit 205, a machine learning module 206, a feedback module 207 and a signal shielding module 208.

[0060] The data acquisition module 201 is used to obtain the physiological data of the target user, the environmental data of the target user's environment, the knowledge points associated with the target user's answers to the questions, the time taken by the target user to answer the questions, and the historical data of the target user's knowledge points in answering the questions. This module mainly includes various sensors, such as heart rate sensors, blood pressure sensors, respiratory rate sensors, etc., which are used to monitor the user's physiological parameters in real time and transmit them to the data analysis module for processing through the data processing unit. Environmental sensors include light sensors, temperature and humidity sensors, noise sensors, etc., which are used to monitor the light intensity, temperature and humidity, noise level, etc. of the target user's environment in real time. These data together constitute the basis for the target user's stress level assessment.

[0061] The data analysis module 202 is used to determine the current stress level of the target user based on the physiological data and the environmental data. The physiological data and the environmental data are pre-processed, the comprehensive value is extracted, and the current stress level is determined using a pre-built stress recognition model. In this process, the comprehensive value of the physiological data includes the ratio of the heart rate, blood pressure, respiratory rate, skin electrode activity and brain wave values ​​obtained in real time to the historical values ​​of the same type of data, and the ratio of the number of exercise steps and sleep quality within a certain threshold time range, combined with the value of the pre-set physiological cycle. For example, when the heart rate value is significantly higher than the historical heart rate value, and the number of exercise steps is less and the sleep quality is low, it is judged that the user is in a high stress state.

[0062] The monitoring module 203 is used to monitor and record the student's heart rate, stress level, sleep quality, exercise steps and physiological cycle in real time. This module obtains real-time physiological data through a heart rate sensor, a stress level sensor, etc., and stores and transmits it to the data analysis module for processing through a data processing unit. For example, when the heart rate is monitored to rise, a comprehensive judgment is made in combination with the user's sleep quality to further confirm whether the user's physiological state is normal.

[0063] The image recognition module 204 is used to capture the target user's expression, answer questions, and environment. These image data include but are not limited to facial expressions, answer question types, etc. The camera collects images and transmits them to the data processing unit for image recognition processing. Image recognition technology can help determine the user's test state. For example, when it is detected that the user's expression is relatively nervous, it is inferred that the user may be under certain pressure, and then the subsequent analysis strategy is adjusted.

[0064] The data processing unit 205 is connected to the monitoring module and the camera module to receive and process monitoring data and image data. This unit includes a central processing unit and a memory, which can perform preliminary processing and storage on the collected data and provide basic data support for subsequent analysis.

[0065] The machine learning module 206 is used to perform in-depth analysis on the collected multi-dimensional data, identify the potential correlation between the heart rate change pattern and the knowledge proficiency, and continuously optimize the analysis model. In this process, the machine learning algorithm can be continuously trained and adjusted to more accurately predict the user's test status and knowledge proficiency. For example, when it is detected that the user takes a long time to answer a certain type of question, combined with the corresponding physiological data (such as increased heart rate), it can be identified that this type of question has a greater impact on the user, and then the corresponding analysis model can be optimized.

[0066] The feedback module 207 provides feedback to the user or teacher based on the real-time analysis results in order to adjust the teaching strategy or learning plan. Through the real-time feedback mechanism, the teaching content or learning method can be quickly adjusted. For example, when the analysis results show that the user performs poorly on a certain type of question, the teaching strategy can be adjusted in time to help students better master the knowledge points.

[0067] The signal shielding module 208 is used to shield other wireless signals to prevent the target user from cheating. This module ensures the authenticity of the data in the examination environment through the wireless signal shielding device to prevent other interference signals from affecting the accuracy of the analysis results. For example, in the examination environment, all wireless signals are shielded by the signal shielding device to ensure the security and fairness of the examination.

[0068] The implementation principle of this embodiment is: by integrating multi-parameter physiological monitoring and advanced data analysis technology, this technical solution can more accurately evaluate students' stress changes during the examination and their proficiency in different question types, thereby providing teachers with more personalized teaching suggestions to help students master knowledge more effectively and improve their academic performance.

[0069] A specific embodiment, a wearable device provided in this embodiment includes a sensor, a processor and a memory. Specifically, the sensor is used to collect the physiological data and environmental data of the target user at the current moment and transmit them to the processor. These sensors include but are not limited to heart rate sensors, blood pressure sensors, respiratory rate sensors, skin electricity sensors, brain wave sensors, etc. For example, the heart rate sensor can be implemented by photoelectric method or mechanical feedback method, and the environmental sensor includes light sensor, temperature and humidity sensor, noise sensor, etc. These sensors work together.

[0070] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A knowledge point proficiency analysis method, characterized in that: The method comprises: Acquire physiological data of a target user, environmental data of the target user's environment, knowledge points associated with the target user's answer to a question, the time taken by the target user to answer the question, and historical data of knowledge points of the target user's answer to the question; determining a current stress level of the target user based on the physiological data and the environmental data; The current test status of the target user is determined according to the knowledge points associated with the questions answered by the target user, the time taken by the target user to answer the questions, and the historical data of the knowledge points of the questions answered by the target user. A fusion analysis is performed on the current stress level and the current test state, and a knowledge point proficiency analysis report is generated based on the fusion analysis result.

2. The method according to claim 1, characterized in that The acquiring of physiological data of the target user includes: acquiring physiological data collected by a physiological parameter sensor, wherein the physiological data includes: at least one of heart rate, blood pressure, respiratory rate, sleep quality, exercise steps, physiological cycle, skin electrical activity and brain waves; And / or, the obtaining of the environment data of the target user's environment includes: Acquire environmental data of the target user's environment collected by an environmental sensor, the environmental data including at least one of light intensity, temperature, humidity, time, geographic location, color, smell, crowd density, and noise level; And / or, the obtaining of historical data of knowledge points of questions answered by the target user includes: The time record information used for historical knowledge points is obtained, and the historical time record information includes: the answer time of a single knowledge point and the cumulative answer time of multiple knowledge points.

3. The method according to claim 1, characterized in that: The determining the current stress level of the target user according to the physiological data and the environmental data includes: Preprocessing the physiological data and the environmental data; extracting the comprehensive value of the physiological data and the comprehensive value of the environmental data; The pre-built stress recognition model is used to determine the current stress level of the target user according to the comprehensive value of the physiological data and the comprehensive value of the environmental data.

4. The method according to claim 3, characterized in that The comprehensive values ​​of the physiological data include the ratio of the real-time heart rate, blood pressure, respiratory rate, skin electrodermal activity and brain wave values ​​to the historical values ​​of the same type of data; the ratio to the number of exercise steps and sleep quality within a certain threshold time range; combined with the values ​​of the pre-set physiological cycle, a pre-built model is used to derive the comprehensive values ​​of the physiological data based on the relevant values ​​and ratios.

5. The method according to claim 1, characterized in that The current test status of the target user is determined according to the knowledge points associated with the questions answered by the target user, the time taken by the target user to answer the questions, and the historical data of the knowledge points of the questions answered by the target user. Including the time the target user takes to answer the question, including the end time of the target user completing the previous question and the time difference before answering the next question; including the sum of the difference between the time the target user puts pen to paper to answer the question and the time the target user lifts pen after answering the question.

6. The method according to claim 1, characterized in that The current stress level of the target user is determined according to the physiological data and the environmental data, where the stress level includes a mild level, a moderate level, and a severe level.

7. The method according to claim 1, characterized in that The current stress level and the current test state are integrated and analyzed, and a knowledge point proficiency analysis report is generated according to the integrated analysis result. The analysis report provides real-time feedback to the guardian of the target user or the teacher of the target user through a feedback mechanism.

8. A knowledge point proficiency analysis system, characterized in that: The system comprises: a data acquisition module, which is used to acquire physiological data of a target user, environmental data of an environment in which the target user is located, knowledge points associated with a question answered by the target user, the time taken by the target user to answer the question, and historical data of knowledge points of the question answered by the target user; a data analysis module, configured to determine a current stress level of the target user based on the physiological data and the environmental data; The monitoring module is used to monitor and record students’ heart rate, stress level, sleep quality, exercise steps and physiological cycle in real time; An image recognition module, used to capture the target user's facial expression, the question answered by the target user, and the environment in which the target user is located; Data processing unit: connected to the monitoring module and the camera module, used to receive and process monitoring data and image data; Machine learning module: used to conduct in-depth analysis of the collected multi-dimensional data, identify the potential correlation between heart rate change patterns and knowledge proficiency, and continuously optimize the analysis model; Feedback module: Based on real-time analysis results, provide feedback to users or teachers in order to adjust teaching strategies or learning plans. The signal shielding module is used to shield other wireless signals to prevent the target user from cheating.

9. A wearable device, characterized in that: include: A sensor, a processor and a memory, wherein the sensor is used to collect physiological data and environmental data of the target user at the current moment and transmit them to the processor; The memory stores a computer program; The processor is used to execute the computer program stored in the memory, and when the computer program is running, the processor executes the method for knowledge point proficiency analysis described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The method stores steps that can be loaded by a processor and executed according to any one of claims 1 to 7.

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