Teacher teaching evaluation method and system based on student concentration degree

Abstract

The invention relates to the technical field of intelligent evaluation, and particularly discloses a teaching level evaluation method and system based on a local account, and the method comprises the steps: obtaining the use process and face data of each student based on the granted authority, determining the concentration degree according to the use process, and synchronously training the face data to a neural network model of the concentration degree; face data of each student is collected regularly, and the concentration degree of each student is determined based on the trained neural network model; receiving a teaching scheme input by a teacher, identifying the teaching scheme, determining importance degrees of different time periods, and determining an evaluation score according to the importance degrees of different moments and the concentration degree of each student at different moments; according to the method, the importance degree of each stage in the teaching process is determined based on the information of the teacher, the concentration degree of the student in each stage is determined based on the information of the student, the process is performed in real time, the evaluation score of the teaching process is jointly determined by combining the concentration degree and the importance degree, the globality is high, and the objectivity is high.

Description

[0001] This application is a divisional application of an invention application with an application date of March 26, 2025, a Chinese application number of 202510363988.6, and an invention title of "A Teaching Level Evaluation Method and System Based on Local Accounts". Technical Field

[0002] The present invention relates to the technical field of intelligent evaluation, and specifically to a teaching level evaluation method and system based on local accounts. Background Art

[0003] With the progress of technology and the development of society, people's desire and access to knowledge are increasing, and the forms and quantities of classrooms are also increasing. The intelligent classroom is a teaching interaction method based on intelligent devices, including online and offline methods.

[0004] There is an important behavior in the teaching interaction process, which is the teaching level evaluation behavior. The existing evaluation methods are still based on manual evaluation. However, the memory of students is limited, and the data sources for evaluation often only come from some teaching segments in the final stage. In fact, on the basis of the sufficient popularity of existing intelligent devices, it is not complicated to introduce a global and objective teaching level evaluation scheme. How to build a global and objective teaching level evaluation framework is the technical problem that the technical solution of the present invention wants to solve. Summary of the Invention

[0005] The purpose of the present invention is to provide a teaching level evaluation method and system based on local accounts to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A teaching level evaluation method based on local accounts, the method includes:

[0008] Sending a permission acquisition request to the user and receiving the permission granted by the user; wherein, the user includes students and teachers;

[0009] Obtaining the usage process and facial data of each student based on the granted permission, determining the concentration according to the usage process, and synchronously training the facial data to the neural network model of the concentration; wherein, one student corresponds to one neural network model;

[0010] Regularly collecting the facial data of each student and determining the concentration of each student based on the trained neural network model;

[0011] Receiving the teaching plan input by the teacher, identifying the teaching plan, determining the importance of different time periods, and determining the evaluation score according to the importance of different times and the concentration of each student at different times.

[0012] As a further solution of the present invention: The steps of obtaining the usage process and facial data of each trainee based on the granted permissions, determining the concentration according to the usage process, and synchronously training the neural network model of the facial data to the concentration include:

[0013] Obtain the browsing content and operation parameters of the user at each moment in real time based on the granted permissions, and determine the concentration at each moment according to the browsing content and operation parameters;

[0014] Obtain the trainee image based on the granted permissions, identify the trainee image, and extract the facial data;

[0015] Perform time-domain registration on the facial data and the concentration, and use the time-domain registered facial data and concentration as samples to train the neural network model; the input of the neural network model is the facial data, and the output is the concentration.

[0016] As a further solution of the present invention: The steps of obtaining the browsing content and operation parameters of the user at each moment in real time based on the granted permissions, and determining the concentration at each moment according to the browsing content and operation parameters include:

[0017] Obtain the browsing content and operation parameters of the user at each moment in real time based on the granted permissions;

[0018] Read the serial number corresponding to the operation parameter at each moment in the preset parameter conversion table; the parameter conversion table contains an operation type item and a serial number item;

[0019] Aggregate the moments according to the browsing content to obtain the time period corresponding to each browsing content;

[0020] Statistically arrange the serial numbers corresponding to the operation parameters in the time period based on the time sequence to obtain an actual operation serial number group;

[0021] Read the preset standard serial number group of each browsing content, compare the actual operation serial number group with the standard serial number group, calculate the similarity, and determine the concentration according to the similarity; wherein, the concentration is proportional to the similarity.

[0022] As a further solution of the present invention: The steps of receiving the teaching plan input by the teacher, identifying the teaching plan, determining the importance of different time periods, and determining the evaluation score according to the importance of different moments and the concentration of each trainee at different moments include:

[0023] Receive the teaching plan input by the teacher, identify the teaching plan, and determine the importance of different time periods; identifying the teaching plan includes a keyword matching process, different keywords correspond to different importance levels, and both the keywords and their importance levels are preset parameters;

[0024] Read the concentration of each student at different times and insert them into the same timeline;

[0025] Intercept matching points on the timeline according to a preset time step, and read the number of people whose concentration at the matching points is less than a preset concentration threshold; the matching points are time points;

[0026] Query the importance corresponding to the matching points, and determine the evaluation score according to the importance and the number of people.

[0027] As a further solution of the present invention: the step of receiving the teaching plan input by the teacher, identifying the teaching plan, and determining the importance of different time periods includes:

[0028] Receive the teaching keywords and their importance of each time period input by the teacher;

[0029] Based on the granted permissions, obtain the audio information of the teacher, locate the teaching keywords, and correct the teaching keywords and their importance of each time period according to the positioning results.

[0030] As a further solution of the present invention: the step of querying the importance corresponding to the matching points and determining the evaluation score according to the importance and the number of people includes:

[0031] Query the number threshold of people for each importance, calculate the ratio of the number of people to the number threshold, and determine the score of each matching point according to the ratio; wherein, the score is inversely proportional to the ratio;

[0032] Determine the weight according to the importance of each matching point, and accumulate all scores based on the weight to obtain the final evaluation score; wherein, the weight is directly proportional to the importance.

[0033] The technical solution of the present invention also provides a teaching level evaluation system based on a local account, and the system includes:

[0034] A permission acquisition module, configured to send a permission acquisition request to a user and receive the permission granted by the user; wherein, the user includes students and teachers;

[0035] A model training module, configured to obtain the usage process and facial data of each student based on the granted permissions, determine the concentration according to the usage process, and synchronously train the neural network model of the facial data to the concentration; wherein, one student corresponds to one neural network model;

[0036] A concentration recognition module, configured to regularly collect the facial data of each student and determine the concentration of each student based on the trained neural network model;

[0037] An evaluation score determination module, configured to receive a teaching plan input by a teacher, identify the teaching plan, determine the importance levels at different time periods, and determine an evaluation score according to the importance levels at different times and the concentration levels of each student at different times.

[0038] As a further solution of the present invention: The model training module includes:

[0039] A preliminary concentration determination unit, configured to obtain the browsing content and operation parameters of the user at each moment in real time based on the granted permissions, and determine the concentration level at each moment according to the browsing content and operation parameters;

[0040] A facial data extraction unit, configured to obtain a student image based on the granted permissions, identify the student image, and extract facial data;

[0041] A training execution unit, configured to perform time-domain registration on the facial data and the concentration level, and use the time-domain registered facial data and concentration level as samples to train a neural network model; the input of the neural network model is the facial data, and the output is the concentration level.

[0042] As a further solution of the present invention: The preliminary concentration determination unit includes:

[0043] A data acquisition subunit, configured to obtain the browsing content and operation parameters of the user at each moment in real time based on the granted permissions;

[0044] A serial number query subunit, configured to read the serial numbers corresponding to the operation parameters at each moment in a preset parameter conversion table; the parameter conversion table contains an operation type item and a serial number item;

[0045] A time aggregation subunit, configured to aggregate the times according to the browsing content to obtain a time period corresponding to each browsing content;

[0046] A serial number arrangement subunit, configured to statistically arrange the serial numbers corresponding to the operation parameters within the time period based on the time sequence to obtain a group of practical operation serial numbers;

[0047] A comparison subunit, configured to read a preset standard serial number group for each browsing content, compare the group of practical operation serial numbers with the standard serial number group, calculate the similarity, and determine the concentration level according to the similarity; wherein, the concentration level is directly proportional to the similarity.

[0048] As a further solution of the present invention: The evaluation score determination module includes:

[0049] A plan identification unit, configured to receive a teaching plan input by a teacher, identify the teaching plan, and determine the importance levels at different time periods; identifying the teaching plan includes a keyword matching process, different keywords corresponding to different importance levels, and both the keywords and their importance levels are preset parameters;

[0050] The concentration statistics unit is used to read the concentration of each student at different times and insert them on the same time axis;

[0051] The number query unit is used to intercept matching points on the time axis according to a preset time step length, and read the number of people whose concentration at the matching points is less than a preset concentration threshold; the matching points are time points;

[0052] The data application unit is used to query the importance corresponding to the matching points and determine the evaluation score according to the importance and the number of people.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention determines the importance of each stage in the teaching process based on the information of the teacher, and determines the concentration of students in each stage based on the information of the students. This process is carried out in real time, and the evaluation score of the teaching process is jointly determined by combining the concentration and the importance, with high globality and strong objectivity. Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0055] Figure 1 It is a flowchart of a teaching level evaluation method based on a local account.

[0056] Figure 2 It is a composition structure diagram of a teaching level evaluation system based on a local account. Detailed Embodiment

[0057] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0058] Figure 1 It is a flowchart of a teaching level evaluation method based on a local account. In an embodiment of the present invention, a teaching level evaluation method based on a local account, the method includes:

[0059] Step S100: Send a permission acquisition request to the user and receive the permission granted by the user; wherein, the user includes students and teachers;

[0060] The application scenario of this application is very clear, which is a smart classroom equipped with electronic devices. Each student is equipped with a tablet computer, and the instructor also has his own computer device. In the technical solution of the present invention, the permission acquisition process is a necessary process, including obtaining the permissions granted by students and the instructor. The obtained permissions include the camera capture permission and the operation information recording permission. The camera capture permission is used to obtain the video of the student or the instructor, and the operation information recording permission is used to obtain the operation behaviors of the student or the instructor on the device, including touch screen signals and keyboard and mouse signals. In the application scenario of this application, the audio signal for operation, that is, the audio control method, is relatively rare.

[0061] Step S200: Obtain the usage process and facial data of each student based on the granted permissions, determine the concentration based on the usage process, and synchronously train the facial data to the neural network model of the concentration; where one student corresponds to one neural network model;

[0062] After receiving the permissions granted by the user, obtain the usage process and facial data of each student based on the granted permissions. The usage process indicates what operations the student has performed on his tablet computer. By analyzing the usage process, the concentration degree of the student during the teaching process can be calculated, which is called concentration. At the same time, this application also obtains facial data. There is a mapping relationship between the facial data and the concentration. Based on the existing facial data and concentration, using the facial data as features (inputs) and the concentration as labels (outputs), a sample set can be constructed. Based on the sample set, a neural network model is trained. After training is completed, inputting the facial data can obtain the concentration.

[0063] It should be noted that in the technical solution of the present invention, the training process of each student is independent. One student corresponds to one neural network model, and the type of facial data of one student is very limited. The training process is very simple, and a simple regression model can be used.

[0064] Step S300: Regularly collect the facial data of each student, and determine the concentration of each student based on the trained neural network model;

[0065] In practical applications, during a teaching activity, regularly collect the facial data of each student, and input the facial data into the trained neural network model to obtain the concentration of each student. Regarding the concept of regular in this application, this application takes the start time of the teaching activity as the zero moment. For example, if a teaching activity has 72 class hours, then the very beginning is the zero moment. Facial data is collected once every minute. The result obtained from the first collection is the result corresponding to one minute, and the result obtained from the second collection is the result corresponding to two minutes, and so on, to obtain multiple results containing time.

[0066] It is worth mentioning that in order to improve the evaluation accuracy, a shorter fixed time interval can be adopted. At this time, the detection granularity is larger and more data is collected. For example, it changes from one minute to thirty seconds, and the amount of data obtained is almost twice the original amount of data.

[0067] Step S400: Receive the teaching plan input by the instructor, identify the teaching plan, determine the importance levels at different time periods, and determine the evaluation score according to the importance levels at different times and the concentration levels of each student at different times;

[0068] The instructor can input the teaching plan for the entire teaching process at the beginning of the teaching (suitable for recorded teaching activities), or input the teaching plan for the current class before each class (suitable for real-time teaching activities). Identify the teaching plan (mainly perform text recognition on the lesson plan). According to the recognition result, the importance levels of each moment in the entire teaching activity can be determined, which is represented by the parameter of importance level; in step S300, the concentration levels of the students at each moment have been calculated. Combine the importance levels at different times and the concentration levels of each student at different times to evaluate the teaching process and obtain the evaluation score; the evaluation score indicates how many students are concentrated on the content of different importance levels in the teaching process and what the concentration degree is. Its practical significance is how much important content is attracted by the students with what concentration degree, which is a very fitting parameter for evaluating the teaching effect.

[0069] Regarding step S100, the steps of obtaining the usage process and facial data of each student based on the granted permissions, determining the concentration degree according to the usage process, and synchronously training the neural network model of facial data to concentration degree include:

[0070] Obtain the browsing content and operation parameters of the user at each moment in real time based on the granted permissions, and determine the concentration degree at each moment according to the browsing content and operation parameters;

[0071] Obtain the student image based on the granted permissions, identify the student image, and extract the facial data;

[0072] Perform time-domain registration on the facial data and the concentration degree, and use the time-domain registered facial data and concentration degree as samples to train the neural network model; the input of the neural network model is the facial data, and the output is the concentration degree.

[0073] In an example of the technical solution of the present invention, the browsing content and operation parameters of the trainee at each moment are obtained in real time based on the granted permissions. The browsing content indicates what content the trainee is viewing on the tablet computer, and the operation parameters indicate what operations are performed. The browsing content and operation parameters at each moment are analyzed to determine the trainee's degree of concentration in the class, which is represented by the parameter of concentration. At the same time, based on the granted permissions, the trainee's image is obtained, and the trainee's image is recognized to extract facial data. This process belongs to the existing facial recognition process and will not be elaborated here. Whether it is the degree of concentration or the facial data, both contain time information. Only the degree of concentration and facial data at the same moment (with a small enough time difference) can be used as the same sample. The facial data and the degree of concentration are registered in the time domain. Based on the facial data and the degree of concentration after time domain registration as samples, a neural network model is trained. Among them, the process of time domain registration is to group the degree of concentration and facial data at the same moment.

[0074] Specifically, the step of obtaining the browsing content and operation parameters of the user at each moment in real time based on the granted permissions and determining the degree of concentration at each moment according to the browsing content and operation parameters includes:

[0075] Obtaining the browsing content and operation parameters of the user at each moment in real time based on the granted permissions;

[0076] Reading the serial number corresponding to the operation parameter at each moment in the preset parameter conversion table; the parameter conversion table contains an operation type item and a serial number item;

[0077] Aggregating the moments according to the browsing content to obtain the time period corresponding to each browsing content;

[0078] Statistically arranging and arranging the serial numbers corresponding to the operation parameters within the time period based on the time sequence to obtain an actual operation serial number group;

[0079] Reading the preset standard serial number group of each browsing content, comparing the actual operation serial number group with the standard serial number group, calculating the similarity, and determining the degree of concentration according to the similarity; wherein, the degree of concentration is proportional to the similarity.

[0080] In an example of the technical solution of the present invention, a specific concentration calculation scheme is provided. The browsing content and operation parameters of the user at each moment are obtained in real time based on the granted permissions. The browsing content is generally the courseware pre-sent by the instructor, which belongs to known data. Regarding the operation parameters, there are only several existing operation parameters, including zooming, sliding, page turning, and marking, etc. They can all be pre-statistically recorded in the parameter conversion table and numbered, so that each operation is quantified as a serial number, and one operation corresponds to a unique serial number. That is, the parameter conversion table contains an operation type item and a serial number item.

[0081] For the registration process of browsing content and operation parameters, the browsing content generally corresponds to a time period. The top - level content is obtained on the tablet based on the granted permissions, which is the browsing content. Since the time corresponding to each browsing content is continuous, it is necessary to aggregate the time according to the browsing content to obtain the time period corresponding to each browsing content.

[0082] Furthermore, regarding the operation information, the time period of each browsing content is determined. The operation information within the time period is the operation process of the trainee regarding this browsing content. Based on the chronological order, the serial numbers corresponding to the operation parameters within the time period are statistically arranged to obtain the practical operation serial number group, which reflects the quantified operation process of the trainee during this period. For each browsing content, since it is known data, its standard operation can be determined in advance. For reading text, it is almost an intermittent sliding and page - turning operation. Its standard operation is pre - statistically determined (the input of the instructor can be received and used as a limitation), and it is represented in the form of a standard serial number group. At this time, the operation information corresponds to the practical operation serial number group, and the standard operation corresponds to the standard serial number group. By comparing the practical operation serial number group and the standard serial number group, calculating the similarity, the concentration degree can be determined according to the similarity.

[0083] Regarding the process of comparing the practical operation serial number group and the standard serial number group and calculating the similarity, the simplest way is to calculate their maximum intersection. The similarity is determined according to the length of the maximum intersection. The longer the length of the maximum intersection, the higher the similarity.

[0084] Regarding step S400, the steps of receiving the teaching plan input by the instructor, identifying the teaching plan, determining the importance of different time periods, and determining the evaluation score according to the importance of different times and the concentration degree of each trainee at different times include:

[0085] Receiving the teaching plan input by the instructor, identifying the teaching plan, and determining the importance of different time periods; identifying the teaching plan includes a keyword matching process. Different keywords correspond to different importance levels, and both the keywords and their importance levels are preset parameters;

[0086] Reading the concentration degree of each trainee at different times and inserting them into the same time axis;

[0087] Intercepting matching points on the time axis according to a preset time step, and reading the number of people whose concentration degree at the matching points is less than the preset concentration degree threshold; the matching points are time points;

[0088] Querying the importance corresponding to the matching points, and determining the evaluation score according to the importance and the number of people.

[0089] In an example of the technical solution of the present invention, the evaluation process is specifically defined. The teaching plan input by the teacher is received and recognized. The recognition of the teaching plan includes a keyword matching process. Different keywords correspond to different importance levels, and both the keywords and their importance levels are preset parameters. The teaching content of different time periods is intercepted from the teaching plan, the keyword matching is performed on the teaching content, the importance level of the matched keywords is read, and then the importance levels of the matched keywords are accumulated to obtain the final importance level. It should be noted that when intercepting the teaching content of different time periods from the teaching plan, the different time periods can be preset time periods, such as two minutes or three minutes, etc. The determination process of the importance level is carried out in segments.

[0090] The concentration levels of each student at different times are read and inserted into the same time axis. The concentration level is the concentration level at each moment. For example, the concentration level is collected every thirty seconds. All students have a concentration level. For the convenience of analysis and processing, it is inserted into the same time axis. Further, there may be a slight difference in the recognition time of the concentration level of each student. For example, there is a difference of a few tenths of a second caused by the data transmission process. Therefore, when analyzing the concentration level, first intercept the matching points on the time axis. The intercepted matching points are to intercept one every thirty seconds (the same as the guiding period of the concentration level analysis process, that is, the period of the image acquisition and recognition process), query the concentration levels of each student at the matching points (moments), and compare them with the preset concentration level threshold. When the concentration level is less than the preset concentration level threshold, it means that the student is lacking attention at the current matching point, and the number of students lacking attention at the matching point (the concentration level is small enough) is obtained, and it can be obtained how many students are absent-minded at the corresponding moment.

[0091] Finally, query the time period where the matching point is located, read the corresponding importance level, and combine the importance level and the number of people to determine the evaluation score.

[0092] Further, the steps of receiving the teaching plan input by the teacher, recognizing the teaching plan, and determining the importance levels of different time periods include:

[0093] Receiving the teaching keywords and their importance levels of each time period input by the teacher;

[0094] Based on the granted permissions, obtaining the audio information of the teacher, locating the teaching keywords, and correcting the teaching keywords and their importance levels of each time period according to the positioning results.

[0095] The above provides a new process for determining a teaching plan. The teaching plan itself is the information uploaded by the trainees, and the teacher knows it best. Therefore, the importance of each time period input by the teacher can be received. At the same time, the teaching keywords for each time period are also received. The purpose of receiving the teaching keywords is to verify the importance. For example, if the corresponding teaching keyword does not appear in a certain time period, that time period is split into two segments and incorporated into the two adjacent time periods respectively, that is, the time period is corrected.

[0096] In addition, the steps for determining the evaluation score based on the importance and the number of people corresponding to the query matching point include:

[0097] Query the threshold of the number of people for each importance, calculate the ratio of the number of people to the threshold of the number of people, and determine the score for each matching point according to the ratio; where the score is inversely proportional to the ratio;

[0098] Determine the weight according to the importance of each matching point, and accumulate all the scores based on the weight to obtain the final evaluation score; where the weight is directly proportional to the importance.

[0099] Specifically, the calculation process of the evaluation score is as follows:

[0100] ; In the formula, represents the evaluation score, represents the total number of matching points, represents the th ratio corresponding to the matching point, represents the th score corresponding to the matching point.

[0101] Among them, the small detail of the above calculation process is that the influence of the importance is greater, so it uses an exponential base.

[0102] The above specifically describes the evaluation process. The evaluation score is comprehensively determined by the number of people and the importance. For each importance, a threshold of the number of people is determined in advance. The threshold of the number of people represents the maximum number of trainees in a state of inattentiveness for that importance; the number of people is the number of people lacking attention at the current matching point. The threshold of the number of people is generally a relatively large value. Calculate the ratio of the number of people to the threshold of the number of people. The ratio is generally less than 1. The smaller the ratio, the fewer people lacking attention, and a higher score is given to it; on this basis, a weight is determined by the importance. The weight can also be understood as the unit score. The higher the importance, the larger the unit score; finally, accumulate all the scores based on the weight to obtain the final evaluation score; the higher the score, the higher the teaching level.

[0103] Figure 2For the component structure diagram of the teaching level evaluation system based on local accounts, in an embodiment of the present invention, a teaching level evaluation system based on local accounts, the system 10 includes:

[0104] A permission acquisition module 11, configured to send a permission acquisition request to a user and receive the permission granted by the user; wherein, the user includes students and teachers;

[0105] A model training module 12, configured to obtain the usage process and facial data of each student based on the granted permission, determine the concentration according to the usage process, and synchronously train the neural network model of the facial data to the concentration; wherein, one student corresponds to one neural network model;

[0106] A concentration recognition module 13, configured to periodically collect the facial data of each student and determine the concentration of each student based on the trained neural network model;

[0107] An evaluation score determination module 14, configured to receive the teaching plan input by the teacher, identify the teaching plan, determine the importance of different time periods, and determine the evaluation score according to the importance of different moments and the concentration of each student at different moments.

[0108] Further, the model training module 12 includes:

[0109] A concentration preliminary determination unit, configured to obtain the browsing content and operation parameters of the user at each moment in real time based on the granted permission, and determine the concentration at each moment according to the browsing content and operation parameters;

[0110] A facial data extraction unit, configured to obtain the student image based on the granted permission, identify the student image, and extract the facial data;

[0111] A training execution unit, configured to perform time domain registration on the facial data and the concentration, and train the neural network model based on the time domain registered facial data and concentration as samples; the input of the neural network model is the facial data, and the output is the concentration.

[0112] Specifically, the concentration preliminary determination unit includes:

[0113] A data acquisition subunit, configured to obtain the browsing content and operation parameters of the user at each moment in real time based on the granted permission;

[0114] A serial number query subunit, configured to read the serial number corresponding to the operation parameter at each moment in a preset parameter conversion table; the parameter conversion table contains an operation type item and a serial number item;

[0115] A moment aggregation subunit, configured to aggregate the moments according to the browsing content to obtain the time period corresponding to each browsing content;

[0116] A serial number arranging subunit, configured to count and arrange serial numbers corresponding to operation parameters within a time period based on chronological order, so as to obtain an actual operation serial number group;

[0117] A comparison subunit, configured to read a preset standard serial number group of each browsing content, compare the actual operation serial number group with the standard serial number group, calculate a similarity, and determine a concentration degree according to the similarity; wherein, the concentration degree is directly proportional to the similarity.

[0118] Furthermore, the evaluation score determination module 14 includes:

[0119] A solution recognition unit, configured to receive a teaching plan input by a teacher, recognize the teaching plan, and determine the importance degrees of different time periods; recognizing the teaching plan includes a keyword matching process, different keywords corresponding to different importance degrees, and both the keywords and their importance degrees are preset parameters;

[0120] A concentration degree statistics unit, configured to read the concentration degrees of each student at different times and insert them into the same time axis;

[0121] A number query unit, configured to intercept matching points on the time axis according to a preset time step length, and read the number of people whose concentration degrees at the matching points are less than a preset concentration degree threshold; the matching points are time points;

[0122] A data application unit, configured to query the importance degree corresponding to the matching point, and determine an evaluation score according to the importance degree and the number of people.

[0123] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A teaching evaluation method for teachers based on the concentration of students, characterized in that, The method includes: Sending a permission acquisition request to the user and receiving the granted permissions; wherein, the users include trainees and instructors; Obtaining the usage progress and facial data of each trainee based on the granted permissions, determining the concentration based on the usage progress, and synchronously training the facial data to a neural network model for concentration; wherein, one trainee corresponds to one neural network model; Regularly collecting the facial data of each trainee and determining the concentration of each trainee based on the trained neural network model; Receiving the teaching plan input by the instructor, identifying the teaching plan, and obtaining the importance levels of different time periods; Intercepting matching points on the time axis, reading the concentration of each trainee at the matching points, and calculating the number of trainees whose concentration is less than the preset concentration threshold; Querying the importance level and the number of people at the matching points to determine the evaluation score.

2. The teaching evaluation method for teachers based on the concentration of students according to claim 1, characterized in that, The step of obtaining the usage progress and facial data of each trainee based on the granted permissions, determining the concentration based on the usage progress, and synchronously training the facial data to a neural network model for concentration includes: Obtaining the browsing content and operation parameters of the user at each moment in real time based on the granted permissions, and determining the concentration at each moment according to the browsing content and operation parameters; Obtaining the trainee images based on the granted permissions, identifying the trainee images, and extracting the facial data; Performing time-domain registration on the facial data and the concentration, and using the time-domain registered facial data and concentration as samples to train the neural network model; the input of the neural network model is the facial data, and the output is the concentration.

3. The teaching evaluation method for teachers based on the attention level of students according to claim 2, characterized in that, The step of obtaining the browsing content and operation parameters of the user at each moment in real time based on the granted permissions, and determining the concentration at each moment according to the browsing content and operation parameters includes: Obtaining the browsing content and operation parameters of the user at each moment in real time based on the granted permissions; Reading the serial numbers corresponding to the operation parameters at each moment in a preset parameter conversion table; the parameter conversion table contains an operation type item and a serial number item; Aggregating the moments according to the browsing content to obtain the time period corresponding to each browsing content; Statistically arranging and arranging the serial numbers corresponding to the operation parameters within the time period based on the time sequence to obtain a group of practical operation serial numbers; Reading the preset standard serial number group of each browsing content, comparing the group of practical operation serial numbers with the standard serial number group, calculating the similarity, and determining the concentration according to the similarity; wherein, the concentration is proportional to the similarity.

4. The teaching evaluation method for teachers based on the concentration of students according to claim 1, wherein The step of receiving the teaching plan input by the instructor, identifying the teaching plan, determining the importance levels of different time periods, and determining the evaluation score according to the importance levels at different moments and the concentration of each trainee at different moments includes: Receiving the teaching plan input by the instructor, identifying the teaching plan, and determining the importance levels of different time periods; identifying the teaching plan includes a keyword matching process, different keywords correspond to different importance levels, and the keywords and their importance levels are all preset parameters; Reading the concentration of each trainee at different moments and inserting them into the same time axis; Intercepting matching points on the time axis at a preset time step, and reading the number of people whose concentration at the matching points is less than the preset concentration threshold; the matching points are time points; Query the importance corresponding to the matching points, and determine the evaluation score according to the importance and the number of people.

5. The teaching evaluation method for teachers based on the attention level of students according to claim 4, characterized in that The steps of receiving the teaching plan input by the instructor, identifying the teaching plan, and determining the importance at different time periods include: Receiving the teaching keywords and their importance at each time period input by the instructor; Obtaining the audio information of the instructor based on the granted permission, locating the teaching keywords, and correcting the teaching keywords and their importance at each time period according to the positioning result.

6. The teaching evaluation method for teachers based on the concentration of students according to claim 4, characterized in that, The steps of querying the importance corresponding to the matching points and determining the evaluation score according to the importance and the number of people include: Query the number threshold for each importance, calculate the ratio of the number of people to the number threshold, and determine the score for each matching point according to the ratio; where the score is inversely proportional to the ratio; Determine the weight according to the importance of each matching point, and accumulate all the scores based on the weight to obtain the final evaluation score; where the weight is directly proportional to the importance.

7. A teaching evaluation system for teachers based on the concentration of students, characterized in that The system includes: A permission acquisition module for sending a permission acquisition request to the user and receiving the permission granted by the user; where the user includes students and instructors; A model training module for obtaining the usage process and facial data of each student based on the granted permission, determining the concentration according to the usage process, and synchronously training the neural network model of the facial data to the concentration; where one student corresponds to one neural network model; A concentration recognition module for regularly collecting the facial data of each student and determining the concentration of each student based on the trained neural network model; An evaluation score determination module for receiving the teaching plan input by the instructor, identifying the teaching plan, and obtaining the importance at different time periods; intercepting the matching points on the time axis, reading the concentration of each student at the matching points, and calculating the number of students whose concentration is less than the preset concentration threshold; querying the importance and the number of people at the matching points to determine the evaluation score.

8. The teaching evaluation system for teachers based on the concentration of students according to claim 7, wherein, The model training module includes: A concentration preliminary determination unit for obtaining the browsing content and operation parameters of the user at each moment in real time based on the granted permission, and determining the concentration at each moment according to the browsing content and operation parameters; A facial data extraction unit for obtaining the student image based on the granted permission, identifying the student image, and extracting the facial data; A training execution unit for performing time-domain registration on the facial data and the concentration, and training the neural network model based on the time-domain registered facial data and concentration as samples; the input of the neural network model is the facial data, and the output is the concentration.

9. The teaching evaluation system for teachers based on the concentration of students according to claim 8, characterized in that The concentration preliminary determination unit includes: A data acquisition subunit for obtaining the browsing content and operation parameters of the user at each moment in real time based on the granted permission; A serial number query subunit for reading the serial number corresponding to the operation parameter at each moment in the preset parameter conversion table; the parameter conversion table contains an operation type item and a serial number item; A moment aggregation subunit for aggregating the moments according to the browsing content to obtain the time period corresponding to each browsing content; A serial number arrangement subunit for statistically arranging the serial numbers corresponding to the operation parameters within the time period based on the time order to obtain an actual operation serial number group; A comparison subunit, configured to read a preset standard serial number group of each browsing content, compare the actual operation serial number group with the standard serial number group, calculate a similarity, and determine a concentration degree according to the similarity; wherein, the concentration degree is directly proportional to the similarity.

10. The teaching evaluation system for teachers based on the concentration of students according to claim 7, characterized in that, The evaluation score determination module includes: A scheme recognition unit, configured to receive a teaching scheme input by a teacher, recognize the teaching scheme, and determine the importance degrees of different time periods; recognizing the teaching scheme includes a keyword matching process, different keywords corresponding to different importance degrees, and both the keywords and their importance degrees being preset parameters; A concentration degree statistics unit, configured to read the concentration degrees of each student at different times and insert them into the same time axis; A number query unit, configured to intercept matching points on the time axis at a preset time step and read the number of people whose concentration degrees at the matching points are less than a preset concentration degree threshold; the matching points are time points; A data application unit, configured to query the importance degree corresponding to the matching point and determine an evaluation score according to the importance degree and the number of people.

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