CN117934220A - Teaching quality evaluation system and method based on 5G network - Google Patents

Abstract

The invention provides a teaching quality evaluation system and method based on a 5G network, and relates to the technical field of teaching quality evaluation systems. According to the invention, the integral growth module, the integral rewarding module and the integral counting module are arranged, and the integral and rewarding module is introduced in course learning to serve as positive feedback of student learning, so that the learning enthusiasm of students can be improved, the students are encouraged to participate in courses all around, communication interaction is carried out with teachers, positive feedback is provided for the students, the effects of activating classroom atmosphere and improving teaching quality are achieved, meanwhile, the teaching quality can be comprehensively evaluated through integral data and score of the students, and the unilaterality of judging the teaching quality only by score is reduced.

Description

A teaching quality evaluation system and method based on 5G network

Technical Field

The present invention relates to the technical field of teaching quality evaluation systems, and specifically to a teaching quality evaluation system and method based on a 5G network.

Background technique

The 5G network service platform is used to provide various telecommunications and data services, including telephone, video data messaging, etc., and has been widely used in people's work and life.

With the development of technology, when it is inconvenient for students to go to school, teachers often use 5G video calls to teach. Since this video call teaching method has not been popular for a long time, it has many technical pain points. For example, in video call teaching, there is a lack of interaction between teachers and students, which can easily lead to students' lack of enthusiasm for class. At the same time, because teachers cannot observe the students as a whole, it is difficult to rely on teaching experience to judge the students' mastery of a certain knowledge point, and there is no way to judge the quality of their own teaching. If they only rely on tests to evaluate the quality of teaching, it is too one-sided to find other problems, and it is easy to cause students to have a rebellious psychology, which will lead to poor test scores in the long run. Therefore, based on the above reasons, it is necessary to provide a system that can not only improve students' enthusiasm for class, increase interaction between students and teachers, but also evaluate the quality of teaching from multiple angles.

The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art.

Summary of the invention

The purpose of the present invention is to provide a teaching quality evaluation system and method based on 5G network to solve the problems raised in the above background technology.

To achieve the above object, the present invention provides the following technical solutions:

A teaching quality evaluation system based on a 5G network includes a teacher terminal and several groups of student terminals, wherein:

The student terminal includes a data acquisition module, a first data transmission module, an operation module and a first storage module;

The data acquisition module is electrically connected to the first data transmission module, and is used to collect learning data of students during video learning and send it to the first data transmission module;

The first data transmission module is electrically connected to the operation module and the first storage module, and is in communication connection with the teacher end, and is used to send the personal points data, points growth data, and points redemption data received from the teacher end to the first storage module, send the test data to the operation module, and send the learning data and score coefficient to the teacher end;

The operation module is used to generate a score coefficient according to the test data and send the score coefficient to the first data transmission module;

The first storage module is electrically connected to the first data transmission module for storing personal points data, points growth data, and points redemption data;

The teacher terminal includes a second data transmission module, a points growth module, a points reward module, a points statistics module, a test module, a second storage module, and a data analysis module;

The second data transmission module is communicatively connected with the first data transmission module, and is used for receiving the learning data and the score coefficient, and sending the learning data and the score coefficient to the score growth module;

The integral growth module is electrically connected to the second data transmission module and the integral statistics module, and is used to generate integral growth data according to the learning data and the score coefficient, and send the integral growth data and the score coefficient to the integral statistics module;

The point reward module is electrically connected to the point statistics module, and is used to set different reward items for redemption, generate point redemption data according to the redemption content, and send the point redemption data to the point statistics module;

The points statistics module is electrically connected to the points growth module, the points reward module, the second storage module, and the data analysis module, and is used to generate personal points data and maximum points data according to the points growth data and the points exchange data, and send the personal points data, the points growth data, and the points exchange data to the second data transmission module and the second storage module, and send the maximum points data and the score coefficient to the data analysis module;

The test module is electrically connected to the second data transmission module, and is used to generate test data and send it to the second data transmission module;

The second storage module is used to store personal points data, points growth data, and points redemption data;

The data analysis module is used to evaluate the teaching quality according to the maximum score data and the score coefficient.

Preferably, the learning data includes learning start time, learning end time, and number of interactions.

Preferably, the test data includes question data and encrypted answer data.

Preferably, the logic for generating the score coefficient is:

Use the operation module to reply to the question data, generate answer data, compare the answer data with the answer data, and calculate the score coefficient A _{(x, y)} . The calculation method is: A _{(x, y)} = n/N, where n represents the number of correctly answered questions in the answer data, N represents the total number of questions in the question data, and the subscripts x and y represent the student-side label and the number of tests respectively. Both x and y are positive integers, x = 1, 2, 3...X, y = 1, 2, 3...Y.

Preferably, the generation logic of the personal points data and the maximum points data is:

At the beginning and end of each course, the student's study start time and study end time are collected and compared with the preset standard start time and standard end time to obtain the number of times the student starts and ends the class on time, and mark it as the punctuality number j. The punctuality rate B _{(x, m)} is generated based on the punctuality number j. The calculation method is B _{(x, m)} = j/m, where m represents the number of courses, m is a positive integer, m = 1, 2, 3...M;

The first growth data Dz1 _{(x , y) is generated according to the score coefficient A (x} , y), and the second growth data Dz2 _{(x, m)} is generated according to the punctuality rate B _{(x, m)} and the number of interactions C _{(x, m)} . The calculation formulas of the first growth data Dz1 _{(x, y)} and the second growth data Dz2 _{(x, m)} are respectively:

After the calculation is completed, the first growth data Dz1 _{(x, y)} and the second growth data Dz2 _{(x, m)} are rounded according to the truncation method, where μ represents the minimum unit integral and μ is a positive integer;

The integral growth data D _x is obtained according to the first growth data Dz1 _{(x, y)} and the second growth data Dz2 _x , and the calculation formula is:

Then calculate the personal integral data F _x and the maximum integral data G, the calculation formula is:

In the formula, E _{(x, p)} represents the points redemption data, the subscript p represents the number of redemptions, p is a positive integer, p = 1, 2, 3...P.

Preferably, the logic for evaluating teaching quality is:

The end of each test is used as an evaluation node for a teaching stage, and the evaluation coefficient is generated according to the score coefficient A _{(x, y)} Using the evaluation coefficient/> The course scores in teaching quality are evaluated and the calculation formula is:

Where A′ represents the preset pass ratio coefficient;

Then the maximum integral data G is compared with the preset integral threshold Gyz to evaluate the teaching atmosphere in the teaching quality;

When the evaluation coefficient When the course results in teaching quality are considered to be up to standard;

When the evaluation coefficient When the course results in teaching quality are considered to be below the standard;

When the maximum integral data G≥Gyz, it is considered that the teaching atmosphere in the teaching quality reaches the standard;

When the maximum integral data G＜Gyz, it is considered that the teaching atmosphere in the teaching quality does not meet the standard.

A teaching quality evaluation method based on 5G network, the evaluation method is applicable to the above-mentioned evaluation system, and the steps are as follows:

Collect learning data and score coefficients of each student;

The points growth data of each student is obtained based on the learning data and the score coefficient, and then the personal points data and the maximum points data are calculated based on the points growth data and the points exchange data;

An evaluation coefficient is generated based on the scoring coefficient, and the teaching quality is comprehensively evaluated using the evaluation coefficient and the maximum score data.

Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention introduces points and rewards as positive feedback for students' learning in course learning by setting up a points growth module, a points reward module and a points statistics module, which can improve students' learning enthusiasm, encourage students to participate in the course wholeheartedly, communicate and interact more with teachers, provide students with positive feedback, play a role in livening up the classroom atmosphere and improving the teaching quality, and at the same time, can comprehensively evaluate the teaching quality through the overall points data and grades of the students, reducing the one-sidedness of judging the teaching quality only by grades.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of an overall module of the present invention;

FIG. 2 is a schematic diagram of the process of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

Example:

Please refer to Figures 1-2, the present invention provides a technical solution:

A teaching quality evaluation system based on 5G network includes a teacher end and several groups of student ends, which are respectively applicable to teachers and students. Both the teacher end and the student end are based on PC computers and use 5G network to form a one-to-many mode, wherein:

The student terminal includes a data acquisition module, a first data transmission module, an operation module and a first storage module.

The data acquisition module is electrically connected to the first data transmission module, and is used to collect learning data of students during video learning and send it to the first data transmission module. The learning data includes learning start time, learning end time, and number of interactions. The learning start time and learning end time are used to determine whether the student has situations such as early or late withdrawal, absenteeism, etc. The interaction method includes asking questions to the teacher through video, answering the teacher's questions, and other teacher-student interactions. The interaction can be initiated by the student end or the teacher end, but it needs to be confirmed by the teacher end to be determined as valid, thereby increasing the number of interactions on the corresponding student end.

The first data transmission module is electrically connected to the operation module and the first storage module, and is communicatively connected to the teacher end, and is used to send the personal points data, points growth data, and points redemption data received from the teacher end to the first storage module, send the test data to the operation module, and send the learning data and score coefficient to the teacher end.

The operation module is used to provide students with an answering environment, including but not limited to input modules such as a touch screen and a virtual keyboard, which allow students to fill in test data, generate score coefficients, and send the score coefficients to the first data transmission module.

The first storage module is electrically connected to the first data transmission module for storing personal points data, points growth data, and points redemption data.

The teacher end includes a second data transmission module, a points growth module, a points reward module, a points statistics module, a test module, a second storage module, and a data analysis module.

The second data transmission module is communicatively connected with the first data transmission module, and is used for receiving the learning data and the score coefficient, and sending the learning data and the score coefficient to the score growth module.

The integral growth module is electrically connected to the second data transmission module and the integral statistics module, and is used to generate integral growth data according to the learning data and the score coefficient, and send the integral growth data and the score coefficient to the integral statistics module.

The point reward module is electrically connected to the point statistics module, and is used to set different reward items for redemption, generate point redemption data according to the redemption content, and send the point redemption data to the point statistics module.

The points statistics module is electrically connected to the points growth module, the points reward module, the second storage module, and the data analysis module, and is used to generate personal points data and maximum points data based on the points growth data and the points redemption data, and send the personal points data, the points growth data, and the points redemption data to the second data transmission module and the second storage module, and send the maximum points data and the score coefficient to the data analysis module.

The test module is electrically connected to the second data transmission module and is used to generate test data and send it to the second data transmission module. The test data includes question data and encrypted answer data.

The second storage module is used to store personal points data, points growth data, and points redemption data. The first storage module and the second storage module are set to store together. The first is to facilitate teachers and students to view data changes, and the second is to serve as a backup to avoid data loss at one end and make it difficult to recover.

The data analysis module is used to evaluate the teaching quality according to the maximum score data and the score coefficient.

The generation logic of the score coefficient is:

Use the operation module to reply to the question data, generate answer data, compare the answer data with the answer data, and calculate the score coefficient A _{(x, y)} , which is used to represent the student's accuracy in the test. The calculation method is: A _{(x, y)} = n/N, where n represents the number of correctly answered questions in the answer data, N represents the total number of questions in the question data, and the subscripts x and y represent the student's label and the number of tests respectively. Both x and y are positive integers, x = 1, 2, 3...X, y = 1, 2, 3...Y.

The generation logic of the personal points data and the maximum points data is:

At the beginning and end of each course, the student's study start time and study end time are collected and compared with the preset standard start time and standard end time to obtain the number of times the student starts and ends class on time, and mark it as the punctuality number j. The punctuality rate B _{(x, m)} is generated based on the punctuality number j. The calculation method is B _{(x, m)} = j/m, where m represents the number of courses, m is a positive integer, m = 1, 2, 3...M, and the punctuality rate B _{(x, m)} represents the proportion of the student who starts and ends class on time in m courses.

The first growth data Dz1 _{(x , y) is generated according to the score coefficient A (x} , y), and the second growth data Dz2 _{(x, m)} is generated according to the punctuality rate B _{(x, m)} and the number of interactions C _{(x, m)} . The calculation formulas of the first growth data Dz1 _{(x, y)} and the second growth data Dz2 _{(x, m)} are respectively:

After the calculation is completed, the first growth data Dz1 _{(x, y)} and the second growth data Dz2 _{(x, m)} are rounded according to the truncation method, that is, all decimal parts are discarded and rounded, where μ represents the minimum unit integral and μ is a positive integer.

In the formula It represents the average score coefficient of the whole class in the test, and the average score coefficient of the whole class is close to the score coefficient A _{(x, y)} of a certain student. Therefore, it can be seen that when the score coefficient A _{(x, y)} of a certain student is greater than or equal to the average score coefficient of the whole class, the value of the first growth data Dz1 _{(x, y)} is greater than or equal to μ and less than 2μ, and it is μ after rounding. When the score coefficient A _{(x, y)} of a certain student is less than the average score coefficient of the whole class, the value of the second growth data Dz2 _{(x, m)} is greater than 0 and less than μ, and it is 0 after rounding. In other words, when a student's test score is greater than or equal to the average score coefficient of the whole class, he or she can obtain μ points, otherwise he or she will not get any.

Similarly, It represents the average punctuality rate of the whole class in m courses. When a student completes the course, if his punctuality rate B _{(x, m)} is greater than or equal to the average punctuality rate of the whole class, he can get μ points, otherwise he will not get any.

It can be seen that fewer points can be obtained by passing tests and attending classes on time, because these are some basic responsibilities of students, and therefore the rewards that can be obtained are also lower. However, more points can be obtained by interacting with teachers, which encourages students to fully participate in the course, communicate and interact more with teachers, provide students with positive feedback, and play a role in livening up the classroom atmosphere and improving the quality of teaching.

The integral growth data D _x is obtained according to the first growth data Dz1 _{(x, y)} and the second growth data Dz2 _x , and the calculation formula is:

The integral growth data _Dx represents the total points obtained by the student through tests, attending classes on time, and interacting with teachers. The second growth data Dz2 _{(x, m)} starts to accumulate from m=3. This is because the teacher may use the first two courses to introduce the course, introduce himself, sort out the outline, etc., and the students may not start on time because they are not familiar with the first class. This avoids these uncontrollable reasons from affecting the subsequent evaluation.

Then calculate the personal integral data F _x and the maximum integral data G, the calculation formula is:

In the formula, E _{(x, p)} represents the points redemption data, the subscript p represents the number of redemptions, p is a positive integer, p = 1, 2, 3...P.

For example, a teacher sets "one exemption from homework" as a reward, which requires a certain number of points to redeem. When a student redeems, the point redemption data E _{(x, p)} is generated. The personal point data F _x represents the number of points remaining after the student has redeemed. The maximum point data G represents the sum of all students' point growth data D _x . It is not difficult to see from the calculation formula of the point growth data D _x that the larger the maximum point data G is, the more harmonious the classroom atmosphere is for the students in that class.

The logic for evaluating teaching quality is:

The end of each test is used as an evaluation node for a teaching stage, and the evaluation coefficient is generated according to the score coefficient A _{(x, y)} Using the evaluation coefficient/> The course scores in teaching quality are evaluated and the calculation formula is:

Where A′ represents the preset passing ratio coefficient.

The maximum integral data G is then compared with the preset integral threshold Gyz to evaluate the teaching atmosphere in the teaching quality.

In the formula Indicates the average score coefficient of the whole class. When the average score coefficient is greater than or equal to the pass rate coefficient A', /> When the average score coefficient is less than the passing rate coefficient A',/>

When the evaluation coefficient When the teaching quality is considered to be up to standard, the course results are considered to be up to standard.

When the evaluation coefficient When the teaching quality is considered to be below the standard, the course results are considered to be below the standard.

When the maximum integral data G≥Gyz, it is considered that the teaching atmosphere in the teaching quality has reached the standard.

When the maximum integral data G＜Gyz, it is considered that the teaching atmosphere in the teaching quality does not meet the standard.

No matter which aspect fails to meet the standards, targeted improvements need to be made.

The present invention also provides a teaching quality evaluation method based on a 5G network, which is applicable to the above-mentioned evaluation system, and the steps are as follows:

Collect learning data and score coefficients of each student.

The points growth data of each student is obtained based on the learning data and the scoring coefficient, and then the personal points data and maximum points data are calculated respectively based on the points growth data and the points redemption data.

Generate an evaluation coefficient based on the scoring coefficient, and use the evaluation coefficient and the maximum score data to conduct a comprehensive evaluation of the teaching quality.

The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The above description is only a specific implementation mode of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims (7)

1. The teaching quality evaluation system based on the 5G network is characterized by comprising a teacher end and a plurality of groups of student ends, wherein:

the student end comprises a data acquisition module, a first data transmission module, an operation module and a first storage module;

The data acquisition module is electrically connected with the first data transmission module and is used for acquiring learning data of students during video learning and transmitting the learning data to the first data transmission module;

The first data transmission module is electrically connected with the operation module and the first storage module, is in communication connection with the teacher end, and is used for sending personal point data, point growth data and point exchange data received from the teacher end to the first storage module, sending test data to the operation module and sending learning data and a score coefficient to the teacher end;

The operation module is used for generating a scoring coefficient according to the test data and sending the scoring coefficient to the first data transmission module;

The first storage module is electrically connected with the first data transmission module and used for storing personal point data, point growth data and point exchange data;

The teacher end comprises a second data transmission module, an integral growth module, an integral rewarding module, an integral statistics module, a test module, a second storage module and a data analysis module;

The second data transmission module is in communication connection with the first data transmission module and is used for receiving the learning data and the score coefficient and sending the learning data and the score coefficient to the integral growth module;

The integral growth module is electrically connected with the second data transmission module and the integral statistics module and is used for generating integral growth data according to the learning data and the score coefficient and sending the integral growth data and the score coefficient to the integral statistics module;

the point rewarding module is electrically connected with the point counting module and is used for setting different rewarding items to be converted, generating point conversion data according to conversion contents and sending the point conversion data to the point counting module;

The point counting module is electrically connected with the point growing module, the point rewarding module, the second storage module and the data analysis module and is used for generating personal point data and maximum point data according to the point growing data and the point exchange data, sending the personal point data, the point growing data and the point exchange data to the second data transmission module and the second storage module and sending the maximum point data and the score coefficient to the data analysis module;

The test module is electrically connected with the second data transmission module and is used for generating test data and transmitting the test data to the second data transmission module;

The second storage module is used for storing personal point data, point growth data and point exchange data;

The data analysis module is used for evaluating the teaching quality according to the maximum integral data and the score coefficient.

2. The teaching quality evaluation system based on the 5G network according to claim 1, wherein: the learning data comprises learning start time, learning end time and interaction times.

3. The teaching quality evaluation system based on the 5G network according to claim 2, wherein: the quiz data includes question data and encrypted answer data.

4. A system for evaluating teaching quality based on a 5G network according to claim 3, wherein: the generation logic of the score coefficient is as follows:

The operation module is utilized to answer the question data, answer data are generated, the answer data are compared with the answer data, a score coefficient A _(x,y) is calculated, and the calculation mode is as follows: a _(x,y) =n/N, where N represents the number of questions in the answer data that are answered correctly, N represents the total number of questions in the question data, X and Y represent the number of tests and the number of labels at the student's end, respectively, X and Y are positive integers, x=1, 2,3 … X, and y=1, 2,3 … Y.

5. The teaching quality evaluation system based on the 5G network according to claim 4, wherein: the generation logic of the personal integral data and the maximum integral data is as follows:

When each course starts and ends, collecting learning starting time and learning ending time of students, comparing the learning starting time and the learning ending time with preset standard starting time and standard ending time to obtain the times of the students on-time class and off-time class, calibrating the times as time keeping times j, generating time keeping rate B _(x,m) according to the time keeping times j, and calculating in a mode of B _(x,m) =j/M, wherein M represents the course times, M is a positive integer, and m=1, 2 and 3 … M;

Generating first growth data Dz1 _(x,y) according to the score coefficient A _(x,y), generating second growth data Dz2 _(x,m) according to the time keeping rate B _(x,m) and the interaction times C _(x,m), wherein the calculation formulas of the first growth data Dz1 _(x,y) and the second growth data Dz2 _(x,m) are respectively as follows:

After the calculation is completed, rounding the first increment data Dz1 _(x,y) and the second increment data Dz2 _(x,m) according to a tail-removing method, wherein mu represents the minimum unit integral, and mu is a positive integer;

The integral growth data D _x is obtained according to the first growth data Dz1 _(x,y) and the second growth data Dz2 _x, and the calculation formula is as follows:

And then calculating personal integral data F _x and maximum integral data G, wherein the calculation formula is as follows:

Wherein E _(x,p) represents point redemption data, subscript P represents redemption times, P is a positive integer, and p=1, 2,3 … P.

6. The teaching quality evaluation system based on the 5G network according to claim 5, wherein: the logic for evaluating the teaching quality is as follows:

taking the end of each test as an evaluation node of a teaching stage, and generating an evaluation coefficient according to the score coefficient A _(x,y) Evaluating course score in the teaching quality by using an evaluation coefficient phi, wherein a calculation formula is as follows:

Wherein A' represents a preset passing ratio coefficient;

Comparing the maximum integral data G with a preset integral threshold Gyz to evaluate the teaching atmosphere in the teaching quality;

when evaluating coefficient When the course score in the teaching quality is considered to reach the standard;

when evaluating coefficient When the course score in the teaching quality is considered to be not up to the standard;

when the maximum integral data G is more than or equal to Gyz, the teaching atmosphere in the teaching quality is considered to reach the standard;

And when the maximum integral data G is less than Gyz, the teaching atmosphere in the teaching quality is considered to be unsatisfied with the standard.

7. A teaching quality evaluation method based on a 5G network is characterized by comprising the following steps: the evaluation method is applicable to the evaluation system of any one of claims 1-6, and comprises the following steps:

Collecting learning data and score coefficients of each student end;

Obtaining integral growth data of each student end according to the learning data and the score coefficient, and respectively calculating personal integral data and maximum integral data according to the integral growth data and the integral exchange data;

And generating an evaluation coefficient according to the score coefficient, and comprehensively evaluating the teaching quality by using the evaluation coefficient and the maximum integral data.