Teaching quality comprehensive evaluation system based on big data analysis

Abstract

The invention discloses a teaching quality comprehensive evaluation system based on big data analysis, and relates to the technical field of big data analysis. The system comprises the following modules: a data acquisition module; a data preprocessing module; the preliminary score calculation module is used for calculating the preliminary scores of the students by utilizing a weighted summation model or other basic algorithms based on the data features; the dynamic weight adjusting module is used for adjusting the weight of each feature in the scoring model according to the real-time student performance or the teaching target; the comprehensive scoring and evaluation module is used for integrating the dynamically adjusted weight and data to generate a final teaching quality scoring result; and the visual display module is used for displaying the final teaching quality evaluation result to a user in a graphical mode. According to the invention, a dynamic weight adjustment formula is adopted, and the weight in the scoring model can be automatically adjusted according to the score fluctuation and the classroom participation degree of the students. Not only can the relevance between the data be quantified, but also the dynamic weight adjustment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular, to a comprehensive teaching quality evaluation system based on big data analysis. Background Art

[0002] In the current education field, teaching quality evaluation, as an important means of teaching management and improvement, has attracted much attention. However, traditional teaching quality evaluation methods often rely on manual evaluation and simple quantitative indicators (such as exam scores, class attendance, etc.), lacking a comprehensive analysis of students' behaviors and learning dynamics. The main problems of this method are as follows:

[0003] Single data dimension: Traditional evaluation methods mainly focus on final exam scores or teachers' subjective evaluations, ignoring students' participation behaviors in the learning process (such as classroom interaction, quality of homework completion) and dynamic changes.

[0004] Lack of real-time performance: Evaluation results are mostly stage summaries, unable to capture changes in students' learning status and teaching effects in real time, resulting in a lag in teaching optimization response.

[0005] In recent years, with the rapid development of information technology and big data analysis, combining big data technology with teaching quality evaluation provides a new opportunity to solve the above problems. Therefore, we propose a comprehensive teaching quality evaluation system based on big data analysis to solve the above problems. Summary of the Invention

[0006] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0007] In view of the problems existing in the above-mentioned prior art, the present invention is proposed.

[0008] To solve the above technical problems, the present invention provides the following technical solution: An evaluation system for a comprehensive teaching quality evaluation method based on big data analysis, the system includes the following modules:

[0009] A data acquisition module, responsible for obtaining raw data related to teaching quality from multiple channels; a data preprocessing module, which cleans and organizes the collected raw data to ensure data quality and prepare for subsequent analysis;

[0010] A preliminary score calculation module, which calculates the preliminary scores of students based on data characteristics using a weighted summation model or other basic algorithms; a dynamic weight adjustment module, which adjusts the weights of each feature in the scoring model according to real-time student performance or teaching objectives;

[0011] A comprehensive scoring and evaluation module that integrates the dynamically adjusted weights and data to generate the final teaching quality scoring result; and a visualization display module that presents the final teaching quality evaluation result to users, including teachers, parents, or school administrators, in a graphical manner.

[0012] An evaluation method applied to the above-mentioned comprehensive teaching quality evaluation system based on big data analysis. This method includes: collecting data related to teaching quality, integrating and preprocessing data from different sources, and after the data processing is completed, constructing a preliminary scoring model through these data;

[0013] Establishing a dynamic adaptive scoring adjustment strategy, that is, according to the performance of students in the teaching process, the scoring model will be adjusted in real time. This strategy specifically includes the following rules:

[0014] Rule 1: If a student's grades fluctuate greatly within a certain period of time, the weights of classroom interaction and homework completion in the scoring will be automatically increased; Rule 2: If a student has a problem of insufficient participation, the weights of classroom interaction and teacher evaluation will be increased;

[0015] After dynamic adjustment, generate the final teaching quality evaluation result and present the result to teachers, school administrators, or parents in a visual way.

[0016] As a preferred solution of the comprehensive teaching quality evaluation method based on big data analysis described in the present invention, wherein: the preliminary scoring model calculates the preliminary teaching score by weighted summation of the data characteristics of each student, and the calculation formula is:

[0017]

[0018] Wherein, represents the preliminary score of student i; X i,k is the value of student i on the kth feature; w k is the preliminary weight related to the kth feature, reflecting the relative importance of the feature; Ω represents the data integration interval, representing the learning process or time range of the student.

[0019] As a preferred solution of the comprehensive teaching quality evaluation method based on big data analysis described in the present invention, wherein: the construction process of the dynamic adaptive scoring adjustment strategy includes:

[0020] S101: According to the performance of students in the teaching process, set dynamic adjustment factors through different rules to change the weights of each feature;

[0021] S102: Adjust the weights of each feature based on real-time student behavior data and update the scoring model;

[0022] S103: Use the updated weights and the student data X i,k to recalculate the dynamic score of the student.

[0023] As a preferred solution of the comprehensive teaching quality evaluation method based on big data analysis described in the present invention, wherein: the dynamic adjustment factor is expressed by the following formula:

[0024]

[0025] wherein, is the weight adjustment amount of the k-th feature at time t, and α k is the adjustment sensitivity of this feature, and f(X i,k , t) is a function reflecting the change of the student's performance;

[0026] Then the adjusted weight

[0027] The expression of the updated teaching quality scoring model is:

[0028]

[0029] wherein, S i t represents the updated dynamic score of the student, is the dynamically adjusted weight of the k-th feature.

[0030] As a preferred solution of the comprehensive teaching quality evaluation method based on big data analysis described in the present invention, wherein: when the dynamic adaptive scoring adjustment strategy adopts Rule 1, at this time f(X i,k , t) represents measuring the score fluctuation by calculating the standard deviation of the student's scores, that is, f(X i,k , t) = σ. If the standard deviation σ is greater than the set standard deviation threshold, it is considered that the score fluctuation is large.

[0031] As a preferred solution of the comprehensive teaching quality evaluation method based on big data analysis described in the present invention, wherein: when the dynamic adaptive scoring adjustment strategy adopts Rule 2, at this time f(X i,k , t) represents the comprehensive influence of score stability and insufficient participation, that is:

[0032]

[0033] wherein, σ max represents the maximum tolerance value of score stability, that is, the standard deviation threshold, σ is the standard deviation of the student's scores, and p is the number of times the student participates in classroom interaction; p min is the minimum participation times threshold.

[0034] As a preferred embodiment of the comprehensive teaching quality evaluation method based on big data analysis of the present invention, wherein: the final teaching quality evaluation result is an evaluation conclusion after comprehensively considering various dimensional factors, and based on the dynamic scoring of students, a comprehensive teaching quality evaluation result S is obtained final ;

[0035]

[0036] wherein, S final is the final comprehensive teaching quality evaluation result; N represents the total number of students; S i t represents the dynamic score of the i-th student at time t, ||X i || 2 represents the Euclidean norm of the data of the i-th student, which measures the volatility of the student's performance, and ∈ represents the Gaussian function parameter;

[0037] is a Gaussian function, which reduces the influence of students with large volatility on the final evaluation result; Z represents a normalization factor to ensure that the final evaluation result is within a reasonable range.

[0038] The present invention also discloses a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned comprehensive teaching quality evaluation system based on big data analysis are implemented.

[0039] The present invention also discloses a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned comprehensive teaching quality evaluation system based on big data analysis are implemented.

[0040] Advantages of the present invention:

[0041] 1. The present invention utilizes big data technology, comprehensively considers multi-dimensional data such as students' grade fluctuations, classroom participation, and homework completion, and overcomes the deficiency of the traditional method with a single data dimension;

[0042] 2. The present invention adopts a dynamic weight adjustment formula, which can automatically adjust the weights in the scoring model according to the students' grade fluctuations (standard deviation) and classroom participation (number of participations). It can not only quantify the correlation between data, but also realize the dynamic adjustment of weights. Specifically, for students with large grade fluctuations: the weights of classroom interaction and homework completion are automatically increased through the formula, so as to guide teachers to focus on the teaching intervention needs of such students. For students with low participation: the importance of classroom interaction is enhanced through the weight promotion mechanism, so that teaching activities pay more attention to interactivity and participation. Brief Description of the Drawings

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0044] Figure 1 It is a schematic diagram of the overall structure of a comprehensive teaching quality evaluation system based on big data analysis proposed by the present invention. Detailed Embodiments

[0045] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0046] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0047] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" appearing in different places in this specification does not all refer to the same embodiment, nor is it a separate or selectively exclusive embodiment from other embodiments.

[0048] Referring to Figure 1 , for an embodiment of the present invention, a comprehensive teaching quality evaluation system based on big data analysis is provided. This system includes: a data collection module responsible for obtaining raw data related to teaching quality from multiple channels; a data preprocessing module for cleaning and organizing the collected raw data to ensure data quality and prepare for subsequent analysis;

[0049] a preliminary score calculation module for calculating the preliminary scores of students based on data characteristics using a weighted summation model or other basic algorithms; a dynamic weight adjustment module for adjusting the weights of each feature in the scoring model according to real-time student performance or teaching goals;

[0050] a comprehensive score and evaluation module for integrating the dynamically adjusted weights and data to generate the final teaching quality score result; and a visualization display module for presenting the final teaching quality evaluation result to users in a graphical manner, including teachers, parents, or school administrators.

[0051] This embodiment also provides an evaluation method applied to the above-mentioned comprehensive teaching quality evaluation system based on big data analysis. This method includes the following steps:

[0052] Step 1: Collect data related to teaching quality, integrate and preprocess data from different sources, and after the data processing is completed, construct a preliminary scoring model through these data;

[0053] Specifically, the preliminary scoring model calculates the preliminary teaching score by weighted summation of various data features of each student. The calculation formula is:

[0054]

[0055] Among them, represents the preliminary score of student i; X i,k is the value of student i on the kth feature; w k is the preliminary weight related to the kth feature, reflecting the relative importance of the feature; Ω represents the data integration interval, indicating the learning process or time range of the student.

[0056] Establish a dynamic adaptive scoring adjustment strategy, that is, according to the performance of students in the teaching process, the scoring model will be adjusted in real time;

[0057] The construction process of the dynamic adaptive scoring adjustment strategy includes:

[0058] S101: According to the performance of students in the teaching process, set dynamic adjustment factors through different rules to change the weights of each feature.

[0059] The dynamic adjustment factor is expressed by the following formula:

[0060]

[0061] Among them, is the weight adjustment amount of the kth feature at time t, α k is the adjustment sensitivity of this feature, f(X i,k , t) is a function reflecting the change of student performance;

[0062] S102: Based on real-time student behavior data, adjust the weights of each feature and update the scoring model. Then the adjusted weight

[0063] S103: Use the updated weight and student data X i,k to recalculate the dynamic score of the student.

[0064] The expression of the updated teaching quality scoring model is:

[0065]

[0066] Among them, represents the updated dynamic score of the student, is the dynamically adjusted weight of the k-th feature.

[0067] This strategy specifically includes the following rules:

[0068] Rule 1: If a student's grades fluctuate greatly within a certain period of time, the weights of classroom interaction and homework completion in the score are automatically increased. At this time, f(X i,k , t) represents measuring the grade fluctuation by calculating the standard deviation of the student's grades, that is, f(X i,k , t) = σ. If the standard deviation σ is greater than the set standard deviation threshold, it is considered that the grade fluctuation is large.

[0069] Here, take students A and B as examples. Among them, student A's grades fluctuate greatly (measuring the grade fluctuation by calculating the standard deviation of the student's grades. If the standard deviation is greater than the set standard deviation threshold, it is considered that the grade fluctuation is large), has frequent classroom interaction, and completes homework in a timely manner; student B's grades are stable, has a medium level of participation, and occasionally submits homework late.

[0070] According to the dynamic adaptive scoring adjustment strategy, it is considered that student A's grades fluctuate greatly, so it meets the conditions of large grade fluctuations. According to Rule 1, increase the weights of classroom interaction and homework completion. The weight adjustment amount formula is expressed as:

[0071] The standard formula for calculating the standard deviation is:

[0072]

[0073] X i is each test score, μ is the average of the scores, and M is the number of scores.

[0074] Student B's grades fluctuate less and has a moderate level of participation. No dynamic adjustment is triggered.

[0075] Rule 2: If a student has a problem of insufficient participation, increase the weights of classroom interaction and teacher evaluation.

[0076] At this time, f(X i,k , t) represents the comprehensive impact of grade stability and insufficient participation, that is:

[0077]

[0078] Among them, σ maxIt represents the maximum tolerance value of grade stability, i.e., the standard deviation threshold, σ is the standard deviation of the student's grade, and p is the number of times the student participates in classroom interaction; p min is the minimum participation threshold. Taking student C as an example, his grade fluctuation is small, that is, σ is small, his class participation is low (the participation is measured by counting the number of times students participate in class discussions and answer questions. If the number of participation is less than the set participation threshold, it is considered that the class interaction is insufficient), and his homework completion is poor. According to Rule 2, the weight of class interaction and teacher evaluation is increased.

[0079] If students meet both Rule 1 and Rule 2, the increase in classroom interaction weight will be based on Rule 1.

[0080] Step 3: After dynamic adjustment, generate the final teaching quality evaluation results and present the results to teachers, school administrators, or parents in a visual way.

[0081] Specifically, the final teaching quality evaluation result is the evaluation conclusion after integrating various dimensional factors, and a comprehensive teaching quality evaluation result S is obtained based on the dynamic scores of students. final ;

[0082]

[0083] Among them, S final is the final comprehensive evaluation result of teaching quality; N represents the total number of students; represents the dynamic score of the i-th student at time t, ||X i || 2 It represents the Euclidean norm of the i-th student data, which measures the volatility of student performance, and ∈ represents the Gaussian function parameter;

[0084] is a Gaussian function that reduces the impact of students with greater volatility on the final evaluation results; Z represents the normalization factor, which ensures that the final evaluation results are within a reasonable range.

[0085] In summary, the present invention utilizes big data technology to comprehensively consider multi-dimensional data such as student performance fluctuations, class participation, and homework completion, thereby overcoming the shortcoming of the traditional method with a single data dimension; a dynamic weight adjustment formula is adopted, which can automatically adjust the weights in the scoring model according to the student's performance fluctuations (standard deviation) and class participation (number of participations). Not only can the correlation between data be quantified, but also the dynamic adjustment of weights is achieved. Specifically, for students with large performance fluctuations: the weights of classroom interaction and homework completion are automatically increased through the formula, thereby guiding teachers to focus on the teaching intervention needs of such students. For students with low participation: the importance of classroom interaction is enhanced through the weight enhancement mechanism, which encourages teaching activities to pay more attention to interactivity and participation.

[0086] This embodiment also provides a computer device, which is applicable to a situation of a comprehensive teaching quality evaluation method based on big data analysis, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a comprehensive teaching quality evaluation method based on big data analysis as proposed in the above embodiment.

[0087] This computer device may be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device may be a liquid crystal display screen or an electronic ink display screen. The input device of this computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0088] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements a comprehensive teaching quality evaluation method based on big data analysis as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A comprehensive teaching quality evaluation system based on big data analysis, characterized in that: include: Data collection module, responsible for obtaining raw data related to teaching quality from multiple channels; The data preprocessing module cleans and organizes the collected raw data to ensure data quality and prepare for subsequent analysis; The preliminary scoring calculation module calculates the preliminary scores of students based on data features using a weighted sum model or other basic algorithms; the dynamic weight adjustment module adjusts the weights of each feature in the scoring model according to real-time student performance or teaching objectives; The comprehensive scoring and evaluation module integrates dynamically adjusted weights and data to generate the final teaching quality scoring results; As well as a visual display module, the final teaching quality evaluation results are presented graphically to users, including teachers, parents, or school administrators.

2. The evaluation method of a teaching quality comprehensive evaluation system based on big data analysis according to claim 1 is characterized by: The method includes: Collect data related to teaching quality, integrate and pre-process data from different sources, and build a preliminary scoring model based on these data after data processing is completed; Establish a dynamic adaptive scoring adjustment strategy, that is, the scoring model will be adjusted in real time according to the student's performance during the teaching process. The strategy specifically includes the following rules: Rule 1: If a student's grades fluctuate greatly over a period of time, the weight of classroom interaction and homework completion will be automatically increased in the grading; Rule 2: If a student has a problem of insufficient participation, the weight of classroom interaction and teacher evaluation will be increased; After dynamic adjustment, the final teaching quality evaluation results are generated and presented to teachers, school administrators, or parents in a visual way.

3. The comprehensive teaching quality evaluation method based on big data analysis according to claim 2 is characterized by: The preliminary scoring model calculates the preliminary teaching score by weighted summing up the data features of each student. The calculation formula is: in, represents the preliminary score of student i; X i,k is the value of student i on the kth feature; w k is the preliminary weight associated with the kth feature, reflecting the relative importance of the feature; Ω represents the data integration interval, which represents the student's learning process or time range.

4. The comprehensive teaching quality evaluation method based on big data analysis according to claim 3 is characterized by: The construction process of the dynamic adaptive scoring adjustment strategy includes: S101: According to the students’ performance in the teaching process, the weight of each feature is changed by setting dynamic adjustment factors through different rules; S102: adjusting the weight of each feature based on real-time student behavior data and updating the scoring model; S103: Use updated weights and student data X i,k Recalculate students' dynamic scores.

5. The comprehensive teaching quality evaluation method based on big data analysis according to claim 4 is characterized by: The dynamic adjustment factor is expressed by the following formula: in, is the weight adjustment of the kth feature at time t, α k is the adjustment sensitivity of the feature, f(X i,k , t) is a function that reflects the change in student performance; The adjusted weight The expression of the updated teaching quality scoring model is: in, Indicates the updated student dynamic score. is the dynamically adjusted weight of the k-th feature.

6. The comprehensive teaching quality evaluation method based on big data analysis according to claim 5 is characterized by: When the dynamic adaptive score adjustment strategy adopts rule 1, f(X i,k , t) means that the fluctuation of grades is measured by calculating the standard deviation of students' grades, that is, f(X i,k , t) = σ. If the standard deviation σ is greater than the set standard deviation threshold, it is considered that the performance fluctuation is large.

7. The comprehensive teaching quality evaluation method based on big data analysis according to claim 5 is characterized by: When the dynamic adaptive score adjustment strategy adopts Rule 2, f(X i,k , t) represents the combined effect of performance stability and lack of participation, namely: Among them, σ max It represents the maximum tolerance value of grade stability, i.e., the standard deviation threshold, σ is the standard deviation of the student's grade, and p is the number of times the student participates in classroom interaction; p min is the minimum participation threshold.

8. The comprehensive teaching quality evaluation method based on big data analysis according to claim 5 is characterized by: The final teaching quality evaluation result is the evaluation conclusion after comprehensive evaluation of various dimensional factors. Based on the dynamic scores of students, a comprehensive teaching quality evaluation result S is obtained. final ; Among them, S final is the final comprehensive evaluation result of teaching quality; N represents the total number of students; represents the dynamic score of the i-th student at time t, ||X i || 2 It represents the Euclidean norm of the i-th student data, which measures the volatility of student performance, and ∈ represents the Gaussian function parameter; is a Gaussian function that reduces the impact of students with greater volatility on the final evaluation results; Z represents the normalization factor, which ensures that the final evaluation results are within a reasonable range.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a comprehensive teaching quality evaluation method based on big data analysis as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a comprehensive teaching quality evaluation method based on big data analysis as described in any one of claims 1 to 7 are implemented.

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