Regional teacher balance evaluation method and device based on big data and storage medium

Abstract

The invention provides a regional teacher balance evaluation method and device based on big data and a storage medium, and relates to the technical field of education management, and the method comprises the following steps: S1, screening regional universal teacher configuration evaluation indexes, and forming a multi-level teacher balance evaluation index system; s2, collecting teacher configuration data in the area, and constructing a data set; s3, calculating the weight of each teacher configuration evaluation index; s4, calculating the score of each teacher configuration evaluation index of the school; s5, performing weighted summation on the score of each teacher configuration evaluation index to obtain a teacher configuration comprehensive evaluation score of each school; s6, calculating an influence factor of each school in the region; and S7, calculating a regional teacher configuration high-quality balanced comprehensive score. According to the method, the influence difference of different schools on the overall situation of the region is considered, the cross-region and cross-time vertical and horizontal comparison scores are obtained, and the objectivity, universality and effectiveness of teacher allocation evaluation are improved.

Description

Technical Field

[0001] The present application relates to the field of educational management technology, and in particular to a method, device and storage medium for regional teacher balance evaluation based on big data. Background Art

[0002] Teachers are a key factor influencing the quality of education. Balanced educational development hinges on teachers. The scientific and rational development of the teaching staff inevitably impacts educational equity and the balanced development of high-quality education. Disparities in teacher allocation have always existed among schools. Factors such as a school's history, geographic location, social reputation, and policy support all influence its teacher allocation capacity. Numerous studies in the field of education have shown that disparities in teacher allocation can affect students' academic performance and overall quality, and school selection is often driven by the desire to secure better teachers. Therefore, achieving high-quality and balanced regional education should begin with improving teacher allocation in compulsory education.

[0003] At the same time, the education sector generally lacks objective and scientific evaluation methods for regional teacher allocation. In scientific research, the indicators selected for evaluating balanced teacher allocation are mostly applicable to small-scale questionnaires, field visits, etc., relying on human and material resources and subjective judgment, and the evaluation purpose is often unclear. This results in education authorities often being unable to fully grasp regional teacher allocation data, and it is also difficult to continuously and systematically monitor and warn of teacher allocation problems in the region. At the same time, the lack of accurate, scientific, and objective evaluation methods also means that education authorities find it difficult to obtain accurate and quantitative guidance, making it difficult to accurately intervene in weak areas of teacher allocation in the region. Summary of the Invention

[0004] This application provides a regional teacher balance evaluation method, device and storage medium based on big data to at least solve the above technical problems existing in the prior art.

[0005] According to the first aspect of the present application, a method for regional teacher balance evaluation based on big data is provided, comprising the following steps: S1, screening the regional common teacher allocation evaluation indicators to form a multi-level teacher balance evaluation indicator system; S2, collects teacher allocation data in the region and constructs a dataset of teacher allocation data for each school in the region; S3, calculate the weight of each teacher configuration evaluation index by entropy weight method; S4, based on the relative difference percentage, calculate the scores of each teacher configuration evaluation indicator of the school; S5, performing a weighted summation on the scores of the teacher allocation evaluation indicators to obtain a comprehensive evaluation score of the teacher allocation of each school; S6, calculate the impact factor of each school in the region based on the proportion of teachers in each school in the region; S7, combining the comprehensive evaluation scores and influencing factors of each school’s teacher allocation, calculates the final comprehensive score of regional teacher allocation quality and balance.

[0006] In certain embodiments of the first aspect of the present application, in S3, the proportion of the i-th sample under the j-th teacher configuration evaluation index is calculated as the probability used in the information entropy calculation. ;

[0007] in, represents the probability that the i-th sample is used in the information entropy calculation under the j-th teacher configuration evaluation index; represents the normalized value of the i-th sample under the j-th teacher configuration evaluation index; Find the information entropy of each teacher configuration evaluation indicator:

[0008]

[0009] in, represents the information entropy of the j-th teacher configuration evaluation index, and , and define that when =0, =0; According to the obtained information entropy, the weight of each teacher allocation evaluation indicator is calculated:

[0010] j = 1, 2, ..., m in, Represents the weight of the j-th teacher configuration evaluation indicator.

[0011] In certain embodiments of the first aspect of the present application, the normalization processing method of the normalized value of the i-th sample under the j-th teacher configuration evaluation indicator is as follows: If the teacher allocation evaluation index is a positive indicator, the normalized calculation formula is:

[0012] If the teacher allocation evaluation index is a reverse index, the normalized calculation formula is:

[0013] in, Represents the value of the i-th sample under the j-th indicator.

[0014] In certain embodiments of the first aspect of the present application, in S4, the method for calculating the scores of the various teacher allocation evaluation indicators of the school is as follows: Calculating the score for the student-teacher ratio indicator ,in The reference standard value for the student-teacher ratio is is the student-teacher ratio of the school, The maximum value of the student-teacher ratio in the area;

[0015] Calculate the score of the teacher-student ratio ,in is the reference standard value of the teacher-student ratio, is the teacher-student ratio of the school, The maximum value of the return ratio in this area;

[0016] Calculating the score of the subject standard ratio indicator ,in The reference standard value for the subject standard is: is the subject standard ratio value of a subject in the school, is the maximum value of the subject standard ratio of a subject in this region, is the minimum value of the subject standard ratio of a subject in the region;

[0017] Calculating Faculty Degree Scores ,in is the reference standard value of teacher degree, The number of teachers with bachelor's degrees per 100 students or the number of teachers with master's degrees per 100 students in the school. The minimum number of teachers with bachelor's degrees per 100 students or teachers with master's degrees per 100 students in the region;

[0018] Calculate teacher title scores ,in is the reference standard value for teacher professional titles, is the number of teachers with various professional titles per 100 students in the school, The minimum number of teachers of various professional titles per 100 students in the region;

[0019] Calculating Teacher Honor Scores , among which Reference standard value for teacher honors, The number of teachers who have received a certain honor per 100 students in the school. The minimum number of teachers who have received a certain honor per 100 students in the district;

[0020] Calculate the scores of educational background structure, age structure, professional title structure, and teaching experience structure; among them, The values ​​corresponding to the school's teachers' academic qualifications, age, professional title, and teaching experience are: is the maximum value of the corresponding values ​​of the school’s teachers’ academic structure, age structure, professional title structure, and teaching experience structure. The minimum value corresponding to the academic qualification structure, age structure, professional title structure, and teaching experience structure of the school’s teachers;

[0021] Wherein, a and b are the lower reference value and the upper reference value respectively.

[0022] In certain embodiments of the first aspect of the present application, the reference standard value of the teacher-student ratio is The value of selects the quarter-place return ratio, and the calculation formula is: .

[0023] In certain embodiments of the first aspect of the present application, in said S5, the weights of the teacher allocation evaluation indicators calculated according to S3 are , K represents the total number of teacher allocation evaluation indicators; S4 is used to obtain the scores of each teacher allocation evaluation indicator of the school Perform weighted summation, where the subscript i represents the school number, and finally obtain the comprehensive evaluation score of the teacher configuration of school i ; .

[0024] In certain embodiments of the first aspect of the present application, in S6, the method for calculating the impact factor of each school in the region is as follows: Calculate the total number of teachers participating in the balanced evaluation of teachers in the region ; Calculate the number of teachers in each school who participated in the balanced evaluation of teacher quality ; Finally, calculate the impact factor of the comprehensive evaluation score of each school's teacher allocation on the regional total score .

[0025] In certain embodiments of the first aspect of the present application, in S7, the method for calculating the comprehensive score of regional teacher allocation quality and balance is as follows:

[0026] in, represents the comprehensive score of the quality and balance of teacher allocation in the region, and n represents the total number of schools participating in the balanced assessment of teacher allocation in the region.

[0027] According to a second aspect of the present application, an electronic device is provided, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method described in this application.

[0028] According to a third aspect of the present application, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the present application.

[0029] Compared with the prior art, this application has the following beneficial effects: This application assigns a score to each school within a region and, using the entropy weight method, weights and adjusts the indicators for regional teacher allocation quality and balance. Based on these two results, a multi-indicator evaluation of the quality and balance of school teacher allocation is conducted. Finally, a comprehensive evaluation of the quality and balance of regional teacher allocation is conducted, taking into account the varying impacts of different schools on the overall regional situation. This results in a single score that can be compared vertically and horizontally across regions and time. This approach improves the objectivity, universality, and effectiveness of teacher allocation evaluations, providing support for improving educational equity.

[0030] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and other objects, features and advantages of the exemplary embodiments of the present application will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present application are shown in an illustrative and non-limiting manner, in which: In the drawings, the same or corresponding reference numerals denote the same or corresponding parts.

[0032] Figure 1 The overall method flow chart of this application is shown.

[0033] Figure 2 A schematic diagram of the teacher allocation evaluation indicators of this application is shown.

[0034] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0035] In order to make the purpose, features, and advantages of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0036] Example 1: This embodiment provides a regional teacher balance evaluation method based on big data. Please refer to Figure 1 , mainly including the following steps: S1. Filter the teacher allocation evaluation indicators that are common in the region and form a multi-level teacher balance evaluation indicator system.

[0037] Through literature review and repeated research, we found that the evaluation of teacher quality and balance often uses a unified primary evaluation indicator, namely quantity, quality, and structure to evaluate the quality and balance of teachers. In this method, these are expressed as: quantity coordination, ability reflection, and structural configuration.

[0038] By reviewing literature, policy requirements, and expert opinions, we collected over twenty potential secondary indicators and corresponding tertiary indicators for quantitative evaluation. We then conducted multiple rounds of anonymous consultations with a selected expert panel using the Delphi method until consensus was reached, ultimately selecting a set of universally applicable regional teacher allocation evaluation indicators.

[0039] Delphi Method: A structured decision support technique that aims to obtain relatively objective information, opinions and insights through the independent and repeated subjective judgments of multiple experts during the information collection process.

[0040] Specifically, the teacher allocation evaluation indicators include: Figure 2 The following are ten secondary indicators and corresponding twenty tertiary indicators. The secondary indicators include student-teacher ratio, class-teacher ratio, subject standard ratio, teacher degree, teacher title, teacher honors, educational background, age, professional title, and teaching experience. Many of these secondary indicators are further broken down into tertiary indicators.

[0041] For example: the subject standard ratio includes eight third-level indicators, namely: Chinese teacher standard ratio, mathematics teacher standard ratio, English teacher standard ratio, science teacher standard ratio, information teacher standard ratio, music teacher standard ratio, art teacher standard ratio, and physical education teacher standard ratio.

[0042] Teacher degree includes two three-level indicators: the number of teachers with master's degree or above per 100 students and the number of teachers with bachelor's degree per 100 students.

[0043] Teacher professional titles include three three-level indicators, namely: the number of senior and above teachers per 100 students, the number of first-level teachers per 100 students, and the number of second-level teachers per 100 students.

[0044] Teacher honors include a three-level indicator, specifically: the number of key teachers at or above the county level per 100 students.

[0045] The educational structure includes two three-level indicators: the proportion of undergraduates and above, and the proportion of postgraduates and above. The proportion of undergraduates and above refers to primary schools, while the proportion of postgraduates and above refers to junior high schools.

[0046] The age structure includes three three-level indicators, namely: the proportion of teachers in the young group, the proportion of teachers in the middle-aged group, and the proportion of teachers in the elderly group.

[0047] The professional title structure includes a three-level indicator, specifically: the proportion of first-level plus senior teachers and above.

[0048] The teaching experience structure includes three three-level indicators, namely: the proportion of teachers with teaching experience <10 years, the proportion of teachers with teaching experience 10-20 years, and the proportion of teachers with teaching experience >20 years.

[0049] What it means is that the student-teacher ratio and class-teacher ratio are used as third-level indicators respectively.

[0050] Finally, a multi-level teacher balance evaluation index system was formed as shown in Table 1 below.

[0051] Table 1: Multi-level teacher balance evaluation index system

[0052] S2, collects teacher allocation data in the region and constructs a dataset of teacher allocation data for each school in the region.

[0053] Specifically, the teacher allocation data includes massive data related to education and teacher conditions in the region. Teacher allocation data often has problems such as numerous data sources, scattered feature data, and complicated early mining and cleaning work. Data is collected based on the dimensions of teacher allocation evaluation indicators, and a data set is constructed for each indicator data of each school: Cluster by n dimensions, D_1 = [B_1, B_2, B_3, ..., B_n] B_i represents the statistical analysis results of teacher data corresponding to the i-th third-level indicator dimension, 1≤i≤n. The following is an example of the resulting data set: B_1 = [Student-teacher ratio of school A, student-teacher ratio of school B, student-teacher ratio of school C, ..., student-teacher ratio of school N] B_2 = [class-teacher ratio of school A, class-teacher ratio of school B, class-teacher ratio of school C, ..., class-teacher ratio of school N] ... B_n = [the number of teachers in school A with teaching experience > 20 years, the number of teachers in school B with teaching experience > 20 years, the number of teachers in school C with teaching experience > 20 years, ..., the number of teachers in school N with teaching experience > 20 years].

[0054] S3, calculate the weight of each teacher configuration evaluation indicator through the entropy weight method.

[0055] Since experts from different regions have different opinions on the importance of different indicators, we choose to use an objective weighting method to weight the total score. In this invention, the entropy weight method is selected.

[0056] The entropy weighting method originates from the concept of information entropy in physics. According to information theory, information is a measure of a system's order, while entropy is a measure of its disorder. When applied to indicator weighting, the entropy weighting method is an objective method. Based on the degree of variation (also known as the degree of dispersion) of each indicator, information entropy is used to calculate the entropy weight of each indicator. The entropy weight is then used to adjust the weight of each indicator, achieving a more objective indicator weighting. The smaller the information entropy value of an indicator, the greater the dispersion of the indicator and the greater the amount of information it provides. Consequently, the greater the impact of the indicator on the comprehensive evaluation, and therefore, its objective weight should also be greater. Conversely, the larger the information entropy value of an indicator, the smaller the dispersion of the indicator and the less information it provides. Consequently, the smaller the impact of the indicator on the comprehensive evaluation, and therefore, its objective weight should also be smaller.

[0057] Each indicator is normalized by feature scaling. The normalization methods of positive indicators and reverse indicators are different, and different formulas are required to ensure that the normalized data are all positive numbers. Normalized calculation formula for positive indicators:

[0058] Normalized calculation formula of the reverse indicator:

[0059] in, represents the value of the i-th sample under the j-th indicator, Represents the normalized value of the i-th sample under the j-th indicator.

[0060] It is worth mentioning that the positive indicators include teacher degree, teacher title, teacher honors and academic qualification structure; the reverse indicators include student-teacher ratio and class-teacher ratio.

[0061] Calculate the proportion of the i-th sample under the j-th indicator as the probability used in the information entropy calculation :

[0062] To find the information entropy of each indicator, according to the definition of information entropy in information theory, the calculation formula of the information entropy of a set of data is:

[0063]

[0064] in, represents the information entropy of the jth indicator, and , and define that when =0, =0.

[0065] According to the obtained information entropy, the weight of each indicator is calculated:

[0066] j = 1, 2, ..., m in, Represents the weight of the j-th teacher configuration evaluation indicator.

[0067] S4, based on the relative difference percentage, calculate the scores of each teacher configuration evaluation indicator of the school.

[0068] Introducing the concept of relative percentage difference in statistics: Relative Percentage Difference: A statistical method for calculating and describing the relative difference between two quantities, using different measurements or samples. Subtracting one measurement from another and taking the absolute value of the difference yields the relative percentage difference between the two points. In this method, the relative percentage difference is used to measure the gap between each school's teacher allocation data and the standard reference value, thereby determining the degree of balance in teacher allocation data.

[0069] By analyzing the evaluation content of each indicator, we believe that indicator evaluation methods can be divided into four main categories: positive indicators, negative indicators, comparative indicators, and interval indicators. For different types of indicators, the calculation formula needs to be fine-tuned based on the basic relative difference percentage formula.

[0070] Positive indicators: These are indicators whose larger the value, the better. In this method, if these indicators meet the standard, they will receive full marks. If they do not meet the standard, the lower the difference from the standard reference value, the more points will be deducted. This type of indicator evaluation method is suitable for the evaluation of teacher configuration, including teacher degrees, teacher titles, teacher honors, and academic qualifications.

[0071] Inverse indicators: These are indicators where the larger the value, the worse the evaluation. In this method, if such an indicator meets the standard, it will be given a full score. The higher the value is, the more points will be deducted. This type of indicator is applicable when the number of students or classes is compared with the number of teachers. In such cases, the more teachers there are, the smaller the value, including student-teacher ratio and class-teacher ratio.

[0072] Comparative Indicators: These are indicators where scores above or below the required value are subject to deduction. In this invention, full marks are awarded when these indicators are equal to the standard reference value, while points are deducted accordingly if they are above or below the standard reference value. For these indicators, both over- and under-staffing can lead to imbalances, including in the standard ratios for each subject.

[0073] Interval indicators: These indicators fall within a certain acceptable range, but are subject to deduction for values ​​above or below that range. In this application, these indicators receive full marks only when they fall between a and b; otherwise, they receive deductions. These indicators are typically applicable to structural evaluations, including age structure, professional title structure, and teaching experience structure.

[0074] Therefore, the scores of the school's various teacher allocation evaluation indicators are as follows: (1) Calculating the score of the student-teacher ratio indicator ,in The reference standard value for the student-teacher ratio is is the student-teacher ratio of the school, This is the maximum student-teacher ratio value in the area. The value selected is mostly based on the student-teacher ratio requirements in national or regional policies.

[0075]

[0076] (2) Calculating the score of the teacher-student ratio ,in is the reference standard value of the teacher-student ratio, is the teacher-student ratio of the school, is the maximum value of the teacher-to-teacher ratio in the region. Since no national or regional policies with detailed configuration requirements for teacher-to-teacher ratio were found when this method was designed, The value of selected is the quarter-place teacher-student ratio, and the calculation formula is .

[0077]

[0078] (2) Calculating the score of the subject standard ratio indicator ,in The reference standard value for the subject standard is: is the subject standard ratio value of a subject in the school, is the maximum value of the subject standard ratio of a subject in this region, The minimum value of the subject standard ratio for a subject in the region. Since the subject standard ratio is the ratio of the teachers that should be equipped and the teachers that are actually equipped, in the case of the best balance, the actual number of teachers equipped in the school should be equal to the number of teachers that should be equipped, so =1. Note that the subject standard ratio is calculated separately for different subjects.

[0079]

[0080]

[0081] (4) Calculation of teacher degree scores ,in is the reference standard value of teacher degree, The number of teachers with a bachelor's degree per 100 students in the school (primary school) or the number of teachers with a master's degree per 100 students in the school (junior high school). It is the minimum number of teachers with a bachelor's degree per 100 students (primary school) or teachers with a master's degree per 100 students (junior high school) in the area. The value of is often selected based on the teacher education requirements in national or regional policies.

[0082]

[0083] (5) Calculation of teacher title scores ,in is the reference standard value for teacher professional titles, is the number of teachers with various professional titles per 100 students in the school, It is the minimum number of teachers with various professional titles per 100 students in the region. The value of is mostly based on the teacher title allocation requirements in national or regional policies.

[0084]

[0085] (6) Calculation of teacher honor scores , among which Reference standard value for teacher honors, The number of teachers who have received a certain honor per 100 students in the school. The minimum number of teachers who have received a certain honor per 100 students in the area. The value of is often selected based on the teacher title allocation requirements in national or regional policies, such as the number of key teachers per 100 students.

[0086]

[0087] (7) Calculate the scores of educational background structure, age structure, professional title structure, and teaching experience structure; The values ​​corresponding to the school's teachers' academic qualifications, age, professional title, and teaching experience are: is the maximum value of the corresponding values ​​of the school’s teachers’ academic structure, age structure, professional title structure, and teaching experience structure. It is the minimum value corresponding to the educational structure, age structure, professional title structure, and teaching experience structure of the school’s teachers.

[0088]

[0089] a and b are the lower and upper reference values, respectively, and are adjusted dynamically based on regional conditions, operational experience, and expert opinion. Table 2 below shows an example of the actual use of a and b values, calculated based on various configuration structures from a list of outstanding schools provided by a district.

[0090] Table 2: Examples of actual use of a and b values

[0091] S5. Perform weighted summation on the scores of the teacher allocation evaluation indicators to obtain a comprehensive evaluation score of the teacher allocation of each school.

[0092] That is, the weight of each teacher configuration evaluation index calculated according to S3 , K represents the total number of teacher allocation evaluation indicators; S4 is used to obtain the scores of each teacher allocation evaluation indicator of the school Perform weighted summation, where the subscript i represents the school number, and finally obtain the comprehensive evaluation score of the teacher configuration of school i .

[0093]

[0094] S6, calculate the impact factor of each school in the region based on the proportion of teachers in each school in the region.

[0095] Calculate the total number of teachers participating in the balanced evaluation of teachers in the region ; Calculate the number of teachers in each school who participated in the balanced evaluation of teacher quality Please note that schools generally have non-staff teachers, teachers on sick leave, and teachers on loan. It is usually the local education management or evaluation department that decides whether to use these teachers in the teacher allocation assessment for that year.

[0096] Finally, calculate the impact factor of the comprehensive evaluation score of each school's teacher allocation on the regional total score .

[0097] This method assumes that the impact of a school's teacher allocation on the district's teacher allocation is determined by the number of teachers in that school. The more teachers a school has participating in the evaluation, the greater its impact on the district's teacher allocation.

[0098] S7, combining the comprehensive evaluation scores and influencing factors of each school’s teacher allocation, calculates the final comprehensive score of regional teacher allocation quality and balance.

[0099]

[0100] in, represents the comprehensive score of the quality and balance of teacher allocation in the region, and n represents the total number of schools participating in the balanced assessment of teacher allocation in the region.

[0101] Through the above steps, this method assigns a score to each school's allocation within the region. Simultaneously, using the entropy weight method, it weights and adjusts the indicators for high-quality and balanced teacher allocation in the region. Based on these two results, a multi-indicator evaluation of high-quality and balanced teacher allocation across schools is conducted. Finally, a comprehensive evaluation of high-quality and balanced teacher allocation in the region is conducted, taking into account the varying impacts of different schools on the overall regional situation. This results in a single score that can be compared vertically and horizontally across regions and time. This method improves the objectivity, universality, and effectiveness of teacher allocation evaluations, providing support for improving educational equity.

[0102] Example 2: According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0103] Figure 3 A schematic block diagram of an example electronic device that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0104] like Figure 3As shown, the device includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). The RAM can also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. An input / output (I / O) interface is also connected to the bus.

[0105] Many components in a device are connected to the I / O interface, including: input units, such as a keyboard and mouse; output units, such as various types of displays and speakers; storage units, such as magnetic disks and optical disks; and communication units, such as network cards, modems, and wireless communication transceivers. The communication unit allows the device to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks.

[0106] The computing unit can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of computing units include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processors, controllers, microcontrollers, etc. The computing unit executes the various methods and processes described above, such as the big data-based balanced regional teacher evaluation method described in Example 1. For example, in some embodiments, the big data-based balanced regional teacher evaluation method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed onto a device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the computing unit, one or more steps of the big data-based balanced regional teacher evaluation method described above can be performed. Alternatively, in other embodiments, the computing unit can be configured to execute the big data-based balanced regional teacher evaluation method through any other suitable means (e.g., via firmware).

[0107] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0108] The program code for implementing the methods of the present application can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow charts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0109] In the context of this application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0110] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0111] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0112] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0113] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved. This is not limited herein.

[0114] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0115] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A regional teacher balance evaluation method based on big data, characterized by: The following steps are involved: S1, screening the regional common teacher allocation evaluation indicators to form a multi-level teacher balance evaluation indicator system; S2, collects teacher allocation data in the region and constructs a dataset of teacher allocation data for each school in the region; S3, calculate the weight of each teacher configuration evaluation index by entropy weight method; S4, based on the relative difference percentage, calculate the scores of each teacher configuration evaluation indicator of the school; S5, performing a weighted summation on the scores of the teacher allocation evaluation indicators to obtain a comprehensive evaluation score of the teacher allocation of each school; S6, calculate the impact factor of each school in the region based on the proportion of teachers in each school in the region; S7, combining the comprehensive evaluation scores and influencing factors of each school’s teacher allocation, calculates the final comprehensive score of regional teacher allocation quality and balance.

2. The regional teacher balance evaluation method based on big data according to claim 1 is characterized in that: In S3, the proportion of the i-th sample under the j-th teacher configuration evaluation index is calculated as the probability used in the information entropy calculation. ; in, represents the probability that the i-th sample is used in the information entropy calculation under the j-th teacher configuration evaluation index; represents the normalized value of the i-th sample under the j-th teacher configuration evaluation index; Find the information entropy of each teacher allocation evaluation indicator: in, represents the information entropy of the j-th teacher configuration evaluation index, and , and define that when =0, =0; According to the obtained information entropy, the weight of each teacher allocation evaluation indicator is calculated: j = 1, 2, ..., m in, Represents the weight of the j-th teacher configuration evaluation indicator.

3. The regional teacher balance evaluation method based on big data according to claim 2 is characterized in that: The normalization processing method of the normalized value of the i-th sample under the j-th teacher configuration evaluation index is as follows: If the teacher allocation evaluation index is a positive indicator, the normalized calculation formula is: If the teacher allocation evaluation index is a reverse index, the normalized calculation formula is: in, Represents the value of the i-th sample under the j-th indicator.

4. The regional teacher balance evaluation method based on big data according to claim 1 is characterized in that: In S4, the method for calculating the scores of the school's teacher allocation evaluation indicators is as follows: Calculating the score for the student-teacher ratio indicator ,in The reference standard value for the student-teacher ratio is is the student-teacher ratio of the school, The maximum value of the student-teacher ratio in the area; Calculate the score of the teacher-student ratio ,in is the reference standard value of the teacher-student ratio, is the teacher-student ratio of the school, The maximum value of the return ratio in this area; Calculating the score for the subject standard ratio indicator ,in The reference standard value for the subject standard is: is the subject standard ratio value of a subject in the school, is the maximum value of the subject standard ratio of a subject in this region, is the minimum value of the subject standard ratio of a subject in the region; Calculating Teacher Degree Scores ,in is the reference standard value of teacher degree, The number of teachers with bachelor's degrees per 100 students or the number of teachers with master's degrees per 100 students in the school. The minimum number of teachers with bachelor's degrees per 100 students or teachers with master's degrees per 100 students in the region; Calculate teacher title scores ,in is the reference standard value for teacher professional titles, is the number of teachers with various professional titles per 100 students in the school, The minimum number of teachers of various professional titles per 100 students in the region; Calculating Teacher Honor Scores , among which Reference standard value for teacher honors, The number of teachers who have received a certain honor per 100 students in the school. The minimum number of teachers who have received a certain honor per 100 students in the district; Calculate the scores of educational background structure, age structure, professional title structure, and teaching experience structure; among them, The values ​​corresponding to the school's teachers' academic qualifications, age, professional title, and teaching experience are: is the maximum value of the corresponding values ​​of the school’s teachers’ academic structure, age structure, professional title structure, and teaching experience structure. The minimum value corresponding to the academic qualification structure, age structure, professional title structure, and teaching experience structure of the school’s teachers; Wherein, a and b are the lower reference value and the upper reference value respectively.

5. The regional teacher balance evaluation method based on big data according to claim 4 is characterized in that: The reference standard value of the teacher-student ratio The value of selects the quarter-place return ratio, and the calculation formula is: .

6. The regional teacher balance evaluation method based on big data according to claim 4 is characterized in that: In S5, the weights of the teacher allocation evaluation indicators calculated according to S3 are , K represents the total number of teacher configuration evaluation indicators; S4 obtains the scores of the school's teacher allocation evaluation indicators Perform weighted summation, where the subscript i represents the school number, and finally obtain the comprehensive evaluation score of the teacher configuration of school i ; 。 7. The regional teacher balance evaluation method based on big data according to claim 6 is characterized in that: In S6, the calculation method of the impact factor of each school in the region is as follows: Calculate the total number of teachers participating in the balanced evaluation of teachers in the region ; Calculate the number of teachers in each school who participated in the balanced evaluation of teacher quality ; Finally, calculate the impact factor of the comprehensive evaluation score of each school's teacher allocation on the regional total score .

8. The regional teacher balance evaluation method based on big data according to claim 7 is characterized in that: In S7, the calculation method for the comprehensive score of regional teacher allocation quality and balance is as follows: in, represents the comprehensive score of the quality and balance of teacher allocation in the region, and n represents the total number of schools participating in the balanced assessment of teacher allocation in the region.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 8.

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