Course reform supervision index correlation analysis method, device and equipment

Abstract

The application is suitable for the technical field of curriculum reform supervision, in particular to a curriculum reform supervision index correlation analysis method, device and equipment, the method comprising: obtaining teaching conditions and curriculum reform data of each class in multiple time periods before and after curriculum reform of multiple classes; establishing a dynamic network model of each class according to each teaching condition and each curriculum reform data; identifying a corresponding index subgroup according to each dynamic network model; determining a corresponding decay subgroup according to a subgroup synergy index of each index subgroup; calculating a network structure similarity of the dynamic network model of each class in adjacent time periods after curriculum reform; and obtaining a cross-period synergy effect of each class according to each decay subgroup and each network structure similarity. Therefore, the curriculum reform supervision index correlation analysis method provided by the application can solve the problem of lacking correlation analysis of multiple indexes in different time periods after curriculum reform.

Description

Technical Field

[0001] This application belongs to the technical field of curriculum reform supervision, and in particular relates to methods, devices and equipment for correlation analysis of curriculum reform supervision indicators. Background Technology

[0002] Curriculum reform supervision is a process in which education management departments or schools systematically supervise, evaluate, and provide feedback on aspects such as the planning and implementation of curriculum reform, teaching implementation, resource allocation, and achievement of results by formulating standards and establishing mechanisms. It aims to ensure that curriculum reform goals and policy requirements are accurately implemented, promptly identify and correct deviations, and promote continuous optimization of curriculum quality.

[0003] In existing technologies, curriculum reform supervision relies on methods such as manual classroom observation, lesson plan review, and student questionnaires. The data collected is mostly immediate and localized indicators, typically using linear weighting or simple comparative analysis. These methods do not consider the impact of time factors on the indicators, providing immediate judgments of curriculum reform results but failing to capture the dynamic relationships between indicators. Therefore, current curriculum reform supervision suffers from a lack of correlation analysis of multiple indicators across different time periods after the reform, making it difficult to determine the sustained impact of the curriculum reform on each indicator. Summary of the Invention

[0004] This application provides a method, apparatus, and equipment for correlation analysis of curriculum reform supervision indicators, which can solve the problem of lacking correlation analysis of multiple indicators in different time periods after curriculum reform.

[0005] Firstly, embodiments of this application provide a method for correlation analysis of curriculum reform supervision indicators, including:

[0006] The study obtains teaching conditions and curriculum reform data for each class in multiple time periods before and after the curriculum reform. The teaching conditions include the teacher-student ratio and the historical performance of students and teachers. The curriculum reform data includes data on various indicators from three aspects: teachers, students, and management. The teacher-related indicators include classroom interaction frequency and / or training duration. The student-related indicators include the number of times they speak and / or their grades. The management-related indicators include equipment utilization rate and / or equipment update cycle.

[0007] A dynamic network model is established for each class based on the teaching conditions and curriculum reform data. The dynamic network model is used to reflect the correlation between various indicators of teachers, students and management in each time period before and after the curriculum reform.

[0008] Identify the corresponding indicator subgroups according to each dynamic network model; wherein, the indicator subgroup is a set of indicators in the dynamic network model that change synchronously in different time periods after the curriculum reform;

[0009] The corresponding decaying subgroup is determined based on the subgroup synergy index of each of the aforementioned subgroups of indicators; wherein, the subgroup synergy index is used to reflect the degree of synergy among the various indicators within the subgroup of indicators, and the decaying subgroup refers to the subgroup of indicators in which the subgroup synergy index continuously decreases or the number of indicators continuously decreases in two or more consecutive time periods.

[0010] Calculate the network structure similarity of the dynamic network model for each class in adjacent time periods after the curriculum reform;

[0011] The intertemporal synergistic effect of each class is obtained based on the similarity of each decaying subgroup and each network structure; wherein, the intertemporal synergistic effect is used to reflect the relationship between various indicators in multiple time periods after the curriculum reform.

[0012] The technical solutions described in this application embodiment have at least the following technical effects:

[0013] The method for correlation analysis of curriculum reform supervision indicators provided in this application involves acquiring teaching conditions and curriculum reform data for each class across multiple time periods before and after curriculum reform; establishing a dynamic network model for each class based on these data; identifying corresponding indicator subgroups based on each dynamic network model; determining corresponding decaying subgroups based on the subgroup synergy index of each indicator subgroup; calculating the network structure similarity of the dynamic network models for adjacent time periods after curriculum reform for each class; and obtaining the inter-period synergistic effect for each class based on each decaying subgroup and the network structure similarity. Therefore, the method for correlation analysis of curriculum reform supervision indicators provided in this application provides a comprehensive analysis of curriculum reform from multiple dimensions and time periods. Through a series of steps, including establishing dynamic network models, identifying indicator subgroups, determining decaying subgroups, and calculating network structure similarity, it is possible to deeply analyze the relationships and evolution of various indicators, and identify problems and weaknesses in curriculum reform. Obtaining inter-period synergistic effects through dynamic network models allows for real-time tracking of changes in the effectiveness of curriculum reform, adapting to the dynamic nature of curriculum reform.

[0014] In one possible implementation of the first aspect, the method further includes:

[0015] Based on the dynamic network models described above, the correlation coefficients between the core indicators and other indicators of each class before and after the curriculum reform are determined; wherein, the core indicators are those that have the greatest impact on other indicators among all indicators.

[0016] The effectiveness of curriculum reform in each class is determined based on the aforementioned curriculum reform data and the correlation coefficients between the core indicators and other indicators of each class; wherein, the effectiveness of curriculum reform is used to evaluate the impact of curriculum reform on teaching.

[0017] The supervision parameters are adjusted according to the effects of each curriculum reform and the corresponding inter-period synergistic effect; wherein, the supervision parameters include the key classes to be supervised and the frequency of supervision.

[0018] In one possible implementation of the first aspect, determining the curriculum reform effectiveness of each class based on the curriculum reform data and the correlation coefficients between the core indicators and other indicators of each class includes:

[0019] The corresponding synergy index is obtained based on the correlation coefficient between the core indicators of each class and other indicators; wherein, the synergy index is used to reflect the degree of synergy among the various indicators of the class.

[0020] Based on the curriculum reform data, the strength of causal relationships between multiple indicators of each class before and after the curriculum reform was obtained;

[0021] The curriculum reform effects for each class are obtained based on the respective synergy indices and the respective causal relationship strengths.

[0022] In one possible implementation of the first aspect, obtaining the causal correlation strength between multiple indicators of each class before and after the curriculum reform based on the curriculum reform data includes:

[0023] Based on the curriculum reform data, establish a first model for each class before the curriculum reform and a second model for each class after the curriculum reform; wherein, the first model includes an autoregressive model and a joint regression model for each indicator before the curriculum reform, and the second model includes an autoregressive model and a joint regression model for each indicator after the curriculum reform.

[0024] The corresponding sum of squared residuals is calculated based on each of the first model and each of the second models;

[0025] The corresponding F-statistic is obtained by performing an F-test on the sum of squared residuals described above.

[0026] The corresponding causal association strength is determined based on each of the aforementioned F-statistics.

[0027] In one possible implementation of the first aspect, determining the correlation coefficient between the core indicators of each class before and after the curriculum reform and other indicators of that class based on the dynamic network models includes:

[0028] Calculate the degree centrality and betweenness centrality of each node in each of the dynamic network models described above;

[0029] The core indicators for each class are determined based on the degree centrality and betweenness centrality of each class.

[0030] Calculate the correlation coefficient between the core indicators and other indicators for each class.

[0031] In one possible implementation of the first aspect, establishing a dynamic network model for each class based on the teaching conditions and curriculum reform data includes:

[0032] The supervision scores for each class are obtained based on the aforementioned teaching conditions and curriculum reform data; wherein, the supervision scores include scores for three aspects: teachers, students, and management;

[0033] A corresponding collaborative relationship matrix is ​​constructed based on the aforementioned curriculum reform data; wherein, the collaborative relationship matrix is ​​used to reflect the degree of correlation between indicators related to teachers, students, and management in different aspects;

[0034] The entropy values ​​corresponding to the teachers, students, and management aspects of each class are calculated based on the aforementioned collaborative relationship matrices.

[0035] The corresponding weight values ​​are obtained based on the inspection scores and entropy values ​​described above.

[0036] The dynamic network models are obtained based on the weight values ​​and the correlation coefficients between the various indicators of teachers, students, and management.

[0037] In one possible implementation of the first aspect, obtaining the supervision score for each class based on the teaching conditions and curriculum reform data includes:

[0038] Based on the aforementioned teaching conditions and curriculum reform data, the first supervisory score for management and the second supervisory score for students were obtained for each class.

[0039] Based on the aforementioned teaching conditions, curriculum reform data, and the second inspection scores, the third inspection scores for each class regarding teachers are obtained.

[0040] In one possible implementation of the first aspect, adjusting the supervision parameters based on the respective curriculum reform effects and the corresponding inter-period synergistic effects includes:

[0041] The key classes to be supervised will be determined based on the described effects of the curriculum reforms.

[0042] The frequency of inspections will be adjusted based on the inter-period synergistic effects and the key inspection classes.

[0043] Secondly, embodiments of this application provide a device for analyzing the correlation of curriculum reform supervision indicators, including:

[0044] The acquisition module is used to acquire teaching conditions and curriculum reform data for each class in multiple time periods before and after the curriculum reform. The teaching conditions include the teacher-student ratio and the historical performance of students and teachers. The curriculum reform data includes data on various indicators in three aspects: teachers, students, and management. The teacher-related indicators include classroom interaction frequency and / or training duration. The student-related indicators include the number of times they speak and / or their grades. The management-related indicators include equipment utilization rate and / or equipment update cycle.

[0045] The dynamic network model module is used to establish a dynamic network model for each class based on the teaching conditions and curriculum reform data. The dynamic network model is used to reflect the correlation between various indicators of teachers, students and management in each time period before and after the curriculum reform.

[0046] The indicator subgroup module is used to identify the corresponding indicator subgroups according to each dynamic network model; wherein, the indicator subgroup is a set of indicators that change synchronously in different time periods after the curriculum reform in the dynamic network model;

[0047] The decay subgroup module is used to determine the corresponding decay subgroup based on the subgroup synergy index of each of the indicator subgroups; wherein, the subgroup synergy index is used to reflect the degree of synergy among the indicators within the indicator subgroup, and the decay subgroup refers to the indicator subgroup in which the subgroup synergy index continuously decreases or the number of indicators continuously decreases in two or more consecutive time periods.

[0048] The network structure similarity module is used to calculate the network structure similarity of the dynamic network model for each class in adjacent time periods after the curriculum reform.

[0049] The intertemporal synergy effect module is used to obtain the intertemporal synergy effect of each class based on the similarity of each decaying subgroup and each network structure; wherein, the intertemporal synergy effect is used to reflect the relationship between various indicators in multiple time periods after the curriculum reform.

[0050] Thirdly, embodiments of this application provide a curriculum reform supervision indicator correlation analysis device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method as described in any one of the first aspects above.

[0051] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the first aspects above.

[0052] Fifthly, embodiments of this application provide a computer program product that, when run on a curriculum reform supervision indicator correlation analysis device, causes the curriculum reform supervision indicator correlation analysis device to execute the method described in any one of the first aspects above.

[0053] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating the correlation analysis method for curriculum reform supervision indicators provided in one embodiment of this application;

[0056] Figure 2 This is a schematic diagram of the subgroup synergy index in the correlation analysis method of curriculum reform supervision indicators provided in an embodiment of this application;

[0057] Figure 3 This is a schematic diagram of the intertemporal synergistic effect in the correlation analysis method of curriculum reform supervision indicators provided in an embodiment of this application;

[0058] Figure 4 This is a schematic diagram illustrating the implementation process of steps S700, S800, S820, and S900 in the method for correlation analysis of curriculum reform supervision indicators provided in an embodiment of this application.

[0059] Figure 5 This is a schematic diagram of the implementation process of steps S200 and S210 in the method for correlation analysis of curriculum reform supervision indicators provided in an embodiment of this application;

[0060] Figure 6 This is an example diagram of the dynamic network model in the correlation analysis method for curriculum reform supervision indicators provided in the embodiments of this application;

[0061] Figure 7 This is a schematic diagram of the structure of the curriculum reform supervision indicator correlation analysis device provided in the embodiments of this application;

[0062] Figure 8 This is a schematic diagram of the structure of the curriculum reform supervision indicator correlation analysis device provided in the embodiments of this application. Detailed Implementation

[0063] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0064] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0065] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0066] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0067] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0068] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0069] In related technologies, curriculum reform supervision relies on methods such as manual classroom observation, lesson plan review, and student questionnaires. The data collected is mostly immediate and localized indicators, typically using linear weighting or simple comparative analysis. These methods do not consider the impact of time factors on the indicators, providing immediate judgments of curriculum reform results but failing to capture the dynamic relationships between indicators. Therefore, existing curriculum reform supervision suffers from a lack of correlation analysis of multiple indicators across different time periods after the reform, making it difficult to determine the sustained impact of the curriculum reform on each indicator.

[0070] To address the aforementioned issues, this application provides a method, apparatus, and equipment for correlation analysis of curriculum reform supervision indicators. The method involves acquiring teaching conditions and curriculum reform data for each class across multiple time periods before and after the reform; establishing a dynamic network model for each class based on these data; identifying corresponding indicator subgroups based on each dynamic network model; determining corresponding decaying subgroups based on the subgroup synergy index of each indicator subgroup; calculating the network structure similarity of the dynamic network models for adjacent time periods after the curriculum reform for each class; and obtaining the inter-period synergistic effect for each class based on each decaying subgroup and the network structure similarity. Therefore, the curriculum reform supervision indicator correlation analysis method provided in this application provides a comprehensive analysis of curriculum reform from multiple dimensions and time periods. Through a series of steps, including establishing a dynamic network model, identifying indicator subgroups, determining decaying subgroups, and calculating network structure similarity, it is possible to deeply analyze the relationships and evolution of various indicators, identifying problems and weaknesses in curriculum reform. Obtaining the inter-period synergistic effect through the dynamic network model allows for real-time tracking of changes in the effectiveness of curriculum reform, adapting to the dynamic nature of curriculum reform.

[0071] The method for correlation analysis of curriculum reform supervision indicators provided in this application embodiment can be applied to the equipment for correlation analysis of curriculum reform supervision indicators. In this case, the equipment for correlation analysis of curriculum reform supervision indicators is the executing subject of the method for correlation analysis of curriculum reform supervision indicators provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of equipment for correlation analysis of curriculum reform supervision indicators.

[0072] For example, the devices used for correlation analysis of curriculum reform supervision indicators can be mobile phones, tablets, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), desktop computers, smart screens, smart TVs, handheld devices with wireless communication capabilities, computing devices or other processing devices connected to a wireless modem, laptops, handheld communication devices, handheld computing devices, etc., but are not limited to these.

[0073] To better understand the method for correlation analysis of curriculum reform supervision indicators provided in the embodiments of this application, the specific implementation process of the method for correlation analysis of curriculum reform supervision indicators provided in the embodiments of this application will be described in the following exemplary manner.

[0074] Figure 1 This illustration shows a schematic flowchart of the correlation analysis method for curriculum reform supervision indicators provided in an embodiment of this application. The correlation analysis method for curriculum reform supervision indicators includes:

[0075] S100 acquires teaching conditions and curriculum reform data for each class across multiple time periods before and after curriculum reform. Teaching conditions include the teacher-student ratio and historical student and teacher performance. Curriculum reform data includes indicators from three aspects: teachers, students, and management. Teacher-related indicators include classroom interaction frequency and / or training duration; student-related indicators include the number of times students speak and / or their grades; and management-related indicators include equipment utilization rate and / or equipment replacement cycle.

[0076] For example, data such as the teacher-student ratio, student historical grades (e.g., the average midterm / final grade of a subject for all students in a class), and teacher historical grades (e.g., the teaching evaluation score of the corresponding subject teacher in the class) can be extracted from the academic affairs system for multiple classes in multiple time periods before and after the curriculum reform; data such as the frequency of classroom interaction of teachers (e.g., the number of questions asked by the corresponding subject teacher in the class / minute) and training duration (e.g., the training duration of the corresponding subject teacher in the class in the teaching and research activity record) can be collected; and data such as equipment utilization rate (e.g., the projector usage time / total time in the class) and equipment update cycle (e.g., the difference between the equipment purchase date and the current date in the class) can be obtained. Among them, equipment includes computers, projectors, and electronic whiteboards. The academic affairs system includes: a grade management module for recording or querying the grades of each student in each class for each exam; a teaching evaluation module for recording or querying the teaching evaluations of each teacher each semester; a classroom recording module that uses electronic devices such as cameras to record classroom images and / or online classroom interaction recording systems to record or query the number of times / minute each teacher asks questions in class; a teacher development or training management module for recording or querying the duration of each training session attended by teachers; and a teaching equipment management module for recording or querying the usage time and purchase date of equipment in each class.

[0077] S200 establishes a dynamic network model for each class based on various teaching conditions and curriculum reform data. This dynamic network model reflects the relationships between various indicators related to teachers, students, and management at each time point before and after the curriculum reform.

[0078] For example, a dynamic network model can be established for each class based on the aforementioned teaching conditions and curriculum reform data to represent the dynamic relationships between various indicators. For instance, a network can be constructed for each class, comprising three networks: teachers, students, and administrators; or a network can be constructed that includes various indicators from the three aspects of teachers, students, and administrators. Nodes within the network represent specific indicators (such as classroom interaction frequency, number of student speeches, etc.). Edge weights are calculated using grey relational analysis to reflect the strength of the relationships between indicators. The network topology of the dynamic network model evolves over time, and edge weights can be updated using a sliding time window (e.g., each month in each semester is a window, i.e., a time period) to capture changes in relationship patterns before and after curriculum reform. Figure 6 As shown, a class is selected, and within a certain period after the curriculum reform, the nodes are specific indicators of three aspects: teachers, students, and management (such as classroom interaction frequency, training duration, number of student speeches, equipment usage rate, student grades, etc.). The correlation strength between nodes is calculated based on the grey relational analysis method (GRA) (i.e., the correlation coefficient between each subsequence and the parent sequence) to obtain the edge weights between nodes. Based on the nodes and edge weights, an undirected graph is constructed using networkx to obtain the corresponding dynamic network model.

[0079] S300 identifies corresponding indicator subgroups based on each dynamic network model. These indicator subgroups are sets of indicators in the dynamic network model that change synchronously over different time periods following the curriculum reform.

[0080] For example, community detection algorithms (such as the Louvain algorithm) based on dynamic network models can identify synchronously changing indicator subgroups. Alternatively, multiple indicator subgroups can be obtained by partitioning the nodes (i.e., indicators) of the dynamic network model using modularity, and it must be determined that each indicator in the subgroup must satisfy synchronicity in the time dimension, i.e., the changing trends of each indicator are consistent within the same time period. The distance matrix of each indicator in the dynamic network model can also be calculated using methods such as the Pearson correlation coefficient, and then the linker function in SciPy can be used based on the distance matrix. gThe `e` and `fcluster` functions perform hierarchical clustering on various indicators of a dynamic network model, dividing the indicators into different subgroups to determine the indicator subgroups. For example, consider a class with data from multiple indicators related to teachers, students, and management during three time periods (time period 1, time period 2, and time period 3) after curriculum reform. There are a total of 6 indicators: teacher classroom interaction frequency (I1), teacher training duration (I2), student speaking frequency (I3), student grades (I4), equipment utilization rate (I5), and equipment update cycle (I6). The values ​​of each indicator in the three time periods are shown in Table 1 below. The Pearson correlation coefficient between each indicator is calculated, resulting in the distance matrix D = 1 − R (R is the correlation coefficient matrix), as shown in Table 2 below. The output result is: Clustering result: [1 1 1 1 1 2], indicating that the first 5 indicators belong to one subgroup, and I6 belongs to another subgroup.

[0081]

[0082] Table 1

[0083]

[0084] Table 2

[0085] S400 determines the corresponding decaying subgroups based on the subgroup synergy index of each indicator subgroup. The subgroup synergy index reflects the degree of synergy among the indicators within a subgroup, while a decaying subgroup refers to an indicator subgroup whose synergy index continuously decreases or whose number of indicators continuously decreases over two or more consecutive time periods.

[0086] For example, the average correlation coefficient can be obtained by calculating the average correlation coefficient of all node pairs (i.e., between each indicator) within each indicator subgroup. A score can then be assigned based on the average correlation coefficient of each indicator subgroup (the average correlation coefficient can be directly multiplied by 100) to obtain the subgroup synergy index. (For example, if the indicator subgroups of a class are teacher indicator subgroups, student indicator subgroups, and management indicator subgroups, the subgroup synergy index of this class would be as follows...) Figure 2 (As shown). The subgroup synergy index sequence of each indicator subgroup is calculated by sliding time window. If the subgroup synergy index continues to decrease or the size of the indicator subgroup (i.e. the number of indicators) continues to decrease for two consecutive time periods, it is identified as a decaying subgroup.

[0087] S500 calculates the network structure similarity of the dynamic network model for each class in adjacent time periods after the curriculum reform.

[0088] For example, graph kernel-based methods (such as the Weisfeiler-Lehman graph kernel) or graph similarity (such as the Jaccard similarity coefficient) can be used to calculate the network structure similarity of the dynamic network models of each class in adjacent time periods after the curriculum reform. Alternatively, the network structure of the dynamic network models of each class in adjacent time periods after the curriculum reform can be transformed into feature vectors, and the cosine similarity or Jensen-Shannon divergence between the feature vectors of adjacent time periods can be calculated to obtain the network structure similarity.

[0089] S600 calculates the intertemporal synergistic effect for each class based on the similarity of each decaying subgroup and network structure. The intertemporal synergistic effect reflects the synergistic relationship between various indicators across multiple time periods following the curriculum reform.

[0090] For example, the intertemporal synergistic effect of each class can be obtained based on the decaying subgroups and network structure similarity of each class, such as... Figure 3 As shown. For example, the intertemporal synergistic effect = [α × (1 - proportion of decaying subgroups) + β × network structure similarity] × 100 (points), where α and β are weight coefficients (e.g., α is 0.6, β is 0.4), and the proportion of decaying subgroups is the ratio of the number of decaying subgroups to the total number of subgroups.

[0091] In one possible implementation, please refer to Figure 4 The methods also include:

[0092] S700 determines the correlation coefficients between the core indicators and other indicators of each class before and after curriculum reform based on various dynamic network models. Among them, the core indicators are those that have the greatest impact on other indicators.

[0093] For example, based on a dynamic network model, the degree centrality algorithm can be used to identify core indicators (i.e., the nodes with the highest connectivity in the network), and the correlation coefficient between the core indicators and other indicators can be calculated using the Pearson correlation coefficient.

[0094] S800 determines the effectiveness of curriculum reform for each class based on the data from each curriculum reform and the correlation coefficients between the core indicators and other indicators for each class. The effectiveness of curriculum reform is used to assess its impact on teaching.

[0095] For example, the changes in each indicator of a class before and after the curriculum reform can be calculated, multiplied by their corresponding weights, summed, and standardized to the 0-1 range to obtain the reform effect for that class. The weight of each indicator is determined by the core indicator and its correlation coefficient with other indicators.

[0096] S900 adjusts the supervision parameters based on the effects of each curriculum reform and the corresponding cross-period synergistic effects. These parameters include the classes to be supervised and the frequency of supervision.

[0097] For example, the supervision parameters can be determined based on the curriculum reform effect of each class and the corresponding inter-period synergistic effect. For instance, if the curriculum reform effect of a class is less than 0.4 or the synergistic effect is less than 60 (points), then the class is identified as a key supervision class; the supervision frequency is: high (curriculum reform effect less than 0.4), medium (curriculum reform effect greater than or equal to 0.4 and less than 0.6), and low (curriculum reform effect greater than or equal to 0.6).

[0098] Through steps S700 to S900, the correlation between indicators is quantified, resolving the problem of subjective allocation of indicator weights in traditional assessments and improving the objectivity of core indicator identification. It breaks through the limitations of single-indicator assessments, achieving multi-dimensional dynamic assessment, which addresses the problem of existing technologies neglecting the synergistic effects between indicators, thus improving the scientific rigor of assessment results. It also enables intelligent allocation of supervisory resources. The dynamic network model can capture synergistic effects and quantify the sustainability of reform outcomes.

[0099] Optionally, please refer to Figure 4 S800 determines the effectiveness of curriculum reform for each class based on the data from each curriculum reform and the correlation coefficients between the core indicators and other indicators of each class, including:

[0100] S810 derives the corresponding synergy index based on the correlation coefficients between the core indicators and other indicators of each class. The synergy index reflects the degree of synergy among the various indicators of a class.

[0101] For example, the corresponding synergy index can be obtained by summing the correlation coefficients between the core indicators of each class and other indicators and averaging them.

[0102] S820, based on the curriculum reform data, obtains the causal relationship strength between multiple indicators of each class before and after the curriculum reform.

[0103] For example, the strength of causal relationship between indicators in each class before and after curriculum reform can be obtained by using Granger causality test analysis of panel data. For instance, stationarity test (ADF test) can be performed on multiple indicators in each class, and first-order differencing can be performed on non-stationary sequences; a VAR model (vector autoregression model) can be constructed to test whether indicator X has a causal relationship with indicator Y; the significance level (p value) can be determined by the F statistic. If p ≥ 0.05, the causal relationship strength = 0; if 0.01 ≤ p < 0.05, the causal relationship strength = 0.5; if p < 0.01, the causal relationship strength = 1.

[0104] S830, based on the synergy index and the strength of each causal relationship, obtained the curriculum reform effect of each class.

[0105] For example, the corresponding curriculum reform effect can be obtained based on the synergy index and causal relationship strength of each class. For instance, curriculum reform effect = a Synergy Index + b Mean strength of causal association + c The mean rate of change of indicators, where a, b, and c are weighting coefficients (which can be determined by the entropy weighting method, such as a = 0.4, b = 0.4, and c = 0.2). The mean rate of change of indicators refers to the average standardized change of each indicator before and after the curriculum reform.

[0106] Through steps S810 to S830 above, the synergy index can reflect the overall correlation strength between indicators. Furthermore, both the synergy index and the causal correlation strength can be updated over a time window, capturing changes in synergistic characteristics at different stages of curriculum reform. This achieves a fusion analysis of synergy, causality, and trends, yielding curriculum reform effects that can be updated over a time window, supporting real-time adjustments to teaching.

[0107] For example, please refer to Figure 4 S820, based on the curriculum reform data, obtained the strength of causal relationships between multiple indicators in each class before and after the curriculum reform, including:

[0108] S821. Based on the curriculum reform data, establish a first model for each class before the curriculum reform and a second model for each class after the curriculum reform. The first model includes autoregressive and joint regression models of each indicator before the curriculum reform, and the second model includes autoregressive and joint regression models of each indicator after the curriculum reform.

[0109] It is understandable that autoregressive models are used to capture the time dependence of a single indicator, while joint regression models are used to analyze the coordinated changes of multiple indicators.

[0110] For example, the curriculum reform data can be standardized (e.g., Z-score standardization) to separate the data into pre-reform and post-reform data, and time windows can be marked (e.g., each month in a semester). For each class, an autoregressive (AR) model and a joint regression (SUR) model are fitted to the pre-reform and post-reform data respectively, resulting in a first model and a second model. For example, the autoregressive model: Y... it =α i +ρY i,t−1 +ϵ it , where Y it Y is the index value of class i at time t, ρ is the autoregressive coefficient; Joint regression model: Y it =α i +ΦY i,t−1 +ϵ it , where Y it Φ is the index vector (the corresponding index vector can be obtained from each index subgroup), and Φ is the autoregressive coefficient matrix.

[0111] S822, the corresponding sum of squared residuals is calculated based on each of the first and second models respectively.

[0112] For example, the predicted values ​​of the first and second models can be obtained based on the fitting results of the first and second models, the actual values ​​can be obtained based on the curriculum reform data of multiple time periods before and after the curriculum reform, the difference (residual) between the actual value and the predicted value can be calculated, and the sum of the squared residuals of all time periods before and after the curriculum reform can be obtained to obtain the sum of squared residuals corresponding to the first and second models respectively.

[0113] S823, perform an F-test based on the sum of squares of each residual to obtain the corresponding F-statistic.

[0114] It is understandable that, when the number of indicators changes before and after curriculum reform, an F-test is performed based on the sum of squared residuals corresponding to the first model for each class to obtain the corresponding F-statistic. When the number of indicator subgroups in a class changes before and after curriculum reform, an F-test is performed based on the sum of squared residuals corresponding to the second model for that class to obtain the corresponding F-statistic. If a class exhibits both of these situations simultaneously, an F-test is performed based on the sums of squared residuals corresponding to the first and second models respectively to obtain the corresponding F-statistic. The largest of these F-statistics is then determined as the strength of the causal association.

[0115] For example, the corresponding F-statistic can be calculated from the sum of squares of the residuals. For instance, the F-statistic = ,in, It is the sum of squared residuals before the curriculum reform. It is the sum of squared residuals after the curriculum reform, k is the difference in the number of model parameters (such as the number of new indicators after the curriculum reform), T is the length of the time window, and p is the number of model parameters before the curriculum reform (such as the number of indicators before the curriculum reform).

[0116] S824, determine the corresponding causal relationship strength based on each F statistic.

[0117] For example, the strength of causal association can be determined by the significance level (p-value) of the F-statistic. If p ≥ 0.05, the strength of causal association is 0; if 0.01 ≤ p < 0.05, the strength of causal association is 0.5; and if p < 0.01, the strength of causal association is 1.

[0118] Through steps S821 to S824, the model is updated over a time window to capture the characteristic changes at different stages of curriculum reform. The F-test provides a rigorous significance test, avoiding subjective assumptions. Key driving factors are identified through causal strength analysis, providing data support for curriculum reform supervision.

[0119] Optionally, please refer to Figure 4S700, based on each dynamic network model, determines the correlation coefficients between the core indicators and other indicators of each class before and after curriculum reform, including:

[0120] S710 calculates the degree centrality and betweenness centrality of each node in each dynamic network model.

[0121] For example, each index corresponds to a node in the dynamic network model. The correlation coefficient between indices is the edge weight between nodes in the dynamic network model. The sum of the in-degree (number of edges pointing to the node) and out-degree (number of edges pointing to other nodes) of each node in the dynamic network model can be calculated, and the degree centrality of the node can be obtained by weighting according to the edge weight. The betweenness centrality of each node is the sum of the proportion of paths passing through the node in the shortest path of all node pairs. The path length between nodes can be the reciprocal of the edge weight.

[0122] S720 determines the core indicators for each class based on degree centrality and betweenness centrality.

[0123] For example, indicators with a degree centrality greater than a preset threshold can be filtered out, and then the core indicators can be determined by sorting them according to betweenness centrality.

[0124] S730 calculates the correlation coefficient between the core indicators and other indicators for each class.

[0125] For example, the time series data of the core indicators can be aligned with those of other indicators, and the correlation coefficient between the core indicators and other indicators for each class can be calculated based on Pearson correlation coefficient, Spearman rank correlation coefficient, etc.

[0126] Through steps S710 to S730 above, the dynamic network model of each class is updated over time, which can track the long-term impact of curriculum reform measures on the correlation pattern of indicators, locate core indicators through centrality analysis, and verify their impact path through correlation coefficients. This is conducive to achieving high-precision identification of key indicators of education reform and quantification of impact paths.

[0127] In one possible implementation, please refer to Figure 5 S200, based on various teaching conditions and curriculum reform data, establishes a dynamic network model for each class, including:

[0128] S210 calculates the supervision score for each class based on various teaching conditions and curriculum reform data. The supervision score includes scores for three aspects: teachers, students, and management.

[0129] For example, min-max standardization (e.g., positive indicator / positive condition: x−x) can be adopted based on teaching conditions and curriculum reform data. min / x max -xmin Negative index / negative condition: 1 - (x - x) min / x max -x min (where x is the indicator value or condition value) to obtain the score of each indicator and to obtain the condition score by weighting each condition. The scores of the three aspects of teachers, students and management are determined according to the weight of each indicator, the score of each indicator and the condition score of each aspect of teachers, students and management.

[0130] S220: Construct corresponding collaborative relationship matrices based on the curriculum reform data. These matrixes reflect the degree of correlation between indicators related to teachers, students, and management.

[0131] For example, the correlation between various indicators of teachers, students and management can be calculated using Pearson correlation coefficient or grey relational analysis based on curriculum reform data, and a collaborative relationship matrix can be constructed based on the correlation between the indicators.

[0132] S230 calculates the entropy values ​​corresponding to teachers, students, and management aspects of each class based on the various collaboration relationship matrices.

[0133] For example, the entropy value of the row (or column) to which the teacher, student, and management aspects belong in each class can be calculated based on each collaboration relation matrix. For instance, the entropy value E of the teacher dimension. 教师 =−k ij ln(p ij ), where k = 1 / ln(m), m is the number of indicators in the teacher dimension, p ij It is the standardized correlation.

[0134] S240, based on the inspection scores and entropy values, yields the corresponding weight values.

[0135] For example, the weight value of each class can be obtained by using a combined weighting method (such as multiplicative composition) based on the inspection scores and entropy values ​​of each class. For example, the weight W of a certain dimension i =S i ×(1−E i ) / j ×(1−E j ), where S i It is the supervisory score of the i-th dimension, E i It is the entropy value of the i-th dimension.

[0136] S250 is derived from the correlation coefficients between the weight values ​​and the various indicators of teachers, students and management.

[0137] For example, the nodes of the dynamic network model are indicators of the teacher, student, and management dimensions. The correlation coefficient between indicators and the weight of the indicator to its corresponding dimension (after normalizing the weight of each indicator in the indicator pair) can be obtained based on the weight values ​​and the correlation coefficient between each indicator of the three aspects of teachers, students, and management. This product is used to determine the edge weights between the indicators, thereby obtaining each dynamic network model.

[0138] Through the steps S210 to S250 above, the correlation strength and information content are quantified by the collaborative relationship matrix and entropy value, which is conducive to analyzing the effect of curriculum reform. The entropy value guides the weight allocation, avoids excessive investment in indicators with low information content, and improves the efficiency of curriculum reform supervision.

[0139] Optionally, please refer to Figure 5 S210, based on various teaching conditions and curriculum reform data, the supervision scores for each class are obtained, including:

[0140] S211, based on various teaching conditions and curriculum reform data, yields the first supervisory score for management and the second supervisory score for students in each class.

[0141] For example, min-max standardization (e.g., positive indicator / condition: x−x) can be adopted based on teaching conditions and curriculum reform data. min / x max -x min Negative index / condition: 1 - (x - x) min / x max -x min (where x is the indicator value or condition value) to obtain the score of each indicator and to obtain the condition score by weighting each condition. Based on the weight of each indicator and the score of each indicator in each aspect of students and management, the student score and management score are determined. Then, based on the calculated condition score of each class, the student score and management score of each class are weighted to obtain the first supervision score of each class in terms of management and the second supervision score of each class in terms of students.

[0142] S212, based on various teaching conditions, curriculum reform data, and second inspection scores, yields the third inspection score for each class regarding teachers.

[0143] For example, min-max standardization (e.g., positive indicator / condition: x−x) can be adopted based on teaching conditions and curriculum reform data. min / x max -x min Negative index / condition: 1 - (x - x) min / x max -x min(where x is the indicator value or condition value) to obtain the score of each indicator and weight the condition to obtain the condition score. The teacher score is determined according to the weight of each indicator and the score of each indicator. Then, the teacher score of each class is weighted according to the calculated condition score and the second supervision score of each class to obtain the third supervision score of each class for the teacher.

[0144] Through the above steps S211 to S212, standardization allows different indicators to be evaluated on a unified scale. By comprehensively considering teaching conditions, curriculum reform data, and the mutual influence between different aspects, such as the impact of student performance on teacher evaluation, it is beneficial to improve the comprehensiveness and accuracy of the evaluation.

[0145] Optionally, please refer to Figure 4 S910, adjust the supervision parameters according to the effects of each curriculum reform and the corresponding inter-period synergistic effects, including:

[0146] S910, determine the key classes to be supervised based on the effectiveness of each curriculum reform.

[0147] For example, the curriculum reform effect of each class can be compared with the preset effect value, and the classes whose curriculum reform effect is less than the preset effect value can be identified as key supervision classes.

[0148] It is understood that the preset effect value means that the effect of curriculum reform does not exceed a preset value. This preset value can be set by those skilled in the art according to actual needs, and is not a unique limitation here.

[0149] For example, a reasonable matching threshold can be determined based on historical data and industry standards. For instance, a preset performance value of 0.4 can be used.

[0150] S920, adjust the frequency of inspections based on the inter-period synergistic effects and the key inspection classes.

[0151] For example, the inspection frequency of key inspection classes can be adjusted to the highest level, and then the non-key inspection classes can be sorted according to the magnitude of the inter-period synergy to determine the corresponding inspection frequency.

[0152] Through steps S910 to S920 above, key classes for supervision are identified based on the effectiveness of curriculum reform, and the frequency of supervision is adjusted according to cross-period synergy. This allows supervision resources to be allocated more precisely to the classes and periods requiring attention, improving the targeting and effectiveness of supervision. Fully considering cross-period synergy helps to better grasp the long-term development trend of the teaching process.

[0153] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0154] Corresponding to the method for correlation analysis of curriculum reform supervision indicators described in the above embodiments, this application also provides a device for correlation analysis of curriculum reform supervision indicators. Each module of the device can realize each step of the method for correlation analysis of curriculum reform supervision indicators. Figure 7 The diagram shows a structural block diagram of the curriculum reform supervision indicator correlation analysis device provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0155] Reference Figure 7 The device includes:

[0156] The acquisition module is used to acquire teaching conditions and curriculum reform data for each class in multiple time periods before and after the curriculum reform. The teaching conditions include the teacher-student ratio and the historical performance of students and teachers. The curriculum reform data includes data on various indicators in three aspects: teachers, students, and management. The teacher indicators include classroom interaction frequency and training duration. The student indicators include the number of times they speak and their grades. The management indicators include equipment utilization rate and equipment update cycle.

[0157] The dynamic network model module is used to establish a dynamic network model for each class based on the teaching conditions and curriculum reform data. The dynamic network model is used to reflect the correlation between various indicators of teachers, students and management in each time period before and after the curriculum reform.

[0158] The indicator subgroup module is used to identify the corresponding indicator subgroups according to each dynamic network model; wherein, the indicator subgroup is a set of indicators that change synchronously in different time periods after the curriculum reform in the dynamic network model;

[0159] The decay subgroup module is used to determine the corresponding decay subgroup based on the subgroup synergy index of each of the indicator subgroups; wherein, the subgroup synergy index is used to reflect the degree of synergy among the indicators within the indicator subgroup, and the decay subgroup refers to the indicator subgroup in which the subgroup synergy index continuously decreases or the number of indicators continuously decreases in two or more consecutive time periods.

[0160] The network structure similarity module is used to calculate the network structure similarity of the dynamic network model for each class in adjacent time periods after the curriculum reform.

[0161] The intertemporal synergy effect module is used to obtain the intertemporal synergy effect of each class based on the similarity of each decaying subgroup and each network structure; wherein, the intertemporal synergy effect is used to reflect the relationship between various indicators in multiple time periods after the curriculum reform.

[0162] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0163] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0164] This application also provides a device for correlation analysis of curriculum reform supervision indicators. Figure 8 A schematic diagram of the structure of a curriculum reform supervision indicator correlation analysis device provided in one embodiment of this application. Figure 8 As shown, the curriculum reform supervision indicator correlation analysis device 8 in this embodiment includes: at least one processor 80 ( Figure 8 Only one is shown in the image), at least one memory 81 ( Figure 8 (Only one is shown in the image) and a computer program 82 stored in the at least one memory 81 and executable on the at least one processor 80. When the processor 80 executes the computer program 82, it causes the curriculum reform supervision indicator correlation analysis device 8 to implement the steps in any of the above-described curriculum reform supervision indicator correlation analysis method embodiments, or causes the curriculum reform supervision indicator correlation analysis device 8 to implement the functions of each module / unit in the above-described device embodiments.

[0165] For example, the computer program 82 can be divided into one or more modules / units, which are stored in the memory 81 and executed by the processor 80 to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 82 in the curriculum reform supervision indicator correlation analysis device 8.

[0166] The curriculum reform supervision indicator correlation analysis device 8 can be a desktop computer, laptop, handheld computer, or cloud server, etc. This device may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will understand that... Figure 8 This is merely an example of the curriculum reform supervision indicator correlation analysis device 8 and does not constitute a limitation on the curriculum reform supervision indicator correlation analysis device 8. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0167] The processor 80 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0168] In some embodiments, the memory 81 may be an internal storage unit of the curriculum reform supervision indicator correlation analysis device 8, such as a hard disk or memory of the device. In other embodiments, the memory 81 may be an external storage device of the device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the device. Furthermore, the memory 81 may include both internal and external storage units of the device. The memory 81 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 81 can also be used to temporarily store data that has been output or will be output.

[0169] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0170] This application provides a computer program product that, when run on a curriculum reform supervision indicator correlation analysis device, enables the curriculum reform supervision indicator correlation analysis device to implement the steps in any of the above method embodiments.

[0171] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the curriculum reform supervision indicator correlation analysis device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0172] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0173] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0174] In the embodiments provided in this application, it should be understood that the disclosed curriculum reform supervision indicator correlation analysis device and method can be implemented in other ways. For example, the above-described embodiments of the curriculum reform supervision indicator correlation analysis device are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0175] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0176] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for correlation analysis of curriculum reform supervision indicators, characterized in that, Applied to computing devices, including: The study obtains teaching conditions and curriculum reform data for each class in multiple time periods before and after the curriculum reform. The teaching conditions include the teacher-student ratio and the historical performance of students and teachers. The curriculum reform data includes data on various indicators from three aspects: teachers, students, and management. The teacher-related indicators include classroom interaction frequency and / or training duration. The student-related indicators include the number of times they speak and / or their grades. The management-related indicators include equipment utilization rate and / or equipment update cycle. A dynamic network model is established for each class based on the teaching conditions and curriculum reform data. The dynamic network model is used to reflect the correlation between various indicators of teachers, students and management in each time period before and after the curriculum reform. Identify the corresponding indicator subgroups according to each dynamic network model; wherein, the indicator subgroup is a set of indicators in the dynamic network model that change synchronously in different time periods after the curriculum reform; The corresponding decaying subgroup is determined based on the subgroup synergy index of each of the aforementioned indicator subgroups; wherein, the subgroup synergy index is used to reflect the degree of synergy among the various indicators within the indicator subgroup, specifically referring to the average correlation coefficient of the indicator subgroup, and the decaying subgroup refers to the indicator subgroup whose subgroup synergy index continuously decreases in two or more consecutive time periods. Calculate the network structure similarity of the dynamic network model for each class in adjacent time periods after the curriculum reform; The inter-period synergistic effect of each class is obtained based on the similarity of each decay subgroup and each network structure; wherein, the inter-period synergistic effect is used to reflect the synergistic relationship between various indicators in multiple time periods after curriculum reform, and the inter-period synergistic effect is used to determine the key supervision classes by comparing with a preset threshold; Based on the dynamic network models described above, the correlation coefficients between the core indicators of each class and other indicators of that class before and after the curriculum reform are determined; wherein, the core indicator is the indicator that has the greatest impact on other indicators among all indicators, and the core indicator refers to the node with the highest degree centrality in the dynamic network model. The effectiveness of curriculum reform in each class is determined based on the data on curriculum reform and the correlation coefficients between the core indicators and other indicators of each class; wherein, the effectiveness of curriculum reform is used to evaluate the impact of curriculum reform on teaching, and the effectiveness of curriculum reform is used to determine the frequency of supervision by comparing with a preset threshold. The supervision parameters are adjusted according to the effects of each curriculum reform and the corresponding inter-period synergistic effect; wherein, the supervision parameters include the key supervision classes and the supervision frequency; The process of establishing a dynamic network model for each class based on the aforementioned teaching conditions and curriculum reform data includes: The supervision scores for each class are obtained based on the aforementioned teaching conditions and curriculum reform data; wherein, the supervision scores include scores for three aspects: teachers, students, and management; A corresponding collaborative relationship matrix is ​​constructed based on the aforementioned curriculum reform data; wherein, the collaborative relationship matrix is ​​used to reflect the degree of correlation between indicators related to teachers, students, and management in different aspects; The entropy values ​​corresponding to the teachers, students, and management aspects of each class are calculated based on the aforementioned collaborative relationship matrices. The corresponding weight values ​​are obtained based on the inspection scores and entropy values ​​described above. The dynamic network models are obtained based on the weight values ​​and the correlation coefficients between the various indicators of teachers, students, and management.

2. The method for correlation analysis of curriculum reform supervision indicators as described in claim 1, characterized in that, The determination of the curriculum reform effectiveness of each class based on the aforementioned curriculum reform data and the correlation coefficients between the core indicators and other indicators of each class includes: The corresponding synergy index is obtained based on the correlation coefficient between the core indicators of each class and other indicators; wherein, the synergy index is used to reflect the degree of synergy among the various indicators of the class. Based on the curriculum reform data, the strength of causal relationships between multiple indicators of each class before and after the curriculum reform was obtained; The curriculum reform effects for each class are obtained based on the respective synergy indices and the respective causal relationship strengths.

3. The method for correlation analysis of curriculum reform supervision indicators as described in claim 2, characterized in that, The determination of the causal relationship strength between multiple indicators of each class before and after the curriculum reform based on the aforementioned curriculum reform data includes: Based on the curriculum reform data, establish a first model for each class before the curriculum reform and a second model for each class after the curriculum reform; wherein, the first model includes an autoregressive model and a joint regression model for each indicator before the curriculum reform, and the second model includes an autoregressive model and a joint regression model for each indicator after the curriculum reform. The corresponding sum of squared residuals is calculated based on each of the first model and each of the second models; The corresponding F-statistic is obtained by performing an F-test on the sum of squared residuals described above. The corresponding causal association strength is determined based on each of the aforementioned F-statistics.

4. The method for correlation analysis of curriculum reform supervision indicators as described in claim 1, characterized in that, The determination of the correlation coefficients between the core indicators and other indicators of each class before and after curriculum reform, based on the dynamic network models described above, includes: Calculate the degree centrality and betweenness centrality of each node in each of the dynamic network models described above; The core indicators for each class are determined based on the degree centrality and betweenness centrality of each class. Calculate the correlation coefficient between the core indicators and other indicators for each class.

5. The method for correlation analysis of curriculum reform supervision indicators as described in claim 1, characterized in that, The supervision scores for each class, obtained based on the aforementioned teaching conditions and curriculum reform data, include: Based on the aforementioned teaching conditions and curriculum reform data, the first supervisory score for management and the second supervisory score for students were obtained for each class. Based on the aforementioned teaching conditions, curriculum reform data, and the second inspection scores, the third inspection scores for each class regarding teachers are obtained.

6. The method for correlation analysis of curriculum reform supervision indicators as described in claim 1, characterized in that, The adjustment of supervision parameters based on the respective curriculum reform effects and the corresponding inter-period synergistic effects includes: The key classes to be supervised will be determined based on the described effects of the curriculum reforms. The frequency of inspections will be adjusted based on the inter-period synergistic effects and the key inspection classes.

7. A correlation analysis device for curriculum reform supervision indicators, characterized in that, For implementing the method as described in any one of claims 1 to 6, the curriculum reform supervision indicator correlation analysis device comprises: The acquisition module is used to acquire teaching conditions and curriculum reform data for each class in multiple time periods before and after the curriculum reform. The teaching conditions include the teacher-student ratio and the historical performance of students and teachers. The curriculum reform data includes data on various indicators in three aspects: teachers, students, and management. The teacher-related indicators include classroom interaction frequency and / or training duration. The student-related indicators include the number of times they speak and / or their grades. The management-related indicators include equipment utilization rate and / or equipment update cycle. The dynamic network model module is used to establish a dynamic network model for each class based on the teaching conditions and curriculum reform data. The dynamic network model is used to reflect the correlation between various indicators of teachers, students and management in each time period before and after the curriculum reform. The indicator subgroup module is used to identify the corresponding indicator subgroups according to each dynamic network model; wherein, the indicator subgroup is a set of indicators that change synchronously in different time periods after the curriculum reform in the dynamic network model; The decaying subgroup module is used to determine the corresponding decaying subgroup based on the subgroup synergy index of each of the indicator subgroups; wherein, the subgroup synergy index is used to reflect the degree of synergy between each indicator within the indicator subgroup, and the decaying subgroup refers to the indicator subgroup whose subgroup synergy index continuously decreases in two or more consecutive time periods. The network structure similarity module is used to calculate the network structure similarity of the dynamic network model for each class in adjacent time periods after the curriculum reform. The intertemporal synergy effect module is used to obtain the intertemporal synergy effect of each class based on the similarity of each decaying subgroup and each network structure; wherein, the intertemporal synergy effect is used to reflect the synergistic relationship between various indicators in multiple time periods after curriculum reform.

8. A computing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 6.

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