Kindergarten management optimization system driven by family interaction data

Abstract

The invention relates to the technical field of education, in particular to a family interaction data driven kindergarten management optimization system, which comprises a satisfaction survey module, a family demand analysis module, a student performance monitoring module, a resource allocation optimization module, a family education support module, a community reputation management module, an enrollment retention strategy module and a crisis response module. Wherein the satisfaction survey module is used for collecting parent feedback, frequency data of participating in school activities and parent-child conference records; the family demand analysis module is used for analyzing and identifying family demands and switches; and the student performance monitoring module is used for adjusting the education service according to the output of the family demand analysis module. According to the invention, through real-time analysis of interaction data, optimization of crisis response and accurate formulation of an enrollment retention strategy, family-school communication efficiency, school safety management capability and student enrollment and retention rates are significantly improved, so that education service quality and school reputation are integrally enhanced.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and particularly to a kindergarten management optimization system driven by family interaction data. Background Art

[0002] In the current educational environment, kindergarten management systems face various challenges, especially in home-school interaction and crisis management. Traditional kindergarten management methods usually rely on basic communication tools (such as telephones, emails or text messages) to maintain the connection between parents and schools, lacking the ability to effectively integrate and analyze home-school interaction data. In addition, existing home-school interaction platforms often fail to provide sufficient support to handle emergencies, such as sudden health crises or safety incidents, which limits the school's ability to take effective actions at critical moments. These platforms also fail to make full use of the collected data to optimize student enrollment and retention strategies, or improve the educational experience of families.

[0003] The limitations of the prior art are mainly manifested in two aspects: First, existing systems lack a comprehensive solution to monitor and respond to the interaction between schools and families in real time, which results in school administrators being unable to fully understand the needs and feedback of parents, thus affecting the formulation and implementation of school policies. Second, existing methods fail to provide an effective crisis response mechanism, unable to quickly and effectively mobilize resources and communicate information when a crisis occurs, thereby increasing the risks faced by schools in the face of emergencies.

[0004] Therefore, it has become extremely necessary to develop a system that can integrate and analyze home-school interaction data, optimize resource allocation, improve family satisfaction, and effectively manage school crises. Summary of the Invention

[0005] Based on the above objectives, the present invention provides a kindergarten management optimization system driven by family interaction data.

[0006] A kindergarten management optimization system driven by family interaction data includes a satisfaction survey module, a family needs analysis module, a student performance monitoring module, a resource allocation optimization module, a family education support module, a community reputation management module, an enrollment and retention strategy module, and a crisis response module; wherein:

[0007] The satisfaction survey module: used to collect parent feedback, data on the frequency of participating in school activities, and parent-teacher conference records;

[0008] The family needs analysis module: receives the parent feedback data provided by the satisfaction survey module, combines the problems and consultation records raised by the family, and analyzes and identifies the needs and concerns of the family;

[0009] Student Performance Monitoring Module: Collect students' attendance records, academic achievements, behavior records, and teacher evaluations, and adjust educational services based on the output of the Family Needs Analysis Module;

[0010] Resource Allocation Optimization Module: Based on the academic achievements and attendance data provided by the Student Performance Monitoring Module, combined with the demand feedback from the Family Needs Analysis Module, optimize the allocation of teaching staff and curriculum resources;

[0011] Family Education Support Module: Based on the concern and problem data provided by the Family Needs Analysis Module, provide parent-child education courses and family counseling services to enhance the support for family education;

[0012] Community Reputation Management Module: Use the parent feedback and community feedback records from the Satisfaction Survey Module to analyze the school's community reputation and image, and the analysis results will be used to improve the school's brand image;

[0013] Enrollment and Retention Strategy Module: Analyze the data provided by the Student Performance Monitoring Module and the Family Needs Analysis Module to identify the retention and leaving patterns of families, and optimize enrollment and student retention strategies;

[0014] Crisis Response Module: Utilize emergency contact information and communication records, combined with the immediate community feedback provided by the Community Reputation Management Module and the Satisfaction Survey Module, to develop and implement a crisis management plan.

[0015] Furthermore, the Satisfaction Survey Module includes a data collection unit, a data processing unit, and a data output unit; among them,

[0016] Data Collection Unit: Automatically collect parents' feedback information and the frequency data of their participation in school activities through a preset regular electronic questionnaire and a mobile application interface. The electronic questionnaire is in a customizable format to adapt to different types of activities and meetings. The mobile application interface is used to provide real-time feedback and sign-in functions to record the specific situation of parents' participation in various school activities, including participation frequency, activity type, and participation duration;

[0017] Data Processing Unit: Use a classification algorithm to process the collected data. First, use the text analysis technology of term frequency-inverse document frequency to extract keywords from parents' feedback and identify the sentiment tendency. The extraction of keywords and sentiment analysis depends on a predefined sentiment dictionary and semantic rules. This data processing unit also uses statistical methods of mean and standard deviation to calculate the statistical data of participation frequency. The specific formula is: Time distribution of participation in activities = standard deviation (activity time points);

[0018] Data Output Unit: Integrates the processed data into a report, which includes a comprehensive score of parental satisfaction, an overview of engagement, and feedback themes. The data output unit also provides a regularly updated data dashboard for kindergarten administrators to monitor changes in parental satisfaction and engagement in real time.

[0019] Furthermore, the family needs analysis module includes a data receiving unit, a natural language processing unit, and a needs analysis unit; specifically:

[0020] Data Receiving Unit: Receives parental feedback data from the satisfaction survey module, ensures the integrity and format consistency of the data, and provides standardized input for subsequent processing.

[0021] Natural Language Processing Unit: Adopts a pre-set BERT model to understand the linguistic nuances in context. The BERT model first encodes the text data into vector form, and this vector is used to capture semantics and sentiment tendencies.

[0022] Needs Analysis Unit: Based on the text vectors provided by the natural language processing unit, uses the support vector machine algorithm for need classification and priority ranking. The support vector machine algorithm classifies different types of needs in a high-dimensional space by constructing one or more hyperplanes. After classification, the decision tree algorithm is used for priority ranking. According to the actual situation of kindergarten resource allocation and the urgency of parental feedback, the needs are evaluated for priority. The calculation formula of the support vector machine is: where x is the input vector to be classified; x i is the feature vector in the training dataset; y i is the class label corresponding to x i and represents the classification of parental needs; α i is the weight parameter learned by the model during the training process and corresponds to each support vector; K(x i , x) is the kernel function used to map the input data to a high-dimensional space to handle non-linear problems; b is the bias term; sign(·) is the sign function.

[0023] Furthermore, the student performance monitoring module includes a data collection unit, an analysis and processing unit, and a service adjustment unit; specifically:

[0024] Data Collection Unit: Used to collect students' attendance records, academic achievements, behavior records, and teacher evaluations. Among them, the attendance records automatically record the arrival and departure times of each student through an electronic attendance system; the academic achievements are collected through the subject score data input by teachers in the electronic grade book; the behavior records are input by teachers through a pre-set behavior monitoring software with detailed descriptions of students' in-school behavior performances; the teacher evaluations are collected through regular teacher assessment forms, including comprehensive evaluations of students' learning attitudes, classroom participation, and peer interactions.

[0025] Analysis and Processing Unit: Integrates and analyzes the data collected by the Data Acquisition Unit using statistical analysis methods. Attendance data analysis includes calculating the attendance rate of each student and identifying abnormal attendance patterns; Academic performance analysis evaluates students' academic performance by calculating average scores, rankings, and indicators of score fluctuations; Behavior record analysis uses preset text analysis techniques to extract behavior patterns; Teacher evaluation analysis relies on sentiment analysis tools to evaluate teachers' overall feelings and concerns about students.

[0026] Service Adjustment Unit: Based on the output of the Analysis and Processing Unit and the feedback of parents' needs provided by the Family Needs Analysis Module, adjusts educational services. Specifically, when the analysis shows that a student's academic performance has declined and parents report a need for additional tutoring, the Service Adjustment Unit will coordinate resources to provide a personalized tutoring plan for the student. When the behavior analysis indicates behavioral problems, a behavior improvement plan will be implemented in combination with parents' concerns.

[0027] Furthermore, the Resource Allocation Optimization Module includes a Data Integration Unit, a Resource Optimization Unit, and a Resource Implementation Unit; among them:

[0028] Data Integration Unit: Receives academic performance and attendance data provided by the Student Performance Monitoring Module, and at the same time combines the demand feedback provided by the Family Needs Analysis Module. Uses data fusion technology to uniformly process data from different sources to ensure data consistency and integrity. The process of data fusion includes standardizing various data formats and aggregating data using the weighted average method. The specific calculation formula is:

[0029] Aggregate score = ω1 × Score of academic performance + ω2 × Score of attendance + ω3 × Score of parents' feedback, where ω1, ω2, and ω3 are weight coefficients.

[0030] Resource Optimization Unit: Based on the output of the Data Integration Unit, uses linear programming methods to optimize teacher allocation and curriculum resources. Defines the resource allocation problem as an optimization model, and the goal is to maximize students' academic performance and meet parents' educational needs. The form of this optimization model is:

[0031]

[0032] where x i represents the amount of resources allocated to the i-th teaching activity, resource consumption i is the resource consumption required for this activity, and the total amount of resources is the total available resources.

[0033] Resource Implementation Unit: According to the planning results of the Resource Optimization Unit, implements specific resource allocation, and converts the optimized resource configuration plan into an actual class schedule and teacher arrangement.

[0034] Furthermore, the family education support module includes a demand recognition unit, a curriculum design unit, and a consulting service unit; specifically:

[0035] Demand recognition unit: Receives the concern and problem data provided by the family demand analysis module, and subdivides and identifies the themes and problem types that parents are concerned about. Specifically, the k-means clustering algorithm is used to classify the feedback into different themes according to the similarity of text data. The calculation formula of the clustering algorithm is:

[0036] where x represents a single data point, C i represents the i-th cluster, and μ i is the center point of the cluster C i , and SSE represents the sum of the squares of the distances from all points to their cluster centers. The goal is to minimize SSE;

[0037] Curriculum design unit: Based on the classification results of the demand recognition unit, develops targeted parent-child education courses, including selecting teaching contents and methods for different demands, formulating the curriculum structure, and developing interactive activities to enhance the participation and practicality of the courses. Specifically, for the demand of homework tutoring skills, designs a course including best practices for homework management and interactive seminars. The curriculum design unit will also compile detailed teaching plans and teaching materials;

[0038] Consulting service unit: According to the specific family demands analyzed by the demand recognition unit, configures education consultants to provide customized consulting services for parents, with a clear service process set, including preliminary consultation, problem diagnosis, solution suggestions, implementation support, and follow-up tracking. Each education consultant proposes evidence-based education strategies according to the specific cases assigned to them, and continuously tracks the service effects and adjusts the consulting content to meet the changing demands of family education.

[0039] Furthermore, the community reputation management module includes a data collection unit, a reputation analysis unit, and an image improvement unit; specifically:

[0040] Data collection unit: Obtains parent feedback data from the satisfaction survey module and collects feedback records in the community. The data collection unit will also be used to organize and standardize all the collected data to ensure data quality and usability;

[0041] Reputation analysis unit: Uses sentiment analysis technology and text mining methods to deeply analyze the collected data. Specifically, natural language processing technology, especially sentiment analysis, is applied to evaluate the sentiment tendencies and opinions of parents and community members. The sentiment analysis is carried out through the following calculation formula:

[0042] Among them, n represents the total number of words in the feedback, the word sentiment weight i is the predefined sentiment weight for each word, and the word frequency i is the frequency of the word appearing in the text;

[0043] Image improvement unit: Based on the analysis results of the reputation analysis unit, formulate specific brand image improvement strategies, including designing and implementing public relations activities, improving the communication methods between the school and the community, and adjusting school policies to respond to the expectations and needs of parents and the community.

[0044] Furthermore, the enrollment retention strategy module includes a data integration unit, a retention analysis unit, and a strategy implementation unit; among them,

[0045] Data integration unit: Collect academic performance, attendance data, and family needs feedback from the student performance monitoring module and the family needs analysis module, and integrate various types of data together to ensure the integrity and consistency of the data, providing accurate basic data for retention analysis;

[0046] Retention analysis unit: Apply a preset logistic regression model to analyze the retention and leaving patterns of families to predict the student retention probability. The specific formula is: Among them, y = 1 represents that the family chooses to stay in the school, and x i represents the factors affecting the family's retention decision, including academic performance, attendance rate, and family satisfaction. β i is the model parameter obtained through data fitting;

[0047] Strategy implementation unit: According to the results of the retention analysis unit, design and implement specific enrollment and retention strategies, including formulating an enrollment promotion plan, optimizing the enrollment experience of students and families, and providing targeted family support services to increase family satisfaction and loyalty.

[0048] Furthermore, the crisis response module includes an information integration unit, a crisis analysis unit, and a crisis management implementation unit;

[0049] Information integration unit: Used to collect and integrate emergency contact information, communication records, and instant community feedback provided by the community reputation management module and the satisfaction survey module. Specifically, use a preset database management system to standardize and store all information to ensure rapid access and utilization during a crisis;

[0050] Crisis Analysis Unit: Analyze the collected data using a pre-set crisis identification algorithm to determine potential crisis types and levels. This crisis identification algorithm is based on crisis indicators, including the frequency of emergencies, the urgency of community feedback, and the intensity of negative emotions, to evaluate the severity of the crisis. The specific formula of the crisis identification algorithm is: Crisis Index = α × Event Frequency + β × Urgency Score + γ × Intensity of Negative Emotions, where α, β, and γ are weight coefficients adjusted according to different crisis types;

[0051] Crisis Management Implementation Unit: Based on the evaluation results of the Crisis Analysis Unit, formulate and implement a specific crisis management plan. This crisis management plan includes immediately notifying relevant personnel, activating the emergency plan, and mobilizing corresponding resources. The Crisis Management Implementation Unit also includes communicating with the community, updating the school's status and response measures to reduce the potential impact of the crisis on the school's reputation.

[0052] Advantages of the present invention:

[0053] In the present invention, by implementing the kindergarten management optimization system driven by family interaction data, the school can monitor and analyze the interaction data between the family and the school in real time. This enhanced data analysis ability enables school administrators to deeply understand the needs and expectations of parents, thereby precisely adjusting educational strategies and improving service quality. Through the immediate response to parents' feedback and personalized educational services, this system greatly enhances the communication and collaboration between the school and the family, and improves parents' satisfaction and participation.

[0054] In the present invention, through its crisis response module, it provides an effective emergency management tool for the kindergarten. In the face of emergencies, it can quickly aggregate and utilize key information, such as emergency contact information and real-time community feedback, to formulate and implement appropriate crisis management plans. This not only reduces potential safety risks but also ensures that the school can maintain the continuity and stability of operations at critical moments, thus protecting the safety of students and teaching staff.

[0055] In the present invention, by optimizing the enrollment and student retention strategies, the school's enrollment efficiency and student retention rate are improved. The systematic data-driven method allows school administrators to accurately identify and respond to factors leading to student dropout, as well as effectively attract new students. The optimization of this strategy not only enhances the school's market competitiveness but also improves the overall educational quality and community reputation of the school, bringing long-term development and a stable student base to the kindergarten. Brief Description of the Drawings

[0056] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0057] Figure 1 Schematic diagram of the kindergarten management optimization system according to an embodiment of the present invention;

[0058] Figure 2 Schematic diagram of the family needs analysis module according to an embodiment of the present invention. Detailed implementation manners

[0059] To make the objectives, technical solutions and advantages of the present invention more clearly understood, the following further details the present invention in conjunction with specific embodiments.

[0060] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the art to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left" and "right" are only used to represent relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0061] As Figure 1 - Figure 2 shown, the kindergarten management optimization system driven by family interaction data includes a satisfaction survey module, a family needs analysis module, a student performance monitoring module, a resource allocation optimization module, a family education support module, a community reputation management module, an enrollment and retention strategy module, and a crisis response module; among which:

[0062] Satisfaction survey module: used to collect parent feedback, frequency data of participating in school activities and parent-teacher conference records, and the data output by this module is used by other modules to analyze the satisfaction and participation of families;

[0063] Family needs analysis module: receives the parent feedback data provided by the satisfaction survey module, combines the problems raised by the family and the consultation records, analyzes and identifies the needs and concerns of the family, and the analysis results of this module will be used to guide the resource allocation optimization module and the family education support module;

[0064] Student Performance Monitoring Module: Collects students' attendance records, academic achievements, behavior records, and teacher evaluations, adjusts educational services based on the output of the Family Needs Analysis Module, and provides necessary student performance data for the Resource Allocation Optimization Module;

[0065] Resource Allocation Optimization Module: Based on the academic performance and attendance data provided by the Student Performance Monitoring Module, combined with the demand feedback from the Family Needs Analysis Module, optimizes the allocation of teaching staff and curriculum resources to ensure that the resource allocation meets the actual needs of students and families;

[0066] Family Education Support Module: Based on the concern and problem data provided by the Family Needs Analysis Module, provides parent-child education courses and family counseling services to enhance the support for family education;

[0067] Community Reputation Management Module: Uses the parent feedback and community feedback records from the Satisfaction Survey Module to analyze the school's community reputation and image, and the analysis results will be used to improve the school's brand image;

[0068] Enrollment and Retention Strategy Module: Analyzes the data provided by the Student Performance Monitoring Module and the Family Needs Analysis Module to identify the retention and departure patterns of families, and optimizes enrollment and student retention strategies;

[0069] Crisis Response Module: Utilizes emergency contact information and communication records, combined with the immediate community feedback provided by the Community Reputation Management Module and the Satisfaction Survey Module, to develop and implement a crisis management plan.

[0070] The Satisfaction Survey Module includes a data collection unit, a data processing unit, and a data output unit; among them,

[0071] Data Collection Unit: Automatically collects parents' feedback information and their frequency data of participating in school activities through a preset regular electronic questionnaire and a mobile application interface. The electronic questionnaire is in a customizable format to adapt to different types of activities and meetings. The mobile application interface is used to provide real-time feedback and check-in functions to record the specific situation of parents' participation in various school activities, including participation frequency, activity type, and participation duration;

[0072] Data Processing Unit: Processes the collected data using a classification algorithm. First, it uses the text analysis technique of term frequency-inverse document frequency (TF-IDF) to extract keywords from parents' feedback and identify the sentiment tendency. The extraction of keywords and sentiment analysis rely on predefined sentiment dictionaries and semantic rules. This data processing unit also uses statistical methods of mean and standard deviation to calculate the statistical data of participation frequency. The specific formula is: Time distribution of participating in activities = standard deviation (activity time points);

[0073] Data Output Unit: Integrates the processed data into a report and outputs it to other modules of the system. The report includes the comprehensive score of parent satisfaction, an overview of participation, and feedback themes. The data output unit also provides a regularly updated data dashboard for kindergarten administrators to monitor the changes in parent satisfaction and participation in real time. Through refined data collection and analysis, this satisfaction survey module effectively improves the accuracy and timeliness of data, enabling kindergarten managers to quickly and accurately understand parents' satisfaction and participation, so as to adjust educational strategies and activity arrangements in a timely manner. In addition, systematic data processing and output provide a powerful decision-making support tool for the kindergarten, helping to improve parent satisfaction and participation, and further enhancing the quality and effectiveness of educational services.

[0074] The Family Needs Analysis Module includes a Data Receiving Unit, a Natural Language Processing Unit, and a Needs Analysis Unit; among them:

[0075] Data Receiving Unit: Receives parent feedback data from the satisfaction survey module, ensures the integrity of the data and the consistency of the format, and provides standardized input for subsequent processing;

[0076] Natural Language Processing Unit: Adopts a pre-set BERT (Bidirectional Encoder Representations from Transformers) model to understand the linguistic nuances in context. The BERT model first encodes the text data into vector form, and this vector is used to capture semantic and sentiment tendencies;

[0077] Needs Analysis Unit: Based on the text vectors provided by the Natural Language Processing Unit, uses the Support Vector Machine (SVM) algorithm for need classification and priority ranking. The SVM algorithm classifies different types of needs in a high-dimensional space by constructing one or more hyperplanes. After classification, the Decision Tree algorithm is used for priority ranking. According to the actual situation of kindergarten resource allocation and the urgency of parent feedback, the needs are evaluated for priority. The calculation formula of the support vector machine is: where x is the input vector to be classified, representing the vector generated after the parent feedback information is processed by the BERT model; x i is the feature vector in the training dataset, and each vector is extracted from historical parent feedback and is also processed by the BERT model; y i is the class label corresponding to x i and represents the classification of parent needs. For example, possible categories include "Educational Quality Needs", "Safety Concerns", "Teacher Issues", etc.; α i is the weight parameter learned by the model during the training process and corresponds to each support vector. These weights are optimized during the training of the SVM to determine the optimal classification hyperplane; K(x i, x) is a kernel function used to map the input data into a high-dimensional space to handle non-linear problems. Common kernel functions include linear kernel, polynomial kernel, radial basis function (RBF) kernel, etc. When dealing with text data, the RBF kernel is a commonly used choice because it can effectively handle the non-linear distribution of data in the original feature space; b is the bias term, which is also learned through the training process of SVM. Together with the weight parameters, it determines the position of the decision boundary and helps the model make classification decisions; sign(·) is the sign function used to determine which class the input vector x belongs to. If the result of the function is positive, then x is classified into the positive class; if it is negative, it is classified into the negative class. By introducing the advanced BERT model and SVM classification technology, the family needs analysis module can more accurately identify and classify the needs and concerns of parents. This method not only improves the accuracy and efficiency of need identification but also ensures that the kindergarten can prioritize and handle the most urgent family needs through priority ranking. Such a data-driven decision support system will ultimately enhance parent satisfaction, improve the quality of educational services, and thus optimize the overall operation effect of the kindergarten.

[0078] The student performance monitoring module includes a data collection unit, an analysis and processing unit, and a service adjustment unit; among them:

[0079] Data collection unit: used to collect students' attendance records, academic achievements, behavior records, and teacher evaluations. Among them, the attendance record automatically records the arrival and departure times of each student through an electronic attendance system; academic achievements are collected through the subject score data input by teachers in the electronic grade book; behavior records are input by teachers through a preset behavior monitoring software with detailed descriptions of students' in-school behavior performance; teacher evaluations are collected through regular teacher evaluation forms, including comprehensive evaluations of students' learning attitudes, classroom participation, and peer interaction;

[0080] Analysis and processing unit: uses statistical analysis methods to integrate and analyze the data collected by the data collection unit. Attendance data analysis includes calculating the attendance rate of each student and identifying abnormal attendance patterns; academic achievement analysis evaluates students' academic performance by calculating average scores, rankings, and indicators of score fluctuations; behavior record analysis uses preset text analysis techniques to extract behavior patterns; teacher evaluation analysis relies on sentiment analysis tools to evaluate teachers' overall feelings and concerns about students;

[0081] Service adjustment unit: based on the output of the analysis and processing unit and the feedback of parents' needs provided by the family needs analysis module, adjusts educational services. Specifically, when the analysis shows that a certain student's academic performance has declined and parents report a need for additional tutoring, the service adjustment unit will coordinate resources to provide a personalized tutoring plan for this student. When the behavior analysis points out behavior problems, a behavior improvement plan will be implemented in combination with parents' concerns;

[0082] By comprehensively analyzing the comprehensive performance data of students and combining with the specific needs of parents to dynamically adjust educational services, this student performance monitoring module can provide more personalized and accurate educational support, which not only helps improve students' academic performance and behavioral performance, but also increases parents' satisfaction and trust, thereby improving the overall educational quality and parental cooperation degree of the entire kindergarten.

[0083] The resource allocation optimization module includes a data integration unit, a resource optimization unit, and a resource implementation unit; among them:

[0084] Data integration unit: Receives the academic performance and attendance data provided by the student performance monitoring module, and at the same time combines the demand feedback provided by the family needs analysis module. Using data fusion technology, it uniformly processes data from different sources to ensure the consistency and integrity of the data. The process of data fusion includes standardizing various data formats and aggregating data using the weighted average method. The specific calculation formula is:

[0085] Aggregate score = ω1 × academic performance score + ω2 × attendance score + ω3 × parent feedback score, where ω1, ω2, and ω3 are weight coefficients, which are specifically adjusted according to the priorities set by the school management;

[0086] Resource optimization unit: Based on the output of the data integration unit, uses the linear programming method to optimize the teacher allocation and curriculum resources. Defines the resource allocation problem as an optimization model, and the goal is to maximize the academic performance of students and meet the educational needs of parents. The form of this optimization model is:

[0087]

[0088] Among them, x i represents the amount of resources allocated to the i-th teaching activity, and the resource consumption i is the resource consumption required for this activity, and the total amount of resources is the total available resources;

[0089] Resource implementation unit: According to the planning results of the resource optimization unit, implements specific resource allocation, converts the optimized resource configuration plan into an actual class schedule and teacher arrangement, and ensures the reasonable allocation and effective utilization of each resource; this resource allocation optimization module can achieve the optimal allocation of teachers and curriculum resources by precisely integrating and analyzing academic and attendance data and family demand feedback. Using the linear programming method not only improves the resource utilization efficiency, but also ensures that educational resources can meet the actual needs of students and parents, thereby improving the educational quality and parental satisfaction. This systematic resource optimization method provides a data-driven decision support tool for the kindergarten, effectively improving the overall effect and efficiency of educational services.

[0090] The family education support module includes a needs identification unit, a curriculum design unit, and a consulting service unit; among which:

[0091] Needs identification unit: Receive the concern and problem data provided by the family needs analysis module, and subdivide and identify the themes and problem types that parents care about. Specifically, use the k-means clustering algorithm to classify the feedback into different themes according to the similarity of text data. The calculation formula of the clustering algorithm is:

[0092] Among them, x represents a single data point (parent feedback), C i represents the i-th cluster, and μ i is the center point of the cluster C i , and SSE represents the sum of the squares of the distances from all points to their cluster centers. The goal is to minimize SSE;

[0093] Curriculum design unit: Based on the classification results of the needs identification unit, develop targeted parent-child education courses, including selecting teaching contents and methods for different needs, formulating the curriculum structure, and developing interactive activities to enhance the participation and practicality of the courses. Specifically, for the needs of homework tutoring skills, design courses that include best practices for homework management and interactive seminars. The curriculum design unit will also compile detailed teaching plans and teaching materials to ensure the systematicness and coherence of educational contents;

[0094] Consulting service unit: According to the specific family needs analyzed by the needs identification unit, allocate education consultants to provide customized consulting services for parents, set clear service processes, including preliminary consultation, problem diagnosis, solution suggestions, implementation support, and follow-up tracking. Each education consultant proposes evidence-based education strategies according to the specific cases assigned to them, and continuously tracks the service effects, adjusting the consulting contents to meet the changing needs of family education; Through the implementation of this family education support module, the kindergarten can accurately meet the diverse needs of families in education, provide more personalized and effective education support. This targeted education service not only improves the satisfaction of parents and students, but also optimizes the communication and cooperation between families and schools, promoting the overall development of children and the improvement of the family education environment.

[0095] The community reputation management module includes a data collection unit, a reputation analysis unit, and an image improvement unit; among which:

[0096] Data collection unit: Obtain parent feedback data from the satisfaction survey module, and collect feedback records in the community. The data collection unit will also be used to organize and standardize all the collected data, ensure data quality and usability, and provide an accurate input basis for subsequent analysis;

[0097] Reputation Analysis Unit: It deeply analyzes the collected data by using sentiment analysis technology and text mining methods. Specifically, it applies natural language processing (NLP) technology, especially sentiment analysis, to evaluate the sentiment tendencies and opinions of parents and community members. The sentiment analysis is carried out through the following calculation formula:

[0098] Where n represents the total number of words in the feedback, the sentiment weight i of the word is the predefined sentiment weight of each word, and the word frequency i is the frequency of the word appearing in the text. Through sentiment analysis, this unit calculates the overall sentiment tendency and provides a quantitative evaluation of the school's community reputation;

[0099] Image Improvement Unit: Based on the analysis results of the Reputation Analysis Unit, it formulates specific brand image improvement strategies, including designing and implementing public relations activities, improving the communication methods between the school and the community, and adjusting school policies to respond to the expectations and needs of parents and the community. The Image Improvement Unit will also monitor the implementation effect of the strategies and adjust the strategies according to the feedback to ensure the continuous improvement of the school image; Through scientific data analysis and systematic improvement measures, this community reputation management module effectively enhances the school's reputation in the family and community. Through the quantitative data provided by sentiment analysis, the kindergarten can specifically understand the views and sentiment attitudes of parents and the community towards the school, so as to formulate targeted improvement strategies, which not only enhances the school's social credibility but also promotes positive interaction with the community, and helps the sustainable development of the school and the stability of community relations in the long run.

[0100] The Enrollment Retention Strategy Module includes a Data Integration Unit, a Retention Analysis Unit, and a Strategy Implementation Unit; Among them,

[0101] Data Integration Unit: It collects academic performance, attendance data, and family needs feedback from the Student Performance Monitoring Module and the Family Needs Analysis Module, and integrates various types of data together to ensure the integrity and consistency of the data, providing accurate basic data for retention analysis;

[0102] Retention Analysis Unit: It applies a preset logistic regression model to analyze the retention and leaving patterns of families to predict the probability of student retention. The specific formula is: Where y = 1 represents that the family chooses to stay in the school, and x i represents the factors affecting the family's retention decision, including academic performance, attendance rate, and family satisfaction. β i is the model parameter obtained through data fitting;

[0103] Strategy Implementation Unit: Based on the results of the Retention Analysis Unit, design and implement specific enrollment and retention strategies, including formulating enrollment promotion plans, optimizing the enrollment experience for students and families, and providing targeted family support services to increase family satisfaction and loyalty; in addition, this unit is also responsible for monitoring the implementation effect of the strategies and adjusting the strategies according to actual feedback. This enrollment and retention strategy module can effectively identify the key factors affecting family retention and departure by precisely analyzing student and family data, and then formulate targeted enrollment and retention strategies. This data-driven approach not only improves enrollment efficiency and student retention rate, but also enhances family satisfaction and loyalty to the school by optimizing the family experience, bringing long-term stable development and improvement of the community reputation to the kindergarten.

[0104] The Crisis Response Module includes an Information Integration Unit, a Crisis Analysis Unit, and a Crisis Management Implementation Unit;

[0105] Information Integration Unit: Used to collect and integrate emergency contact information, communication records, and instant community feedback provided by the Community Reputation Management Module and the Satisfaction Survey Module, and specifically use a preset database management system to standardize and store all information to ensure rapid access and utilization in case of a crisis;

[0106] Crisis Analysis Unit: Analyze the collected data using a preset crisis identification algorithm to determine potential crisis types and levels. This crisis identification algorithm is based on crisis indicators, including the frequency of emergencies, the urgency of community feedback, and the intensity of negative emotions, to evaluate the severity of the crisis. The specific formula of the crisis identification algorithm is: Crisis Index = α × Event Frequency + β × Urgency Score + γ × Negative Emotion Intensity, where α, β, and γ are weight coefficients adjusted according to different crisis types;

[0107] Crisis Management Implementation Unit: Based on the evaluation results of the Crisis Analysis Unit, formulate and implement a specific crisis management plan. This crisis management plan includes immediately notifying relevant personnel, activating the emergency plan, and mobilizing corresponding resources. The Crisis Management Implementation Unit also includes communicating with the community, updating the school's status and response measures to reduce the potential impact of the crisis on the school's reputation; this crisis response module can quickly identify and respond to various emergencies by systematically integrating and analyzing key information. Through an effective crisis management plan, the kindergarten can minimize the negative impact of crisis events, protect the safety of students and faculty, and maintain good relations between the school and the community. The implementation of this module not only improves the school's emergency response ability, but also enhances the trust and satisfaction of parents and the community in the school's management ability.

[0108] The present invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A kindergarten management optimization system driven by family interaction data, characterized by: It includes satisfaction survey module, family needs analysis module, student performance monitoring module, resource allocation optimization module, family education support module, community reputation management module, enrollment retention strategy module and crisis response module; among them: Satisfaction survey module: used to collect parent feedback, frequency of participation in school activities and parent-child meeting records; Family needs analysis module: Receives parent feedback data from the satisfaction survey module, combines questions raised by the family and consultation records, and analyzes and identifies the family's needs and concerns; Student performance monitoring module: collects students' attendance records, academic performance, behavior records and teacher evaluations, and adjusts educational services based on the output of the family needs analysis module; Resource allocation optimization module: Based on the academic performance and attendance data provided by the student performance monitoring module and the demand feedback from the family demand analysis module, optimize the allocation of teaching staff and course resources; Family education support module: Based on the concerns and problem data provided by the family needs analysis module, it provides parent-child education courses and family consultation services to enhance support for family education; Community Reputation Management Module: Use parent feedback and community feedback records from the satisfaction survey module to analyze the school's community reputation and image. The analysis results will be used to improve the school's brand image; Enrollment and retention strategy module: Analyzes data provided by the student performance monitoring module and the family needs analysis module to identify family retention and departure patterns and optimize enrollment and student retention strategies; Crisis Response Module: Develop and implement a crisis management plan using emergency contact information and communication records, combined with immediate community feedback provided by the Community Reputation Management Module and Satisfaction Survey Module.

2. The family interaction data-driven kindergarten management optimization system according to claim 1, characterized in that: The satisfaction survey module includes a data collection unit, a data processing unit and a data output unit; wherein, Data Collection Unit: Automatically collects parents’ feedback and frequency of participation in school activities through a pre-set periodic electronic questionnaire and mobile application interface. The electronic questionnaire is in a customizable format to accommodate different types of activities and meetings. The mobile application interface is used to provide real-time feedback and sign-in functions to record the specific circumstances of parents’ participation in various school activities, including frequency of participation, activity type and duration of participation; Data processing unit: The collected data is processed using a classification algorithm. First, the word frequency-inverse document frequency text analysis technology is used to extract keywords from parent feedback and identify sentiment tendencies. Keyword and sentiment analysis relies on predefined sentiment dictionaries and semantic rules for extraction. The data processing unit also uses the statistical method of mean and standard deviation to calculate the statistical data of participation frequency. The specific formula is: Time distribution of participation in activities = standard deviation (activity time point); Data Output Unit: Integrates the processed data into a report that includes a comprehensive score of parent satisfaction, an overview of engagement, and feedback topics. The data output unit also provides a regularly updated data dashboard for kindergarten managers to monitor changes in parent satisfaction and engagement in real time.

3. The family interaction data-driven kindergarten management optimization system according to claim 1, characterized in that: The family demand analysis module includes a data receiving unit, a natural language processing unit, and a demand analysis unit; wherein: Data receiving unit: Receives parent feedback data from the satisfaction survey module, ensures data integrity and format consistency, and provides standardized input for subsequent processing; Natural Language Processing Unit: Uses the preset BERT model to understand the language nuances in context. The BERT model first encodes text data into a vector form, which is used to capture semantic and emotional tendencies; Demand analysis unit: Based on the text vectors provided by the natural language processing unit, the support vector machine algorithm is used to classify and prioritize the needs. The support vector machine algorithm constructs one or more hyperplanes to classify different types of needs in a high-dimensional space. After classification, the decision tree algorithm is used to prioritize the needs. The needs are prioritized according to the actual situation of kindergarten resource allocation and the urgency of parent feedback. The calculation formula of the support vector machine is: Among them, x is the input vector to be classified; x i is the feature vector in the training data set; y i is with x i The corresponding class label represents the classification of parental needs; α i is the weight parameter learned by the model during training, corresponding to each support vector; K(x i , x) is the kernel function, which is used to map the input data into a high-dimensional space so as to be able to handle nonlinear problems; b is the bias term; sign(·) is the sign function.

4. The family interaction data-driven kindergarten management optimization system according to claim 1, characterized in that: The student performance monitoring module includes a data collection unit, an analysis and processing unit, and a service adjustment unit; wherein: Data collection unit: used to collect students' attendance records, academic performance, behavior records and teacher evaluations. Attendance records are automatically recorded by the electronic attendance system to record the arrival and departure time of each student; academic performance is collected through the grade data of each subject entered by the teacher in the electronic grade book; behavior records are entered by teachers through the preset behavior monitoring software to describe the detailed behavior of students in school; teacher evaluations are collected through regular teacher evaluation forms, including comprehensive evaluations of students' learning attitudes, classroom participation and peer interaction; Analysis and processing unit: Use statistical analysis methods to integrate and analyze the data collected by the data collection unit. Attendance data analysis includes calculating the attendance rate of each student and identifying abnormal attendance patterns; academic performance analysis evaluates students' academic performance by calculating average scores, rankings, and indicators of grade fluctuations; behavioral record analysis uses preset text analysis technology to extract behavioral patterns; teacher evaluation analysis relies on sentiment analysis tools to evaluate teachers' overall feelings and concerns about students; Service Adjustment Unit: Adjusts educational services based on the output of the analysis and processing unit and the parent needs feedback provided by the family needs analysis module. Specifically, when the analysis shows that a student's academic performance has declined and the parents reflect the need for additional tutoring, the service adjustment unit will coordinate resources to provide the student with a personalized tutoring plan. When the behavioral analysis points out behavioral problems, a behavioral improvement plan will be implemented in combination with the parents' concerns.

5. The family interaction data-driven kindergarten management optimization system according to claim 1, characterized in that: The resource allocation optimization module includes a data integration unit, a resource optimization unit and a resource implementation unit; wherein: Data Integration Unit: Receives the academic performance and attendance data provided by the student performance monitoring module, and combines it with the demand feedback provided by the family demand analysis module. It uses data fusion technology to unify the data from different sources to ensure the consistency and integrity of the data. The data fusion process includes standardizing various data formats and aggregating data using the weighted average method. The specific calculation formula is: Aggregate score = ω1 × grade score + ω2 × attendance score + ω3 × parent feedback score, where ω1, ω2, and ω3 are weight coefficients; Resource optimization unit: Based on the output of the data integration unit, the linear programming method is used to optimize the allocation of teachers and course resources. The resource allocation problem is defined as an optimization model. The goal is to maximize students' academic performance and meet parents' educational needs. The form of the optimization model is: Student satisfaction i ·x i ; subjectto: Resource consumption i ·x i ≤Total amount of resources, where x i represents the amount of resources allocated to the i-th teaching activity, and the resource consumption i is the resource consumption required for the activity, and the total amount of resources is the total amount of available resources; Resource implementation unit: Based on the planning results of the resource optimization unit, implement specific resource allocation and convert the optimized resource allocation plan into an actual curriculum schedule and teacher arrangements.

6. The family interaction data-driven kindergarten management optimization system according to claim 1, characterized in that: The family education support module includes a demand identification unit, a curriculum design unit and a consulting service unit; wherein: Demand identification unit: Receives the concern and problem data provided by the family demand analysis module, and segments the data to identify the topics and problem types that parents are concerned about. Specifically, the k-means clustering algorithm is used to classify the feedback into different topics based on the similarity of the text data. The calculation formula of the clustering algorithm is: Where x represents a single data point, C i represents the i-th cluster, μ i is cluster C i The center point of , SSE represents the sum of the squares of the distances from all points to their cluster centers, and the goal is to minimize SSE; Curriculum Design Unit: Based on the classification results of the Needs Identification Unit, develop targeted parent-child education courses, including selecting teaching content and methods for different needs, formulating course structure, and developing interactive activities to enhance course participation and practicality. Specifically for the needs of homework tutoring skills, design courses that include best practices for homework management and interactive workshops. The Curriculum Design Unit will also prepare detailed teaching plans and teaching materials; Consulting Service Unit: Based on the specific family needs analyzed by the Needs Identification Unit, educational consultants are assigned to provide customized consulting services to parents, with a clear service process set up, including initial consultation, problem diagnosis, solution recommendations, implementation support and follow-up tracking. Each educational consultant proposes evidence-based education strategies based on the specific cases assigned to them, continuously tracks service results, and adjusts consulting content to adapt to the changing needs of family education.

7. The family interaction data-driven kindergarten management optimization system according to claim 1, characterized in that: The community reputation management module includes a data collection unit, a reputation analysis unit and an image improvement unit; wherein: Data Collection Unit: Obtain parent feedback data from the satisfaction survey module and collect feedback records from the community. The data collection unit will also be used to organize and standardize all collected data to ensure data quality and availability; Reputation Analysis Unit: Use sentiment analysis technology and text mining methods to conduct in-depth analysis of the collected data. Specifically, natural language processing technology, especially sentiment analysis, is used to evaluate the emotional tendencies and opinions of parents and community members. Sentiment analysis is performed using the following calculation formula: Where n represents the total number of words in the feedback, word sentiment weight i is the predefined sentiment weight of each word, and word frequency i is the frequency of the word appearing in the text; Image Improvement Unit: Based on the analysis results of the reputation analysis unit, develop specific brand image improvement strategies, including designing and implementing public relations activities, improving the way the school communicates with the community, and adjusting school policies to respond to the expectations and needs of parents and the community.

8. The family interaction data-driven kindergarten management optimization system according to claim 1, characterized in that: The enrollment retention strategy module includes a data integration unit, a retention analysis unit and a strategy implementation unit; wherein, Data Integration Unit: collects academic performance, attendance data and family demand feedback from the student performance monitoring module and the family demand analysis module, and integrates various data together to ensure the integrity and consistency of the data, providing accurate basic data for retention analysis; Retention Analysis Unit: Apply the preset logistic regression model to analyze the family's retention and departure patterns to predict the student retention probability. The specific formula is: Among them, y = 1 represents the family chooses to stay in school, x i represents the factors that influence family retention decisions, including academic performance, attendance rate, and family satisfaction, β i are model parameters, obtained by data fitting; Strategy Implementation Unit: Based on the results of the retention analysis unit, design and implement specific enrollment and retention strategies, including developing enrollment promotion plans, optimizing the enrollment experience for students and families, and providing targeted family support services to increase family satisfaction and loyalty.

9. The family interaction data-driven kindergarten management optimization system according to claim 1, characterized in that: The crisis response module includes an information integration unit, a crisis analysis unit and a crisis management implementation unit; Information Integration Unit: used to collect and integrate emergency contact information, communication records, and immediate community feedback provided by the community reputation management module and satisfaction survey module. Specifically, a preset database management system is used to standardize and store all information to ensure that it can be quickly accessed and used when a crisis occurs; Crisis analysis unit: Analyze the collected data using a pre-set crisis identification algorithm to determine the potential crisis type and level. The crisis identification algorithm is based on crisis indicators, including the frequency of emergencies, the urgency of community feedback, and the intensity of negative emotions, to assess the severity of the crisis. The specific formula of the crisis identification algorithm is: Crisis Index = α × Event Frequency + β × Urgency Score + γ × Negative Emotion Intensity, where α, β, and γ are weight coefficients that are adjusted according to different crisis types; Crisis Management Implementation Unit: Based on the assessment results of the crisis analysis unit, a specific crisis management plan is formulated and implemented. The crisis management plan includes immediate notification of relevant personnel, activation of emergency plans and mobilization of corresponding resources. The crisis management implementation unit also includes communication with the community to update the school's status and response measures to reduce the potential impact of the crisis on the school's reputation.

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