Psychological childbearing data analysis method

Abstract

The invention provides a psychological childbearing data analysis method which comprises the following steps: step 1, data collection: collecting to-be-analyzed data; step 2, data arrangement: cleaning and processing the collected data and integrating and classifying the data, including removing abnormal values, filling missing values and standardizing the data, and integrating and classifying the data of different sources and different types; 3, data analysis: extracting valuable information and insight by using various data mining and analysis algorithms; and step 4, decision support: personalized intervention suggestions are provided for teachers and counselers according to a data analysis result, the method provides insight and decision support for the teachers and the counselers through the steps of data collection, arrangement, analysis and the like, can help education practitioners manage and apply psychological childbearing data more effectively, and improves the psychological childbearing efficiency. Therefore, more personalized and targeted student support and intervention can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method for analyzing psychological education data. Background Art

[0002] Mental health education is an education that uses psychological educational methods based on the laws of students' physiological and psychological development to cultivate students' good psychological qualities and promote the overall improvement of students' overall quality. Mental health education is an important part of quality-oriented education.

[0003] Currently, teachers and counselors face difficulties in processing large amounts of psychological education data, as these data need to be collected, organized, and analyzed to obtain effective insights and decision support. Traditional data processing methods may lack efficiency and accuracy, so a specially designed software tool is needed to help teachers and counselors analyze psychological education data efficiently. Summary of the invention

[0004] In view of this, in order to solve the problems existing in the technical background, the present invention proposes a psychological education data analysis method, and the specific technical solution is as follows:

[0005] A psychological education data analysis method, comprising the following steps

[0006] Step 1, data collection, collect the data to be analyzed;

[0007] Step 2: Data collation, which involves cleaning and processing the collected data, as well as integrating and classifying the data, including removing outliers, filling missing values, and standardizing data, integrating and classifying data from different sources and types;

[0008] Step three, data analysis, uses various data mining and analysis algorithms to extract valuable information and insights;

[0009] Step 4, decision support, provides personalized intervention suggestions to teachers and counselors based on the results of data analysis;

[0010] Step 3 includes cluster analysis, association rule mining and predictive modeling. By clustering student data, students are divided into groups with similar characteristics to discover student group characteristics and trends. The algorithm used is as follows:

[0011]

[0012] Among them, x_i and y_i are the characteristic values ​​of students;

[0013] The hierarchical clustering algorithm is as follows:

[0014]

[0015] Among them, x_{ij} and y_{ij} are the characteristic data of student i and student j respectively;

[0016] Association rule mining discovers the correlation between different variables by analyzing the association rules in student data. The algorithm used is as follows:

[0017]

[0018]

[0019] Where X and Y are variables in the student data.

[0020]

[0021] Where (X) is the item set in the student data;

[0022] Predictive modeling uses machine learning algorithms to build predictive models to predict students' academic performance, learning difficulties, etc. The algorithms used are as follows:

[0023]

[0024] Among them, D is the student data set, and A is the feature in the data set;

[0025]

[0026] Among them, (X_1,...,X_p) is the characteristic data of students, (β0,...,,βp) is the model parameter, and the support vector machine f(X)=β0+β1X1+…+β p X p , where (X_1,...,X_p) are the characteristic data of students, (β_0,β_1,...,β_p) are the model parameters;

[0027] Time series analysis, autoregressive model (AR model):

[0028] X t =β0+β1X t-1 +…+β p X t-p +∈ t

[0029] Where (X_t) is the student data at time (t), (β_0,β_1,...,β_p) are the model parameters, and (ε_t) is the error term;

[0030] Moving average model (MA model):

[0031] X t=μ+∈ t +θ1∈ t-1 +…+θ q ∈ t-q

[0032] Where (X_t) is the student data at time (t), (μ) is the mean, (θ_1,...,θ_q) is the moving average term, and (ε_t) is the white noise error;

[0033] Autoregressive Moving Average Model (ARMA Model):

[0034] X t =β0+β1X t-1 +…+β p X t-p +∈ t +θ1∈ t-1 +…+θ q ∈ t-q

[0035] Among them, (X_t) is the student data at time (t), (β_0,β_1,...,β_p) are the autoregressive term parameters, (θ_1,...,θ_q) are the moving average term parameters, and (ε_t) is the white noise error; by analyzing students' physiological data (such as heart rate, skin conductance, etc.) or behavioral data (such as sleep time, social media use, etc.) over a period of time, the changing patterns of students' stress and anxiety can be identified, and time series analysis can be used to monitor students' stress and anxiety levels. This helps to detect students' psychological stress in a timely manner and provide corresponding support and intervention measures.

[0036] Furthermore, it also includes steps such as personalized intervention suggestions, action plan formulation, monitoring and evaluation. The personalized intervention suggestions provide personalized intervention suggestions to teachers and counselors based on the results of data analysis, and recommend corresponding intervention measures based on students' characteristics and problems, such as providing additional tutoring, formulating individual learning plans, etc.

[0037] Furthermore, the action plan system formulates a specific action plan based on the intervention suggestions, including specific steps, timetable and responsible persons of the intervention measures.

[0038] Furthermore, the monitoring and evaluation monitors the implementation of intervention measures, collects relevant data, and evaluates and analyzes the intervention effects, which helps to adjust the intervention strategy and improve the effectiveness of decision support.

[0039] Furthermore, the data collection includes student psychological assessment data collection, behavior record data collection, learning performance data collection, and physiological data collection.

[0040] The above technical solution has the following beneficial effects:

[0041] The present invention provides more accurate, comprehensive and timely insights through data analysis, helping teachers and counselors make more informed decisions and develop more effective intervention measures;

[0042] Data collection and analysis capabilities can help education practitioners better understand each student’s individual needs and issues, thereby providing more personalized and targeted support;

[0043] Automated and intelligent analysis methods can reduce the workload of teachers and counselors and improve the efficiency and accuracy of data processing.

[0044] The present invention provides teachers and counselors with insights and decision support through data collection, collation and analysis, and can help education practitioners more effectively manage and apply psychological education data to achieve more personalized and targeted student support and intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The present invention is a flowchart of a psychological education data analysis method. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] See also Figure 1 A psychological education data analysis method shown includes the following steps

[0048] Step 1, data collection, collect the data to be analyzed;

[0049] Step 2: Data organization, data cleaning and preprocessing: Clean and process the collected data, including removing outliers, filling missing values, data standardization, etc. The methods used include mean value filling, interpolation, data normalization, etc.

[0050] Data integration and classification: Integrate and classify data from different sources and types, and organize and classify them according to students, time, specific events, etc. This will help with subsequent data analysis and decision support.

[0051] Step three, data analysis, uses various data mining and analysis algorithms to extract valuable information and insights;

[0052] Step 4, decision support, provides personalized intervention suggestions to teachers and counselors based on the results of data analysis;

[0053] Step 3 includes cluster analysis, association rule mining and predictive modeling. By clustering student data, students are divided into groups with similar characteristics to discover student group characteristics and trends. The algorithm used is as follows:

[0054]

[0055] Among them, x_i and y_i are the characteristic values ​​of students;

[0056] The hierarchical clustering algorithm is as follows:

[0057]

[0058] Among them, x_{ij} and y_{ij} are the characteristic data of student i and student j respectively;

[0059] Association rule mining discovers the correlation between different variables by analyzing the association rules in student data. The algorithm used is as follows:

[0060]

[0061] Where X and Y are variables in the student data.

[0062]

[0063] Where (X) is the item set in the student data;

[0064] Predictive modeling uses machine learning algorithms to build predictive models to predict students' academic performance, learning difficulties, etc. The algorithms used are as follows:

[0065]

[0066] Among them, D is the student data set and A is the feature in the data set.

[0067]

[0068] Among them, (X_1,...,X_p) is the characteristic data of students, (β0,...,,βp) is the model parameter, and the support vector machine f(X)=β0+β1X1+…+β p X p , where (X_1,...,X_p) are the characteristic data of students, (β_0,β_1,...,β_p) are the model parameters;

[0069] Time series analysis, autoregressive model (AR model):

[0070] X t =β0+β1X t-1 +…+β p X t-p +∈ t

[0071] Where (X_t) is the student data at time (t), (β_0,β_1,...,β_p) are the model parameters, and (ε_t) is the error term;

[0072] Moving average model (MA model):

[0073] X t =μ+∈ t +θ1∈ t-1 +…+θ q ∈ t-q

[0074] Where (X_t) is the student data at time (t), (μ) is the mean, (θ_1,...,θ_q) is the moving average term, and (ε_t) is the white noise error;

[0075] Autoregressive Moving Average Model (ARMA Model):

[0076] X t =β0+β1X t-1 +…+β p X t-p +∈ t +θ1∈ t-1 +…+θ q ∈ t-q

[0077] Among them, (X_t) is the student data at time (t), (β_0,β_1,...,β_p) are the autoregressive term parameters, (θ

[0078] _1,...,θ_q) are moving average parameters, (ε_t) is white noise error; by analyzing students' physiological data (such as heart rate, skin conductance, etc.) or behavioral data (such as sleep time, social media use, etc.) over a period of time, the changing patterns of students' stress and anxiety can be identified, and time series analysis can be used to monitor students' stress and anxiety levels. This helps to detect students' psychological stress in a timely manner and provide corresponding support and intervention measures.

[0079] It also includes steps such as personalized intervention suggestions, action plan formulation, monitoring and evaluation. The personalized intervention suggestions provide personalized intervention suggestions to teachers and counselors based on the results of data analysis, and recommend corresponding intervention measures based on students' characteristics and problems, such as providing additional tutoring, formulating individual learning plans, etc.

[0080] The action plan system formulates a specific action plan based on the intervention recommendations, including specific steps, timetables, and responsible persons for the intervention measures.

[0081] The monitoring and evaluation monitors the implementation of intervention measures, collects relevant data, and evaluates and analyzes the effectiveness of the intervention. This helps to adjust the intervention strategy and improve the effectiveness of decision support.

[0082] The data collection includes student psychological assessment data collection, behavior record data collection, learning performance data collection, and physiological data collection;

[0083] Student psychological assessment data collection, collect students' psychological assessment data through psychological assessment tools. The psychological assessment tools used include Beck Depression Inventory, Spielberg Self-Rating Scale, etc. Data collection can be carried out through online questionnaires, face-to-face interviews, etc.

[0084] Behavior record data collection, collect students' behavior record data, including students' behavioral problems, discipline problems, etc. Data can be obtained through teacher observation, students' self-report, parents' feedback, etc.

[0085] Learning performance data collection collects students' learning performance data, such as classroom performance, test scores, homework completion, etc. The data can be obtained through the school's academic affairs system, teachers' records, etc.

[0086] Physiological data collection, heart rate monitoring, skin conductance measurement, electroencephalogram (EEG) recording, sleep monitoring, and exercise monitoring. (When collecting physiological data, it is necessary to ensure data security and privacy protection. Students and parents should be clearly informed of the purpose, method, and protection measures of data collection, and their informed consent should be obtained. In addition, the storage and processing of data must also comply with relevant laws, regulations, and ethical standards.)

[0087] The present invention provides teachers and counselors with insights and decision support through data collection, collation and analysis, and can help education practitioners more effectively manage and apply psychological education data to achieve more personalized and targeted student support and intervention.

[0088] The basic principles and main features of the present invention are described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for illustrating the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which shall fall within the scope of the present invention to be protected. The scope of protection of the invention shall be defined by the attached claims and their equivalents.

Claims

1. A psychological education data analysis method, It is characterized in that The following steps are included Step 1, data collection, collect the data to be analyzed; Step 2: Data collation, which involves cleaning and processing the collected data, as well as integrating and classifying the data, including removing outliers, filling missing values, and standardizing data, integrating and classifying data from different sources and types; Step three, data analysis, uses various data mining and analysis algorithms to extract valuable information and insights; Step 4, decision support, provides personalized intervention suggestions to teachers and counselors based on the results of data analysis; Step 3 includes cluster analysis, association rule mining and predictive modeling. By clustering student data, students are divided into groups with similar characteristics to discover student group characteristics and trends. The algorithm used is as follows: Among them, x_i and y_i are the characteristic values ​​of students; The hierarchical clustering algorithm is as follows: Among them, x_{ij} and y_{ij} are the characteristic data of student i and student j respectively; Association rule mining discovers the correlation between different variables by analyzing the association rules in student data. The algorithm used is as follows: Where X and Y are variables in the student data. Where (X) is the item set in the student data; Predictive modeling uses machine learning algorithms to build predictive models to predict students' academic performance, learning difficulties, etc. The algorithms used are as follows: Among them, D is the student data set, and A is the feature in the data set; Among them, (X_1,...,X_p) is the characteristic data of students, (β0,...,,βp) is the model parameter, and the support vector machine f(X) = β 0 +β 1 X 1 +…+β p X p , where (X_1,...,X_p) are the characteristic data of students, (β_0,β_1,...,β_p) are the model parameters; Time series analysis, autoregressive model (AR model): X t =b 0 +b 1 X t-1 +…+b p X t-p +∈ t Where (X_t) is the student data at time (t), (β_0,β_1,...,β_p) are the model parameters, and (ε_t) is the error term; Moving average model (MA model): X t =μ+∈ t +θ 1 ∈ t-1 +…+θ q ∈ t-q Where (X_t) is the student data at time (t), (μ) is the mean, (θ_1,...,θ_q) is the moving average term, and (ε_t) is the white noise error; Autoregressive Moving Average Model (ARMA Model): X t =b 0 +b 1 X t-1 +…+b p X t-p +∈ t +θ 1 ∈ t-1 +…+θ q ∈ t-q Among them, (X_t) is the student data at time (t), (β_0,β_1,...,β_p) are the autoregressive term parameters, (θ_1,...,θ_q) are the moving average term parameters, and (ε_t) is the white noise error; by analyzing students' physiological data (such as heart rate, skin conductance, etc.) or behavioral data (such as sleep time, social media use, etc.) over a period of time, the changing patterns of students' stress and anxiety can be identified, and time series analysis can be used to monitor students' stress and anxiety levels. This helps to detect students' psychological stress in a timely manner and provide corresponding support and intervention measures.

2. A psychological education data analysis method according to claim 1, It is characterized in that It also includes steps such as personalized intervention suggestions, action plan formulation, monitoring and evaluation. The personalized intervention suggestions provide personalized intervention suggestions to teachers and counselors based on the results of data analysis, and recommend corresponding intervention measures based on students' characteristics and problems, such as providing additional tutoring, formulating individual learning plans, etc.

3. A psychological education data analysis method according to claim 2, It is characterized in that The action plan system formulates a specific action plan based on the intervention recommendations, including specific steps, timetables, and responsible persons for the intervention measures.

4. A psychological education data analysis method according to claim 2, It is characterized in that The monitoring and evaluation monitors the implementation of intervention measures, collects relevant data, and evaluates and analyzes the effectiveness of the intervention. This helps to adjust the intervention strategy and improve the effectiveness of decision support.

5. A psychological education data analysis method according to claim 1, It is characterized in that The data collection includes student psychological assessment data collection, behavior record data collection, learning performance data collection, and physiological data collection.

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