CN117036126A - College student comprehensive quality management system and method based on data analysis - Google Patents
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Abstract
A college student comprehensive quality management system and method based on data analysis acquire educational administration system data, library system data, campus card system data and community system data of college student objects to be evaluated; performing joint analysis on the educational administration system data, the library system data, the campus card system data and the community system data to obtain a multi-dimensional semantic association feature vector of a student object; and determining the comprehensive quality estimation value of the college student object to be evaluated based on the multi-dimensional semantic association feature vector of the student object. Thus, the comprehensive quality estimated value of the college students can be intelligently calculated, and deep insight and intelligent decision making are provided for college education management.
Description
Technical Field
The application relates to the technical field of intelligent management, in particular to a college student comprehensive quality management system and method based on data analysis.
Background
With the popularization and development of higher education, the comprehensive quality of college students becomes one of important indexes for education evaluation. The comprehensive quality of college students not only comprises learning achievements, but also comprises a plurality of aspects such as reading habits, life style, social ability and the like.
However, the conventional college student comprehensive quality evaluation method generally only depends on a subjective questionnaire or a single data source, lacks objectivity and comprehensiveness, and cannot truly reflect the multidimensional characteristics and the potential value of college students. Thus, an optimized college student comprehensive quality management scheme is desired.
Disclosure of Invention
The present application has been made to solve the above-mentioned technical problems. The embodiment of the application provides a college student comprehensive quality management system and method based on data analysis, which are used for acquiring educational administration system data, library system data, campus card system data and community system data of college student objects to be evaluated; performing joint analysis on the educational administration system data, the library system data, the campus card system data and the community system data to obtain a multi-dimensional semantic association feature vector of a student object; and determining the comprehensive quality estimation value of the college student object to be evaluated based on the multi-dimensional semantic association feature vector of the student object. Thus, the comprehensive quality estimated value of the college students can be intelligently calculated, and deep insight and intelligent decision making are provided for college education management.
In a first aspect, a method for comprehensive quality management of college students based on data analysis is provided, which includes:
acquiring educational administration system data, library system data, campus card system data and community system data of a college student object to be evaluated;
performing joint analysis on the educational administration system data, the library system data, the campus card system data and the community system data to obtain a multi-dimensional semantic association feature vector of a student object; a kind of electronic device with high-pressure air-conditioning system
And determining the comprehensive quality estimated value of the college student object to be evaluated based on the multi-dimensional semantic association feature vector of the student object.
In a second aspect, there is provided a college student comprehensive diathesis management system based on data analysis, comprising:
the data acquisition module is used for acquiring educational administration system data, library system data, campus card system data and community system data of the college student object to be evaluated;
the joint analysis module is used for carrying out joint analysis on the educational administration system data, the library system data, the campus card system data and the community system data to obtain a multi-dimensional semantic association feature vector of the student object; a kind of electronic device with high-pressure air-conditioning system
And the comprehensive quality estimation value determining module is used for determining the comprehensive quality estimation value of the college student object to be evaluated based on the multi-dimensional semantic association feature vector of the student object.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a college student comprehensive quality management method based on data analysis according to an embodiment of the present application.
Fig. 2 is a schematic architecture diagram of a college student comprehensive quality management method based on data analysis according to an embodiment of the present application.
FIG. 3 is a block diagram of a college student comprehensive quality management system based on data analysis according to an embodiment of the application.
Fig. 4 is a schematic view of a scenario of a college student comprehensive quality management method based on data analysis according to an embodiment of the present application.
Detailed Description
The following description of the technical solutions according to the embodiments of the present application will be given with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Unless defined otherwise, all technical and scientific terms used in the embodiments of the application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application.
In describing embodiments of the present application, unless otherwise indicated and limited thereto, the term "connected" should be construed broadly, for example, it may be an electrical connection, or may be a communication between two elements, or may be a direct connection, or may be an indirect connection via an intermediate medium, and it will be understood by those skilled in the art that the specific meaning of the term may be interpreted according to circumstances.
It should be noted that, the term "first\second\third" related to the embodiment of the present application is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second\third" may interchange a specific order or sequence where allowed. It is to be understood that the "first\second\third" distinguishing objects may be interchanged where appropriate such that embodiments of the application described herein may be practiced in sequences other than those illustrated or described herein.
The comprehensive quality of college students refers to comprehensive capability and literacy of college students in learning, thinking, interpersonal interaction, innovation capability, social responsibility feeling and the like, and comprises not only the grasping of discipline knowledge and the improvement of academic capability, but also wide personal literacy and social adaptability.
The following are several important aspects of college student comprehensive quality:
academic ability: college students should have firm discipline knowledge and academic research ability, be able to understand and master basic theory and practice skills of the learned profession, and have criticism thinking and problem solving ability.
Comprehensive literacy: college students need to culture extensive knowledge and culture literacy, including basic knowledge and understanding capability in various fields such as humanity science, social science, natural science and the like, and have comprehensive thinking and comprehensive analysis capability across disciplines.
Innovation capability: college students should have creative thinking and innovative capabilities, can independently think, propose new ideas and solve problems, and have innovative consciousness and practical capabilities.
Social ability: college students need to have good interpersonal interaction and cooperation capability, can effectively communicate with, cooperate with and resolve conflicts with other people, and have team cooperation and leadership capability.
Practical capabilities: universities should have practical capabilities, be able to apply the learned knowledge to the resolution of actual problems, have practical operation and experimental design capabilities.
Humane care and social responsibility feeling: college students should be concerned with others, respecting diversity and quality with sense of social responsibility, be able to actively participate in social public welfare activities, be concerned with social problems and put forward solutions.
The comprehensive quality of college students is a multi-dimensional concept, and needs to comprehensively consider the performances of academic capability, comprehensive literacy, innovation capability, social capability, practical capability, personal care, social responsibility feeling and the like, and the culture of the quality is not only a goal of college education, but also an important component of the comprehensive development of college students.
Traditional college student comprehensive quality assessment methods typically rely on subjective questionnaires or single data sources, lacking objectivity and comprehensiveness, which include:
subjective questionnaire investigation: by distributing questionnaires to students, teachers or other related personnel, subjective evaluation and opinion of comprehensive quality of college students are collected, and the method is easily affected by subjective factors, and subjective deviation of evaluation results is possible.
Student self-evaluation: students are required to evaluate and dislike their comprehensive quality, and this approach can learn their own knowledge, but may have a tendency to self-exaggerate or underestimate.
Academic performance evaluation: the comprehensive quality of students is mainly evaluated according to the achievement of the students on academic subjects, and the method only focuses on academic manifestations and ignores other important dimensions such as social capacity, practical capacity and the like.
Prize evaluation: the comprehensive quality of the student is evaluated based on the prize, honor or competition score obtained by the student, and the method is biased to the performance of the student in a specific field and cannot comprehensively reflect the comprehensive quality of the student.
Individual interviews and interviews: through face-to-face communication, the quality of students in terms of oral expression capability, interpersonal interaction capability, thinking logic and the like is evaluated, subjective judgment of an evaluator is needed, and a certain subjective deviation may exist in an evaluation result.
Conventional evaluation methods typically rely on subjective evaluations, such as subjective questionnaires and student self-evaluation, which are susceptible to personal subjective preferences, misunderstandings, or bias, and the evaluation results may not be objective and accurate. Traditional evaluation methods often focus on only a few aspects or indicators, and cannot comprehensively evaluate the comprehensive quality of students, for example, focus on academic performance only and neglect other important quality, or focus on discipline knowledge only and neglect comprehensive literacy and practical ability. Conventional evaluation methods are usually disposable, cannot track and evaluate the development and progress of students for a long time, cannot discover potential and problems of students in time, and provide personalized support and guidance. The traditional evaluation method lacks support of multi-source data, and cannot fully utilize data generated by students in learning, practice and social activities, so that the accuracy and the comprehensiveness of evaluation are limited. Conventional evaluation methods typically require a significant amount of time and human resources, such as collecting and analyzing questionnaire data, organizing interview and review processes, and the like, which increases the cost and effort of the evaluation.
The traditional college student comprehensive quality evaluation method has some limitations, and cannot comprehensively and objectively evaluate the multidimensional characteristics and the potential value of students. Therefore, new methods such as multisource data and intelligent calculation are needed to improve the accuracy and the comprehensiveness of evaluation.
FIG. 1 is a flow chart of a college student comprehensive quality management method based on data analysis according to an embodiment of the present application. Fig. 2 is a schematic architecture diagram of a college student comprehensive quality management method based on data analysis according to an embodiment of the present application. As shown in fig. 1 and 2, the college student comprehensive quality management method based on data analysis includes: 110, acquiring educational administration system data, library system data, campus card system data and community system data of a college student object to be evaluated; 120, performing joint analysis on the educational administration system data, the library system data, the campus card system data and the community system data to obtain a multi-dimensional semantic association feature vector of the student object; and 130, determining the comprehensive quality estimated value of the college student object to be evaluated based on the multi-dimensional semantic association feature vector of the student object.
In the step 110, it is ensured that the process of acquiring data complies with the relevant privacy protection laws and regulations, and the personal privacy and data security of the students are protected. The acquired data is ensured to be accurate, and errors or distortion in the data acquisition or processing process are avoided. By acquiring data of different systems, information of students in multiple dimensions such as academic, practice, social contact and the like can be acquired, and the data sources of evaluation are enriched. Integrating data of multiple systems can provide more comprehensive student information, and help to comprehensively understand comprehensive quality and potential of students.
In the step 120, the acquired data is cleaned, denoised and processed to ensure consistency and availability of the data. And extracting relevant features from the data of each system according to the estimated targets, and fusing to form the multi-dimensional semantic association feature vector of the student object. Joint analysis of data from different systems may reveal associations and interactions between different dimensions, providing a more comprehensive representation of the student. Through feature extraction and fusion, rich and various features can be obtained, so that comprehensive quality and potential of students can be described more accurately.
In the step 130, suitable algorithms and models are selected to analyze and evaluate feature vectors of the student object, such as machine learning algorithms, data mining techniques, and the like. According to the estimated targets, corresponding estimated indexes such as comprehensive quality estimated values, capability grades and the like are defined. Evaluation based on the multidimensional semantic association feature vector can improve the objectivity and accuracy of the evaluation and reduce the influence of subjective factors. By integrating the quality estimation values, personalized assessment results and development suggestions can be provided for each student, and the comprehensive development and growth of the students are promoted.
Through the steps, the accuracy, the comprehensiveness and the individuation degree of the evaluation can be improved by utilizing the multi-source data and the intelligent calculation method, and the comprehensive quality of college students can be better evaluated.
Aiming at the technical problems, the technical concept of the application is to combine multi-source data to capture potential characteristics of the comprehensive quality of college students, intelligently calculate the comprehensive quality estimated value of the college students and provide profound insight and intelligent decision for college education management.
Specifically, in the technical scheme of the application, firstly, educational administration system data, library system data, campus card system data and community system data of a college student object to be evaluated are obtained. The educational administration system data records the information of the student such as the learning condition, course score, course selection record and the like, and the data can reflect the academic performance and capability of the student; the library system data records information such as borrowing records, reading preferences and library activity participation of students, and the data can reflect the reading capacity, subject interests and autonomous learning capacity of the students; the campus card system data records information such as consumption records, access records and campus activity participation conditions of students, and the data can reflect daily living habits, social circles and the degree of participation in campus activities of the students; the community system data records the information of the situation that the students participate in the community activities, the role of the students and the achievement of the communities, and the data can reflect the organization capacity, the leadership capacity and the team cooperation capacity of the students.
The educational administration system data comprise information such as academic achievement, course selection and repair condition, score completion condition, academic honor and the like of students, and the data can reflect the performance and achievement of the students in academic aspects. For example, a student's average score point (GPA) may measure the quality of its academic performance, and the score completion may know whether the student is completing the academic requirement on time. Educational administration system data provides an important basis for assessing students' learning ability and academic level.
The library system data comprises information such as borrowing records of students, library use frequency, reading preference and the like, and the data can reflect reading habits of the students, knowledge acquisition conditions and interests in different fields. For example, the type and number of books a student borrows may display their discipline breadth and depth, and the frequency of library use may reflect the aggressiveness of the student's knowledge acquisition. Library system data provides important clues for assessing students' knowledge reserves and information literacy.
Campus card system data comprises information such as consumption records, access records, activity participation conditions and the like of students, and the data can reflect participation degree, social capacity and activity experience of the students in campus life. For example, a student's activity participation may display his team cooperation and leadership, and a consumption record may learn about the student's lifestyle and social circle. Campus card system data provides an important clue to assess the overall quality and social ability of students.
The community system data comprises communities in which students participate, roles of the students, activity participation and the like, and the data can reflect the organization capacity, the leadership capacity and the team cooperation capacity of the students in the community activities. For example, the role and activity participation of students in a community may display their lead potential and organizational capacity. The community system data provides important clues for assessing the leadership and social abilities of students. The comprehensive utilization of educational administration system data, library system data, campus card system data and community system data can comprehensively understand the comprehensive quality and potential of students from multiple dimensions of academic, practice, social contact and the like, and provides more accurate and comprehensive basis for evaluation.
The acquisition of educational administration system data, library system data, campus card system data and community system data of the college student object to be evaluated plays an important role in finally determining the comprehensive quality estimation value of the college student object to be evaluated. The data of each system provides information of different dimensions, the quality of students can be comprehensively estimated from multiple angles of academic, practice, social contact and the like, educational administration system data reflects academic performance and academic performance of the students, library system data reflects reading and knowledge acquisition conditions of the students, campus card system data reflects activity participation and campus life of the students, community system data reflects team cooperation and leadership capability of the students and the like, and comprehensive quality of the students can be estimated more accurately by integrating the data.
By acquiring data of different systems, the information of the students in multiple aspects can be acquired, the interests, the particulars, the participation degree, the development potential and the like of the students can be known, and the comprehensive evaluation of the personality characteristics, the development trend and the adaptability of the students can be facilitated. By comprehensively analyzing the data of multiple systems, the potential and problems of the student can be discovered, for example, educational system data may show that the student is excellent in a certain discipline, while community system data may show that the student has potential in terms of leadership, which can provide personalized support and development guidance for the student. The comprehensive analysis of the multi-source data can improve the accuracy of evaluation, the data of different systems are mutually verified, the credibility of the evaluation result is improved, and meanwhile, the association and the mode hidden in the data can be mined by using technologies such as big data analysis, machine learning and the like, so that the comprehensive quality of students can be estimated more accurately.
The data of a plurality of systems can provide more comprehensive, accurate and objective evaluation basis, is helpful for determining the comprehensive quality evaluation value of the college student object to be evaluated, and provides targeted support and guidance for the personalized development of students.
In one embodiment of the present application, performing joint analysis on the educational administration system data, the library system data, the campus card system data, and the community system data to obtain a multi-dimensional semantic association feature vector of a student object includes: semantic coding is respectively carried out on the educational administration system data, the library system data, the campus card system data and the community system data to obtain an educational administration system data semantic coding feature vector, a library system data semantic coding feature vector, a campus card system data semantic coding feature vector and a community system data semantic coding feature vector; and extracting semantic association features among the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector and the community system data semantic coding feature vector to obtain a student object multidimensional semantic association feature vector.
And then, carrying out semantic coding on the educational administration system data, the library system data, the campus card system data and the community system data to obtain an educational administration system data semantic coding feature vector, a library system data semantic coding feature vector, a campus card system data semantic coding feature vector and a community system data semantic coding feature vector. That is, the educational administration system data, the library system data, the campus card system data, and the community system data are converted into a structured vector representation to facilitate reading and identification of subsequent models.
In one embodiment of the present application, extracting semantic association features among the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector, and the community system data semantic coding feature vector to obtain a student object multidimensional semantic association feature vector includes: and arranging the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector and the community system data semantic coding feature vector into a two-dimensional feature matrix, and then obtaining the student object multidimensional semantic association feature vector through a text convolutional neural network model.
And then, arranging the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector and the community system data semantic coding feature vector into a two-dimensional feature matrix, and obtaining the multi-dimensional semantic association feature vector of the student object through a text convolutional neural network model. That is, a text convolutional neural network model is utilized to extract implicit associated feature information in the multi-source data. It should be appreciated that assessing the overall quality of college students needs to encompass a number of aspects including academic abilities, innovation abilities, team cooperation abilities, communication abilities, social abilities, practice abilities, and the like. Thus, individual data often cannot fully assess the overall quality of a student. And comprehensively analyzing and feature mining the high-dimensional feature distribution under each data source through the text convolutional neural network model so as to capture more comprehensive feature expression and provide more accurate information sources for comprehensive quality assessment of college students.
In one embodiment of the application, determining the comprehensive quality estimate of the college student object to be evaluated based on the student object multidimensional semantic association feature vector comprises: performing feature distribution optimization on the student object multi-dimensional semantic association feature vector to obtain an optimized student object multi-dimensional semantic association feature vector; and carrying out decoding regression on the multi-dimensional semantic association feature vector of the optimized student object through a decoder to obtain a decoding value, wherein the decoding value is used for representing the comprehensive quality estimated value of the college student object to be evaluated.
Further, the multi-dimensional semantic association feature vector of the student object is subjected to decoding regression through a decoder to obtain a decoding value, wherein the decoding value is used for representing the comprehensive quality estimated value of the college student object to be evaluated. The data of a plurality of systems are converted into the feature vectors, and decoding regression is carried out through the decoder, so that information of each dimension can be integrated to obtain an integrated evaluation value, and therefore, the performances of students in academic, practical, social and other aspects can be comprehensively considered, and the situation that one-sided evaluation or over dependence on data in a certain aspect is avoided.
The process of adopting the decoder to carry out decoding regression is objective calculation based on data and algorithm, so that the interference and deviation of subjective evaluation are reduced. Compared with the traditional subjective evaluation method, the evaluation mode based on the data is more objective and fair, and the influence of subjective factors on the evaluation result can be reduced. The decoding value can be used for representing the comprehensive quality estimation value of the college student object to be evaluated, which means that each student can obtain a personalized evaluation result, the decoding value can reflect the performances and potentials of the students in different dimensions, and provides targeted development advice and guidance for the students, thereby helping the students to develop and promote the comprehensive quality of the students better. The method of decoding regression using the decoder can efficiently process a large amount of data and can adapt to different student objects and features, and since the decoder is a machine learning and model training based method, the performance and accuracy of the decoder can be continuously improved by adding training data and optimization algorithms.
The multi-dimensional semantic association feature vector of the student object is decoded and returned through the decoder, so that a more comprehensive, objective and personalized evaluation result can be provided, the influence of subjective factors is reduced, and targeted support and guidance are provided for the development of the student. The method has good effect and can be applied to large-scale student assessment.
In the technical proposal of the application, the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector and the community system data semantic coding feature vector are arranged into two-dimensional feature matrixes, and then through a text convolution neural network model, the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector and the community system data semantic coding feature vector respectively express the educational administration system data, the library system data, the campus card system data and the community system data of the college student object to be evaluated, which are respectively expressed, of single-sample semantic features, however, if the coded text semantic feature of each of the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector, and the community system data semantic coding feature vector is used as a foreground object feature, background distribution noise related to feature distribution interference of single-sample semantic features is also introduced when cross-sample high-order semantic association feature extraction is performed, and the student object multidimensional semantic association feature vector has feature expression of feature order hierarchical association, thereby it is desirable to enhance the expression effect thereof based on the distribution characteristics of the student object multidimensional semantic association feature vector.
Therefore, the applicant of the present application performs a distribution gain based on a probability density feature simulation paradigm on the multi-dimensional semantic association feature vector of the student object, specifically expressed as: carrying out distribution gain based on a probability density characteristic simulation paradigm on the multi-dimensional semantic association feature vector of the student object by using the following optimization formula; wherein, the optimization formula is:
wherein V is the multi-dimensional semantic association feature vector of the student object, V i ′ Is the feature value of the ith position of the multi-dimensional semantic association feature vector of the optimized student object, L is the length of the multi-dimensional semantic association feature vector of the student object, v i Is the feature value of the ith position of the multi-dimensional semantically-related feature vector V of the student object,representing the square of the two norms of the multi-dimensional semantic association feature vector V of the student object, and alpha is a weighted hyper-parameter, exp (·) representing calculating a natural exponential function value with a value as a power.
Here, based on the characteristic simulation paradigm of the standard cauchy distribution on the probability density for the natural gaussian distribution, the distribution gain based on the probability density characteristic simulation paradigm can use the characteristic scale as a simulation mask to distinguish foreground object characteristics and background distribution noise in a high-dimensional characteristic space, so that semantic cognition distribution soft matching of characteristic space mapping is carried out on the high-dimensional space based on time domain space hierarchical semantics of the high-dimensional characteristics, unconstrained distribution gain of the high-dimensional characteristic distribution is obtained, the expression effect of the student object multi-dimensional semantic association characteristic vector based on the characteristic distribution characteristic is improved, and the accuracy of a decoding value obtained by the student object multi-dimensional semantic association characteristic vector through a decoder is improved.
In summary, the college student comprehensive diathesis management method 100 based on data analysis according to the embodiment of the application is illustrated, which combines multi-source data to capture potential characteristics of college student comprehensive diathesis, intelligently calculates the college student comprehensive diathesis estimated value, and provides deep insight and intelligent decision-making for college education management.
In one embodiment of the application, FIG. 3 is a block diagram of a college student comprehensive quality management system based on data analysis in accordance with an embodiment of the application. As shown in fig. 3, the college student comprehensive quality management system 200 based on data analysis according to an embodiment of the present application includes: the data acquisition module 210 is configured to acquire educational administration system data, library system data, campus card system data and community system data of an object of a college student to be evaluated; the joint analysis module 220 is configured to perform joint analysis on the educational administration system data, the library system data, the campus card system data, and the community system data to obtain a multi-dimensional semantic association feature vector of the student object; and a comprehensive quality estimation value determining module 230, configured to determine a comprehensive quality estimation value of the college student object to be evaluated based on the multi-dimensional semantic association feature vector of the student object.
In the college student comprehensive quality management system based on data analysis, the joint analysis module comprises: the semantic coding unit is used for respectively carrying out semantic coding on the educational administration system data, the library system data, the campus card system data and the community system data to obtain an educational administration system data semantic coding feature vector, a library system data semantic coding feature vector, a campus card system data semantic coding feature vector and a community system data semantic coding feature vector; and the feature vector extraction unit is used for extracting semantic association features among the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector and the community system data semantic coding feature vector to obtain a student object multidimensional semantic association feature vector.
In the college student comprehensive quality management system based on data analysis, the feature vector extraction unit is configured to: and arranging the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector and the community system data semantic coding feature vector into a two-dimensional feature matrix, and then obtaining the student object multidimensional semantic association feature vector through a text convolutional neural network model.
In the college student comprehensive quality management system based on data analysis, the comprehensive quality estimation value determining module comprises: the optimization unit is used for carrying out feature distribution optimization on the multi-dimensional semantic association feature vectors of the student objects to obtain optimized multi-dimensional semantic association feature vectors of the student objects; and the decoding unit is used for carrying out decoding regression on the multi-dimensional semantic association feature vector of the optimized student object through a decoder to obtain a decoding value, wherein the decoding value is used for representing the comprehensive quality estimated value of the college student object to be evaluated.
In the college student comprehensive quality management system based on data analysis, the optimizing unit is used for: carrying out distribution gain based on a probability density characteristic simulation paradigm on the multi-dimensional semantic association feature vector of the student object by using the following optimization formula; wherein, the optimization formula is:
wherein V is the multi-dimensional semantic association feature vector of the student object, V i ′ Is the feature value of the ith position of the multi-dimensional semantic association feature vector of the optimized student object, L is the length of the multi-dimensional semantic association feature vector of the student object, v i Is the feature value of the ith position of the multi-dimensional semantically-related feature vector V of the student object,representing the square of the two norms of the multi-dimensional semantic association feature vector V of the student object, and alpha is a weighted hyper-parameter, exp (·) representing calculating a natural exponential function value with a value as a power.
Here, it will be understood by those skilled in the art that the specific functions and operations of the respective units and modules in the above-described college student integrated quality management system based on data analysis have been described in detail in the above description of the college student integrated quality management method based on data analysis with reference to fig. 1 to 2, and thus, repetitive descriptions thereof will be omitted.
As described above, the college student comprehensive quality management system 200 based on data analysis according to the embodiment of the present application may be implemented in various terminal devices, for example, a server or the like for college student comprehensive quality management based on data analysis. In one example, the college student comprehensive quality management system 200 based on data analysis according to an embodiment of the present application may be integrated into the terminal device as one software module and/or hardware module. For example, the data analysis-based college student comprehensive quality management system 200 may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the university student comprehensive diathesis management system 200 based on data analysis may also be one of the plurality of hardware modules of the terminal device.
Alternatively, in another example, the data analysis-based college student integrated quality management system 200 and the terminal device may be separate devices, and the data analysis-based college student integrated quality management system 200 may be connected to the terminal device through a wired and/or wireless network and transmit interactive information in a contracted data format.
Fig. 4 is a schematic view of a scenario of a college student comprehensive quality management method based on data analysis according to an embodiment of the present application. As shown in fig. 4, in this application scenario, first, educational system data (e.g., C1 as illustrated in fig. 4), library system data (e.g., C2 as illustrated in fig. 4), campus card system data (e.g., C3 as illustrated in fig. 4), and community system data (e.g., C4 as illustrated in fig. 4) of a college student object to be evaluated are acquired; the acquired educational administration system data, library system data, campus card system data, and community system data are then input to a server (e.g., S as illustrated in fig. 4) deployed with a data analysis-based college student integrated quality management algorithm, wherein the server is capable of processing the educational administration system data, the library system data, the campus card system data, and the community system data based on the data analysis-based college student integrated quality management algorithm to determine an integrated quality estimate of the college student object to be evaluated.
It is also noted that in the apparatus, devices and methods of the present application, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (10)
1. The utility model provides a university student synthesizes quality management method based on data analysis which characterized in that includes:
acquiring educational administration system data, library system data, campus card system data and community system data of a college student object to be evaluated;
performing joint analysis on the educational administration system data, the library system data, the campus card system data and the community system data to obtain a multi-dimensional semantic association feature vector of a student object; a kind of electronic device with high-pressure air-conditioning system
And determining the comprehensive quality estimated value of the college student object to be evaluated based on the multi-dimensional semantic association feature vector of the student object.
2. The data analysis-based college student comprehensive diathesis management method of claim 1, wherein performing joint analysis on the educational administration system data, the library system data, the campus card system data, and the community system data to obtain a student object multidimensional semantic association feature vector, comprises:
semantic coding is respectively carried out on the educational administration system data, the library system data, the campus card system data and the community system data to obtain an educational administration system data semantic coding feature vector, a library system data semantic coding feature vector, a campus card system data semantic coding feature vector and a community system data semantic coding feature vector; and
and extracting semantic association features among the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector and the community system data semantic coding feature vector to obtain a student object multidimensional semantic association feature vector.
3. The data analysis-based college student comprehensive diathesis management method of claim 2, wherein extracting semantic association features among the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector, and the community system data semantic coding feature vector to obtain a student object multidimensional semantic association feature vector, comprises:
and arranging the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector and the community system data semantic coding feature vector into a two-dimensional feature matrix, and then obtaining the student object multidimensional semantic association feature vector through a text convolutional neural network model.
4. A college student comprehensive diathesis management method based on data analysis as in claim 3, wherein determining the comprehensive diathesis estimate of the college student object to be evaluated based on the student object multidimensional semantic association feature vector comprises:
performing feature distribution optimization on the student object multi-dimensional semantic association feature vector to obtain an optimized student object multi-dimensional semantic association feature vector; and
and carrying out decoding regression on the multi-dimensional semantic association feature vector of the optimized student object through a decoder to obtain a decoding value, wherein the decoding value is used for representing the comprehensive quality estimated value of the college student object to be evaluated.
5. The method for overall quality management of college students based on data analysis according to claim 4, wherein performing feature distribution optimization on the student object multi-dimensional semantic association feature vector to obtain an optimized student object multi-dimensional semantic association feature vector comprises: carrying out distribution gain based on a probability density characteristic simulation paradigm on the multi-dimensional semantic association feature vector of the student object by using the following optimization formula;
wherein, the optimization formula is:
wherein V is the multi-dimensional semantic association feature vector of the student object, V' i Is the feature value of the ith position of the optimized student object multi-dimensional semantic association feature vector, and L is the student object multi-dimensional semantic associationLength of feature vector, v i Is the feature value of the ith position of the multi-dimensional semantically-related feature vector V of the student object,representing the square of the two norms of the multi-dimensional semantic association feature vector V of the student object, and alpha is a weighted hyper-parameter, exp (·) representing calculating a natural exponential function value with a value as a power.
6. A college student comprehensive diathesis management system based on data analysis, comprising:
the data acquisition module is used for acquiring educational administration system data, library system data, campus card system data and community system data of the college student object to be evaluated;
the joint analysis module is used for carrying out joint analysis on the educational administration system data, the library system data, the campus card system data and the community system data to obtain a multi-dimensional semantic association feature vector of the student object; a kind of electronic device with high-pressure air-conditioning system
And the comprehensive quality estimation value determining module is used for determining the comprehensive quality estimation value of the college student object to be evaluated based on the multi-dimensional semantic association feature vector of the student object.
7. The data analysis-based college student comprehensive diathesis management system of claim 6, wherein the joint analysis module comprises:
the semantic coding unit is used for respectively carrying out semantic coding on the educational administration system data, the library system data, the campus card system data and the community system data to obtain an educational administration system data semantic coding feature vector, a library system data semantic coding feature vector, a campus card system data semantic coding feature vector and a community system data semantic coding feature vector; and
the feature vector extraction unit is used for extracting semantic association features among the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector and the community system data semantic coding feature vector to obtain a student object multidimensional semantic association feature vector.
8. The comprehensive quality management system for college students based on data analysis according to claim 7, wherein the feature vector extraction unit is configured to:
and arranging the educational administration system data semantic coding feature vector, the library system data semantic coding feature vector, the campus card system data semantic coding feature vector and the community system data semantic coding feature vector into a two-dimensional feature matrix, and then obtaining the student object multidimensional semantic association feature vector through a text convolutional neural network model.
9. The college student comprehensive diathesis management system based on data analysis of claim 8, wherein the comprehensive diathesis estimate determination module comprises:
the optimization unit is used for carrying out feature distribution optimization on the multi-dimensional semantic association feature vectors of the student objects to obtain optimized multi-dimensional semantic association feature vectors of the student objects; and
and the decoding unit is used for carrying out decoding regression on the multi-dimensional semantic association feature vector of the optimized student object through a decoder to obtain a decoding value, and the decoding value is used for representing the comprehensive quality estimated value of the college student object to be evaluated.
10. The college student comprehensive diathesis management system based on data analysis of claim 9, wherein the optimization unit is configured to: carrying out distribution gain based on a probability density characteristic simulation paradigm on the multi-dimensional semantic association feature vector of the student object by using the following optimization formula;
wherein, the optimization formula is:
wherein V is the multi-dimensional semantic association feature vector of the student object, V i ′ Is the feature value of the ith position of the multi-dimensional semantic association feature vector of the optimized student object, L is the length of the multi-dimensional semantic association feature vector of the student object, v i Is the feature value of the ith position of the multi-dimensional semantically-related feature vector V of the student object,representing the square of the two norms of the multi-dimensional semantic association feature vector V of the student object, and alpha is a weighted hyper-parameter, exp (·) representing calculating a natural exponential function value with a value as a power.
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