CN112101786A - Student data analysis method and device based on big data and computer equipment - Google Patents
Student data analysis method and device based on big data and computer equipment Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention discloses a student data analysis method, a student data analysis device, computer equipment and a storage medium based on big data, wherein the method comprises the steps of receiving an initial student behavior data set; each initial student behavior data in the initial student behavior data set comprises student all-purpose card personal consumption data, library information resource usage records, dormitory access data, physical testing data, psychological assessment data, online learning system usage records, teaching resource library usage records and teaching recorded broadcast data; carrying out data combination on the initial student behavior data set to obtain a student behavior data set; performing correlation analysis according to the student behavior data set to obtain a correlation network; obtaining quantitative evaluation indexes by the student behavior data set and the association network according to grey association analysis; so as to obtain the weight of each quantitative evaluation index, and to calculate and obtain the comprehensive score of each student. The automatic processing calculation of the student evaluation data is realized after the data preprocessing is carried out on the basis of the big data, and the data processing efficiency is improved.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a student data analysis method and device based on big data, computer equipment and a storage medium.
Background
Under the promotion of the government, the Internet and education are developed vigorously, and the construction of the smart campus is developed in many higher-vocational schools, so that the level of education informatization is improved. Along with the construction of the smart campus, colleges and universities accumulate a large amount of data of personal information and behavior information of students, how to analyze the data to obtain valuable information, better serve the management work of the students, and become a new research direction for the innovative student management work of the colleges and universities at present.
Currently, the assessment of the high-position students is mostly finished by manpower, a score is given according to the existing student score information and the quantization integral and a corresponding management system, and the assessment method is single in assessment mode and cannot comprehensively reflect the school situation of the students.
Disclosure of Invention
The embodiment of the invention provides a student data analysis method and device based on big data, computer equipment and a storage medium, and aims to solve the problems that in the prior art, student evaluation is carried out according to accumulated data of a large amount of student personal information and behavior information by manpower, so that the evaluation method is single in evaluation mode and low in efficiency.
In a first aspect, an embodiment of the present invention provides a student data analysis method based on big data, which includes:
receiving and storing an initial student behavior data set uploaded by an acquisition terminal; each initial student behavior data in the initial student behavior data set comprises student all-purpose card personal consumption data, library information resource usage records, dormitory access data, physical testing data, psychological assessment data, online learning system usage records, teaching resource library usage records and teaching recorded broadcast data;
carrying out data combination on the initial student behavior data set to obtain a student behavior data set;
performing correlation analysis according to the student behavior data set to obtain a correlation network;
obtaining quantitative evaluation indexes by the student behavior data set and the association network according to grey association analysis; and
and acquiring the weight of each quantitative evaluation index to calculate and acquire the comprehensive score of each student.
In a second aspect, an embodiment of the present invention provides a big data based student data analysis apparatus, including:
the initial data set acquisition unit is used for receiving and storing an initial student behavior data set uploaded by the acquisition end; each initial student behavior data in the initial student behavior data set comprises student all-purpose card personal consumption data, library information resource usage records, dormitory access data, physical testing data, psychological assessment data, online learning system usage records, teaching resource library usage records and teaching recorded broadcast data;
the data merging unit is used for carrying out data merging on the initial student behavior data set to obtain a student behavior data set;
the gateway network acquisition unit is used for performing correlation analysis according to the student behavior data set to acquire a gateway network;
the quantitative evaluation index acquisition unit is used for acquiring quantitative evaluation indexes of the student behavior data set and the association network according to grey association analysis; and
and the comprehensive score calculating unit is used for acquiring the weight of each quantitative evaluation index so as to calculate and acquire the comprehensive score of each student.
In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the processor implements the big-data-based student data analysis method according to the first aspect.
In a fourth aspect, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the big-data-based student data analysis method according to the first aspect.
The embodiment of the invention provides a student data analysis method and device based on big data, computer equipment and a storage medium, wherein an initial student behavior data set uploaded by an acquisition end is received and stored; each initial student behavior data in the initial student behavior data set comprises student all-purpose card personal consumption data, library information resource usage records, dormitory access data, physical testing data, psychological assessment data, online learning system usage records, teaching resource library usage records and teaching recorded broadcast data; carrying out data combination on the initial student behavior data set to obtain a student behavior data set; performing correlation analysis according to the student behavior data set to obtain a correlation network; obtaining quantitative evaluation indexes by the student behavior data set and the association network according to grey association analysis; so as to obtain the weight of each quantitative evaluation index, and to calculate and obtain the comprehensive score of each student. The automatic processing calculation of the student evaluation data is realized after the data preprocessing is carried out on the basis of the big data, and the data processing efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic view of an application scenario of a big data-based student data analysis method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a big data-based student data analysis method according to an embodiment of the present invention;
FIG. 3 is a schematic block diagram of a big data based student data analysis apparatus provided by an embodiment of the present invention;
FIG. 4 is a schematic block diagram of a computer device provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic view of an application scenario of a big data-based student data analysis method according to an embodiment of the present invention; fig. 2 is a schematic flow chart of a big data-based student data analysis method according to an embodiment of the present invention, where the big data-based student data analysis method is applied to a server, and the method is executed by application software installed in the server.
As shown in fig. 2, the method includes steps S110 to S150.
S110, receiving and storing an initial student behavior data set uploaded by an acquisition end; the initial student behavior data set comprises student all-purpose card personal consumption data, library information resource usage records, dormitory access data, physical testing data, psychological assessment data, online learning system usage records, teaching resource library usage records and teaching recorded broadcast data.
In the embodiment, the learning behavior and process collection is carried out through the hybrid learning platform, various online learning systems, a teaching resource library and normalized teaching recording and broadcasting; wifi, Internet of things, monitoring and access control are used for collecting student behavior tracks; collecting offline consumption conditions of campus card, WeChat, Paibao and the like; and collecting internet behavior data.
The collected student behavior data is stored in a big data storage and analysis platform built by Hadoop and Spark. The big data storage and analysis platform adopts a distributed architecture, can support flexible expansion of services and support a service scene with large concurrency; secondly, a distributed account book is introduced into the storage layer, so that high expansion and high availability of data can be guaranteed.
And S120, carrying out data combination on the initial student behavior data set to obtain a student behavior data set.
In one embodiment, step S120 includes:
acquiring a unique student identifier corresponding to each piece of initial student behavior data of the initial student behavior data set;
the initial student behavior data with the same student unique identifier are merged to form a student behavior data set.
In the embodiment, the evaluation system of the high-position students is most important to comprehensively reflect all aspects of learning, activities of participation, daily performance and the like of the students during the school, and the students can generate a large amount of valuable data during the school along with the application of the informatization technology. Such data collectively fall into two categories:
one is behavioral data generated by the student himself. For example, time data such as in dormitories, classrooms, libraries, campuses, schools and other places, consumption data such as dining room diet and market consumption, second classroom data such as the number of people participating in various activities, activity conditions in different periods, evaluation and sharing of activities by students on different platforms, health data such as medical drug use, action tracks and extraclass physical exercises, and network data such as comments, posts and messages published in the network, etc.
The second category is data collected and collated in a targeted manner for various behaviors of students. For example, the data collection of students on class attendance, class attendance and performance, question answering and homework performance conditions can be carried out through various software; the student can input the statistics and arrangement for the out-of-class activity participation and the performance condition and the participation in various competitions into the system.
And S130, performing correlation analysis according to the student behavior data set to obtain a correlation network.
In one embodiment, step S130 includes:
acquiring a correlation coefficient between student behavior data in the student behavior data set;
and establishing an association relation between the student behavior data of which the correlation coefficient between the student behavior data exceeds a preset correlation coefficient threshold value to obtain a correlation network.
In this embodiment, by collecting massive behavior data and performing statistical search, comparison, association, classification, and other analysis and induction on the massive data, a mutual relationship network (gateway network) hidden in a data set is found, and the correlation is generally reflected by parameters such as support degree, reliability degree, interest degree, and the like
And S140, obtaining quantitative evaluation indexes by the student behavior data set and the association network according to grey association analysis.
In one embodiment, step S140 includes:
acquiring a data field value included in each student behavior data in the student behavior data set;
and performing grey correlation analysis according to the data field value included in each student behavior data to obtain quantitative evaluation indexes.
According to the correlation analysis, behavior characteristics are found out, quantitative assessment indexes are firstly graded according to assessment content items, and the content indexes are quantitative, such as examination scores, and qualitative, such as political expression; and then, according to all the subentry indexes, making comprehensive judgment on each student.
In the past, the method generally carries out comprehensive conversion through mathematical modes according to various set evaluation indexes, but many mathematical modes cannot perfectly reflect the quality of comprehensive qualities of students.
The grey correlation analysis can well process the problem, is particularly suitable for large-scale, multi-factor and multi-index system evaluation, and is scientific and close to objective practice. The grey system theory is evaluated by combining with the practical work of student management and evaluation and applying a very important grey correlation analysis method in the grey system theory to the comprehensive quality of college students, the viewpoints and methods of a general system theory, an information theory and a control theory are extended to abstract systems of society, economy, ecology and the like, and a set of theories and methods for solving an information incomplete system are developed by combining with a mathematical method.
The grey correlation analysis in the grey system theory is used, and the correlation of all factors to be analyzed and researched can be found out among random factor sequences through certain data processing in incomplete information, so that the main contradiction is found, and the main characteristics and the main influence factors are found. Because the correlation analysis is analyzed according to the development trend, the requirement on the size of the sample volume is not high, the typical distribution rule is not required during the analysis, and the analysis result is generally consistent with the qualitative analysis, so the method has wide practicability. The general steps of the grey correlation analysis method calculation are that a comparison number series and a reference number series are determined; calculating a correlation coefficient; calculating the degree of association; and sorting the relevance sizes.
Specifically, step S140 includes the following steps:
the comprehensive quality of college students is evaluated by using a grey correlation analysis method, different professions with collected data respectively represent different types of different students, and the application of the method is described by taking the students as an example.
411) Collecting and establishing an original data table;
and (4) sorting the data of the questionnaire according to the data of the questionnaire to obtain the scores of various indexes of classmates. The score value of each evaluation item is obtained by accumulating the scores of the corresponding specific questions, and the score of each student represents the average value of the scores of all the surveyed objects in the profession;
412) modeling and calculating a gray system;
4121) determining an index;
4122) and carrying out quantitative processing on the qualitative indexes. The scoring standards of all indexes can be properly adjusted according to the actual conditions of all schools;
4123) and normalizing the weight of each index. The product of the weight of the first-level index and the weight of the second-level index;
413) analyzing and evaluating a calculation result;
414) determining an evaluation index:
determining a comprehensive evaluation index system is the basis of evaluation work, comprehensive quality evaluation is a complex evaluation process involving multiple indexes, the evaluation index system is divided into three layers according to a general target, a primary index and a secondary index according to the principle of highlighting, giving consideration to comprehensiveness, clear layers, conciseness, science and effectiveness; the first layer is a comprehensive evaluation layer, the second layer is a primary index layer, the third layer is a secondary index layer, a plurality of factors of the same layer belong to or influence the factors of the upper layer, and simultaneously dominate or are influenced by the factors of the lower layer, and the factors of the same layer are mutually independent; the connection among all the layers is represented by connected straight lines, and a comprehensive quality evaluation index system of students is provided on the basis of in-depth analysis according to the characteristics of the students in the higher vocational colleges and universities, the culture target and the requirements of the society on high-level skill talents.
And S150, acquiring the weight of each quantitative evaluation index to calculate and acquire the comprehensive score of each student.
In the embodiment, a specific comprehensive evaluation method is compiled on the basis of a comprehensive evaluation system of the senior students, the meaning of an evaluation index combining quantitative description and qualitative description is refined, a clear meaning is determined for the qualitative index, and the requirements of quantity and quality are refined while the clear meaning is determined for the quantitative index, so that a specific scoring rule is established, and the evaluation system is more systematic. The definition or measurement design of the index has certain challenges and difficulty, and students can achieve the definition or measurement design through the effort to avoid too high or too low design. And finally, inputting the evaluation information of the students into a computer system by utilizing an advanced computer technology, and calculating scores through a background of the computer system by data mining and processing to realize comprehensive evaluation of the students.
In an embodiment, step S150 is followed by:
and sending the comprehensive scores of the students to a target receiving end.
In this embodiment, after the comprehensive scores of the students are obtained, the comprehensive scores can be timely sent to the target receiving end for viewing.
The method realizes automatic processing calculation of the student evaluation data after data preprocessing based on big data, and improves data processing efficiency.
The embodiment of the invention also provides a student data analysis device based on big data, which is used for executing any embodiment of the student data analysis method based on big data. Specifically, referring to fig. 3, fig. 3 is a schematic block diagram of a big data-based student data analysis device according to an embodiment of the present invention. The big data-based student data analysis apparatus 100 may be configured in a server.
As shown in fig. 3, the big-data-based student data analysis apparatus 100 includes: an initial data set acquisition unit 110, a data merging unit 120, an association network acquisition unit 130, a quantitative evaluation index acquisition unit 140, and a comprehensive score calculation unit 150.
An initial data set obtaining unit 110, configured to receive and store an initial student behavior data set uploaded by an acquisition end; the initial student behavior data set comprises student all-purpose card personal consumption data, library information resource usage records, dormitory access data, physical testing data, psychological assessment data, online learning system usage records, teaching resource library usage records and teaching recorded broadcast data.
In the embodiment, the learning behavior and process collection is carried out through the hybrid learning platform, various online learning systems, a teaching resource library and normalized teaching recording and broadcasting; wifi, Internet of things, monitoring and access control are used for collecting student behavior tracks; collecting offline consumption conditions of campus card, WeChat, Paibao and the like; and collecting internet behavior data.
The collected student behavior data is stored in a big data storage and analysis platform built by Hadoop and Spark. The big data storage and analysis platform adopts a distributed architecture, can support flexible expansion of services and support a service scene with large concurrency; secondly, a distributed account book is introduced into the storage layer, so that high expansion and high availability of data can be guaranteed.
And a data merging unit 120, configured to perform data merging on the initial student behavior data set to obtain a student behavior data set.
In one embodiment, the data merging unit 120 includes:
the unique identifier acquisition unit is used for acquiring a student unique identifier corresponding to each piece of initial student behavior data of the initial student behavior data set;
and the homogeneous data merging unit is used for merging the initial student behavior data with the same student unique identifier to form a student behavior data set.
In the embodiment, the evaluation system of the high-position students is most important to comprehensively reflect all aspects of learning, activities of participation, daily performance and the like of the students during the school, and the students can generate a large amount of valuable data during the school along with the application of the informatization technology. Such data collectively fall into two categories:
one is behavioral data generated by the student himself. For example, time data such as in dormitories, classrooms, libraries, campuses, schools and other places, consumption data such as dining room diet and market consumption, second classroom data such as the number of people participating in various activities, activity conditions in different periods, evaluation and sharing of activities by students on different platforms, health data such as medical drug use, action tracks and extraclass physical exercises, and network data such as comments, posts and messages published in the network, etc.
The second category is data collected and collated in a targeted manner for various behaviors of students. For example, the data collection of students on class attendance, class attendance and performance, question answering and homework performance conditions can be carried out through various software; the student can input the statistics and arrangement for the out-of-class activity participation and the performance condition and the participation in various competitions into the system.
And the association network obtaining unit 130 is configured to perform correlation analysis according to the student behavior data set to obtain an association network.
In an embodiment, the associated network obtaining unit 130 includes:
the correlation coefficient calculation unit is used for acquiring correlation coefficients among the student behavior data in the student behavior data set;
and the association relationship establishing unit is used for establishing an association relationship between the student behavior data of which the correlation coefficient between the student behavior data exceeds a preset correlation coefficient threshold value to obtain an association network.
In this embodiment, by collecting massive behavior data and performing statistical search, comparison, association, classification, and other analysis and induction on the massive data, a mutual relationship network (gateway network) hidden in a data set is found, and the correlation is generally reflected by parameters such as support degree, reliability degree, interest degree, and the like
And a quantitative evaluation index obtaining unit 140, configured to obtain quantitative evaluation indexes from the student behavior data set and the association network according to gray association analysis.
In one embodiment, the quantitative evaluation index acquisition unit 140 includes:
the data field value acquisition unit is used for acquiring a data field value included by each student behavior data in the student behavior data set;
and the grey correlation analysis unit is used for carrying out grey correlation analysis according to the data field value included in each student behavior data to acquire quantitative evaluation indexes.
According to the correlation analysis, behavior characteristics are found out, quantitative assessment indexes are firstly graded according to assessment content items, and the content indexes are quantitative, such as examination scores, and qualitative, such as political expression; and then, according to all the subentry indexes, making comprehensive judgment on each student.
In the past, the method generally carries out comprehensive conversion through mathematical modes according to various set evaluation indexes, but many mathematical modes cannot perfectly reflect the quality of comprehensive qualities of students.
The grey correlation analysis can well process the problem, is particularly suitable for large-scale, multi-factor and multi-index system evaluation, and is scientific and close to objective practice. The grey system theory is evaluated by combining with the practical work of student management and evaluation and applying a very important grey correlation analysis method in the grey system theory to the comprehensive quality of college students, the viewpoints and methods of a general system theory, an information theory and a control theory are extended to abstract systems of society, economy, ecology and the like, and a set of theories and methods for solving an information incomplete system are developed by combining with a mathematical method.
The grey correlation analysis in the grey system theory is used, and the correlation of all factors to be analyzed and researched can be found out among random factor sequences through certain data processing in incomplete information, so that the main contradiction is found, and the main characteristics and the main influence factors are found. Because the correlation analysis is analyzed according to the development trend, the requirement on the size of the sample volume is not high, the typical distribution rule is not required during the analysis, and the analysis result is generally consistent with the qualitative analysis, so the method has wide practicability. The general steps of the grey correlation analysis method calculation are that a comparison number series and a reference number series are determined; calculating a correlation coefficient; calculating the degree of association; and sorting the relevance sizes.
And the comprehensive score calculating unit 150 is used for acquiring the weight of each quantitative evaluation index so as to calculate and acquire the comprehensive score of each student.
In the embodiment, a specific comprehensive evaluation method is compiled on the basis of a comprehensive evaluation system of the senior students, the meaning of an evaluation index combining quantitative description and qualitative description is refined, a clear meaning is determined for the qualitative index, and the requirements of quantity and quality are refined while the clear meaning is determined for the quantitative index, so that a specific scoring rule is established, and the evaluation system is more systematic. The definition or measurement design of the index has certain challenges and difficulty, and students can achieve the definition or measurement design through the effort to avoid too high or too low design. And finally, inputting the evaluation information of the students into a computer system by utilizing an advanced computer technology, and calculating scores through a background of the computer system by data mining and processing to realize comprehensive evaluation of the students.
In one embodiment, the big-data-based student data analysis apparatus 100 further includes:
and the comprehensive score sending unit is used for sending the comprehensive scores of the students to the target receiving end.
In this embodiment, after the comprehensive scores of the students are obtained, the comprehensive scores can be timely sent to the target receiving end for viewing.
The device realizes automatic processing calculation of the student evaluation data after data preprocessing based on big data, and improves the data processing efficiency.
The above-mentioned big data based student data analysis apparatus may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 4.
Referring to fig. 4, fig. 4 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device 500 is a server, and the server may be an independent server or a server cluster composed of a plurality of servers.
Referring to fig. 4, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, may cause the processor 502 to perform a big data based student data analysis method.
The processor 502 is used to provide computing and control capabilities that support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 may be caused to perform a big data based student data analysis method.
The network interface 505 is used for network communication, such as providing transmission of data information. Those skilled in the art will appreciate that the configuration shown in fig. 4 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing device 500 to which aspects of the present invention may be applied, and that a particular computing device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The processor 502 is configured to run the computer program 5032 stored in the memory to implement the big data based student data analysis method disclosed in the embodiment of the present invention.
Those skilled in the art will appreciate that the embodiment of a computer device illustrated in fig. 4 does not constitute a limitation on the specific construction of the computer device, and that in other embodiments a computer device may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 4, and are not described herein again.
It should be understood that, in the embodiment of the present invention, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a non-volatile computer readable storage medium. The computer readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the big data based student data analysis method disclosed by the embodiment of the invention.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A student data analysis method based on big data is characterized by comprising the following steps:
receiving and storing an initial student behavior data set uploaded by an acquisition terminal; each initial student behavior data in the initial student behavior data set comprises student all-purpose card personal consumption data, library information resource usage records, dormitory access data, physical testing data, psychological assessment data, online learning system usage records, teaching resource library usage records and teaching recorded broadcast data;
carrying out data combination on the initial student behavior data set to obtain a student behavior data set;
performing correlation analysis according to the student behavior data set to obtain a correlation network;
obtaining quantitative evaluation indexes by the student behavior data set and the association network according to grey association analysis; and
and acquiring the weight of each quantitative evaluation index to calculate and acquire the comprehensive score of each student.
2. The big data based student data analysis method according to claim 1, wherein the data merging of the initial student behavior data sets to obtain the student behavior data sets comprises:
acquiring a unique student identifier corresponding to each piece of initial student behavior data of the initial student behavior data set;
the initial student behavior data with the same student unique identifier are merged to form a student behavior data set.
3. The big data based student data analysis method according to claim 1, wherein the performing correlation analysis according to the student behavior data set to obtain a correlation network comprises:
acquiring a correlation coefficient between student behavior data in the student behavior data set;
and establishing an association relation between the student behavior data of which the correlation coefficient between the student behavior data exceeds a preset correlation coefficient threshold value to obtain a correlation network.
4. The big data-based student data analysis method according to claim 1, wherein the obtaining of quantitative evaluation indexes from the student behavior data set and the association network according to grey association analysis comprises:
acquiring a data field value included in each student behavior data in the student behavior data set;
and performing grey correlation analysis according to the data field value included in each student behavior data to obtain quantitative evaluation indexes.
5. The big data-based student data analysis method according to claim 1, wherein after obtaining the weight of each quantitative evaluation index to calculate and obtain the comprehensive score of each student, the method further comprises:
and sending the comprehensive scores of the students to a target receiving end.
6. A student data analysis device based on big data, comprising:
the initial data set acquisition unit is used for receiving and storing an initial student behavior data set uploaded by the acquisition end; each initial student behavior data in the initial student behavior data set comprises student all-purpose card personal consumption data, library information resource usage records, dormitory access data, physical testing data, psychological assessment data, online learning system usage records, teaching resource library usage records and teaching recorded broadcast data;
the data merging unit is used for carrying out data merging on the initial student behavior data set to obtain a student behavior data set;
the gateway network acquisition unit is used for performing correlation analysis according to the student behavior data set to acquire a gateway network;
the quantitative evaluation index acquisition unit is used for acquiring quantitative evaluation indexes of the student behavior data set and the association network according to grey association analysis; and
and the comprehensive score calculating unit is used for acquiring the weight of each quantitative evaluation index so as to calculate and acquire the comprehensive score of each student.
7. The big-data-based student data analysis apparatus according to claim 6, wherein the data merging unit includes:
the unique identifier acquisition unit is used for acquiring a student unique identifier corresponding to each piece of initial student behavior data of the initial student behavior data set;
and the homogeneous data merging unit is used for merging the initial student behavior data with the same student unique identifier to form a student behavior data set.
8. The big-data-based student data analysis apparatus according to claim 6, wherein the gateway network acquisition unit includes:
the correlation coefficient calculation unit is used for acquiring correlation coefficients among the student behavior data in the student behavior data set;
and the association relationship establishing unit is used for establishing an association relationship between the student behavior data of which the correlation coefficient between the student behavior data exceeds a preset correlation coefficient threshold value to obtain an association network.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements a big data based student data analysis method according to any one of claims 1 to 5.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to execute the big-data based student data analysis method according to any one of claims 1 to 5.
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