Student information attainment evaluation method and system based on big data

Abstract

The invention discloses a student information quality evaluation method and system based on big data, and the method comprises the steps: determining a mutual logic relation between information quality components, and building an information quality evaluation system based on the mutual logic relation; determining evaluation indexes of multiple abilities according to the information quality evaluation system, determining a quantitative evaluation mode of each evaluation index, and obtaining related data through a big data platform based on the quantitative evaluation modes to perform quantitative analysis on students; determining a strong item parameter and a weak item parameter of the information attainment of each student according to an analysis result, and comparing and analyzing the strong item parameter and the weak item parameter to determine key factors influencing the information attainment level of the student; and determining the potential value of each student in the aspect of information attainment through a machine learning algorithm based on the key factors and the omnibearing test ability diagram of each student. Comprehensive information attainment evaluation can be carried out through objective big data, and the objectivity of an evaluation result is ensured.

Description

Technical Field

[0001] The present invention relates to the field of big data analysis education technology, and in particular to a student information literacy assessment method and system based on big data. Background Art

[0002] At present, with the development of network technology, data has exploded, resulting in a huge amount of information that people are exposed to every day. Therefore, how to accurately find the information you need in the complex information world requires information users to have relatively high information literacy. The digital era is an era of high development of digitization and intelligence. Information technology and big data have become key factors in promoting social progress and economic development. In this era, the ability to obtain, deeply mine and efficiently use information has become particularly important, and higher requirements have been placed on the information literacy of talents. The cultivation of information literacy of college talents has become an important task to adapt to the development needs of the digital era.

[0003] In the digital age, having good information literacy can make college students more competitive in the job search process. Regardless of the industry, having the ability of information retrieval, data analysis, and information technology application can bring more career opportunities and development space for college students. The cultivation of information literacy can not only help college students obtain and process information more efficiently, but also guide them to form independent thinking and active exploration learning attitudes and habits, improve their ability of independent learning, and enable them to continuously update their knowledge and skills in their future careers and achieve lifelong learning. Before cultivating students' information literacy, they need to be assessed for their information literacy to determine the potential development direction of each student. The existing information literacy assessment methods are all conducted through questionnaire surveys for human subjective assessment. The assessment results are not only seriously subjective, but also unable to conduct a comprehensive assessment of various data operation capabilities, making the final assessment results lack objectivity and reference value. Summary of the invention

[0004] In response to the above-mentioned problems, the present invention provides a student information literacy assessment method and system based on big data to solve the problem mentioned in the background technology that the assessment results of human subjective assessment through questionnaire surveys are not only seriously subjective but also unable to conduct a comprehensive assessment of various data operation capabilities, making the final assessment results lack objectivity and reference value.

[0005] A student information literacy assessment method based on big data includes the following steps: Determine the mutual logical relationship between the components of information literacy and establish an information literacy evaluation system based on the mutual logical relationship; Determine the evaluation indicators of multiple abilities according to the information literacy evaluation system, determine the quantitative evaluation method for each evaluation indicator, and obtain relevant data through the big data platform based on the quantitative evaluation method to conduct quantitative analysis of students; Determine the strong and weak parameters of each student's information literacy based on the analysis results, and compare and analyze the strong and weak parameters to determine the key factors that affect the students' information literacy level; The potential value of each student in information literacy is determined through a machine learning algorithm based on key factors and each student's comprehensive test ability map.

[0006] Preferably, the determining of the mutual logical relationship between the components of information literacy and establishing an information literacy evaluation system based on the mutual logical relationship includes: Identify multiple operational factors related to information, evaluate the daily operational weight of each operational factor through a preset evaluation method, and screen out the key components of information quality based on the daily operational weight; Determine the operation process parameters of each key component, determine the association attributes between the key components based on the operation process parameters, and determine the mutual logical relationship between the key components according to the association attributes; Determine the specific evaluation reference parameters for each key component based on their mutual logical relationships and set evaluation criteria; An information literacy evaluation system is established based on the specific evaluation reference parameters and evaluation standards of each key component as well as their mutual logical relationships.

[0007] Preferably, the step of determining evaluation indicators of multiple abilities according to the information literacy evaluation system, determining a quantitative evaluation method for each evaluation indicator, and obtaining relevant data through a big data platform based on the quantitative evaluation method to conduct quantitative analysis on students includes: Determine the evaluation reference parameters of multiple capabilities based on the information literacy evaluation system, and define the evaluation indicators of each capability based on the evaluation reference parameters; Obtain the performance data corresponding to the evaluation indicators of each capability, and determine the quantitative evaluation method of each evaluation indicator based on the performance data; Based on quantitative evaluation, data mining technology of big data platform is used to obtain the network resources and search engine data used by students and the activity data on the technology platform; The information literacy evaluation system is used to conduct quantitative analysis of students' information literacy based on evaluation indicators and evaluation standards according to the network resources and search engine data used by students and their activity data on the technology platform.

[0008] Preferably, the step of determining the strength parameters and weakness parameters of each student's information literacy based on the analysis results, and comparing and analyzing the strength parameters and weakness parameters to determine the key factors affecting the student's information literacy level includes: Determine each student's strong and weak information operation abilities based on the analysis results; According to the evaluation parameters corresponding to the strong information operation ability and the weak information operation ability, the strong parameters and weak parameters of each student's information literacy are screened out from the relevant data; Compare each student's strength and weakness parameters with the same parameters of other students, and determine the predicted target variables that affect each information operation ability based on the comparison results; Determine the reflective characteristic factors that predict the target variable and identify the reflective characteristic factors as the key factors affecting students' information literacy level.

[0009] Preferably, the method of determining the potential value of each student in information literacy by a machine learning algorithm based on the key factors and the all-round test ability map of each student includes: Determine the most direct presentation method of key factors, and determine the test method for each key factor based on the most direct presentation method; Each student is tested by a test method to obtain a comprehensive test ability map of each student, and the strength factors and weakness factors of each student are determined based on the comprehensive test ability map; Obtain the simple behavior parameters and complex behavior parameters of each student under each information operation ability; Through the decision tree algorithm, the information literacy emphasis pattern of each student is determined according to the strong factors and weak factors of each student and the associated factors of each information operation ability, as well as the simple behavior parameters and complex behavior parameters of each student under each information operation ability; According to the information literacy emphasis model, the emphasis weight of each student on each information operation ability is determined, and according to the emphasis weight, the potential value of each student in each information operation ability is determined.

[0010] A student information literacy assessment system based on big data, the system comprising: Establish a module to determine the mutual logical relationship between the components of information literacy and establish an information literacy evaluation system based on the mutual logical relationship; The analysis module is used to determine the evaluation indicators of multiple abilities according to the information literacy evaluation system, determine the quantitative evaluation method of each evaluation indicator, and obtain relevant data through the big data platform based on the quantitative evaluation method to conduct quantitative analysis of students; The first determination module is used to determine the strong and weak parameters of each student's information literacy according to the analysis results, and compare and analyze the strong and weak parameters to determine the key factors affecting the students' information literacy level; The second determination module is used to determine the potential value of each student in information literacy through a machine learning algorithm based on key factors and each student's all-round test ability map.

[0011] Preferably, the establishment module includes: The first screening submodule is used to determine multiple operational factors related to information, evaluate the daily operational weight of each operational factor through a preset evaluation method, and screen out the key components of information quality based on the daily operational weight; A first determination submodule is used to determine the operation process parameters of each key component, determine the association attributes between the key components based on the operation process parameters, and determine the mutual logical relationship between the key components according to the association attributes; The second determination submodule is used to determine the specific evaluation reference parameters of each key component and set the evaluation criteria according to the mutual logical relationship; Sub-modules are established to build an information literacy evaluation system based on the specific evaluation reference parameters and evaluation criteria of each key component and their mutual logical relationships.

[0012] Preferably, the analysis module comprises: A definition submodule is used to determine the evaluation reference parameters of multiple capabilities according to the information literacy evaluation system, and define the evaluation indicators of each capability according to the evaluation reference parameters; The third determination submodule is used to obtain the performance data corresponding to the evaluation index of each capability, and determine the quantitative evaluation method of each evaluation index according to the performance data; The first acquisition submodule is used to acquire the network resources and search engine data used by students and the activity data on the technology platform through the data mining technology of the big data platform based on the quantitative evaluation method; The analysis submodule is used to conduct quantitative analysis of students' information literacy based on the evaluation indicators and evaluation standards using the information literacy evaluation system according to the network resources and search engine data used by students and the activity data on the technology platform.

[0013] Preferably, the first determining module includes: The fourth determination submodule is used to determine the strong information operation ability and the weak information operation ability of each student according to the analysis results; The second screening submodule is used to screen out the strong parameters and weak parameters of each student's information literacy from the relevant data according to the evaluation parameters corresponding to the strong information operation ability and the weak information operation ability; The comparison submodule is used to compare the strength and weakness parameters of each student with the same parameters of other students, and determine the predicted target variables that affect each information operation ability based on the comparison results; The fifth determination submodule is used to determine the reflective characteristic factors of the predicted target variable and confirm the reflective characteristic factors as the key factors affecting the students' information literacy level.

[0014] Preferably, the second determining module includes: A sixth determination submodule is used to determine the most direct presentation method of the key factors, and determine the test method of each key factor based on the most direct presentation method; The seventh determination submodule is used to test each student by means of a test method, obtain a comprehensive test ability map of each student, and determine the strength factors and weakness factors of each student according to the comprehensive test ability map; The second acquisition submodule is used to obtain the simple behavior parameters and complex behavior parameters of each student under each information operation ability; The eighth determination submodule is used to determine the information literacy emphasis mode of each student according to the strengths and weaknesses of each student and the associated factors of each information operation ability and the simple behavior parameters and complex behavior parameters of each student under each information operation ability through a decision tree algorithm; The ninth determination submodule is used to determine the emphasis weight of each student for each information operation ability according to the information literacy emphasis model, and to determine the potential value of each student under each information operation ability according to the emphasis weight.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0016] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0018] Figure 1 A workflow diagram of a student information literacy assessment method based on big data provided by the present invention; Figure 2 Another workflow diagram of a student information literacy assessment method based on big data provided by the present invention; Figure 3 This is a schematic diagram of the structure of a student information literacy assessment system based on big data provided by the present invention; Figure 4 This is a structural schematic diagram of a module established in a student information literacy assessment system based on big data provided by the present invention. DETAILED DESCRIPTION

[0019] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0020] At present, with the development of network technology, data has exploded, resulting in a huge amount of information that people are exposed to every day. Therefore, how to accurately find the information you need in the complex information world requires information users to have relatively high information literacy. The digital era is an era of high development of digitization and intelligence. Information technology and big data have become key factors in promoting social progress and economic development. In this era, the ability to obtain, deeply mine and efficiently use information has become particularly important, and higher requirements have been placed on the information literacy of talents. The cultivation of information literacy of college talents has become an important task to adapt to the development needs of the digital era.

[0021] In the digital age, having good information literacy can make college students more competitive in the job search process. Regardless of the industry, having the ability of information retrieval, data analysis, information technology application, etc. can bring more career opportunities and development space for college students. The cultivation of information literacy can not only help college students obtain and process information more efficiently, but also guide them to form independent thinking and active exploration learning attitudes and habits, improve their ability of autonomous learning, and enable them to continuously update their knowledge and skills in their future careers and achieve lifelong learning. Before cultivating students' information literacy, they need to conduct information literacy assessment to determine the potential development direction of each student. The existing information literacy assessment methods are all conducted through questionnaire surveys for human subjective assessment. The assessment results are not only seriously subjective, but also unable to conduct a comprehensive assessment of various data operation capabilities, which makes the final assessment results lack objectivity and reference value. In order to solve the above problems, this embodiment discloses a student information literacy assessment method based on big data.

[0022] A method for assessing students' information literacy based on big data, such as Figure 1 As shown, the following steps are included: Step S101, determining the mutual logical relationship between the components of information literacy, and establishing an information literacy evaluation system based on the mutual logical relationship; Step S102: Determine the evaluation indicators of multiple capabilities according to the information literacy evaluation system, determine the quantitative evaluation method of each evaluation indicator, and obtain relevant data through the big data platform based on the quantitative evaluation method to conduct quantitative analysis on students; Step S103, determining the strength parameters and weakness parameters of each student's information literacy according to the analysis results, and comparing and analyzing the strength parameters and weakness parameters to determine the key factors affecting the students' information literacy level; Step S104: Determine the potential value of each student in information literacy through a machine learning algorithm based on key factors and each student's all-round test ability graph.

[0023] In this embodiment, the information literacy components are represented as the elements related to information operations for assessing students' information literacy, such as: information acquisition elements, information processing elements, and information technology application elements; In this embodiment, the mutual logical relationship is represented as an interlaced logical relationship between the various components, such as a progressive relationship, a parallel relationship, etc.; In this embodiment, the information literacy evaluation system is represented as an evaluation system for students' comprehensive information operation literacy; In this embodiment, the multiple capabilities may be information acquisition capability, information judgment capability, information processing capability, and information technology application capability; In this embodiment, the evaluation index is expressed as the evaluation index of each capability, for example, the evaluation index of information acquisition capability may be information acquisition efficiency, information acquisition accuracy, information acquisition channel security, etc.; In this embodiment, the quantitative evaluation method refers to a method of performing quantitative evaluation for each evaluation indicator; In this embodiment, the quantitative analysis is represented by a method of conducting a comprehensive statistical analysis of students' various information operation abilities.

[0024] The working principle of the above technical solution is: determine the mutual logical relationship between the components of information literacy, and establish an information literacy evaluation system based on the mutual logical relationship; determine the evaluation indicators of multiple abilities according to the information literacy evaluation system, determine the quantitative evaluation method of each evaluation indicator, and obtain relevant data through the big data platform based on the quantitative evaluation method to conduct quantitative analysis of students; determine the strong and weak parameters of each student's information literacy according to the analysis results, and compare and analyze the strong and weak parameters to determine the key factors affecting the students' information literacy level; determine the potential value of each student in information literacy through a machine learning algorithm based on the key factors and each student's comprehensive test ability map.

[0025] The beneficial effects of the above technical solution are: by constructing an information literacy evaluation system, the evaluation indicators of multiple data operation capabilities can be accurately determined, and then the evaluation values ​​of students' various data operation capabilities can be evaluated from students' search data and platform operation data through data mining. Comprehensive and comprehensive information literacy evaluation can be performed through objective big data, ensuring the objectivity of the evaluation results. Furthermore, by screening out students' strong and weak abilities and key factors affecting their information literacy levels, the potential development direction of students' information operation capabilities can be determined according to the key factor values ​​corresponding to students under different data operation capabilities. It is possible to mine the potential of students' information literacy, improve the reference value of evaluation data, and solve the problem mentioned in the prior art that the evaluation results of subjective human evaluations through questionnaires are not only seriously subjective, but also unable to conduct comprehensive evaluations of various data operation capabilities, making the final evaluation results lack objectivity and reference value.

[0026] In one embodiment, Figure 2 As shown, the mutual logical relationship between the components of information literacy is determined, and the information literacy evaluation system is established based on the mutual logical relationship, including: Step S201: determine multiple operational factors related to information, evaluate the daily operational weight of each operational factor through a preset evaluation method, and screen out key components of information quality based on the daily operational weight; Step S202: determining the operation process parameters of each key component, determining the association attributes between the key components based on the operation process parameters, and determining the mutual logical relationship between the key components according to the association attributes; Step S203, determining specific evaluation reference parameters for each key component according to the mutual logical relationship and setting evaluation criteria; Step S204: Establish an information literacy evaluation system based on the specific evaluation reference parameters and evaluation criteria of each key component and their mutual logical relationships.

[0027] In this embodiment, the multiple operation factors are represented as operation factors corresponding to multiple operation modes for information operation, such as: information reading, information recognition, information processing, information acquisition, etc.; In this embodiment, the key components are represented as components that are highly relevant to information literacy, including: information technology skills, information acquisition capabilities, information processing capabilities alone and information collaborative processing capabilities, as well as information learning capabilities and information innovation capabilities; In this embodiment, the operation process parameters are represented by specific operation description parameters of each key component; In this embodiment, the association attributes are represented as flow attributes between the key components, including: progressive attributes, causal attributes, parallel attributes, etc.; In this embodiment, the specific evaluation reference parameter is represented as a reference parameter for evaluating each key component, for example: the specific evaluation reference parameter of information technology capability may be a parameter for the use of information technology tools; In this embodiment, the evaluation criteria are expressed as high-level, qualified-level and low-level evaluation criteria for each key component. For example, for information technology skills, the high-level criteria may be "proficient in a variety of information technology tools and able to flexibly apply them to actual work; In this embodiment, the same specific evaluation reference parameter can be used to evaluate a single key component or two or more key components at the same time, which is determined specifically according to the mutual logical relationship between the key components.

[0028] The beneficial effects of the above technical solution are: by determining the operating process parameters of the key components of information literacy, determining the correlation attributes between the various components, determining the specific evaluation reference parameters for each key component and setting the evaluation standards, it is possible to ensure the reliability, comprehensiveness and accuracy of the information literacy evaluation system, improve the persuasiveness of the evaluation results, and take into account the correlation between the key components, conduct progressive evaluation and synchronous evaluation, thereby improving the evaluation efficiency.

[0029] In one embodiment, the evaluation indicators of multiple abilities are determined according to the information literacy evaluation system, a quantitative evaluation method for each evaluation indicator is determined, and relevant data is obtained through a big data platform based on the quantitative evaluation method to perform quantitative analysis on students, including: Determine the evaluation reference parameters of multiple capabilities based on the information literacy evaluation system, and define the evaluation indicators of each capability based on the evaluation reference parameters; Obtain the performance data corresponding to the evaluation indicators of each capability, and determine the quantitative evaluation method of each evaluation indicator based on the performance data; Based on quantitative evaluation, data mining technology of big data platform is used to obtain the network resources and search engine data used by students and the activity data on the technology platform; The information literacy evaluation system is used to conduct quantitative analysis of students' information literacy based on evaluation indicators and evaluation standards according to the network resources and search engine data used by students and their activity data on the technology platform.

[0030] The beneficial effects of the above technical solution are: obtaining the performance data corresponding to the evaluation indicators of each ability, thereby determining the quantitative evaluation method of each evaluation indicator, and being able to quickly and accurately evaluate students' various abilities through quantitative evaluation methods, further improving the evaluation efficiency, and further, using the information literacy evaluation system to conduct quantitative analysis of students' information literacy, so as to have a more comprehensive understanding of students' information literacy level and accurately identify students' strengths and weaknesses.

[0031] In one embodiment, the determination of the strength parameters and weakness parameters of each student's information literacy based on the analysis results, and the comparison and analysis of the strength parameters and weakness parameters to determine the key factors affecting the student's information literacy level include: Determine each student's strong and weak information operation abilities based on the analysis results; According to the evaluation parameters corresponding to the strong information operation ability and the weak information operation ability, the strong parameters and weak parameters of each student's information literacy are screened out from the relevant data; Compare each student's strength and weakness parameters with the same parameters of other students, and determine the predicted target variables that affect each information operation ability based on the comparison results; Determine the reflective characteristic factors that predict the target variable and identify the reflective characteristic factors as the key factors affecting students' information literacy level.

[0032] In this embodiment, the prediction target variable is represented as a reflection variable of each information operation ability, for example, the reflection variable of information acquisition ability is the student's grasp of keywords and the accuracy of document screening retrieved by keywords; In this embodiment, the reflection characteristic factors are expressed as human reflection characteristic factors of the reflection variables for each information operation ability, including: mental intelligence factors, reaction time factors, comprehension ability factors, etc. For example: the reflection characteristic factor corresponding to the student's grasp of keywords is the student's comprehension ability.

[0033] The beneficial effects of the above technical solution are: determining the corresponding strength parameters and weakness parameters according to each student's strong information operation ability and weak information operation ability, determining the reflection characteristic factors of the predicted target variables affecting each information operation ability, and being able to accurately and quickly determine the multiple factors affecting the students' information operation ability and then make effective targeted adjustments, make improvements based on the causes, and improve the students' ability and literacy levels.

[0034] In one embodiment, the method of determining each student's potential value in information literacy based on key factors and each student's all-round test ability map by a machine learning algorithm includes: Determine the most direct presentation method of key factors, and determine the test method for each key factor based on the most direct presentation method; Each student is tested by a test method to obtain a comprehensive test ability map of each student, and the strength factors and weakness factors of each student are determined based on the comprehensive test ability map; Obtain the simple behavior parameters and complex behavior parameters of each student under each information operation ability; Through the decision tree algorithm, the information literacy emphasis pattern of each student is determined according to the strong factors and weak factors of each student and the associated factors of each information operation ability, as well as the simple behavior parameters and complex behavior parameters of each student under each information operation ability; According to the information literacy emphasis model, the emphasis weight of each student on each information operation ability is determined, and according to the emphasis weight, the potential value of each student in each information operation ability is determined.

[0035] The beneficial effects of the above technical solution are: determining the strengths and weaknesses of each student through a comprehensive test ability map of each student, and determining the information literacy emphasis pattern of each student based on a decision tree algorithm in combination with the simple behavior parameters and complex behavior parameters of each student under each information operation ability, thereby determining the potential value of each student under each information operation ability, and being able to quickly determine the potential advantage direction of each student's information literacy and focus on developing the ability in that direction.

[0036] In this embodiment, the simple behavior parameters and complex behavior parameters of each student under each information operation capability are obtained, including: Obtain the operation behavior parameters of each student at multiple stages under each information operation ability, and determine the behavior value of the operation behavior parameters at each stage through the fuzzy state algorithm; The multi-stage operation behavior parameters are preliminarily divided into simple behavior parameter and complex behavior parameter categories according to the behavior values; Conduct parameter analysis on the operational behavior parameters of each divided stage in the categories of simple behavior parameters and complex behavior parameters; Determine the behavior control description state parameters, behavior flow description state parameters and behavior level description state parameters of the operation behavior parameters of each divided stage according to the analysis results; Determine the operation parameter threshold based on the behavior control description state parameter through the preset control behavior scoring rule, determine the process parameter threshold based on the process description state parameter based on the preset process indicator scoring rule, and determine the level parameter threshold based on the level description state parameter based on the preset behavior association parameter scoring rule; Determine the evaluation score of the operation behavior parameter at each division stage according to the operation parameter threshold, process parameter threshold and level parameter threshold of the operation behavior parameter at each division stage and their respective preset parameter weights; Determine the accuracy of the division of the operational behavior parameters at each division stage according to the evaluation scores and the respective score ranges of the simple behavior parameters and the complex behavior parameters; According to the accuracy of division, the inaccurate division stage operation behavior parameters in the simple behavior parameter and complex behavior parameter categories are adjusted; According to the adjustment results, the simple behavior parameters and complex behavior parameters of each student under each information operation ability are obtained.

[0037] The beneficial effects of the above technical solution are: by preliminarily dividing and further dividing the stage operation behavior parameters of each student under each information operation ability to determine the final simple behavior parameters and complex behavior parameters, the operation behavior parameters of each stage can be accurately divided, and high-precision division can be performed based on the behavioral attributes of the operation behavior parameters of each stage, thereby ensuring the objectivity and rationality of the division results.

[0038] In one embodiment, this embodiment also discloses a student information literacy assessment system based on big data, such as Figure 3 As shown, the system includes: Establishing module 301, for determining the mutual logical relationship between the components of information literacy, and establishing an information literacy evaluation system based on the mutual logical relationship; The analysis module 302 is used to determine the evaluation indicators of multiple abilities according to the information literacy evaluation system, determine the quantitative evaluation method of each evaluation indicator, and obtain relevant data through the big data platform based on the quantitative evaluation method to conduct quantitative analysis on students; The first determination module 303 is used to determine the strength parameters and weakness parameters of each student's information literacy according to the analysis results, and compare and analyze the strength parameters and weakness parameters to determine the key factors affecting the student's information literacy level; The second determination module 304 is used to determine the potential value of each student in information literacy through a machine learning algorithm based on key factors and the comprehensive test ability map of each student.

[0039] The working principle and beneficial effects of the above technical solution have been explained in the method embodiment and will not be repeated here.

[0040] In one embodiment, Figure 4 As shown, the establishment module 301 includes: The first screening submodule 3011 is used to determine multiple operational factors related to information, evaluate the daily operational weight of each operational factor through a preset evaluation method, and screen out key components of information quality based on the daily operational weight; The first determination submodule 3012 is used to determine the operation process parameters of each key component, determine the association attributes between the key components based on the operation process parameters, and determine the mutual logical relationship between the key components according to the association attributes; The second determination submodule 3013 is used to determine specific evaluation reference parameters of each key component according to the mutual logical relationship and set the evaluation criteria; Submodule 3014 is established to establish an information literacy evaluation system based on the specific evaluation reference parameters and evaluation criteria of each key component and their mutual logical relationships.

[0041] In one embodiment, the analysis module comprises: A definition submodule is used to determine the evaluation reference parameters of multiple capabilities according to the information literacy evaluation system, and define the evaluation indicators of each capability according to the evaluation reference parameters; The third determination submodule is used to obtain the performance data corresponding to the evaluation index of each capability, and determine the quantitative evaluation method of each evaluation index according to the performance data; The first acquisition submodule is used to acquire the network resources and search engine data used by students and the activity data on the technology platform through the data mining technology of the big data platform based on the quantitative evaluation method; The analysis submodule is used to conduct quantitative analysis of students' information literacy based on the evaluation indicators and evaluation standards using the information literacy evaluation system according to the network resources and search engine data used by students and the activity data on the technology platform.

[0042] In one embodiment, the first determining module includes: The fourth determination submodule is used to determine the strong information operation ability and the weak information operation ability of each student according to the analysis results; The second screening submodule is used to screen out the strong parameters and weak parameters of each student's information literacy from the relevant data according to the evaluation parameters corresponding to the strong information operation ability and the weak information operation ability; The comparison submodule is used to compare the strength and weakness parameters of each student with the same parameters of other students, and determine the predicted target variables that affect each information operation ability based on the comparison results; The fifth determination submodule is used to determine the reflective characteristic factors of the predicted target variable and confirm the reflective characteristic factors as the key factors affecting the students' information literacy level.

[0043] In one embodiment, the second determining module includes: A sixth determination submodule is used to determine the most direct presentation method of the key factors, and determine the test method of each key factor based on the most direct presentation method; The seventh determination submodule is used to test each student by means of a test method, obtain a comprehensive test ability map of each student, and determine the strength factors and weakness factors of each student according to the comprehensive test ability map; The second acquisition submodule is used to obtain the simple behavior parameters and complex behavior parameters of each student under each information operation ability; The eighth determination submodule is used to determine the information literacy emphasis mode of each student according to the strengths and weaknesses of each student and the associated factors of each information operation ability and the simple behavior parameters and complex behavior parameters of each student under each information operation ability through a decision tree algorithm; The ninth determination submodule is used to determine the emphasis weight of each student for each information operation ability according to the information literacy emphasis model, and to determine the potential value of each student under each information operation ability according to the emphasis weight.

[0044] Those skilled in the art should understand that the first and second in the present invention merely refer to different application stages.

[0045] Other embodiments of the present disclosure will be readily apparent to those skilled in the art after considering the specification and practicing the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0046] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for assessing student information literacy based on big data, characterized in that: The following steps are involved: Determine the mutual logical relationship between the components of information literacy and establish an information literacy evaluation system based on the mutual logical relationship; Determine the evaluation indicators of multiple abilities according to the information literacy evaluation system, determine the quantitative evaluation method for each evaluation indicator, and obtain relevant data through the big data platform based on the quantitative evaluation method to conduct quantitative analysis of students; Determine the strong and weak parameters of each student's information literacy based on the analysis results, and compare and analyze the strong and weak parameters to determine the key factors that affect the students' information literacy level; The potential value of each student in information literacy is determined through a machine learning algorithm based on key factors and each student's comprehensive test ability map.

2. According to the method for evaluating student information literacy based on big data in claim 1, it is characterized in that: Determining the mutual logical relationship between the components of information literacy and establishing an information literacy evaluation system based on the mutual logical relationship includes: Identify multiple operational factors related to information, evaluate the daily operational weight of each operational factor through a preset evaluation method, and screen out the key components of information quality based on the daily operational weight; Determine the operation process parameters of each key component, determine the association attributes between the key components based on the operation process parameters, and determine the mutual logical relationship between the key components according to the association attributes; Determine the specific evaluation reference parameters for each key component based on their mutual logical relationships and set evaluation criteria; An information literacy evaluation system is established based on the specific evaluation reference parameters and evaluation standards of each key component as well as their mutual logical relationships.

3. According to the method for evaluating student information literacy based on big data in claim 1, it is characterized in that: The evaluation indicators of multiple abilities are determined according to the information literacy evaluation system, and the quantitative evaluation method of each evaluation indicator is determined. Based on the quantitative evaluation method, relevant data are obtained through the big data platform to conduct quantitative analysis on students, including: Determine the evaluation reference parameters of multiple capabilities based on the information literacy evaluation system, and define the evaluation indicators of each capability based on the evaluation reference parameters; Obtain the performance data corresponding to the evaluation indicators of each capability, and determine the quantitative evaluation method of each evaluation indicator based on the performance data; Based on quantitative evaluation, data mining technology of big data platform is used to obtain the network resources and search engine data used by students and the activity data on the technology platform; The information literacy evaluation system is used to conduct quantitative analysis of students' information literacy based on evaluation indicators and evaluation standards according to the network resources and search engine data used by students and their activity data on the technology platform.

4. According to the method for evaluating student information literacy based on big data in claim 1, it is characterized in that: The strength and weakness parameters of each student's information literacy are determined based on the analysis results, and the strength and weakness parameters are compared and analyzed to determine the key factors affecting the student's information literacy level, including: Determine each student's strong and weak information operation abilities based on the analysis results; According to the evaluation parameters corresponding to the strong information operation ability and the weak information operation ability, the strong parameters and weak parameters of each student's information literacy are screened out from the relevant data; Compare each student's strength and weakness parameters with the same parameters of other students, and determine the predicted target variables that affect each information operation ability based on the comparison results; Determine the reflective characteristic factors that predict the target variable and identify the reflective characteristic factors as the key factors affecting students' information literacy level.

5. According to the method for evaluating student information literacy based on big data in claim 1, it is characterized in that: The key factors and the comprehensive test ability map of each student are used to determine each student's potential value in information literacy through a machine learning algorithm, including: Determine the most direct presentation method of key factors, and determine the test method for each key factor based on the most direct presentation method; Each student is tested by a test method to obtain a comprehensive test ability map of each student, and the strength factors and weakness factors of each student are determined based on the comprehensive test ability map; Obtain the simple behavior parameters and complex behavior parameters of each student under each information operation ability; Through the decision tree algorithm, the information literacy emphasis pattern of each student is determined according to the strong factors and weak factors of each student and the associated factors of each information operation ability, as well as the simple behavior parameters and complex behavior parameters of each student under each information operation ability; According to the information literacy emphasis model, the emphasis weight of each student on each information operation ability is determined, and according to the emphasis weight, the potential value of each student in each information operation ability is determined.

6. A student information literacy assessment system based on big data, characterized in that: The system includes: Establish a module to determine the mutual logical relationship between the components of information literacy and establish an information literacy evaluation system based on the mutual logical relationship; The analysis module is used to determine the evaluation indicators of multiple abilities according to the information literacy evaluation system, determine the quantitative evaluation method of each evaluation indicator, and obtain relevant data through the big data platform based on the quantitative evaluation method to conduct quantitative analysis of students; The first determination module is used to determine the strong and weak parameters of each student's information literacy according to the analysis results, and compare and analyze the strong and weak parameters to determine the key factors affecting the students' information literacy level; The second determination module is used to determine the potential value of each student in information literacy through a machine learning algorithm based on key factors and each student's all-round test ability map.

7. The student information literacy assessment system based on big data according to claim 6 is characterized in that: The establishment module comprises: The first screening submodule is used to determine multiple operational factors related to information, evaluate the daily operational weight of each operational factor through a preset evaluation method, and screen out the key components of information quality based on the daily operational weight; A first determination submodule is used to determine the operation process parameters of each key component, determine the association attributes between the key components based on the operation process parameters, and determine the mutual logical relationship between the key components according to the association attributes; The second determination submodule is used to determine the specific evaluation reference parameters of each key component and set the evaluation criteria according to the mutual logical relationship; Sub-modules are established to build an information literacy evaluation system based on the specific evaluation reference parameters and evaluation criteria of each key component and their mutual logical relationships.

8. The student information literacy assessment system based on big data according to claim 6 is characterized in that: The analysis module comprises: A definition submodule is used to determine the evaluation reference parameters of multiple capabilities according to the information literacy evaluation system, and define the evaluation indicators of each capability according to the evaluation reference parameters; The third determination submodule is used to obtain the performance data corresponding to the evaluation index of each capability, and determine the quantitative evaluation method of each evaluation index according to the performance data; The first acquisition submodule is used to acquire the network resources and search engine data used by students and the activity data on the technology platform through the data mining technology of the big data platform based on the quantitative evaluation method; The analysis submodule is used to conduct quantitative analysis of students' information literacy based on the evaluation indicators and evaluation standards using the information literacy evaluation system according to the network resources and search engine data used by students and the activity data on the technology platform.

9. The student information literacy assessment system based on big data according to claim 6 is characterized in that: The first determining module includes: The fourth determination submodule is used to determine the strong information operation ability and the weak information operation ability of each student according to the analysis results; The second screening submodule is used to screen out the strong parameters and weak parameters of each student's information literacy from the relevant data according to the evaluation parameters corresponding to the strong information operation ability and the weak information operation ability; The comparison submodule is used to compare the strength and weakness parameters of each student with the same parameters of other students, and determine the predicted target variables that affect each information operation ability based on the comparison results; The fifth determination submodule is used to determine the reflective characteristic factors of the predicted target variable and confirm the reflective characteristic factors as the key factors affecting the students' information literacy level.

10. The student information literacy assessment system based on big data according to claim 6 is characterized in that: The second determining module includes: A sixth determination submodule is used to determine the most direct presentation method of the key factors, and determine the test method of each key factor based on the most direct presentation method; The seventh determination submodule is used to test each student by means of a test method, obtain a comprehensive test ability map of each student, and determine the strength factors and weakness factors of each student according to the comprehensive test ability map; The second acquisition submodule is used to obtain the simple behavior parameters and complex behavior parameters of each student under each information operation ability; The eighth determination submodule is used to determine the information literacy emphasis mode of each student according to the strengths and weaknesses of each student and the associated factors of each information operation ability and the simple behavior parameters and complex behavior parameters of each student under each information operation ability through a decision tree algorithm; The ninth determination submodule is used to determine the emphasis weight of each student for each information operation ability according to the information literacy emphasis model, and to determine the potential value of each student under each information operation ability according to the emphasis weight.

Images