CN115659129A - Questionnaire survey credibility analysis method based on VARK scale - Google Patents
Questionnaire survey credibility analysis method based on VARK scale Download PDFInfo
- Publication number
- CN115659129A CN115659129A CN202211601063.3A CN202211601063A CN115659129A CN 115659129 A CN115659129 A CN 115659129A CN 202211601063 A CN202211601063 A CN 202211601063A CN 115659129 A CN115659129 A CN 115659129A
- Authority
- CN
- China
- Prior art keywords
- user
- questionnaire
- vark
- scale
- credibility
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention relates to the technical field of questionnaire survey, in particular to a questionnaire survey credibility analysis method based on a VARK scale, which comprises the steps of drawing user portrait data based on the style tendency of a user, and configuring the user requirements of related users; inputting the requirements of the user into a preset value database; setting questionnaire classification, group aiming and supervision rules; distributing questionnaire answering requests to the server of each user; constructing a user portrait and a user value map; generating a questionnaire according to the attribute information, and judging the questions answered one by one according to the attribute information to analyze the related credibility of the user; generating an optimal solution matched with the demand according to the value database; the invention solves the problems that in the existing questionnaire interaction mode, the user needs to answer in sequence, the user experience is poor, the reliability is low, and the questionnaire has no reference value.
Description
Technical Field
The invention relates to the technical field of questionnaire survey, in particular to a questionnaire survey credibility analysis method based on a VARK scale.
Background
A questionnaire typically consists of a series of questions, survey items, alternative answers, or written instructions. The primary goal is to collect project-related statistics from respondents. The questionnaire, as a tool for collecting research data, can acquire a large amount of statistical data, perform in-depth analysis on the information, verify research assumptions, and make scientific explanations and explanations of the studied questions. In an actual survey, to collect highly efficient and reliable research data through a questionnaire, questionnaire design is one of key points for ensuring validity of survey results. When designing a questionnaire, a designer often needs to consider the problems of determining the structure of the questionnaire, compiling and sequencing test questions, selecting answer options and scales, and describing the relationship between test question expressions and survey objects, and the like, and cannot quickly adjust the questionnaire according to the currently collected information. Therefore, effective automatic questionnaire generation and automatic survey data acquisition can be greatly facilitated, the workload of related personnel is reduced, and the possibility of manual errors is reduced.
With the rapid development of the internet, on-line questionnaire survey and evaluation become important means for people to maintain own rights and interests and improve service quality. At present, the existing questionnaire design is often uniform, and different objects are taken to be the same questionnaire. The existing questionnaire design idea is that questions are extracted from a database, for different objects, or the same questionnaire is taken, even if different questionnaires are taken, only different questions are randomly extracted from the database, but no pertinence is achieved, and user experience is poor. In the existing questionnaire interaction, users generally answer questions in sequence, and each user is a normal question regardless of strong or weak correlation between the question and the user, but sometimes, partial questions and the user correlation are weak, or corresponding information is filled in before, and according to the existing questionnaire interaction mode, the user still needs to answer in sequence, so that the user experience is poor, the reliability is low, and the reference value is not available.
Disclosure of Invention
Aiming at the defects of the prior art, the invention discloses a questionnaire survey credibility analysis method based on a VARK scale, which is used for solving the problems that a user still needs to answer in sequence in the existing questionnaire interaction mode, the user experience is poor, the credibility is low, and the reference value is not available;
the invention is realized by the following technical scheme:
the invention discloses a questionnaire survey credibility analysis method based on a VARK scale, which comprises the following steps:
step1: drawing user portrait data based on style tendency of a user, and configuring user requirements of related users;
step2: inputting the requirements of the user into a preset value database;
step3: setting questionnaire classification, group aiming and supervision rules; distributing questionnaire answering requests to the server of each user;
step4: acquiring attribute information of an answering user, and constructing a user portrait and a user value map;
step5: generating a questionnaire according to the attribute information, and analyzing the related credibility of the user by judging the questions answered one by one according to the attribute information, wherein the method specifically comprises the following steps:
based on the credibility rating and the user similarity as weights, the following weighted credibility is obtained:
wherein, the first and the second end of the pipe are connected with each other,
indicating the average deviation, i and j indicate the item number and the user number, respectively,
representing the trust degree of the jth user on the ith item, and R (j) representing the trust rating of the jth user as an index parameter;
representing the evaluation deviation of the jth user to the ith item, and T (i) representing the user similarity of the ith item as an index parameter;
the value database is used for generating a solution matched with the requirement; the definition formula of the solution is as follows:
performing linear or nonlinear combination definition generalized analysis on two or more selection criteria in the VARK scale as follows:
wherein c is a generalized evaluation, d is a correlation evaluation, and t is an evaluation criterion; alpha (alpha is more than or equal to 0 and less than or equal to 1) is a weight coefficient, and if the value of the alpha is 0, the shortest effectiveness of the evaluation criterion is selected as a selection criterion; if the value of alpha is 1, selecting the shortest evaluation criterion utility as a selection criterion; if (0 < α < l), the selection criterion is a trade-off between the evaluation criterion and the utility of the associated evaluation;
step6: generating an optimal solution matched with the demand according to the value database;
step7: and encrypting the user independent personal information by a distributed block chain technology, and then transmitting and storing the encrypted user independent personal information.
Further, the attribute information includes basic information of the user and historical answer information of the user.
Further, the Step5 of the correlation reliability analysis comprises the following steps:
step501: obtaining the current answer condition of the user to the questionnaire to obtain the question condition answered by the user;
step502: judging the relevance of the answering questions and the users one by one according to the attribute information;
step503: and if the related credibility score exceeds a preset scoring standard, collecting and storing the questionnaire evaluation information of the user.
Further, the Step of Step6 matching optimal solution comprises the following steps:
step601: establishing at least one corresponding label for the requirement of the user, and judging whether the basic information of the user is stored in a database or not when the attribute information is marked in the database;
step602: and when the basic information of the user exists, searching question categories corresponding to the basic information from a database according to the basic information, selecting questions with preset question quantity to generate a questionnaire, and calculating an optimal solution in the value database through a preset calculation model according to the label.
Further, the predetermined scoring criteria defines the following formula:
E= aL+bT+dS+eD+fP+gR+hM
wherein E is credibility score, L is the user attribute, T is answer time, S is modification times, D is delay, R is topic proportion, R is the user level, M is marginal requirement, and a-h are weight coefficients.
Furthermore, in Step6, the value range of the ratio of the reliability is set to 0-1, and the ratio PI (1-P) is taken as the natural logarithm Ln (P/(1-P)), i.e. the Logit is transformed to the r; establishing a linear regression equation by taking LogitP as a variable:
wherein, the parameter α is a constant term representing a natural logarithm of a ratio (ratio of probabilities of Y =1 and Y = 0) when the values of the respective variables x are all 0, and the parameter α is a parameter
Referred to as the regression coefficient, indicates the amount of change in the ratio (OR) natural logarithm that results from increasing the value of an independent variable by one unit while the values of other independent variables remain unchanged.
Still further, the user representation data includes dynamic user requirements, and the user value map includes user information provided by the user for resolving solutions to the corresponding user requirements.
Still further, the server includes, but is not limited to, a web browser application, a search-type application, an instant messaging tool, a mailbox client, social platform software.
Further, the server is applied to the network device.
Still further, the network devices are various electronic devices having display screens and supporting web browsing, including but not limited to in-vehicle smart screens, smart phones, tablets, laptop portable computers, and desktop computers.
The invention has the beneficial effects that:
1. the present invention determines a learning style or tendency based on a user's questionnaire. The method has the advantages that the user and the interested direction of the user are increased through the adaptive adjustment of the style, the learning style or preference of the user is considered by utilizing the VARK, the efficiency and the effect of questionnaires of the user are improved, and the research of teaching reform is carried out through the user style and according to the learning style of the user. The questionnaire consists of a plurality of multiple choice questions, each of which has multiple choices, although only one of them may be selected. Those with strong literacy will likely read the guideline carefully. Those students that prefer word senses will likely be more concerned about word senses in the questionnaire. Students with kinesthetic tendencies may require additional situational or content information. Because visual enthusiasts tend to use charts, flow diagrams, symbolic arrows, circles, hierarchical taxonomies, and other methods that can be used to represent interpreted text; auditory enthusiasts tend to learn by lectures, teacher explanations, or other classmates; the read-write lovers tend to learn by using the information expressed by the word materials; kinesthetic enthusiasts learn by experience, case, practice, or simulation.
2. The invention verifies that the questionnaires are closely related by adopting a fixed effect model and applying section data through empirical verification. The invention can not only inform the type of the learning style of the tested person in detail; and the user can be conveniently analyzed as a whole by educators and educational researchers, and the difference of users of different groups can be compared. The present invention is a scientific improvement and adjustment based on the problems that arise in the practical use of the VARK scale, which is expected to complement the existing scale. The invention can see whether the different levels contain the proportion difference of V, A, R and K or not, and has statistical significance.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for analyzing the credibility of questionnaires based on a VARK scale according to the present invention;
FIG. 2 is a schematic flow chart of step5 of the questionnaire survey credibility analysis method based on the VARK scale of the present invention;
FIG. 3 is a flowchart illustrating a step6 of the questionnaire credibility analysis method based on the VARK scale according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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, but 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.
Example 1
The present embodiment provides a method for analyzing reliability of questionnaire based on a VARK scale, referring to fig. 1, fig. 2, and fig. 3, according to embodiment 1 of the present invention, the method for analyzing reliability of questionnaire based on a VARK scale includes the following steps:
step1: drawing user portrait data based on style trends of users, and configuring user requirements of related users;
step2: inputting the requirements of the user into a preset value database;
step3: setting questionnaire classification, group aiming and supervision rules; distributing questionnaire answering requests to the server of each user;
step4: acquiring attribute information of a question answering user, and constructing a user portrait and a user value map;
step5: generating a questionnaire according to the attribute information, and judging answered questions one by one according to the attribute information to analyze the related credibility of the user, wherein the specific steps are as follows:
based on the credibility rating and the user similarity as weights, the following weighted credibility is obtained:
wherein, the first and the second end of the pipe are connected with each other,
indicating the average deviation, i and j indicating the item number and the user number, respectively,
representing the trust degree of the jth user on the ith item, and R (j) representing the trust rating of the jth user as an index parameter;
representing the evaluation deviation of the jth user to the ith item, and T (i) representing the user similarity of the ith item as an index parameter;
the Step5 of correlation reliability analysis comprises the following steps:
step501: obtaining the current answer condition of the user to the questionnaire to obtain the question condition answered by the user;
step502: judging the relevance of the answering questions and the user one by one according to the attribute information;
step503: if the related credibility score exceeds a preset scoring standard, collecting and storing the questionnaire evaluation information of the user;
step6: generating an optimal solution matched with the demand according to the value database;
the optimal solution of Step6 matching comprises the following steps:
step601: establishing at least one corresponding label for the requirement of the user, and judging whether the basic information of the user is stored in the database when the attribute information is marked in the database;
step602: when the basic information of the user exists, searching question categories corresponding to the basic information from a database according to the basic information, selecting questions with preset question quantity to generate a questionnaire, and calculating an optimal solution in the value database through a preset calculation model according to the label;
step7: and encrypting the user independent personal information by a distributed block chain technology, and then transmitting and storing the encrypted user independent personal information.
The attribute information comprises basic information of the user and historical answer condition information of the user.
The VARK questionnaire, which is called Visual Aural Read/write learning (VARK) learning style questionnaire in English, is built by Nei flight in 1998 and is specially used for researching learning methods, teaching methods and analyzing information processing trends of students so as to know the questionnaires of learning styles of the students, and the trends of the learning styles are methods for receiving and giving information. The Visual mode is Visual, aural is auditory, read/write is Read-write and Kinesthetic is behavioral. The questionnaire is totally divided into 16 questions, tendency modes (V, A, R and T) of each question are obtained through a grading table, the maximum number of the sensory modes is comprehensively calculated, the learning style tendency of the user is judged, and if the number of the sensory modes is consistent, the learning style tendency is considered to be a mixed type. There are studies that suggest that most students who complete the VARK questionnaire are multi-modal learners with kinesthetic and visual preferences.
The single-style Visual (V) type students are usually more sensitive to information such as charts, flow charts, schematic diagrams, maps and the like, and the graphic information helps the students biased to the Visual learning style, so that the impression is increased more easily, and the learning efficiency is improved. In the test group, the recommended students can use the picture and chart data to carry out pre-study, and the recommended students can summarize and summarize the chart by themselves.
Single style hearing (a) type students are more suitable for learning by listening to radio, discussion, watching video with commentary, chat over the internet, or speaking by themselves. In the test group, students will be recommended to preview by watching video material and encouraged to find companion discussions by themselves.
Single-style Read/write (R) type students are more sensitive to textual information and more comfortable to learn using text manuals, text reports, traditional text books, text lists, and the like. In the test group, the recommended students can preview through a text manual and literature data, and the recommended students can list the text outline by themselves. Students with single style of behavioral activity (K) prefer to acquire more information by simulating actual operation and practice, and improve efficiency. In the test group, it will be recommended to encourage students to perform simulation operations, practice in person. The learning style tends to be multi-modal (two or more of the four learning style trends of V, A, R and K have the same score, i.e. the learning style is considered to be prone to be multi-modal).
The value database is used for generating a solution matched with the requirement; the definition formula of the solution is as follows:
performing linear or nonlinear combination definition generalized analysis on two or more selection criteria in the VARK scale as follows:
where c is the generalized evaluation, d is the correlation evaluation, and t is the evaluation criterion. Alpha (alpha is more than or equal to 0 and less than or equal to 1) is a weight coefficient, and if the value of alpha is 0, the shortest effectiveness of the evaluation criterion is selected as a selection criterion; if the value of alpha is 1, selecting the shortest evaluation criterion utility as a selection criterion; if (O < α < l), then the selection criteria is a trade-off between the two utilities of the evaluation criteria and the associated evaluation.
The calculation model in this embodiment may be at least one of the following algorithms, including: decision tree: providing decision basis for people; naive bayes classifier: the method mainly comprises the steps of detecting junk information, classifying information and the like; least square method: accurately matching the demand and the value; a clustering algorithm: processing a pile of data, clustering data according to similarity, and the like.
The formula defined by the preset scoring standard is as follows:
E= aL+bT+dS+eD+fP+gR+hM
wherein E is the credibility score, L is the user attribute, T is the answering time, S is the modification times, D is the delay, the theme proportion, R is the user level, M is the marginal requirement, and a-h is the weight coefficient.
In the Step6, the ratio of the reliability is set as the ratio of the reliability, the value range is 0-1, and the ratio PI (1-P) is taken from the natural logarithm Ln (P/(1-P)), namely the Logit conversion is carried out on the P; taking LogitP as a variable, establishing a linear regression equation:
wherein, the parameter α is a constant term representing a natural logarithm of a ratio (ratio of probabilities of Y =1 and Y = 0) when the values of the respective variables x are all 0, and the parameter α is a parameter
Referred to as the regression coefficient, indicates the amount of change in the natural logarithm of the ratio (OR) caused by an increase in one unit of the independent variable value when the other independent variable values remain unchanged.
The user representation data includes dynamic user requirements, and the user value graph includes user information provided by a user for solutions to the corresponding user requirements.
The present invention determines a learning style or tendency based on a user's questionnaire. Through the adaptive adjustment of styles, users and the interested directions of the users are increased, the learning styles or preferences of the users are considered by using VARK, the efficiency and the effect of questionnaires of the users are improved, and the research of teaching reform is carried out according to the user styles and the learning styles of the users. The questionnaire is composed of a plurality of multiple choice questions, each of which has multiple choices, although only one of them may be selected. Those with strong reading and writing tendencies will likely peruse the guideline. Those students that prefer word senses will likely be more concerned about word senses in the questionnaire. Students with kinesthetic tendencies may require additional situational or content information. Because visual enthusiasts tend to use charts, flow diagrams, symbolic arrows, circles, hierarchical taxonomies, and other methods that can be used to represent interpreted text; auditory enthusiasts tend to learn by lectures, teacher explanations, or other classmates; the read-write lovers tend to learn by using the information expressed by the word materials; kinesthetic enthusiasts learn by experience, case, practice, or simulation.
The invention verifies that the questionnaires are closely related by adopting a fixed effect model and applying section data through empirical verification. The invention not only can inform the learning style type of the tested person in detail, but also is convenient for educators and educational researchers to analyze the user as a whole and compare the difference of users of different groups. The present invention is a scientific improvement and adjustment based on the problems that arise in the practical use of the VARK scale, which is expected to complement the existing scale. The invention can see whether the different levels contain the proportion difference of V, A, R and K or not, and has statistical significance.
Example 2
The embodiment discloses a questionnaire credibility analysis method based on the VARK scale in the embodiment 1, wherein the server includes but is not limited to a web browser application, a search application, an instant messaging tool, a mailbox client and social platform software. The server is applied to the network equipment; the network devices are various electronic devices having display screens and supporting web browsing, including but not limited to in-vehicle smart screens, smart phones, tablets, laptop portable computers, and desktop computers.
The requirements of the users can be collected through various channels. In particular, the present embodiment preferably collects the user's needs through the following channels: offline interview: basic understanding is carried out on the user through a specially customized sop standard interview form; and (3) moving organization: through special activities, user requirements are supplemented in links of activity conversation, question answering and the like; and (3) online communication between housekeepers: through 13 high-level customized exclusive services, different opinions and requirements of users are known; multidimensional clicking technology: the method comprises the following steps of pushing a point selection diagram according to multiple demand dimensions such as interest, sports, health, fashion, life, education, consultation, technology, investment, travel, lease, leisure, home and the like, and directly selecting corresponding demand items to finish data acquisition without filling by a user; investigation of the questionnaire: different questionnaires are customized for the interest points of the user, and data are further captured; the demand is independently issued: the channel for supplementing is carried out after the user finishes watching the required portrait; daily question answering: through the acquisition of knowledge points, the user requirements are learned in a humanized and implicit way; and (4) browsing traces: intelligent big data acquisition and mining requirements.
There may be multiple tags to the user's needs. For example, the clothing needs may have various needs such as gender, style, color, etc., and these needs may each establish a corresponding label, such as a male, european, black, etc. label representing the needs of the user. Here, the present embodiment is not listed.
And judging the relevance of the unanswered questions and the users one by one according to the attribute information. The attribute information is information which can represent the historical answering condition of the user and is related to the basic information of the user, and the relevance of the unanswered questions and the user is judged through the question types and the question contents of the unanswered questions. Therefore, the unanswered questions related and unrelated to the user attribute information can be screened out, and the subsequent operation is facilitated.
The present invention determines a learning style or tendency based on a user's questionnaire. Through the adaptive adjustment of styles, users and the interested directions of the users are increased, the learning styles or preferences of the users are considered by using VARK, the efficiency and the effect of questionnaires of the users are improved, and the research of teaching reform is carried out according to the user styles and the learning styles of the users. The questionnaire consists of a plurality of multiple choice questions, each of which has multiple choices, although only one of them may be selected. Those with strong reading and writing tendencies will likely peruse the guideline. Those students who prefer word senses will likely be more concerned about word senses in the questionnaire. Students with kinesthetic tendencies may require additional situation or content information. Because visual enthusiasts tend to use charts, flow diagrams, symbolic arrows, circles, hierarchical taxonomies, and other methods that can be used to represent interpreted text; auditory enthusiasts tend to learn by lectures, teacher lectures, or other classmates; the fans who read and write tend to learn by using the information expressed by the word materials; kinesthetic enthusiasts learn by experience, case, practice, or simulation.
The invention verifies that the questionnaires are closely related by adopting a fixed effect model and applying section data through empirical verification. The invention not only can inform the learning style type of the tested person in detail, but also is convenient for educators and educational researchers to analyze the user as a whole and compare the difference of users of different groups. The present invention is a scientific improvement and adjustment based on the problems that arise in the practical use of the VARK scale, and is expected to beneficially supplement the original scale. The invention can see whether the different levels contain the proportion difference of V, A, R and K or not, and has statistical significance.
Example 3
This embodiment discloses a questionnaire credibility analysis method based on the VARK scale in embodiment 2, wherein if the attribute information is not marked in the database, after the user fills in data related to the attribute information in the questionnaire, a corresponding relationship between the user id and the attribute information is established in the database. So as to generate a personalized questionnaire for the user when the user answers again.
The present invention determines a learning style or tendency based on a user's questionnaire. Through the adaptive adjustment of styles, users and the interested directions of the users are increased, the learning styles or preferences of the users are considered by using VARK, the efficiency and the effect of questionnaires of the users are improved, and the research of teaching reform is carried out according to the user styles and the learning styles of the users. The questionnaire consists of a plurality of multiple choice questions, each of which has multiple choices, although only one of them may be selected. Those with strong reading and writing tendencies will likely peruse the guideline. Those students that prefer word senses will likely be more concerned about word senses in the questionnaire. Students with kinesthetic tendencies may require additional situational or content information. Because visual enthusiasts tend to use charts, flow diagrams, symbolic arrows, circles, hierarchical taxonomies, and other methods that can be used to represent interpreted text; auditory enthusiasts tend to learn by lectures, teacher lectures, or other classmates; the read-write lovers tend to learn by using the information expressed by the word materials; kinesthetic enthusiasts learn by experience, case, practice, or simulation.
The invention verifies that the questionnaires are closely related by adopting a fixed effect model and applying section data through empirical verification. The invention not only can inform the type of the learning style of the tested person in detail, but also is convenient for educators and educational researchers to analyze the users as a whole and compare the difference of the users of different groups. The present invention is a scientific improvement and adjustment based on the problems that arise in the practical use of the VARK scale, and is expected to beneficially supplement the original scale. The invention can see whether the different levels contain the proportion difference of V, A, R and K or not, and has statistical significance.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A questionnaire survey credibility analysis method based on a VARK scale is characterized by comprising the following steps:
step1: drawing user portrait data based on style tendency of a user, and configuring user requirements of related users;
step2: inputting the requirements of the user into a preset value database;
step3: setting questionnaire classification, group aiming and supervision rules; distributing questionnaire answering requests to the server of each user;
step4: acquiring attribute information of an answering user, and constructing a user portrait and a user value map;
step5: generating a questionnaire according to the attribute information, and judging the questions answered one by one according to the attribute information to analyze the related credibility of the user, wherein the specific steps are as follows:
based on the credibility rating and the user similarity as weights, the following weighted credibility is obtained:
wherein, the first and the second end of the pipe are connected with each other,
indicating the average deviation, i and j indicating the item number and the user number, respectively,
representing the trust degree of the jth user to the ith item, and R (j) representing the trust rating of the jth user as an index parameter;
representing the evaluation deviation of the jth user to the ith item, and T (i) representing the user similarity of the ith item as an index parameter;
the value database is used for generating a solution matched with the requirement; the definition formula of the solution is as follows:
the generalized analysis of the combination of two or more selection criteria in the VARK scale, linear or non-linear, is defined as:
wherein c is generalized evaluation, d is related evaluation, and t is evaluation criterion; alpha is a weight coefficient, and alpha is more than or equal to 0 and less than or equal to 1; if the value of alpha is 0, selecting the shortest evaluation criterion utility as a selection criterion; if the value of alpha is 1, selecting the shortest evaluation criterion utility as a selection criterion; if 0< alpha < l, the selection criterion is a trade-off between the evaluation criterion and the associated evaluation utility;
step6: generating an optimal solution matched with the demand according to the value database;
step7: and encrypting the user independent personal information by a distributed block chain technology, and then transmitting and storing the encrypted user independent personal information.
2. The method as claimed in claim 1, wherein the attribute information includes basic information of the user and historical answer information of the user.
3. The method for analyzing the credibility of questionnaire survey based on the VARK scale as claimed in claim 1, wherein the Step of Step5 related credibility analysis comprises the following steps:
step501: obtaining the current answer condition of the user to the questionnaire to obtain the question condition answered by the user;
step502: judging the relevance of the answering questions and the users one by one according to the attribute information;
step503: and if the related credibility score exceeds a preset scoring standard, collecting and storing the questionnaire evaluation information of the user.
4. The method according to claim 1, wherein the reliability of questionnaires is analyzed based on the VARK scale, and the method comprises the following steps: the optimal solution of Step6 matching comprises the following steps:
step601: establishing at least one corresponding label for the requirement of the user, and judging whether the basic information of the user is stored in a database or not when the attribute information is marked in the database;
step602: and when the basic information of the user exists, searching question categories corresponding to the basic information from a database according to the basic information, selecting questions with preset question quantity to generate a questionnaire, and calculating an optimal solution in the value database through a preset calculation model according to the label.
5. The method according to claim 4, wherein the predetermined scoring criteria define the following formula:
E= aL+bT+dS+eD+fP+gR+hM
wherein E is the credibility score, L is the user attribute, T is the answering time, S is the modification times, D is the delay, the theme proportion, R is the user level, M is the marginal requirement, and a-h is the weight coefficient.
6. The method according to claim 5, wherein the reliability analysis method of questionnaire survey based on the VARK scale comprises the following steps: in the Step6, the ratio of the reliability is set as the ratio of the reliability, the value range is 0-1, and the ratio PI (1-P) is taken from the natural logarithm Ln (P/(1-P)), namely the Logit conversion is carried out on the P; taking LogitP as a variable, establishing a linear regression equation:
wherein, the parameter α is a constant term representing a natural logarithm of a ratio (ratio of probabilities of Y =1 and Y = 0) when the values of the respective variables x are all 0, and the parameter α is a parameter
Is a regression coefficient obtained by fitting, and represents other factorsWhen the argument value remains unchanged, the argument value increases by one unit causing a change in ratio from the natural logarithm.
7. The method according to claim 6, wherein the reliability of questionnaires is analyzed based on the VARK scale, and the method comprises the following steps: the user representation data includes dynamic user requirements, and the user value graph includes user information provided by a user for solutions to the corresponding user requirements.
8. The method according to claim 7, wherein the reliability analysis method of questionnaire survey based on the VARK scale comprises the following steps: the server comprises a web browser application, a search application, an instant messaging tool, a mailbox client and social platform software.
9. The method according to claim 8, wherein the reliability analysis method for questionnaire survey based on the VARK scale comprises the following steps: a server is used as the network device.
10. The method according to claim 9, wherein the reliability analysis method for questionnaire survey based on the VARK scale comprises the following steps: the network devices are various electronic devices having display screens and supporting web browsing, including in-vehicle smart screens, smart phones, tablets, laptop portable computers, and desktop computers.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211601063.3A CN115659129B (en) | 2022-12-14 | 2022-12-14 | Questionnaire credibility analysis method based on VARK scale |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211601063.3A CN115659129B (en) | 2022-12-14 | 2022-12-14 | Questionnaire credibility analysis method based on VARK scale |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115659129A true CN115659129A (en) | 2023-01-31 |
| CN115659129B CN115659129B (en) | 2023-05-12 |
Family
ID=85022620
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211601063.3A Active CN115659129B (en) | 2022-12-14 | 2022-12-14 | Questionnaire credibility analysis method based on VARK scale |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115659129B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150056597A1 (en) * | 2013-08-22 | 2015-02-26 | LoudCloud Systems Inc. | System and method facilitating adaptive learning based on user behavioral profiles |
| US20200233925A1 (en) * | 2019-01-23 | 2020-07-23 | International Business Machines Corporation | Summarizing information from different sources based on personal learning styles |
| CN113971243A (en) * | 2021-10-12 | 2022-01-25 | 上海众言网络科技有限公司 | Data processing method, system, equipment and storage medium applied to questionnaire survey |
| CN114297476A (en) * | 2021-12-08 | 2022-04-08 | 苏州众言网络科技股份有限公司 | Questionnaire survey method, system, electronic equipment and storage medium based on user tags |
-
2022
- 2022-12-14 CN CN202211601063.3A patent/CN115659129B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150056597A1 (en) * | 2013-08-22 | 2015-02-26 | LoudCloud Systems Inc. | System and method facilitating adaptive learning based on user behavioral profiles |
| US20200233925A1 (en) * | 2019-01-23 | 2020-07-23 | International Business Machines Corporation | Summarizing information from different sources based on personal learning styles |
| CN113971243A (en) * | 2021-10-12 | 2022-01-25 | 上海众言网络科技有限公司 | Data processing method, system, equipment and storage medium applied to questionnaire survey |
| CN114297476A (en) * | 2021-12-08 | 2022-04-08 | 苏州众言网络科技股份有限公司 | Questionnaire survey method, system, electronic equipment and storage medium based on user tags |
Non-Patent Citations (1)
| Title |
|---|
| 朱聪等: "VARK学习风格问卷在作业治疗临床带教中的运用" * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115659129B (en) | 2023-05-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109919810B (en) | Student modeling and personalized course recommendation method in online learning system | |
| Papamitsiou et al. | Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence | |
| US20150363795A1 (en) | System and Method for gathering, identifying and analyzing learning patterns | |
| US20130311409A1 (en) | Web-Based Education System | |
| CN101203895A (en) | Systems and methods for semantic knowledge assessment, instruction and acquisition | |
| CN107423851A (en) | Adaptive learning method based on learning style context aware | |
| US20180068576A1 (en) | Automatic learning curriculum generation | |
| Zhong et al. | Design of a personalized recommendation system for learning resources based on collaborative filtering | |
| Aydoğdu | Educational data mining studies in Turkey: A systematic review | |
| US20180182256A1 (en) | Method and system to optimize education content-learner engagement-performance pathways | |
| Marshall | Contribution of open-ended questions in student evaluation of teaching | |
| Baird et al. | Rater accuracy and training group effects in Expert-and Supervisor-based monitoring systems | |
| CN113656687B (en) | Teacher portrait construction method based on teaching and research data | |
| Peng et al. | A pattern recognition method of personalized adaptive learning in online education | |
| Su et al. | Research on the improvement effect of machine learning and neural network algorithms on the prediction of learning achievement | |
| Harjanto et al. | Application of analytic hierarchy process and weighted product methods in determining the best employees | |
| Oreski et al. | CRISP-DM process model in educational setting | |
| Salehi et al. | Attribute-based recommender system for learning resource by learner preference tree | |
| CN115081965B (en) | Big data analysis system of condition of learning and condition of learning server | |
| CN111401525A (en) | Adaptive learning system and method based on deep learning | |
| Kong et al. | Towards recommender systems supporting knowledge sharing and transfer in vocational education and training | |
| Kim et al. | Measuring teacher effectiveness for equity: value-added model of teachers’ distributive effects on classroom achievement gaps | |
| CN115659129A (en) | Questionnaire survey credibility analysis method based on VARK scale | |
| El Moustamid et al. | Integration of data mining techniques in e-learning systems: Clustering Profil of Lerners and Recommender Course System | |
| CN108491547A (en) | A kind of internet teaching system based on big data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |