CN113238751A - Railway multi-professional problem composition method based on problem error tendency data - Google Patents

Abstract

The invention relates to a railway multi-specialty problem forming method based on error tendency data, which comprises the steps of obtaining a user default specialty and obtaining parameters set by the user; requesting an existing question group, if the existing question group exists, loading the existing question group and loading a user answer record; if the user parameter does not exist, generating a new question group according to the user parameter, and loading the new question group; the front end requests to query the API of the topic group, and the back end returns the query result; the front end generates a new question group according to the user parameter request; the front-end request API is submitted to background processing; the server side generates a new question group; and under the condition that no question bank exists in the specialty selected by the user, the system pushes a part of the test questions selected from all the question banks according to the specified rule to the user. The invention can be customized to push test questions for users, meets the requirements of industries such as railways, buildings and the like, supports various question types, optimizes the question setting result according to the wrong question data of the users and improves the user viscosity.

Description

Railway multi-professional problem composition method based on problem error tendency data

Technical Field

The invention relates to the technical field of information technology, analysis and measurement control, in particular to a railway multi-specialty problem forming method based on problem error tendency data.

Background

In the field of railway construction, the work speciality of a user is extremely strong, for example, people who engage in signal specialties may have little knowledge about house construction specialties or contact net specialties, and in actual learning, learning materials required by the user need to be limited within a certain range and often have further subdivision, such as design specifications, design standards and the like. Therefore, the topic construction method must support a certain degree of customization on a multi-professional basis.

In addition, in the question refreshing module of the initial online learning system, the system randomly extracts a group of a certain number of test questions from the question bank by using a random algorithm and returns a foreground interface to the user. The user selects or inputs answers and submits a background, and the background calculates answer results. The test questions generated by adopting a common random algorithm have some problems, for example, the test questions in the current group are repeated with the test questions in the previous group or the next group; the test questions which are frequently answered by the user repeatedly appear, and the test questions which are answered by mistake rarely appear repeatedly. The disadvantage of using a random algorithm is that the random algorithm is completely obtained randomly, and the user cannot be helped to master the required knowledge more efficiently and quickly.

Disclosure of Invention

The invention aims to provide a railway multi-professional problem grouping method based on problem-error tendency data, which is suitable for users in the railway construction industry, optimizes the problem grouping result according to the user problem error records, helps the users to learn and prepare for examination more efficiently, and provides better learning experience.

The technical scheme adopted by the invention is as follows:

the railway multi-specialty problem composition method based on the problem error tendency data is characterized in that:

the method comprises the following steps:

p1: acquiring a user default specialty and acquiring parameters set by a user;

p2: requesting an existing question group, if the existing question group exists, loading the existing question group and loading a user answer record; if the user parameter does not exist, generating a new question group according to the user parameter, and loading the new question group; the front end requests to query the API of the topic group, and the back end returns the query result;

p3: the front end generates a new question group according to the user parameter request; the front-end request API is submitted to background processing;

p4: the server side generates a new question group; and under the condition that no question bank exists in the specialty selected by the user, the system pushes a part of the test questions selected from all the question banks according to the specified rule to the user.

In step P1, the parameters set by the user include difficulty, label, question source, whether to prompt, and the user inputs the parameters through the interface.

In step P2, find that there are 3 conditions in the existing topic group:

the user: creating that the user is a current user;

topic group type: self-test;

the state is as follows: not completed.

In step P3, the request parameters are divided into a fixed part and a variable part, the fixed part is a part of parameters that will not change according to the user and the use environment, including the type of the test question, whether it is a comprehensive question type, the number of the test questions, and the user submitted; the variable portion includes specialty, labels.

In step P4, the process of generating a new topic group by the server side is as follows:

according to the conditions: specialization, difficulty interval, test question classification, question type and test question quantity query, if the background query quantity is 0, no test question meeting the condition is shown; then the background algorithm automatically removes the speciality for re-query to newly generate a question group, the question group creator is a requester, a specified number of test questions are obtained from the query result, and the obtained test question IDs and the new question group form a record and are returned to the front end;

under the condition of no query result, the variable part needs to be removed and then queried again, when the query result is 0, the variable part is deserialized, professional conditions are removed, the variable part is serialized, and then query is combined;

inquiring results according to the parameters, and if the number of the results is less than the number of the requested test questions, directly returning all the inquiry results; then, taking out a part from the wrong question array, taking out a part from the rest results, combining the two parts and returning;

if the number of the results is larger than or equal to the number of the requested test questions, firstly inquiring user data in a user wrong question tendency table, splitting the wrong question list into arrays, and placing the test questions in the inquiry results, which have the same ID as that in the wrong question array, in a wrong question alternative array a; judging the number of the array a, if the number is less than 1/4 of the number of the requested test questions, putting all the array a into an alternative array A; if the number is greater than or equal to 1/4 of the requested number, 1/4 of the number of the requested test questions is taken from the tendency table and is placed in the alternative array A; removing the result in the array A from the query result to obtain a residual array B, taking out the residual required number of test questions in the array B, putting the test questions into the array B, combining the array A and the array B, and returning the combined result;

and finally, establishing a relationship between all the test questions in the final result question combination and the new question group, and returning to the new question group.

The method further comprises a step P5 of brushing question page design:

the page content is divided into three parts:

(1) question stem display area: as part of the parent container, a standardizable, fixed part;

(2) options/answers display area: for single-choice/multiple-choice questions, options are displayed here; displaying answers aiming at question types such as short answer, judgment, blank filling and the like; non-standardized, variable parts;

(3) answer result display area: after the user finishes answering the test questions, the answering results are displayed, and the parts can be standardized and fixed.

The method further comprises a step P6 of displaying data:

inquiring relevant information of the test questions according to the ID of the first question for creating the new group, displaying the question stem in a parent component, and displaying and switching corresponding contents in the option/answer display area according to the current question type; the single-choice, multi-choice and judgment question types are displayed as options, the blank filling questions display corresponding number of input frames according to the number of answers, and the question and answer questions display the input frames.

The invention has the following advantages:

the method can push test questions corresponding to the specialties according to the specialties of the users by default, and combines the results required by the users according to the parameters specified by the users, so that the examination preparation and the learning are more efficient; according to the mastering condition of the user on the test questions, the subsequent question composition result is optimized, and the unsophisticated knowledge appears in the question composition result for multiple times, so that the user is promoted to better master the related knowledge; the frequency of occurrence of the knowledge mastered by the user is reduced, and the learning and examination preparation efficiency is improved.

Drawings

FIG. 1 is a schematic diagram of a user interface for setting parameters.

FIG. 2 is a schematic diagram illustrating a process for handling professional non-data flow.

FIG. 3 is a schematic view of a parameter processing flow.

FIG. 4 is a flow chart illustrating the test question result combining process according to the error question tendency table.

FIG. 5 is a schematic diagram of a page design.

FIG. 6 is a diagram illustrating the display of subcomponents when switching to a radio topic.

FIG. 7 is a diagram illustrating the display of subcomponents when switching to a multiple choice question.

FIG. 8 is a diagram illustrating the display of subcomponents when switching to filling in a gap.

FIG. 9 is a diagram illustrating a sub-component display when switching to a judgment question.

FIG. 10 is a diagram illustrating the display of subcomponents when switching to a simple answer.

Detailed Description

The present invention will be described in detail with reference to specific embodiments.

The invention relates to a railway multi-professional question setting method based on wrong question tendency data. The inclined table is recorded by using a user ID as a main key, a core field records the ID of the error of the user, the ID is recorded in a form separated by commas, at most 25 IDs are stored, and the IDs are sorted according to the frequency and the time of the error of the user. The storage mode can avoid the infinite expansion of data, occupy the infinite expansion of data storage space, and simultaneously can improve the efficiency of extracting wrong questions.

The method specifically comprises the following steps:

p1: acquiring the default specialty of the user, and acquiring the parameters set by the user, wherein the parameters include: difficulty, label, test question source, question amount, whether to prompt or not, and input by a user through an interface.

And (4) data description.

Parameters to be set:

difficulty: difficulty level of the test question;

labeling: preset keywords;

the sources of test questions are as follows: determining according to the field source when the test questions are uploaded;

whether to prompt: whether the question brushing process comprises a prompt related to the answer or not;

p2: requesting an existing question group, and if the existing question group exists, loading the existing question group and loading the user answer records. And if the user parameter does not exist, generating a new topic group according to the user parameter, and loading the new topic group. The specific implementation is that the front end requests the API of the query topic group, and the back end returns the query result.

There are 3 conditions to find the existing problem group:

the user: creating that the user is a current user;

topic group type: self-test;

the state is as follows: not completed.

P3: and the front end generates a new topic group according to the user parameter request. The request parameters are divided into a fixed part and a variable part, wherein the fixed part is a part of parameters which cannot be changed according to users and use environments, such as test question types, whether the test question types are comprehensive or not, the number of test questions, submitted users and the like; the variable portion includes specialties, tags, and the like. And the front end requests the API and submits the API to the background for processing.

P4: the server side generates a new topic group. Since the system functions are to satisfy the user's ease of use principle, consider all users: users with definite learning targets, new users, users without learning targets, or users who need to find a specialty of interest to learn by trying the basic functions of the system first. In order to avoid the situation that the system has no feedback or no feedback blank, under the situation that the specialty selected by the user has no question bank, the system selects a part of test questions from all question banks according to the specified rule and pushes the selected part of test questions to the user.

The overall steps are as follows:

according to the conditions: specialization, difficulty interval, test question classification, question type and test question quantity query, if the background query quantity is 0, no test question meeting the condition is shown; then the background algorithm automatically removes the speciality for re-query to generate a new question group, the question group creator is the requester, obtains a specified number of test questions from the query result, and the obtained test question IDs and the new question group form a record and return the record to the front end.

Since the query condition is divided into two parts, a variable part and a fixed part. And when the query result is 0, firstly performing deserialization on the variable part, removing professional conditions, then serializing the variable part, and then combining the queries.

And inquiring results according to the parameters, and if the number of the results is less than the number of the requested test questions, directly returning all the inquiry results. The next operation is to take out a part from the error problem array, take out a part from the rest results, merge the two parts and return.

If the number of the results is larger than or equal to the number of the requested test questions, firstly inquiring user data in a user wrong question tendency table, splitting the wrong question list into arrays, and placing the test questions with the same ID as that in the wrong question array in the inquiry result in a wrong question alternative array a. Judging the number of the arrays a, if the number is less than 1/4 of the number of the requested test questions, putting all the arrays into the alternative array A. If greater than or equal to 1/4 for the number of requests, 1/4 for the number of requested questions is taken from the tendency table and placed in alternative array A. And (4) eliminating the result in the array A from the query result to obtain a residual array B, taking out the residual required number of test questions in the array B, putting the test questions into the array B, combining the array A and the array B, and returning the combined result.

And finally, establishing a relationship between all the test questions in the final result question combination and the new question group, and returning to the new question group.

P5: and (5) designing a question brushing page. The front end adopts single-page application component type development, so that the corresponding speed of the page is higher, the multiplexing rate is higher, and the redundant code quantity is less. The test question is divided into four parts of a question stem, options, answers and answer results. The question stem is owned by each question, and the data can be standardized to the display mode without difference; the options are only partial types; the display mode of the answers is almost different from each topic type, and needs to be processed separately. Thus, the page content is divided into three parts:

(1) question stem display area: as part of the parent container, the part may be standardized, fixed.

(2) Options/answers display area: for single-choice/multiple-choice questions, options are displayed here; displaying answers aiming at question types such as short answer, judgment, blank filling and the like; non-standardized, variable parts.

(3) Answer result display area: after the user finishes answering the test questions, the answering results are displayed, and the parts can be standardized and fixed.

P6: and displaying the data. Inquiring relevant information of the test questions according to the ID of the first question for creating the new group, displaying the question stem in a parent component, and displaying and switching corresponding contents in the option/answer display area according to the current question type; the single-choice, multi-choice and judgment question types are displayed as options, the blank filling questions display corresponding number of input frames according to the number of answers, and the question and answer questions display the input frames.

The technical tasks which can be completed by the invention comprise: and setting and combining test questions meeting the conditions according to the parameters, optimizing and generating a question combination result according to a wrong question table of the user, pausing and continuing in answering, and setting out the questions independently or in combination according to multiple question types.

The invention is realized by adopting a B/S mode, and the front end adopts an Vue single-page application framework, so that the response speed is high and the reuse rate is high; the background is realized by using C # WebApi to realize specific logic; the database adopts SQL Server. And recording user tendency data in an ID combination mode as one basis for generating the question group.

The invention is not limited to the examples, and any equivalent changes to the technical solution of the invention by a person skilled in the art after reading the description of the invention are covered by the claims of the invention.

Claims (7)

1. The railway multi-professional question grouping method based on the error tendency data is characterized in that: The method includes the following steps: P1: Obtain the user's default major, and obtain the parameters set by the user; P2: Request an existing question group, if it exists, load the existing question group and load the user answer record; if it does not exist, generate a new question group according to the user parameters, and load the new question group; the front-end requests the API for querying the question group , the backend returns the query result; P3: The front end generates a new question group according to the user parameter request; the front end requests the API and submits it to the background for processing; P4: A new question group is generated on the server side; if there is no question bank for the major selected by the user, the system will select a part of the test questions from all the question banks and push them to the user according to the specified rules. 2. the railway multi-professional question-making method based on wrong question tendency data according to claim 1, is characterized in that: In step P1, the parameters set by the user include difficulty, label, test question source, and whether to prompt, and the user inputs through the interface. 3. the railway multi-professional question-grouping method based on wrong question tendency data according to claim 2, is characterized in that: In step P2, there are 3 conditions for finding existing question groups: User: The created user is the current user; Question group type: self-test; Status: Not completed. 4. the railway multi-professional question-grouping method based on wrong question tendency data according to claim 3, is characterized in that: In step P3, the request parameters are divided into a fixed part and a variable part. The fixed part is a part of the parameters that will not change according to the user and the use environment, including the type of test question, whether it is a comprehensive question type, the number of test questions, and the user who submitted it; the variable part Including professional, label. 5. The railway multi-professional question grouping method based on wrong question tendency data according to claim 4, is characterized in that: In step P4, the process of generating a new question group on the server side is as follows: According to the conditions: major, difficulty interval, question classification, question type, number of questions, if the number of background queries is 0, it means that there are no questions that meet the conditions; then the background algorithm will automatically remove the major and re-query, and generate a new question group, question group The creator is the requester, obtains the specified number of questions in the query result, forms a record with the obtained question ID and the new question group, and returns it to the front end; Under the condition that there is no query result, the variable part needs to be removed and re-queried. When the query result is 0, the variable part is first deserialized, the professional condition is removed, and then the variable part is serialized, and then the query is combined; According to the parameter query results, if the number of results is less than the number of requested questions, all query results can be returned directly; then a part is taken from the wrong question array, and another part is taken out of the remaining results, and the two parts are combined and returned; If the number of results is greater than or equal to the number of requested questions, first query the user data in the user error tendency table, split the list of error questions into arrays, and put the questions with the same ID in the query result as the error question array in the wrong question option. In array a; judge the number of array a, if it is less than 1/4 of the requested number of questions, put it all into the alternative array A; if it is greater than or equal to 1/4 of the requested number, take 1 of the requested number of questions from the tendency table /4, put it into the candidate array A; exclude the results in the array A from the query results, get the remaining array b, take the remaining required number of test questions from b, put them into the B group, merge A and B and The merge result is returned; Finally, create a relationship between all the questions in the final question group and the new question group, and return the new question group. 6. The railway multi-professional question grouping method based on wrong question tendency data according to claim 5, is characterized in that: The method also includes step P5, brushing the design of the title page: Divide the page content into three parts: (1) Title stem display area: as part of the parent container, it can be standardized and fixed; (2) Option/answer display area: For single-choice/multiple-choice questions, options are displayed here; for short answer/judgment/fill-in-the-blank questions, answers are displayed; cannot be standardized, variable parts; (3) Answering result display area: After the user answers the question, the answering result is displayed, which can be standardized and fixed. 7. The railway multi-professional question grouping method based on wrong question tendency data according to claim 6, is characterized in that: The method also includes step P6, displaying data: According to the first question ID of the new group, the relevant information of the test question is inquired, and the question stem is displayed in the parent component. The option/answer display area displays and switches the corresponding content according to the current question type; For options, fill-in-the-blank questions display the corresponding number of input boxes according to the number of answers, and Q&A questions display as input boxes.

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