CN114780679A - Method and device for generating test paper - Google Patents
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Abstract
The application discloses a method and a device for generating test paper, wherein the method comprises the steps of obtaining the total number of questions required by the test paper, preset generation conditions, categories contained by each preset generation condition and the number of questions required by different categories; determining the row number and the column number of a table according to the total number of the questions and the number of preset generation conditions, and filling the numerical results of different categories into the table according to the number of the questions required by different categories to generate a temporary table; performing row and column combination calculation on the temporary table, wherein each row obtains a condition value, and each condition value corresponds to a test paper question; inquiring a test question database according to each condition value; taking out the inquired questions with the same condition numerical values from a test question database; and generating the test paper by corresponding all the taken-out questions to the question sequence of the test paper in the temporary table. The problem of how to improve the efficiency of automatic generation paper is solved in this application.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for generating a test paper.
Background
In order to help users to perform examination paper-out operation more easily and greatly improve the working efficiency of users, automatic paper-out software systems and the like are provided. The automatic paper output needs to match test questions meeting various conditions according to various conditions, and the test questions are combined into a test paper according to a certain random sequence. At present, the general process of automatic unwinding is as follows: and matching the questions in the question bank item by item according to the generation conditions, randomly selecting one question from the matching result query set to fill in the test paper, and circulating the process until the whole test paper is filled. And finally, disordering the test question sequence. I.e. a random test paper is generated. The inventor finds that when questions meeting the generation conditions are matched and inquired, the database needs to be linked for many times, the inquiry efficiency changes along with the number of the generation conditions and the number of the questions in the database, the inquiry efficiency is slower when the number is larger, and the test paper generation efficiency is slower when the inquiry efficiency is slower.
Disclosure of Invention
The application mainly aims to provide a method and a device for generating test paper, and the problem of how to improve the efficiency of automatically generating the test paper is solved.
To achieve the above object, according to a first aspect of the present application, there is provided a method of generating a test paper.
The method for generating the test paper comprises the following steps: acquiring the total number of questions required by the test paper, preset generation conditions, categories contained in each preset generation condition and the number of questions required by different categories; determining the number of rows and columns of the table according to the total number of the questions and the number of preset generating conditions, and filling the numerical results of different categories into the table according to the number of the questions required by different categories to generate a temporary table; performing row and column combination calculation on the temporary table, wherein each row obtains a condition value, and each condition value corresponds to a test paper question; inquiring a test question database according to each condition value; taking out the inquired questions with the same condition numerical values from a test question database; and generating the test paper by corresponding all the taken-out questions to the question sequence of the test paper in the temporary table.
Optionally, the filling the table with the digitized results of different categories according to the number of questions required by the different categories to generate a temporary table includes: determining the required number of questions as class filling amount corresponding to the class; and randomly filling the numerical results of the class filling quantities of different classes belonging to the same preset generation condition into the column corresponding to each preset generation condition to generate the temporary table.
Optionally, the performing column merging calculation on the temporary table includes: and multiplying the numerical result of each column in the temporary table by the column coefficient of each column respectively, and then performing summation calculation.
Optionally, the retrieving the queried topic with the same condition value from the test question database further includes: if the question which is the same as the condition numerical value cannot be inquired, carrying out tail number removing processing on the condition numerical value; continuously inquiring the test question database according to the condition value after the mantissa is removed; if the questions with the same condition value as the denoised condition value can be inquired, taking out the questions from the test question database; and if the topics with the same condition values as the condition values subjected to the tail number removal cannot be inquired, performing tail number removal processing on the condition values subjected to the tail number removal again until all topics corresponding to the condition values are inquired.
Optionally, the querying the test question database according to each condition value includes: carrying out homonymy grouping on the condition values corresponding to all test paper questions to obtain a binary value sequence of the question numbers corresponding to the condition values; and inquiring the corresponding question number of the test question database according to each condition value in the binary value sequence.
Optionally, the method further includes: and setting different number levels of column coefficients according to the importance degree of each preset generation condition and the number of categories contained in each preset generation condition.
Optionally, the setting of different number levels of column coefficients according to the importance degree of each preset generating condition and the number of categories included in each preset generating condition includes: determining a proportional value between column coefficients according to the magnitude of the category number in the preset generation condition with the maximum category number; and sequentially determining terms in the geometric proportion series with the leading term of 1 and the common ratio of the proportional value as the series coefficients of different number levels according to the importance degree of each preset generation condition.
Optionally, the removing the mantissa of the condition value includes: and dividing the condition numerical value by the proportion value and rounding to obtain the condition numerical value after mantissa removal.
Optionally, after the test paper is generated in the order that all the retrieved subjects correspond to the test paper subjects in the temporary table, the method further includes: receiving a replacement command for replacing a question in a test paper; inquiring the question bank again according to the condition value corresponding to the question contained in the replacing command; and recommending the inquired questions as the replacement questions.
Optionally, the preset generating condition includes one or more of a question type, a knowledge point, a difficulty level, whether to use, and a repetition rate.
In order to achieve the above object, according to a second aspect of the present application, there is provided an apparatus for generating a test paper.
The device for generating the test paper comprises the following components: the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the total number of questions required by the test paper, preset generation conditions, categories contained in each preset generation condition and the number of questions required by different categories; the table generating unit is used for determining the number of rows and columns of the table according to the total number of the questions and the number of preset generating conditions, and filling the numerical results of different categories into the table according to the number of the questions required by different categories to generate a temporary table; the calculation unit is used for performing row and column combination calculation on the temporary table, each row obtains a condition value, and each condition value corresponds to a test paper question; the query unit is used for querying the test question database according to each condition value; the extracting unit is used for extracting the inquired questions which are the same as the condition numerical values from the test question database; and the test paper generating unit is used for generating test papers by corresponding all the taken questions to the test paper questions in the temporary table.
Optionally, the table generating unit further includes: the first determining module is used for determining the required question number as a category filling amount corresponding to a category; and the filling module is used for randomly filling the numerical results of the filling quantities of different categories belonging to the same preset generation condition into the column corresponding to each preset generation condition to generate the temporary table.
Optionally, the computing unit is further configured to: and multiplying the numerical result of each column in the temporary table by the column coefficient of each column respectively, and then performing summation calculation.
Optionally, the extracting unit further includes: the mantissa removing module is used for removing the mantissas of the condition numerical value if the question which is the same as the condition numerical value cannot be inquired; the first query module is used for continuously querying the test question database according to the condition numerical value after the mantissa is removed; the extraction module is used for extracting the questions from the test question database if the questions with the same condition numerical values after the mantissas are removed can be inquired; and the mantissa removing module is also used for carrying out mantissa removing processing on the condition numerical value after the mantissa is removed again until all the topics corresponding to the condition numerical value are inquired if the topics identical to the condition numerical value after the mantissa is removed cannot be inquired.
Optionally, the query unit further includes: the grouping module is used for carrying out homonymy grouping on the condition numerical values corresponding to all test paper questions to obtain a binary value sequence of the question numbers corresponding to the condition numerical values; and the second query module is used for querying the corresponding question number of the test question database according to each condition value in the binary value sequence.
Optionally, the apparatus further comprises: and the setting unit is used for setting column coefficients of different number levels according to the importance degree of each preset generation condition and the number of categories contained in each preset generation condition.
Optionally, the setting unit includes: the second determining module is used for determining a proportional value between the column coefficients according to the magnitude of the category number in the preset generating condition with the maximum category number; and the third determining module is used for sequentially determining the terms in the equal ratio number series with the first term of 1 and the common ratio of the proportional value as the series coefficients of different number levels according to the importance degree of each preset generating condition.
Optionally, the mantissa removing module is further configured to: and dividing the condition numerical value by the proportion value and rounding to obtain the condition numerical value after mantissa removal.
Optionally, the apparatus further comprises: a receiving unit, configured to receive a replacement command for replacing the questions in the test paper after the test paper is generated in a manner that all the questions to be taken out correspond to the test paper question orders in the temporary table; the re-query unit is used for re-querying the test question bank according to the condition value corresponding to the question contained in the replacement command; and the recommending unit is used for recommending the inquired questions as the replacement questions.
Optionally, the preset generating condition includes one or more of a question type, a knowledge point, difficulty, whether used, and a repetition rate.
In order to achieve the above object, according to a third aspect of the present application, there is provided a computer-readable storage medium storing computer instructions for causing the computer to execute the method for generating a test paper of any one of the above first aspects.
In order to achieve the above object, according to a fourth aspect of the present application, there is provided an electronic apparatus comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the method of generating test sheets of any one of the first aspect.
In the method and the device for generating the test paper, the total number of questions required by the test paper, preset generation conditions, categories included in each preset generation condition and the number of questions required by different categories are obtained; then, determining the number of rows and columns of the table according to the total number of the questions and the number of preset generating conditions, and filling the numerical results of different categories into the table according to the number of the questions required by different categories to generate a temporary table; performing row and column combination calculation on the temporary table, wherein each row obtains a condition value, and each condition value corresponds to a test paper question; inquiring a test question database according to each condition value; taking out the inquired questions with the same condition values from the test question database; finally, all the questions taken out are corresponding to the question sequence of the test paper in the temporary table to generate the test paper. It can be seen that, when the test paper is generated, a temporary table is generated according to some conditions (total question number, preset generation conditions, categories included in each preset generation condition and question numbers required by different categories), the table realizes the randomization and serialization of the test paper, and a condition value is calculated for each test paper question based on the table, wherein the randomness corresponding to the question is basically determined in the step, and a pseudo-random effect is achieved. And then, based on the condition value, similarity matching calculation is carried out on the questions in the test paper database, the questions in the test paper to be generated can be taken out as long as the condition values are the same, compared with a mode of matching and obtaining item by item, matching calculation with each question in the test paper database is not needed, the query times are greatly reduced, the obtaining difficulty is simplified, and therefore the efficiency of generating the test paper can be greatly improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a flow chart of a method for generating test paper according to an embodiment of the present application;
FIG. 2 is a block diagram of an apparatus for generating test paper according to an embodiment of the present disclosure;
fig. 3 is a block diagram of another apparatus for generating test paper according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the accompanying drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
According to an embodiment of the present application, there is provided a method for generating a test paper, as shown in fig. 1, the method includes the following steps S101-S106: s101, acquiring the total number of questions required by the test paper, preset generation conditions, categories contained in each preset generation condition and the number of questions required by different categories; s102, determining the row number and the column number of a table according to the total number of the questions and the number of preset generation conditions, and filling the numerical results of different categories into the table according to the number of the questions required by the different categories to generate a temporary table; s103, performing row-column combination calculation on the temporary table, wherein each row obtains a condition value, and each condition value corresponds to a test paper question; s104, inquiring a test question database according to each condition value; s105, taking out the inquired questions with the same condition numerical values from a test question database; and S106, generating the test paper by corresponding all the taken-out questions to the question sequence of the test paper in the temporary table.
In step S101, the total number of questions required for the test paper is the total number of questions of the test paper to be generated. The preset generating conditions are a part of conditions for generating the test paper set by the user according to the requirements of the user, the preset generating conditions can be question types, knowledge points, difficulty, whether the test paper is used or not, repetition rate and the like, and one or more of the preset generating conditions can be selected when the user actually selects the test paper. The category included in each preset generating condition represents the type included in each preset generating condition, for example, the question type may include a single-choice question type, a multiple-choice question type, and the like; the knowledge points can comprise integral calculation, calculus calculation and the like, and the specific type of the knowledge points can be determined according to the use range of the actual test paper; the difficulty can comprise common difficulty, medium difficulty, high difficulty and the like; whether used may include used in other test papers and unused in other test papers; the repetition rate may be 10%, 20%, or the like. Each preset condition contains a type that can be set by the user. In addition, the number of questions required for different categories needs to be determined. For example, for the preset generation condition of the "question type", the number of questions required for the single-choice question type may be set to 30, and the number of questions required for the multiple-choice question type may be set to 40.
In addition, the embodiment of the application can be applied to automatic paper output software or a system, and after a user sets (selects, manually inputs and the like) the total number of questions required by the test paper, the preset generation conditions, the categories included in each preset generation condition and the number of questions required by different categories according to the requirements of the user, the paper output software or the system background can acquire the total number of questions required by the test paper, the preset generation conditions, the categories included in each preset generation condition and the number of questions required by different categories.
In step S102, the number of rows and columns of the table is determined according to the total number of questions and the number of preset generating conditions, that is, the number of rows of the table is determined according to the total number of questions, and the number of columns of the table is determined according to the number of preset generating conditions. That is, each row corresponds to a test paper title of a test paper to be generated, and each column corresponds to a preset generation condition. In addition, for convenience of subsequent processing, the columns in the table may be arranged in order according to the magnitude of the importance degree of the preset generation condition.
Filling the numerical results of different categories into a table according to the number of questions required by the different categories to generate a temporary table, which specifically comprises the following steps:
firstly, determining the required number of questions as class filling amount corresponding to the class; a specific example is given for explanation, and assuming that the number of questions required by the radio topic type included in the preset generation condition of the topic type is 30, the category filling amount corresponding to the radio topic type is 30; if the number of questions required for the multiple choice question type is 40, the class filling amount corresponding to the multiple choice question type is 40.
And then randomly filling the numerical results of the class filling quantities of different classes belonging to the same preset generation condition into the column corresponding to each preset generation condition to generate the temporary table. Before filling, the category included in each preset generation condition needs to be digitized. For example, the preset generation condition of the question type includes a single-choice question type and a multiple-choice question type, and after the numerical processing is performed on the preset generation condition, the numerical result can be a single-choice question type-1; multiple choice question type-2 "; for another example, for the preset generating condition of "used or not used", including used or unused, after performing the numerical processing on the preset generating condition, the numerical result may be "used-1; no-0 "used, etc. The specific filling process is described by taking the column corresponding to the question type as an example, and assuming that the category filling amount corresponding to the single-choice question type is 30, the numerical result is 1, the category filling amount corresponding to the multiple-choice question type is 40, and the numerical result is 2, the filling process is that 30 items of 1 and 40 items of 2 are randomly arranged in the column where the question type is located. And filling the columns corresponding to each preset generating condition according to the filling mode, and finally obtaining a temporary table.
In step S103, "performing row-merging calculation on the temporary table" specifically includes: and multiplying the numerical result of each column in the temporary table by the column coefficient of each column respectively, and then performing summation calculation. Thus, for column merging, the column coefficients are first determined. In the embodiment of the present application, the column coefficients are also set in advance, and the specific setting rule sets column coefficients of different number levels according to the importance degree of each preset generation condition and the number of categories included in each preset generation condition.
Setting column coefficients of different number levels according to the importance degree of each preset generating condition and the number of categories contained in each preset generating condition, and comprising the following steps of:
firstly, determining a proportional value between column coefficients according to the magnitude of the number of categories in a preset generation condition with the largest number of categories; assuming that the preset generation condition with the largest number of categories is knowledge points, and the number of categories of the knowledge points is of the order of hundreds (greater than 10 and less than or equal to 100), the proportion value may be determined to be 100. If the scale value is of the order of thousands of bits (greater than 100 and less than or equal to 1000), the scale value can be determined to be 1000.
And then, sequentially determining the terms in the geometric proportion series with the leading term of 1 and the common ratio of the proportional value as the series coefficients of different number levels according to the importance degree of each preset generation condition. Specifically, the greater the importance of the preset generation condition is, the greater the corresponding column coefficient is. Therefore, the terms of the geometric progression are sequentially determined from 1 to the column coefficients of the corresponding columns corresponding to the preset generating conditions in the order of increasing importance.
Specific examples are given for illustration: the preset generating conditions are assumed to comprise question types, knowledge points and difficulty according to the importance degree. The number of the categories of the knowledge points is the largest and the category is hundreds, the proportion value can be determined to be 100, and the column coefficients corresponding to three preset generating conditions of difficulty, knowledge points and question types can be further determined to be 1, 100 and 10000 respectively.
In addition, the column coefficients of different columns may not be equal-ratio columns, for example, for the examples of 1, 100, and 10000, the order of the categories included in the topic model is usually ten-bit, and in this case, the column ratio coefficient of the topic model may be multiplied by 10 on the column coefficient of the knowledge point column to obtain the column coefficient 1000 corresponding to the topic model. The column coefficients are determined in such a manner that the proportional values between the column coefficients are determined with respect to the magnitude of the preset generation condition. The scaling values for different column coefficients may be the same or different, and thus the column coefficients for different columns may not be equal-scaled columns.
After the column coefficients are determined, a condition value of each row is obtained according to a calculation mode of multiplying the numerical result of each column by the column coefficients of each column and then performing summation calculation, and each condition value corresponds to a test paper subject. The condition values for different rows may be the same or different. The digitized results in different corresponding columns in each row corresponding to the same condition value must also be the same, which is also determined by the column coefficients of different number levels.
In step S104, the test question database is queried according to each condition value, that is, the condition value calculated in step S103 is used as a query condition, and is subjected to similarity matching with the condition value corresponding to the question in the test question database. The condition values of the questions in the test question database are calculated in the same manner as the condition values obtained by row-column combination based on the temporary table. The questions in the test question database can also be obtained by calculation according to the attribute characteristics corresponding to the preset generating conditions. It should be noted that the condition values of the questions in the test question database are not generated in advance, but are generated during the query, that is, the condition value of each question is calculated by querying which question. Then, the condition values in the query conditions are matched with the condition values of the test questions in the test question database in similarity. If the topic with the similarity of 100% is found, that is, the topic with the same condition value is found in the query in step S105, the topic is taken out, and then the next condition value is used as the query condition to continue the query, so that the topic with the same condition value is obtained and taken out until all the condition values in step S103 have the corresponding topics. Finally, step S106 is executed, in which all the questions to be taken out are generated into test paper corresponding to the test paper question sequence in the temporary table.
From the above description, it can be seen that, in the method for generating a test paper in the embodiment of the present application, first, the total number of questions required for the test paper, the preset generation conditions, the categories included in each preset generation condition, and the number of questions required for different categories are obtained; then, determining the number of rows and columns of the table according to the total number of the questions and the number of preset generating conditions, and filling the numerical results of different categories into the table according to the number of the questions required by different categories to generate a temporary table; performing row-column combination calculation on the temporary table, wherein each row obtains a condition value, and each condition value corresponds to a test paper question; inquiring a test question database according to each condition value; taking out the inquired questions with the same condition numerical values from the test question database; and finally, generating the test paper by corresponding all the taken questions to the test paper question sequence in the temporary table. It can be seen that, when the test paper is generated, a temporary table is generated according to some conditions (total number of questions, preset generation conditions, categories included in each preset generation condition, and number of questions required by different categories), the table realizes randomization and serialization of the test paper, and a condition value is calculated for each test paper question based on the table, which is equivalent to that the randomness of the question is basically determined in the step, so that a pseudo-random effect is achieved. And then, based on the condition value, similarity matching calculation is carried out on the questions in the test paper database, the questions in the test paper to be generated can be taken out as long as the condition values are the same, compared with a mode of matching and obtaining item by item, matching calculation with each question in the test paper database is not needed, the query times are greatly reduced, the obtaining difficulty is simplified, and therefore the efficiency of generating the test paper can be greatly improved.
In order to further improve the efficiency of generating test paper, the embodiment of the present application further provides another implementation manner for step S104, and the specific implementation steps are as follows:
firstly, carrying out homonymy grouping on condition values corresponding to all test paper questions to obtain a binary value sequence of the question numbers corresponding to the condition values; because the numerical results in different corresponding columns in each row corresponding to the same condition value are also necessarily the same, in order to further improve the query efficiency, the condition values can be divided into one group, and the query is also quicker when one condition value is in one group. The specific implementation mode is as follows: and carrying out homonymy grouping on the condition values corresponding to all test paper topics, and calculating the number of the same condition values in each group to obtain a binary value sequence of the condition values corresponding to the topics (the number of the same condition values).
Then, the question database is queried according to the corresponding question number of each condition value in the binary value sequence. Firstly, one condition value in the binary value sequence is used as a query condition, and then the condition value in the query condition is matched with the condition value of the test questions in the test question database in the similarity. If the question with the similarity of 100% is inquired, that is, the question with the same condition value is inquired in the step S105, the question is taken out, then whether the number of the questions taken out based on the condition value in the current inquiry condition is smaller than the number of the questions corresponding to the condition value in the binary value sequence is judged, if the number of the taken-out questions is smaller than the number of the questions corresponding to the binary value sequence, the inquiry is continued by using the condition value; if the number of the extracted questions is equal to the number of the questions corresponding to the binary value sequence, it indicates that the number of the questions corresponding to the condition value is enough, and the next condition value in the binary value sequence needs to be obtained as the query condition for continuous query. And querying in the query mode until each condition value in the binary value sequence queries the topic of the corresponding topic number. Finally, all the taken questions are corresponding to the question sequence of the test paper in the temporary table to generate the test paper.
Further, no matter whether the condition values are subjected to the same-value grouping or not, in the process of inquiring the test question database according to the condition values, the situation that questions with the condition values in the inquiry conditions and the condition values of the test questions in the test question database having the similarity of 100% cannot be found may occur. In this case, the embodiment of the present application may reduce the requirement for matching, that is, according to the importance degree of the preset generating condition, the preset generating condition with the minimum importance degree is removed first, and query is performed using other preset generating conditions. The specific implementation is as follows: carrying out mantissa removing treatment on the condition numerical values, wherein the mantissa removing treatment is to divide the condition numerical values by a proportional value (the proportional value is a proportional value between two corresponding column coefficients which are least important in the current preset generation condition) and obtain the condition numerical values after mantissa removing by rounding, and then continuously inquiring the test question database according to the condition numerical values after mantissa removing; if the questions with the same condition numerical values as the conditions numerical values after the mantissas are removed can be inquired, taking out the questions from the test question database; if the topics which are the same as the condition values after the mantissa removal cannot be queried, the mantissa removal processing is performed on the condition values after the mantissa removal again (the condition values after the mantissa removal processing are the preset generation conditions from which the least important is removed, so when the condition values after the mantissa removal processing are performed again, the two least important preset generation conditions are selected from the other preset generation conditions from which the least important preset generation conditions are removed to determine the proportion value) until topics corresponding to all the condition values are queried.
For explaining the reasonability of the mantissa removing processing, the lower the importance degree of the preset generating condition is, the lower the corresponding column coefficient is, and the lower the column coefficient is, the lower the number of bits is corresponding to the condition value, so that the preset generating condition with the lowest importance degree at present can be removed by performing the mantissa removing processing on the condition value every time.
Further, after step S106, if the user is not satisfied with some questions in the generated test paper and needs to replace the questions, a replacement command may be provided, and when the software or the system background receives the replacement command for replacing the questions in the test paper; the question bank can be inquired again according to the condition value corresponding to the question contained in the replacement command; and recommending the inquired questions as the replacement questions. The specific implementation manner of re-querying the test question library is the same as the query manner when generating the test paper, and reference may be made to the description related to the foregoing description, which is not repeated herein.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than here.
According to an embodiment of the present application, there is also provided an apparatus 200 for generating a test paper for implementing the method of fig. 1, as shown in fig. 2, the apparatus includes: an obtaining unit 201, configured to obtain a total number of questions required for a test paper, preset generation conditions, categories included in each preset generation condition, and a number of questions required for different categories; a table generating unit 202, configured to determine the number of rows and columns of a table according to the total number of questions and the number of preset generating conditions, and fill the table with the digitized results of different categories according to the number of questions required by the different categories to generate a temporary table; a calculating unit 203, configured to perform row-column combination calculation on the temporary table, where each row obtains a condition value, and each condition value corresponds to a test paper question; the query unit 204 is configured to query the test question database according to each condition value; a fetching unit 205, configured to fetch the queried question that is the same as the condition value from the test question database; and a test paper generating unit 206, configured to generate test papers by corresponding all the retrieved topics to the test paper topic order in the temporary table.
Specifically, the specific process of implementing the functions of each unit and module in the device in the embodiment of the present application may refer to the related description in the method embodiment, and is not described herein again.
From the above description, it can be seen that, in the apparatus for generating a test paper according to the embodiment of the present application, first, the total number of questions required for the test paper, the preset generation conditions, the categories included in each preset generation condition, and the number of questions required for different categories are obtained; then, determining the number of rows and columns of the table according to the total number of the questions and the number of preset generating conditions, and filling the numerical results of different categories into the table according to the number of the questions required by different categories to generate a temporary table; performing row-column combination calculation on the temporary table, wherein each row obtains a condition value, and each condition value corresponds to a test paper question; inquiring the test question database according to each condition value; taking out the inquired questions with the same condition numerical values from the test question database; finally, all the questions taken out are corresponding to the question sequence of the test paper in the temporary table to generate the test paper. It can be seen that, when the test paper is generated, a temporary table is generated according to some conditions (total question number, preset generation conditions, categories included in each preset generation condition and question numbers required by different categories), the table realizes the randomization and serialization of the test paper, and a condition value is calculated for each test paper question based on the table, wherein the randomness corresponding to the question is basically determined in the step, and a pseudo-random effect is achieved. And then, based on the condition value, similarity matching calculation is carried out on the questions in the test paper database, the questions in the test paper to be generated can be taken out as long as the condition values are the same, compared with a mode of matching and obtaining item by item, matching calculation with each question in the test paper database is not needed, the query times are greatly reduced, the obtaining difficulty is simplified, and therefore the efficiency of generating the test paper can be greatly improved.
Further, as shown in fig. 3, the table generating unit 202 further includes: a first determining module 2021, configured to determine the required number of questions as a category filling amount corresponding to a category; the filling module 2022 is configured to randomly fill the digitized results of the class filling amounts of different classes that belong to the same preset generation condition into a column corresponding to each preset generation condition to generate the temporary table.
Further, the calculating unit 203 is further configured to: and multiplying the numerical result of each column in the temporary table by the column coefficient of each column respectively, and then performing summation calculation.
Further, as shown in fig. 3, the extracting unit 205 further includes: a tail number removing module 2051, configured to, if a question that is the same as the condition value is not queried, perform tail number removing processing on the condition value; the first query module 2052 is configured to continue querying the test question database according to the condition value after the mantissa is removed; a fetching module 2053, configured to fetch a question from the test question database if a question with the same condition value as the removed mantissa can be queried; the tailing number removing module 2051 is further configured to, if a topic that is the same as the condition value after the tailing number is removed cannot be queried, perform tailing number removing processing on the condition value after the tailing number is removed again until topics corresponding to all condition values are queried.
Further, as shown in fig. 3, the querying unit 204 includes: the grouping module 2041 is configured to perform homonymous grouping on the condition values corresponding to all test paper topics to obtain a binary value sequence of the condition values corresponding to the topics; the second query module 2042 is configured to query the test question database for the number of corresponding questions according to each condition value in the binary value sequence.
Further, as shown in fig. 3, the apparatus further includes: the setting unit 207 is configured to set column coefficients of different number levels according to the importance degree of each preset generation condition and the number of categories included in each preset generation condition.
Further, as shown in fig. 3, the setting unit 207 includes: a second determining module 2071, configured to determine a ratio value between column coefficients according to an order of magnitude of the number of categories in the preset generation condition with the largest number of categories; a third determining module 2072, configured to sequentially determine, according to the importance degree of each preset generating condition, terms in the equal ratio series with a first term being 1 and a common ratio being the ratio value as the series coefficients of different number levels.
Further, as shown in fig. 3, the mantissa removing module 2051 is further configured to: and dividing the condition numerical value by the proportional value and rounding to obtain the condition numerical value after mantissa removal.
Further, as shown in fig. 3, the apparatus further includes: a receiving unit 208, configured to receive a replacement command for replacing the questions in the test paper after the test paper is generated in a manner that all the questions to be taken out correspond to the test paper question orders in the temporary table; a re-query unit 209, configured to re-query the question bank according to the condition value corresponding to the question included in the replacement command; and the recommending unit 210 is configured to recommend the newly queried title as a replacement title.
Further, the preset generating conditions comprise one or more of a question type, a knowledge point, difficulty, whether the preset generating conditions are used or not and a repetition rate.
Specifically, the specific process of implementing the functions of each unit and module in the device in the embodiment of the present application may refer to the related description in the method embodiment, and is not described herein again.
According to an embodiment of the present application, there is further provided a computer-readable storage medium, where the computer-readable storage medium stores computer instructions for causing the computer to execute the method for generating test paper in the above method embodiment.
According to an embodiment of the present application, there is also provided an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform the method of generating test paper in the above method embodiment.
It should be obvious to those skilled in the art that the modules or steps of the present application described above can be implemented by a general-purpose computing device, they can be centralized on a single computing device or distributed on a network composed of a plurality of computing devices, and they can alternatively be implemented by program code executable by the computing device, so that they can be stored in a storage device and executed by the computing device, or they can be separately manufactured as integrated circuit modules, or a plurality of modules or steps in them can be manufactured as a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method of generating a test paper, the method comprising:
acquiring the total number of questions required by the test paper, preset generation conditions, categories contained in each preset generation condition and the number of questions required by different categories;
determining the number of rows and columns of the table according to the total number of the questions and the number of preset generating conditions, and filling the numerical results of different categories into the table according to the number of the questions required by different categories to generate a temporary table;
performing row and column combination calculation on the temporary table, wherein each row obtains a condition value, and each condition value corresponds to a test paper question;
inquiring a test question database according to each condition value;
taking out the inquired questions with the same condition numerical values from a test question database;
and generating the test paper by corresponding all the taken-out questions to the question sequence of the test paper in the temporary table.
2. The method of claim 1, wherein the populating the table with the digitized results of different categories based on the number of questions required for the different categories to generate a temporary table comprises:
determining the required question number as a category filling amount corresponding to a category;
and randomly filling the numerical results of the class filling quantities of different classes belonging to the same preset generation condition into the column corresponding to each preset generation condition to generate the temporary table.
3. The method of generating test paper of claim 1, wherein said performing row-merge-calculation on said temporary table comprises:
and multiplying the numerical result of each column in the temporary table by the column coefficient of each column respectively, and then performing summation calculation.
4. The method for generating test paper according to claim 1, wherein the retrieving the queried question with the same value as the condition value from the question database further comprises:
if the questions which are the same as the condition numerical values cannot be inquired, carrying out tailing number removing processing on the condition numerical values;
continuously inquiring the test question database according to the condition value after the mantissa is removed;
if the questions with the same condition value as the denoised condition value can be inquired, taking out the questions from the test question database;
and if the topics which are the same as the condition values after the tail number removal cannot be inquired, carrying out tail number removal processing on the condition values after the tail number removal again until the topics corresponding to all the condition values are inquired.
5. The method of generating test paper according to claim 1, wherein said querying the test question database according to each condition value comprises:
carrying out homonymy grouping on the condition values corresponding to all test paper questions to obtain a binary value sequence of the question numbers corresponding to the condition values;
and inquiring the corresponding question number of the test question database according to each condition value in the binary value sequence.
6. The method of generating a test paper according to claim 3 or 4, wherein the method further comprises:
and setting different number levels of column coefficients according to the importance degree of each preset generation condition and the number of categories contained in each preset generation condition.
7. The method of claim 6, wherein the setting of different levels of column coefficients according to the importance of each preset generating condition and the number of categories included in each preset generating condition comprises:
determining a proportional value between column coefficients according to the magnitude of the category number in the preset generation condition with the maximum category number;
and sequentially determining the terms in the equal ratio number series with the first term of 1 and the common ratio of the equal ratio number series as the series coefficients of different number levels according to the importance degree of each preset generating condition.
8. An apparatus for generating a test paper, the apparatus comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the total number of questions required by the test paper, preset generation conditions, categories contained in each preset generation condition and the number of questions required by different categories;
the table generating unit is used for determining the number of rows and columns of the table according to the total number of the questions and the number of preset generating conditions, and filling the numerical results of different categories into the table according to the number of the questions required by different categories to generate a temporary table;
the calculation unit is used for performing row-column combination calculation on the temporary table, each row obtains a condition value, and each condition value corresponds to a test paper subject;
the query unit is used for querying the test question database according to each condition value;
the extracting unit is used for extracting the inquired questions which are the same as the condition numerical values from the test question database;
and the test paper generating unit is used for generating test papers by corresponding all the taken questions to the test paper questions in the temporary table.
9. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of generating test sheets of any one of claims 1 to 7.
10. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the method of generating a test paper of any one of claims 1 to 7.
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