CN116662305A - Question bank management method, system, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses a method, a system, electronic equipment and a storage medium for managing a question library, which comprise the steps of selecting a question to be input and classifying the content of the question to be input; extracting keywords from the classified topics, and taking the extracted keywords as the labels of the topics; performing topic duplication elimination processing based on topic labels; and obtaining the topics which are confirmed to be input into the topic library through structural judgment and content quality judgment. By the aid of the scheme, the question quality can be improved from different dimensions, and the auditing rate is improved.
Description
Technical Field
The application relates to the technical field of question bank systems, in particular to a question bank management method, a system, electronic equipment and a storage medium.
Background
The conventional question library system performs classification management according to categories, and the quality of questions cannot be guaranteed, including the repetition rate, the accuracy rate and the like of the questions. And no strict auditing flow specification exists, and corresponding auditing scheme formulation is required to be carried out on the existing question bank content according to different question bank directions and different auditing flows and standards. Through the examination of patent data, no related data suitable for the treatment design of the question bank is found.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides a question bank management method, a system, electronic equipment and a storage medium, wherein the requirement of computing equipment is low, the study design of question bank management is developed, the question quality can be improved from different dimensions, and the auditing rate is improved, and the method is mainly characterized in that:
(1) The examination of the question bank is more organized and flow-processed. After uploading the questions, firstly, the questions are subjected to system duplication elimination, checking and online, analysis, knowledge points, labels and the like are arranged and added, and star-level standards and the like related to the questions are obtained through the integrity of the questions, so that the questions are checked and checked with regularity.
(2) The accuracy of the question bank is improved. The accuracy of the questions is further demonstrated through a series of examination of the questions, such as the addition of the analysis, the description of the link sources and the like.
In order to achieve the above purpose, the technical scheme of the application is as follows:
a method of question bank management, the method comprising:
selecting the questions to be input, and classifying the contents of the questions to be input;
extracting keywords from the classified topics, and taking the extracted keywords as the labels of the topics;
performing topic duplication elimination processing based on topic labels;
and obtaining the topics which are confirmed to be input into the topic library through structural judgment and content quality judgment.
Preferably, the selecting the questions to be input and classifying the contents of the questions to be input include:
reading text data of at least one topic category, wherein the text data is provided with a category label;
learning the text data and generating a classifier corresponding to the topic category;
and calling the classifier to scan the questions to be classified, and judging the category to which the questions to be classified belong.
Preferably, the keyword extraction of the stem after the classification treatment includes: extracting keywords in the stem by adopting a keyword extraction algorithm, and correspondingly generating a keyword label for each keyword; and manually checking the keyword label extracted by the algorithm.
Preferably, the topic-based tag performs topic ranking processing, including:
selecting noun words in the keyword label information of one to-be-input question and noun words in the keyword label information of other to-be-input questions for comparison, and generating a first repeated mark if the repeated number of the current noun words exceeds a preset first threshold value;
when the first repeated mark exists, comparing the adjective vocabulary in the keyword label information of the current to-be-input question with the adjective vocabulary in the keyword label information of other to-be-input questions, and generating a second repeated mark if the repeated number of the current adjective vocabulary exceeds a preset second threshold;
when the second repeated mark exists, comparing the data parameters in the key word label information of the current question to be recorded with the data parameters in the key word label information of other questions to be recorded, and generating a third repeated mark if the repeated number of the data parameters exceeds a preset third threshold value;
when the third repeat mark is present, a topic repeat alert is generated and the repeated topic is deleted.
Preferably, the obtaining the questions of the confirmed input question bank through structural judgment and content quality judgment includes:
searching whether links, text descriptions and correct rates are contained in the analysis, setting dominant items and inferior items, and customizing star rating standards for topics; let n denote the title of the highest star level; let m=n-2, m denote the title of the basic star stage; performing star grade grading on the topics based on the dominant items and the subtractive items of the topics;
and secondly, judging the content quality based on the aspects of accuracy, feedback error correction mechanism, set assessment area and professional teacher examination.
Further, the advantage item includes: parsing the links and parsing the textual description; wherein the parsing link includes: official links, community links, and other links; the parsing text description includes: parsing for each option, parsing for correct answers, and parsing for wrong answers.
Further, the disadvantaged terms include: the correct rate and the analysis link have the information of link failure or mismatching of the link content and the topic; wherein the accuracy comprises: the number of pairs of answers and the percentage of the number of answers.
A question bank management system, comprising:
the input module is used for selecting the questions to be input and classifying the contents of the questions to be input;
the acquisition module is used for extracting keywords from the classified topics, and taking the extracted keywords as the labels of the topics;
the screening module is used for carrying out topic duplication elimination processing based on the topic labels;
and the evaluation module is used for obtaining the questions confirmed to be input into the question bank through structural judgment and content quality judgment.
An electronic device comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to perform the steps of the question bank management method of any of claims 1 to 7.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the question bank management method of any of claims 1 to 7.
Compared with the prior art, the application has the beneficial effects that:
the application provides a method, a system, electronic equipment and a storage medium for managing a question library, which comprise the steps of selecting a question to be input and classifying the content of the question to be input; extracting keywords from the classified stems, taking the extracted keywords as the labels of the topics, and carrying out topic duplication eliminating treatment based on the labels of the topics; and the repetition rate of the question bank is reduced. And the subjects are subjected to similarity matching based on the labels, the subjects are subjected to similarity matching, and the speed is improved.
Finally, the topics subjected to the duplication elimination treatment are subjected to structural judgment and content quality judgment to obtain the topics confirmed to be input into the topic library; the examination of the question bank is enhanced step by step from basic to perfect, and the objectivity and the strictness of the test questions are ensured; thereby improving the quality of the questions and enabling the question bank to be more efficient and authoritative.
The problem library management scheme provided by the application can establish a problem library highly related to the proposition, and realizes the automation, the programming and the optimization of the proposition process through the large-scale collection, the analysis, the storage, the retrieval, the optimization and the combination of the problem information.
The scheme provided by the application has low requirements on computing equipment, greatly reduces data redundancy, can improve the question quality from different dimensions and improves the auditing rate:
(1) The examination of the question bank is more organized and flow-processed. After uploading the questions, firstly, the questions are subjected to system duplication elimination, checking and online, analysis, knowledge points, labels and the like are arranged and added, and star-level standards and the like related to the questions are obtained through the integrity of the questions, so that the questions are checked and checked with regularity.
(2) The accuracy of the question bank is improved. The accuracy of the questions is further demonstrated through a series of examination of the questions, such as the addition of the analysis, the description of the link sources and the like.
Drawings
In order to more clearly illustrate the embodiments of the present application 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. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flow chart of a method for managing a question bank according to the present application;
FIG. 2 is a schematic diagram of a library management system according to the present application;
FIG. 3 is a schematic diagram of an electronic device module according to an embodiment of the present application;
reference numerals illustrate: 31. a processor; 32. a communication bus; 33. a computer program; 34. a user interface; 35. a network interface; 36. a memory.
Detailed Description
Embodiments of the technical scheme of the present application will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and thus are merely examples, and are not intended to limit the scope of the present application.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which the application pertains.
Example 1: the specific embodiment of the application provides a question bank management method, which comprises the following steps:
s1, selecting a question to be input, and classifying the content of the question to be input;
s2, extracting keywords from the classified topics, and taking the extracted keywords as the labels of the topics;
s3, performing topic duplication elimination processing based on topic labels;
s4, the topics subjected to the duplication elimination treatment are subjected to structural judgment and content quality judgment to obtain the topics confirmed to be input into the topic library.
In step S1, the selecting the questions to be input, and classifying the contents of the questions to be input includes:
reading text data of at least one topic category, wherein the text data is provided with a category label;
learning the text data and generating a classifier corresponding to the topic category;
and calling the classifier to scan the questions to be classified, and judging the category to which the questions to be classified belong.
After the classification processing in step S1, the classification result may be entered into an Excel table according to a predetermined template.
The classifier is an SVM classifier, and the construction method of the SVM classifier comprises the following steps: after the TF-IDF algorithm is adopted to extract and acquire data characteristics, the characteristic vectors are input into the SVM algorithm model for training, and a classification model can be obtained. When the system needs to classify the text data of the new topic class, the new topic is input into the model, and a classification result can be obtained.
The basic idea of SVM algorithm classification is to maximize the separation between classes by finding the optimal hyperplane. Assume that the training sample is (x i ，y i )，i＝1，2，...，1，x∈R n Y ε { +1, -1},1 represents the number of samples and n represents the input dimension;
when the sample linearity is variable, the optimal hyperplane can be expressed as:
wx+b＝0
at this time, the liquid crystal display device, classification interval is 2/|| w is the same as the original one. When the classification interval is largest when the classification is smallest, the classification can be described as:
min||w|| 2 /2
s.t.y i (w.x i +b)-1≥0，i＝1，2，...，l
when the training sample set is linearly infeasible, a non-negative relaxation variable xi is introduced i I=1, 2, where, and l, solving the optimal hyperplane as the formula.
s.t.y i (w.x i +b)-1≥1-ξ i ，ξ i ≥0，i＝1，2，...，l
Where C represents a penalty parameter, the larger its value represents the greater penalty for error classification. Solving the above equation by using lagrange multiplier method to obtain the optimal decision function:
where a represents a lagrange coefficient.
The feature extraction method for extracting and acquiring data features by adopting the TF-IDF algorithm comprises the following steps:
feature extraction is to extract numerical features from the acquired text data and convert the numerical features into vectors. Common text data sign extraction algorithms include TF-IDF and Doc2vec based feature extraction. The TF-IDF algorithm is an algorithm for extracting data features by calculating word weights, is more prone to retaining important words and filtering common words, and can better represent the data features, so that the method is used for extracting the features of the acquired text data.
The mathematical expression of the TF-IDF algorithm can be expressed by the following formula:
TF-IDF＝tf×idf
tf(w，D)＝f wD
where tf represents word frequency and idf represents inverse document frequency; f (f) wD Representing the frequency of word w in topic D; c represents the total number of topics, idf (t) represents the idf of word t, df (t) represents the topic number frequency of t.
Using tfidf to represent the euclidean L2 norm of the matrix, the eigenvectors of TF-IDF can be expressed as:
step S2, extracting keywords from the stems subjected to the classification processing, wherein the step S2 comprises the steps of taking the extracted keywords as the labels of the topics: extracting keywords in the stem by adopting a keyword extraction algorithm, and correspondingly generating a keyword label for each keyword; and manually checking the keyword label extracted by the algorithm.
In step S3, the task-based tag performs task duplication elimination processing, including:
selecting noun words in the keyword label information of one to-be-input question and noun words in the keyword label information of other to-be-input questions for comparison, and generating a first repeated mark if the repeated number of the current noun words exceeds a preset first threshold value;
when the first repeated mark exists, comparing the adjective vocabulary in the keyword label information of the current to-be-input question with the adjective vocabulary in the keyword label information of other to-be-input questions, and generating a second repeated mark if the repeated number of the current adjective vocabulary exceeds a preset second threshold;
when the second repeated mark exists, comparing the data parameters in the key word label information of the current question to be recorded with the data parameters in the key word label information of other questions to be recorded, and generating a third repeated mark if the repeated number of the data parameters exceeds a preset third threshold value;
when the third repeat mark is present, a topic repeat alert is generated and the repeated topic is deleted.
The similarity matching of the topics based on the labels is compared with the similarity matching of the topics, so that the speed and accuracy are improved.
In step S4, the obtaining the questions of the confirmed input question bank through structural judgment and content quality judgment includes:
searching whether links, text descriptions and correct rates are contained in the analysis, setting dominant items and inferior items, and customizing star rating standards for topics; let n denote the title of the highest star level; let m=n-2, m denote the title of the basic star stage; performing star grade grading on the topics based on the dominant items and the subtractive items of the topics;
and secondly, judging the content quality based on the aspects of accuracy, feedback error correction mechanism, set assessment area and professional teacher examination.
The advantage items include: parsing the links and parsing the textual description; wherein the parsing link includes: official links, community links, and other links; the parsing text description includes: parsing for each option, parsing for correct answers, and parsing for wrong answers.
The disadvantaged terms include: the correct rate and the analysis link have the information of link failure or mismatching of the link content and the topic; wherein the accuracy comprises: the number of pairs of answers and the percentage of the number of answers.
For example: 1. structural evaluation: star grading
(1) Customizing star level standards for the topics, and judging whether links and text descriptions are contained in the analysis; accuracy rate (answer pair number-
Number of answers), etc.;
(2) 5 stars are excellent topics, 3 stars are taken as the basis, the advantages are added items, and the disadvantages are subtracted items;
advantages are:
1) Resolving links: a. official links; b. linking communities; c. other links
2) Analyzing the text description: a. parsing for each option; b. resolving the correct answer; c. and analyzing the wrong answer.
Disadvantages:
1) Resolving links: a. a link failure; b. the link content does not match the title.
2) Accuracy rate: number of answers/number of answers
A.80% > correct rate >60%
B. Accuracy >40%
C. Correct rate >20%
D. Accuracy >0%
2. Content quality judgment: correct rate, feedback error correction mechanism, set comment area, professional teacher audit
(1) Accuracy rate: the system obtains the correct rate by calculating the answer number and the answer number, and the question quality with the correct rate lower than 50% is relatively lower and is checked preferentially.
(2) Feedback error correction mechanism: providing feedback and error correction mechanism, encouraging students to correct errors, and laterally feeding back the quality of questions
(3) Setting an evaluation area: the question brushing interface displays an evaluation area, mobilizes enthusiasm of people, participates in question discussion, and improves the question quality.
(4) Professional teacher checks: based on external factors (error correction and feedback of students) +internal factors (internal proofreading), the problematic questions are obtained, and professional teachers are arranged to answer, so that the quality of the questions is further improved.
Example 2: based on the same technical concept, the present application provides a question bank management system according to embodiment 1, as shown in fig. 2, the system includes:
the input module is used for selecting the questions to be input and classifying the contents of the questions to be input;
the acquisition module is used for extracting keywords from the classified topics, and taking the extracted keywords as the labels of the topics;
the screening module is used for carrying out topic duplication elimination processing based on the topic labels;
and the evaluation module is used for obtaining the questions confirmed to be input into the question bank through structural judgment and content quality judgment.
Example 3: the embodiment of the application also provides an electronic device and a computer-readable storage medium corresponding to embodiments 1 and 2.
The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the computer program when executed by the processor causes the processor to execute the steps of the question bank management method in any one of S1-S4.
As shown in fig. 3, the electronic device may include: at least one processor 31, at least one network interface 35, a user interface 34, a memory 36, at least one communication bus 32.
Wherein the communication bus 32 is used to enable connected communication between these components.
The user interface 34 may include a Display screen (Display), a Camera (Camera), and the optional user interface 34 may further include a standard wired interface, a wireless interface, among others.
The network interface 35 may optionally include a standard wired interface, a wireless interface (e.g., WIFI interface), among others.
Wherein the processor 31 may comprise one or more processing cores. The processor 31 connects various parts within the overall server using various interfaces and lines, performs various functions of the server and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 36, and invoking data stored in the memory 36. Alternatively, the processor 31 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 31 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 31 and may be implemented by a single chip.
The Memory 36 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 36 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 36 may be used to store instructions, programs, code, a set of codes, or a set of instructions. The memory 36 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 36 may alternatively be at least one memory device located remotely from the aforementioned processor 31. As shown in fig. 3, the memory 36, which is a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program of a multi-screen frame synchronization method and system.
In the electronic device shown in fig. 3, the user interface 34 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 31 may be configured to invoke an application program in the memory 36 that stores a question bank management method that, when executed by one or more processors 31, causes the electronic device to perform the method as described in one or more of the above-described embodiment steps S1-S4.
It will be clear to a person skilled in the art that the solution according to the application can be implemented by means of software and/or hardware. "Unit" and "module" in this specification refer to software and/or hardware capable of performing a specific function, either alone or in combination with other components, such as Field programmable gate arrays (Field-Programma BLE Gate Array, FPGAs), integrated circuits (Integrated Circuit, ICs), etc.
A computer readable storage medium storing a computer program which when executed by a processor performs the steps of the question bank management method of any one of S1-S4.
In particular, the processor may be a central processing unit (Central Processing Unit, CPU), which may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein the code of the computer program may be in the form of source code, object code, executable files or some intermediate form, etc.
The computer-readable storage medium may include Cache (RAM), high-speed Random Access Memory (RAM), such as the common double data rate synchronous dynamic random access memory (DDR SDRAM), and may also include non-volatile memory (NVRAM), such as one or more read-only memory (ROM), magnetic disk storage devices, flash memory (Flash) memory devices, or other non-volatile solid-state memory devices, such as compact discs (CD-ROM, DVD-ROM), floppy disks, or data tapes, among others.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application 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 scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description.
Claims (10)
1. A method for managing a question bank, the method comprising:
selecting the questions to be input, and classifying the contents of the questions to be input;
extracting keywords from the classified topics, and taking the extracted keywords as the labels of the topics;
performing topic duplication elimination processing based on topic labels;
and obtaining the topics which are confirmed to be input into the topic library through structural judgment and content quality judgment.
2. The method of claim 1, wherein selecting the title to be entered and classifying the content of the title to be entered comprises:
reading text data of at least one topic category, wherein the text data is provided with a category label;
learning the text data and generating a classifier corresponding to the topic category;
and calling the classifier to scan the questions to be classified, and judging the category to which the questions to be classified belong.
3. The method of claim 1, wherein extracting keywords from the categorized stems and using the extracted keywords as tags for the topics comprises: extracting keywords in the stem by adopting a keyword extraction algorithm, and correspondingly generating a keyword label for each keyword; and manually checking the keyword label extracted by the algorithm.
4. The system of claim 1, wherein the performing the topic de-duplication process based on the topic tag comprises:
selecting noun words in the keyword label information of one to-be-input question and noun words in the keyword label information of other to-be-input questions for comparison, and generating a first repeated mark if the repeated number of the current noun words exceeds a preset first threshold value;
when the first repeated mark exists, comparing the adjective vocabulary in the keyword label information of the current to-be-input question with the adjective vocabulary in the keyword label information of other to-be-input questions, and generating a second repeated mark if the repeated number of the current adjective vocabulary exceeds a preset second threshold;
when the second repeated mark exists, comparing the data parameters in the key word label information of the current question to be recorded with the data parameters in the key word label information of other questions to be recorded, and generating a third repeated mark if the repeated number of the data parameters exceeds a preset third threshold value;
when the third repeat mark is present, a topic repeat alert is generated and the repeated topic is deleted.
5. The method of claim 1, wherein obtaining the topics determined to be entered into the topic library by structural and content quality evaluation of the topics subjected to the de-duplication process comprises:
searching whether links, text descriptions and correct rates are contained in the analysis, setting dominant items and inferior items, and customizing star rating standards for topics; let n denote the title of the highest star level; let m=n-2, m denote the title of the basic star stage; performing star grade grading on the topics based on the dominant items and the subtractive items of the topics;
and secondly, judging the content quality based on the aspects of accuracy, feedback error correction mechanism, set assessment area and professional teacher examination.
6. The method of claim 5, wherein the dominance term comprises: parsing the links and parsing the textual description; wherein the parsing link includes: official links, community links, and other links; the parsing text description includes: parsing for each option, parsing for correct answers, and parsing for wrong answers.
7. The method of claim 5, wherein the disadvantaged item comprises: the correct rate and the analysis link have the information of link failure or mismatching of the link content and the topic; wherein the accuracy comprises: the number of pairs of answers and the percentage of the number of answers.
8. A question bank management system, comprising:
the input module is used for selecting the questions to be input and classifying the contents of the questions to be input;
the acquisition module is used for extracting keywords from the classified topics, and taking the extracted keywords as the labels of the topics;
the screening module is used for carrying out topic duplication elimination processing based on the topic labels;
and the evaluation module is used for obtaining the questions confirmed to be input into the question bank through structural judgment and content quality judgment.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of a question bank management method as claimed in any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the question bank management method of any of claims 1 to 7.
Priority Applications (1)
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