CN117216195B - Intelligent paper-making method, system, equipment and storage medium for course examination of universities - Google Patents
Intelligent paper-making method, system, equipment and storage medium for course examination of universities Download PDFInfo
- Publication number
- CN117216195B CN117216195B CN202311479064.XA CN202311479064A CN117216195B CN 117216195 B CN117216195 B CN 117216195B CN 202311479064 A CN202311479064 A CN 202311479064A CN 117216195 B CN117216195 B CN 117216195B
- Authority
- CN
- China
- Prior art keywords
- question
- soft
- requirement
- representing
- questions
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000004804 winding Methods 0.000 claims abstract description 20
- 230000006870 function Effects 0.000 claims description 84
- 238000000605 extraction Methods 0.000 claims description 15
- 238000010276 construction Methods 0.000 claims description 12
- 238000004891 communication Methods 0.000 claims description 11
- 230000003247 decreasing effect Effects 0.000 claims description 5
- 230000000694 effects Effects 0.000 description 8
- 238000012360 testing method Methods 0.000 description 8
- 238000011835 investigation Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000670 limiting effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 230000002829 reductive effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an intelligent paper-making method, system, equipment and storage medium for course examination in universities, which comprises the steps of constructing a question dictionary, a question difficulty dictionary, a chapter dictionary and an error-prone question set; constructing a soft requirement moderate function based on the question dictionary, the question difficulty dictionary, the chapter dictionary and the error prone question set; calculating a soft requirement moderate value by adopting a soft requirement moderate function and judging that the soft requirement meets the condition; under the condition of meeting the hard requirement and the soft requirement, constructing an exploration function according to the moderate value of the soft requirement; and calculating the maximum satisfaction rate of each extracted question by adopting an exploration function, and carrying out winding by adopting the questions corresponding to the maximum satisfaction rate. The invention can improve the winding efficiency and the winding quality.
Description
Technical Field
The invention relates to the technical field of intelligent scroll grouping, in particular to an intelligent scroll grouping method, system, equipment and storage medium for course examination in universities.
Background
During the annual period or the end of the period of the university, staff is required to make paper for each examination question, and then examination is carried out according to the paper making result. When the method is used for assembling, both hard requirements of the assembled coil and soft requirements such as rationality of the assembled coil are considered, most of traditional coiling methods only draw questions from the hard requirements (namely, the total score requirements and the score requirements of all types are met), and few methods consider soft requirements such as difficulty distribution of the assembled coil, but do not consider in the aspects of balance, chapter coverage, error-prone question extraction proportion and the like, so that the coiling effect is not good.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides an intelligent scroll grouping method, system, equipment and storage medium for the course examination of a college, which can improve scroll grouping efficiency and scroll grouping quality.
In a first aspect, an embodiment of the present invention provides an intelligent college course examination paper making method, where the intelligent college course examination paper making method includes:
constructing a question dictionary, a question difficulty dictionary, a chapter dictionary and an error-prone question set;
constructing a soft requirement moderate function based on the question dictionary, the question difficulty dictionary, the chapter dictionary and the error prone question set;
calculating a soft requirement moderate value by adopting the soft requirement moderate function and judging that the soft requirement meets the condition;
under the condition of meeting the hard requirement and the soft requirement, constructing an exploration function according to the soft requirement moderate value;
and calculating the maximum satisfaction rate of each extracted question by adopting the exploration function, and carrying out winding by adopting the questions corresponding to the maximum satisfaction rate.
Compared with the prior art, the first aspect of the invention has the following beneficial effects:
the method constructs a soft requirement moderate function based on the question dictionary, the question difficulty dictionary, the chapter dictionary and the error prone question set, judges that the soft requirement meets the condition by adopting the soft requirement moderate function, and improves the comprehensiveness of the investigation of students and the grasp of the students on knowledge points by comprehensively considering the question, the question difficulty, the chapter and the error prone questions; under the condition of meeting the hard requirement and the soft requirement, constructing an exploration function according to the moderate value of the soft requirement, calculating the maximum satisfaction rate of each extracted question by adopting the exploration function, and assembling the questions corresponding to the maximum satisfaction rate, and under the condition of definitely combining the evaluation standards of each test paper, effectively evaluating each extracted question to the greatest extent by adopting exploratory calculation, thereby improving the soft effect of the assembled paper, further improving the efficiency of the assembled paper and the quality of the assembled paper.
According to some embodiments of the invention, the soft-demand moderation function is constructed by:
wherein,indicates simultaneous satisfaction of->Mean coverage, +_for representing number of questions per question type in the question dictionary>Representing the question dictionary->Middle->The first ratio of the number of questions of the question type to the total number of questions of the group roll, +.>Representing the minimum value of said first ratio, < >>Represents the maximum value of said first ratio, +.>Represents the average satisfaction rate of the question difficulty level and the +.>A dictionary for indicating the difficulty level of the subject>Middle->The first question of the difficulty-like degree>The number of questions of the question type accounts for +.>Second ratio of total number of questions of class difficulty/ease/questions>Representing the minimum value of said second ratio, < >>Represents the maximum value of said second ratio, +.>The average coverage of the chapter is indicated,representing the chapter dictionary->In->Chapter->The number of questions of the question type accounts for +.>Third ratio of chapter total title, +.>Representing the minimum value of said third ratio,/->Represents the maximum value of said third ratio, +.>Represents the average satisfaction rate of error prone questions, < >>Representing said error prone question set->Middle->The fourth ratio of the number of the questions with error prone questions to the total number of the group roll,/>Representing the minimum value of said fourth ratio, is->Representing the maximum value of said fourth ratio.
According to some embodiments of the invention, the determining soft requirement compliance using the soft requirement fitness function includes:
judging whether the average coverage rate of the number of questions of each type of questions in the question dictionary meets a first soft requirement or not by adopting the soft requirement moderate function;
judging whether the average satisfaction rate of the question difficulty level meets a second soft requirement or not by adopting the soft requirement moderate function;
judging whether the average coverage rate of the chapters meets a third soft requirement or not by adopting the soft requirement moderate function;
judging whether the average satisfaction rate of the error prone questions meets a fourth soft requirement or not by adopting the soft requirement moderate function;
when the first soft requirement, the second soft requirement, the third soft requirement and the fourth soft requirement are all met, the question meets the soft requirement.
According to some embodiments of the invention, the constructing the exploratory function according to the soft requirement moderate value in the case of meeting the hard requirement and the soft requirement includes:
judging the hard requirement and the soft requirement for all the topics to be selected;
under the condition of meeting the hard requirement and the soft requirement, constructing a plurality of exploration channels for each question, and presetting the exploration times of each exploration channel;
and constructing an exploration function according to the exploration times and the soft requirement moderate value.
According to some embodiments of the invention, the heuristic function is constructed by:
wherein,coefficients representing the current extraction topic, +.>Representing a decreasing coefficient +.>Representing the soft demand appropriateness value calculated by the soft demand appropriateness function, < + >>And->Represents learning step size->Indicating +.>Maximum satisfaction rate among all exploration times of each exploration channel,/->Representing all ∈explored>Exploring the maximum satisfaction rate in a channel, +.>And->Initial value equal to->，/>Representing the actual satisfaction rate per extraction of a question,/->Representation->Maximum satisfaction rate in each exploration channel, < >>And->Representing a random number.
According to some embodiments of the invention, before calculating the maximum satisfaction rate for each extracted question using the exploration function, the intelligent college course examination grouping method further includes:
and when each question is extracted, the questions to be selected are inversely ordered according to the frequency of the selected questions for winding.
According to some embodiments of the invention, the frequency is calculated by:
wherein,representing the initial frequency of each topic, +.>Indicating the frequency of each topic selected, < >>Represents the time from the first selection +.>Time to last selection +.>Time difference of->Representing the attenuation coefficient.
In a second aspect, the embodiment of the invention also provides an intelligent college course examination paper making system, which comprises:
the first construction unit is used for constructing a question dictionary, a question difficulty dictionary, a chapter dictionary and an error-prone question set;
the second construction unit is used for constructing a soft requirement moderate function based on the question dictionary, the question difficulty dictionary, the chapter dictionary and the error prone question set;
the data calculation unit is used for calculating a soft requirement moderate value by adopting the soft requirement moderate function and judging that the soft requirement meets the condition;
the third construction unit is used for constructing an exploration function according to the moderate value of the soft requirement under the condition of meeting the hard requirement and the soft requirement;
and the topic group volume unit is used for calculating the maximum satisfaction rate of each extracted topic by adopting the exploration function and grouping the topics corresponding to the maximum satisfaction rate.
In a third aspect, the embodiment of the invention also provides intelligent college course examination paper making equipment, which comprises at least one control processor and a memory, wherein the memory is used for being in communication connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform a college course examination intelligent composition method as described above.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where computer-executable instructions are stored, where the computer-executable instructions are configured to cause a computer to perform an intelligent method for composing a course examination for a college as described above.
It is to be understood that the advantages of the second to fourth aspects compared with the related art are the same as those of the first aspect compared with the related art, and reference may be made to the related description in the first aspect, which is not repeated herein.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of an intelligent method for composing a course examination in a college according to an embodiment of the invention;
FIG. 2 is a flow chart of an intelligent method for composing a course examination at a college in accordance with another embodiment of the present invention;
FIG. 3 is a block diagram of an intelligent college course examination paper system according to an embodiment of the invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In the description of the present invention, the description of first, second, etc. is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, it should be understood that the direction or positional relationship indicated with respect to the description of the orientation, such as up, down, etc., is based on the direction or positional relationship shown in the drawings, is merely for convenience of describing the present invention and simplifying the description, and does not indicate or imply that the apparatus or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In the description of the present invention, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present invention can be determined reasonably by a person skilled in the art in combination with the specific content of the technical solution.
During the annual period or the end of the period of the university, staff is required to make paper for each examination question, and then examination is carried out according to the paper making result. When the method is used for assembling, both hard requirements of the assembled coil and soft requirements such as rationality of the assembled coil are considered, most of traditional coiling methods only draw questions from the hard requirements (namely, the total score requirements and the score requirements of all types are met), and few methods consider soft requirements such as difficulty distribution of the assembled coil, but do not consider in the aspects of balance, chapter coverage, error-prone question extraction proportion and the like, so that the coiling effect is not good.
In order to solve the problems, the invention constructs a soft requirement moderate function based on the question dictionary, the question difficulty dictionary, the chapter dictionary and the error prone question set, judges that the soft requirement meets the condition by adopting the soft requirement moderate function, and improves the comprehensiveness of the investigation of students and the grasp of the knowledge points by comprehensively considering the question, the question difficulty, the chapter and the error prone questions; under the condition of meeting the hard requirement and the soft requirement, constructing an exploration function according to the moderate value of the soft requirement, calculating the maximum satisfaction rate of each extracted question by adopting the exploration function, and assembling the questions corresponding to the maximum satisfaction rate, and under the condition of definitely combining the evaluation standards of each test paper, effectively evaluating each extracted question to the greatest extent by adopting exploratory calculation, thereby improving the soft effect of the assembled paper, further improving the efficiency of the assembled paper and the quality of the assembled paper.
Referring to fig. 1, an embodiment of the present invention provides an intelligent college course examination paper making method, including but not limited to steps S100 to S500, where:
step S100, constructing a question dictionary, a question difficulty dictionary, a chapter dictionary and an error-prone question set;
step S200, constructing a soft requirement moderate function based on the question dictionary, the question difficulty dictionary, the chapter dictionary and the error prone question set;
step S300, calculating a soft requirement moderate value by adopting a soft requirement moderate function and judging that the soft requirement meets the condition;
step S400, under the condition of meeting the hard requirement and the soft requirement, constructing an exploration function according to the moderate value of the soft requirement;
and S500, calculating the maximum satisfaction rate of each extracted question by adopting an exploration function, and carrying out winding by adopting the questions corresponding to the maximum satisfaction rate.
In the embodiment, in order to improve the comprehensiveness of student investigation and improve the knowledge point grasp of students, the soft requirement moderate function is constructed by constructing a question dictionary, a question difficulty dictionary, a chapter dictionary and an error prone question set, and calculating soft requirement moderate values and judging soft requirement compliance conditions by adopting the soft requirement moderate function based on the question dictionary, the question difficulty dictionary, the chapter dictionary and the error prone question set; in order to improve the winding efficiency and the winding quality, the embodiment constructs an exploration function according to the moderate value of the soft requirement under the condition of meeting the hard requirement and the soft requirement, calculates the maximum satisfaction rate of each extracted question by adopting the exploration function, and performs winding by adopting the questions corresponding to the maximum satisfaction rate.
In some embodiments, the soft-demand moderation function is constructed by:
wherein,indicates simultaneous satisfaction of->Represents the average coverage of the number of topics per type in the topic dictionary,dictionary for representing questions->Middle->The number of questions of the type question is a first ratio of the total number of questions of the group volume,represents the minimum of the first ratio, +.>Represents the maximum value of the first ratio,/>Represents the average satisfaction rate of the question difficulty level and the +.>Dictionary for indicating difficulty level of title>Middle->The first question of the difficulty-like degree>The number of questions of the question type accounts for +.>Question type of difficulty and difficultyA second ratio of total title, +.>Representing the minimum value of the second ratio,represents the maximum value of the second ratio, +.>Mean coverage of chapter,/->Representation chapter dictionary->In->Chapter->The number of questions of the question type accounts for +.>Third ratio of chapter total title, +.>Represents the minimum of the third ratio, +.>Represents the maximum value of the third ratio, +.>Represents the average satisfaction rate of error prone questions, < >>Representing error prone topic set->Middle->Error-prone questionsA fourth ratio of the number of questions to the total number of questions in the group roll,/A>Represents the minimum of the fourth ratio, +.>The maximum value of the fourth ratio is indicated.
In the embodiment, whether the questions, the question difficulty, the chapters and the error prone questions are met or not is comprehensively considered, so that the comprehensiveness of the investigation of the students can be improved, and the knowledge points of the students can be mastered.
In some embodiments, determining compliance with soft requirements using soft requirement fitness functions includes:
judging whether the average coverage rate of the number of questions of each type of questions in the question dictionary meets the first soft requirement or not by adopting a soft requirement moderate function;
judging whether the question difficulty average satisfaction rate meets the second soft requirement by adopting a soft requirement moderate function;
judging whether the average coverage rate of the chapters meets a third soft requirement or not by adopting a soft requirement moderate function;
judging whether the average satisfaction rate of the error prone questions meets the fourth soft requirement or not by adopting a soft requirement moderate function;
when the first soft requirement, the second soft requirement, the third soft requirement and the fourth soft requirement are all met, the title meets the soft requirement.
In this embodiment, only when the first soft requirement, the second soft requirement, the third soft requirement and the fourth soft requirement are all met, the soft requirements are met, so that the quality of the winding can be improved.
In some embodiments, where the hard and soft requirements are met, constructing the exploration function from soft requirement moderation values includes:
judging the hardness requirement and the softness requirement of all the topics to be selected;
under the condition of meeting the hard requirement and the soft requirement, constructing a plurality of exploration channels for each question, and presetting the exploration times of each exploration channel;
and constructing an exploration function according to the exploration times and the soft requirement moderate value.
In the embodiment, under the condition of meeting the hard requirement and the soft requirement, an exploration function is constructed, and each extracted question is effectively evaluated to the greatest extent through exploratory calculation, so that the soft effect of the winding can be improved, and the winding efficiency is improved.
In some embodiments, the heuristic function is constructed by:
wherein,coefficients representing the current extraction topic, +.>Representing a decreasing coefficient +.>A soft demand moderate value calculated by representing a soft demand moderate function,/->And->Represents learning step size->Indicating +.>Maximum satisfaction rate among all exploration times of each exploration channel,/->Representing all ∈explored>Exploring the maximum satisfaction rate in a channel, +.>And->Initial value equal to->，/>Representing the actual satisfaction rate per extraction of a question,/->Representation->Maximum satisfaction rate in each exploration channel, < >>And->Representing a random number.
In some embodiments, the college course examination intelligent grouping method further comprises, before calculating the maximum satisfaction rate for each of the extracted topics using the exploration function:
and when each question is extracted, the questions to be selected are inversely ordered according to the frequency of the selected questions for winding.
In this embodiment, the inverse ranking is performed by frequency, so that some questions can be prevented from being drawn, each question drawn is further effectively evaluated, and the soft effect of the group coil is improved.
In some embodiments, the frequency is calculated by:
wherein,representing the initial frequency of each topic, +.>Indicating the frequency of each topic selected, < >>Represents the time from the first selection +.>Time to last selection +.>Time difference of->Representing the attenuation coefficient.
For ease of understanding by those skilled in the art, a set of preferred embodiments are provided below:
according to the embodiment, on the base surface meeting the hardness requirement, the soft requirement of the assembled paper is emphasized, the extracted each question is effectively evaluated to the greatest extent through clear moderate requirement and exploratory calculation, the adaptability and the rationality of the test paper are comprehensively evaluated, and the final assembled paper effect is improved. Referring to fig. 2, the present embodiment specifically includes the following steps:
1. and (5) setting parameters.
Designing a question bank C0; designing the search channel number C1, wherein the default value of the search channel number C1 is 5; a design topic dictionary C2, a topic difficulty degree dictionary C3, a chapter dictionary C4 and an error-prone topic set C5.
2. And customizing the moderate model.
Evaluating a test paper, wherein the test paper is required to meet the hardness requirement (namely, the total score of the test paper cannot exceed a limit value and the score of each type of question cannot exceed a specified value), and also meets the software requirement, and the soft requirement is judged by a moderate function, wherein the moderate function expression is as follows:
wherein,indicates simultaneous satisfaction of->Representing the average coverage rate of the number of questions of each type in the question type dictionary, wherein the default value is more than or equal to 0.9,/for>Representing the question dictionary->Middle->The first ratio of the number of questions of the question type to the total number of questions of the group roll, +.>A range of values representing said first ratio, < > is provided>Representing the minimum value of said first ratio, < >>Represents the maximum value of said first ratio, +.>The average satisfaction rate of the question difficulty is represented, and the default value is more than or equal to 0.9 and is->A dictionary for indicating the difficulty level of the subject>Middle->The first question of the difficulty-like degree>The number of questions of the question type accounts for +.>A second ratio of the total number of questions of the difficulty class question type,a range of values representing said second ratio, < >>Representing the minimum value of said second ratio,represents the maximum value of said second ratio, +.>Represents the average coverage rate of chapters, the default value of the average coverage rate is more than or equal to 0.85,representing the chapter dictionary->In->Chapter->The number of questions of the question type accounts for +.>Third ratio of chapter total title, +.>A value range representing said third ratio,/->Representing the minimum value of said third ratio,/->Represents the maximum value of said third ratio, +.>Indicating the average satisfaction rate of error prone questions, the default value is more than or equal to 0.3,/for the questions>Representing said error prone question set->Middle->The fourth ratio of the number of the questions with error prone questions to the total number of the group roll,/>A value range representing said fourth ratio,/->Representing the minimum value of said fourth ratio, is->Representing the maximum value of said fourth ratio.
3. And (5) exploring a calculation model.
When the method is used for winding, the questions are extracted from the question bank C0, each question to be selected needs to be subjected to the following process, C1 exploration channels are firstly constructed, and the exploration times R of each channel and the default value of R are 20; then, when each time of searching the title, except for meeting the hardness requirement, the following search function meeting F1 is defined as follows:
wherein,the coefficient representing the current extraction question, whose default value is 0.9,/is>Representing a decreasing coefficient with a default value of 0.0001, -/->Represents a soft-demand moderating function (specifically, a soft-demand moderating function F1 +.>，/>，/>，/>Function) calculated soft requirement moderate values, initial value of 0.7,/for each>And->Representing learning step length, default value of two learning step lengths is 1.5,/for learning step length>Indicating +.>Maximum satisfaction rate among all exploration times of each exploration channel,/->Representing all ∈explored>Exploring the maximum satisfaction rate in a channel, +.>And->Initial value equal to->，/>Representing the actual satisfaction rate per extraction of a question by +.>(immediately before +.>And->Sum assigned to the current calculation +.>) Obtaining, iterating R times each channel, and updating +.>And->，/>And->Representing random numbers, the range of values of both random numbers is 0,1]，/>Representation->And searching the largest satisfaction rate in the channels, and taking the question with the largest satisfaction rate as the result of the extraction. When one question is extracted, the extraction times, extraction time and frequency of the question are updated, and the frequency calculation function is as follows:
wherein,representing the initial frequency of each question, default to 0.1,/for>Indicating the frequency of each question selected, with a default value of 1,/for>Represents the time from the first selection +.>Time to last selection +.>Is used for the time difference of (a),represents the decay factor, which defaults to 0.001. And when F2 draws questions each time, reversing all the selectable question sources according to the frequency. By the method, a test paper is circularly built.
It should be noted that, the default value, the limit value, and the predetermined value in this embodiment may be changed according to actual situations, and this embodiment is not particularly limited.
In the embodiment, firstly, the evaluation standard of each test paper is definitely combined, and each extracted examination question is effectively evaluated to the greatest extent through exploratory calculation, so that the soft effect of the group paper is improved; secondly, the winding can be completed through a small amount of parameters, so that the working complexity of staff is reduced; thirdly, the comprehensiveness of the study of the students can be improved, such as consideration of error-prone questions and balanced study of chapters, and knowledge points of the students are improved. Through implementation of the method, through partial college operation, the method is compared with the traditional winding method, and the method has obvious winding quality improvement.
Referring to fig. 3, the embodiment of the present invention further provides an intelligent college course examination paper making system, which includes a first construction unit 100, a second construction unit 200, a data calculation unit 300, a third construction unit 400, and a theme paper making unit 500, wherein:
a first construction unit 100 for constructing a question dictionary, a question difficulty dictionary, a chapter dictionary, and an error-prone question set;
a second construction unit 200, configured to construct a soft-requirement moderation function based on the question dictionary, the question difficulty dictionary, the chapter dictionary, and the error-prone question set;
a data calculating unit 300, configured to calculate a soft requirement suitability value by using a soft requirement suitability function and determine that the soft requirement meets the condition;
a third construction unit 400, configured to construct an exploration function according to the soft requirement moderate value under the condition that the hard requirement and the soft requirement are met;
the topic group volume unit 500 is configured to calculate a maximum satisfaction rate for each extracted topic by using an exploration function, and group volumes by using the topic corresponding to the maximum satisfaction rate.
It should be noted that, since the intelligent paper-making system for course examination in colleges and universities and the intelligent paper-making method for course examination in colleges and universities described above are based on the same inventive concept, the corresponding content in the method embodiment is also applicable to the system embodiment, and will not be described in detail here.
Referring to fig. 4, the embodiment of the application further provides a college course examination intelligent scroll forming device, which includes:
at least one memory;
at least one processor;
at least one program;
the program is stored in the memory, and the processor executes at least one program to implement the present disclosure to implement the intelligent winding method for course examination at universities.
The electronic equipment can be any intelligent terminal including a mobile phone, a tablet personal computer, a personal digital assistant (PersonalDigitalAssistant, PDA), a vehicle-mounted computer and the like.
The electronic device according to the embodiment of the present application is described in detail below.
Processor 1600, which may be implemented by a general purpose central processing unit (CentralProcessingUnit, CPU), a microprocessor, an application specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, is configured to execute related programs to implement the technical solutions provided by the embodiments of the present disclosure;
memory 1700 may be implemented in the form of read-only memory (ReadOnlyMemory, ROM), static storage, dynamic storage, or random access memory (RandomAccessMemory, RAM). Memory 1700 may store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present disclosure are implemented in software or firmware, relevant program code is stored in memory 1700 and invoked by processor 1600 to perform the intelligent composition method of the college course examination of the embodiments of the present disclosure.
An input/output interface 1800 for implementing information input and output;
the communication interface 1900 is used for realizing communication interaction between the device and other devices, and can realize communication in a wired manner (such as USB, network cable, etc.), or can realize communication in a wireless manner (such as mobile network, WIFI, bluetooth, etc.);
bus 2000, which transfers information between the various components of the device (e.g., processor 1600, memory 1700, input/output interface 1800, and communication interface 1900);
wherein processor 1600, memory 1700, input/output interface 1800, and communication interface 1900 enable communication connections within the device between each other via bus 2000.
The embodiment of the disclosure also provides a storage medium which is a computer readable storage medium, wherein the computer readable storage medium stores computer executable instructions for causing a computer to execute the intelligent college course examination paper assembly method.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiments described in the embodiments of the present disclosure are for more clearly describing the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure, and as those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present disclosure are equally applicable to similar technical problems.
It will be appreciated by those skilled in the art that the technical solutions shown in the figures do not limit the embodiments of the present disclosure, and may include more or fewer steps than shown, or may combine certain steps, or different steps.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the present application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. 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 understood that in this application, "at least one" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution, in the form of a software product stored in a storage medium, including multiple instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RandomAccessMemory, RAM), a magnetic disk, an optical disk, or other various media capable of storing programs. The embodiments of the present application have been described in detail above with reference to the accompanying drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present application.
Claims (6)
1. The intelligent college course examination paper making method is characterized by comprising the following steps of:
constructing a question dictionary, a question difficulty dictionary, a chapter dictionary and an error-prone question set;
constructing a soft requirement moderate function based on the question dictionary, the question difficulty dictionary, the chapter dictionary and the error prone question set; wherein the soft-demand moderation function is constructed by:
；
wherein,indicates simultaneous satisfaction of->Representing the average coverage of the number of topics per type in the topic dictionary,representing the question dictionary->Middle->The number of questions of the type question is a first ratio of the total number of questions of the group volume,representing the minimum value of said first ratio, < >>Represents the maximum value of said first ratio, +.>Represents the average satisfaction rate of the question difficulty level and the +.>A dictionary for indicating the difficulty level of the subject>Middle->The first question of the difficulty-like degree>The number of questions of the question type accounts for +.>Second ratio of total number of questions of class difficulty/ease/questions>Representing the minimum value of said second ratio, < >>Represents the maximum value of said second ratio, +.>The average coverage of the chapter is indicated,representing the chapter dictionary->In->Chapter->The number of questions of the question type accounts for +.>Third ratio of chapter total title, +.>Representing the minimum value of said third ratio,/->Represents the maximum value of said third ratio,represents the average satisfaction rate of error prone questions, < >>Representing said error prone question set->Middle->The fourth ratio of the number of the questions with error prone questions to the total number of the group roll,/>Representing the minimum value of said fourth ratio, is->Representing the maximum value of the fourth ratio;
calculating a soft requirement moderate value by adopting the soft requirement moderate function and judging that the soft requirement meets the condition; wherein:
judging whether the average coverage rate of the number of questions of each type of questions in the question dictionary meets a first soft requirement or not by adopting the soft requirement moderate function;
judging whether the average satisfaction rate of the question difficulty level meets a second soft requirement or not by adopting the soft requirement moderate function;
judging whether the average coverage rate of the chapters meets a third soft requirement or not by adopting the soft requirement moderate function;
judging whether the average satisfaction rate of the error prone questions meets a fourth soft requirement or not by adopting the soft requirement moderate function;
when the first soft requirement, the second soft requirement, the third soft requirement and the fourth soft requirement are all met, the question meets the soft requirement;
under the condition of meeting the hard requirement and the soft requirement, constructing an exploration function according to the soft requirement moderate value; wherein:
judging the hard requirement and the soft requirement for all the topics to be selected;
under the condition of meeting the hard requirement and the soft requirement, constructing a plurality of exploration channels for each question, and presetting the exploration times of each exploration channel;
constructing an exploration function according to the exploration times and the soft requirement moderate value; wherein the exploration function is constructed by:
；
wherein,coefficients representing the current extraction topic, +.>Representing a decreasing coefficient +.>Representing the soft demand appropriateness value calculated by the soft demand appropriateness function, < + >>And->Represents learning step size->Indicating +.>Maximum satisfaction rate among all exploration times of each exploration channel,/->Representing all ∈explored>Exploring the maximum satisfaction rate in a channel, +.>And->Initial value equal to->，/>Representing the actual satisfaction rate per extraction of a question,/->Representation->Maximum satisfaction rate in each exploration channel, < >>And->Representing a random number;
and calculating the maximum satisfaction rate of each extracted question by adopting the exploration function, and carrying out winding by adopting the questions corresponding to the maximum satisfaction rate.
2. The college course examination intelligent composition method as set forth in claim 1, wherein before calculating a maximum satisfaction rate for each of the extracted topics using the exploration function, the college course examination intelligent composition method further includes:
and when each question is extracted, the questions to be selected are inversely ordered according to the frequency of the selected questions for winding.
3. The intelligent college course examination paper making method according to claim 2, wherein the frequency is calculated by:
；
wherein,representing the initial frequency of each topic, +.>Indicating the frequency of each topic selected, < >>Represents the time from the first selection +.>Time to last selection +.>Time difference of->Representing the attenuation coefficient.
4. The intelligent university course examination paper making system is characterized in that the intelligent university course examination paper making system comprises:
the first construction unit is used for constructing a question dictionary, a question difficulty dictionary, a chapter dictionary and an error-prone question set;
the second construction unit is used for constructing a soft requirement moderate function based on the question dictionary, the question difficulty dictionary, the chapter dictionary and the error prone question set; wherein the soft-demand moderation function is constructed by:
；
wherein,indicates simultaneous satisfaction of->Representing the average coverage of the number of topics per type in the topic dictionary,representing the question dictionary->Middle->The first ratio of the number of questions of the question type to the total number of questions of the group roll, +.>Representing the minimum value of said first ratio, < >>Represents the maximum value of said first ratio, +.>Represents the average satisfaction rate of the question difficulty level and the +.>A dictionary for indicating the difficulty level of the subject>Middle->The first question of the difficulty-like degree>The number of questions of the question type accounts for +.>Second ratio of total number of questions of class difficulty/ease/questions>Representing the minimum value of said second ratio, < >>Represents the maximum value of said second ratio, +.>The average coverage of the chapter is indicated,representing the chapter dictionary->In->Chapter->The number of questions of the question type accounts for +.>Third ratio of chapter total title, +.>Representing the minimum value of said third ratio,/->Represents the maximum value of said third ratio, +.>Represents the average satisfaction rate of error prone questions, < >>Representing said error prone question set->Middle->The fourth ratio of the number of the questions with error prone questions to the total number of the group roll,/>Representing the minimum value of said fourth ratio, is->Representing the maximum value of the fourth ratio;
the data calculation unit is used for calculating a soft requirement moderate value by adopting the soft requirement moderate function and judging that the soft requirement meets the condition; wherein:
judging whether the average coverage rate of the number of questions of each type of questions in the question dictionary meets a first soft requirement or not by adopting the soft requirement moderate function;
judging whether the average satisfaction rate of the question difficulty level meets a second soft requirement or not by adopting the soft requirement moderate function;
judging whether the average coverage rate of the chapters meets a third soft requirement or not by adopting the soft requirement moderate function;
judging whether the average satisfaction rate of the error prone questions meets a fourth soft requirement or not by adopting the soft requirement moderate function;
when the first soft requirement, the second soft requirement, the third soft requirement and the fourth soft requirement are all met, the question meets the soft requirement;
the third construction unit is used for constructing an exploration function according to the moderate value of the soft requirement under the condition of meeting the hard requirement and the soft requirement; wherein:
judging the hard requirement and the soft requirement for all the topics to be selected;
under the condition of meeting the hard requirement and the soft requirement, constructing a plurality of exploration channels for each question, and presetting the exploration times of each exploration channel;
constructing an exploration function according to the exploration times and the soft requirement moderate value; wherein the exploration function is constructed by:
；
wherein,coefficients representing the current extraction topic, +.>Representing a decreasing coefficient +.>Representing the soft demand appropriateness value calculated by the soft demand appropriateness function, < + >>And->Represents learning step size->Indicating +.>Maximum satisfaction rate among all exploration times of each exploration channel,/->Representing all ∈explored>Exploring the maximum satisfaction rate in a channel, +.>And->Initial value equal to->，/>Representing the actual satisfaction rate per extraction of a question,/->Representation->Maximum satisfaction rate in each exploration channel, < >>And->Representing a random number;
and the topic group volume unit is used for calculating the maximum satisfaction rate of each extracted topic by adopting the exploration function and grouping the topics corresponding to the maximum satisfaction rate.
5. The intelligent college course examination paper making device is characterized by comprising at least one control processor and a memory, wherein the memory is used for being in communication connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the college course examination intelligent composition method of any one of claims 1 to 3.
6. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the college course examination intelligent composition method according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311479064.XA CN117216195B (en) | 2023-11-08 | 2023-11-08 | Intelligent paper-making method, system, equipment and storage medium for course examination of universities |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311479064.XA CN117216195B (en) | 2023-11-08 | 2023-11-08 | Intelligent paper-making method, system, equipment and storage medium for course examination of universities |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117216195A CN117216195A (en) | 2023-12-12 |
| CN117216195B true CN117216195B (en) | 2024-02-02 |
Family
ID=89046574
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311479064.XA Active CN117216195B (en) | 2023-11-08 | 2023-11-08 | Intelligent paper-making method, system, equipment and storage medium for course examination of universities |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117216195B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110123325A (en) * | 2010-05-07 | 2011-11-15 | (주)엠피디에이 | Method of service for matching test paper and test paper matching service system for the same |
| KR20130026027A (en) * | 2011-09-05 | 2013-03-13 | 강대식 | Hierarchical problem setting and automatic test paper forming system using internet multiple servers |
| CN103136453A (en) * | 2013-03-25 | 2013-06-05 | 华北电力大学（保定） | Automatic test paper formation method and automatic scoring method of document manipulation subjects |
| CN104111953A (en) * | 2013-04-19 | 2014-10-22 | 北京竞业达数码科技有限公司 | Role driving based intelligent question-bank test paper composition method and system |
| CN104820707A (en) * | 2015-05-14 | 2015-08-05 | 西安交通大学 | Automatic test paper composition method in B/S (Brower/Server) mode based on knowledge hierarchy in field of computers |
| CN105469145A (en) * | 2016-01-15 | 2016-04-06 | 清华大学 | Intelligent test paper method based on genetic particle swarm optimization algorithm |
| CN112989783A (en) * | 2021-03-31 | 2021-06-18 | 武汉烽火技术服务有限公司 | Intelligent volume assembling device and method |
| CN116263782A (en) * | 2021-12-10 | 2023-06-16 | 上海终身教育科技有限公司 | Intelligent winding method, system and storage medium based on question bank |
| CN116542829A (en) * | 2023-07-03 | 2023-08-04 | 江西师范大学 | Personalized intelligent group scroll and on-line examination method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE112018007578T5 (en) * | 2018-05-07 | 2021-07-01 | Bayerische Motoren Werke Aktiengesellschaft | Dialog system capable of semantic understanding mapping between user intentions and machine services |
| US11106728B2 (en) * | 2019-05-14 | 2021-08-31 | Accenture Global Solutions Limited | Artificial intelligence based music playlist reordering and song performance assessment |
| US20220198949A1 (en) * | 2020-12-22 | 2022-06-23 | Vedantu Innovations Pvt. Ltd. | System and method for determining real-time engagement scores in interactive online learning sessions |
-
2023
- 2023-11-08 CN CN202311479064.XA patent/CN117216195B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110123325A (en) * | 2010-05-07 | 2011-11-15 | (주)엠피디에이 | Method of service for matching test paper and test paper matching service system for the same |
| KR20130026027A (en) * | 2011-09-05 | 2013-03-13 | 강대식 | Hierarchical problem setting and automatic test paper forming system using internet multiple servers |
| CN103136453A (en) * | 2013-03-25 | 2013-06-05 | 华北电力大学（保定） | Automatic test paper formation method and automatic scoring method of document manipulation subjects |
| CN104111953A (en) * | 2013-04-19 | 2014-10-22 | 北京竞业达数码科技有限公司 | Role driving based intelligent question-bank test paper composition method and system |
| CN104820707A (en) * | 2015-05-14 | 2015-08-05 | 西安交通大学 | Automatic test paper composition method in B/S (Brower/Server) mode based on knowledge hierarchy in field of computers |
| CN105469145A (en) * | 2016-01-15 | 2016-04-06 | 清华大学 | Intelligent test paper method based on genetic particle swarm optimization algorithm |
| CN112989783A (en) * | 2021-03-31 | 2021-06-18 | 武汉烽火技术服务有限公司 | Intelligent volume assembling device and method |
| CN116263782A (en) * | 2021-12-10 | 2023-06-16 | 上海终身教育科技有限公司 | Intelligent winding method, system and storage medium based on question bank |
| CN116542829A (en) * | 2023-07-03 | 2023-08-04 | 江西师范大学 | Personalized intelligent group scroll and on-line examination method |
Non-Patent Citations (1)
| Title |
|---|
| 基于多策略的个性化智能组卷的研究;孙蓓蕾等;《成都信息工程大学学报》;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117216195A (en) | 2023-12-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109460513A (en) | Method and apparatus for generating clicking rate prediction model | |
| US8086253B1 (en) | Graphical mobile e-mail | |
| CN105631538A (en) | User activity prediction method and device, and application method and system thereof | |
| US10853563B1 (en) | Method and system for configuring form filling application to minimize form filling effort | |
| CN107992595A (en) | A kind of learning Content recommends method, apparatus and smart machine | |
| CN107302793A (en) | A kind of localization method based on wireless signal, server, terminal and system | |
| CN116957874B (en) | Intelligent automatic course arrangement method, system and equipment for universities and storage medium | |
| CN110333864A (en) | A kind of tree data rendering method, device, equipment and medium | |
| CN112417002A (en) | Information literacy data mining method and system applied to education informatization | |
| CN109992715A (en) | Information displaying method, device, medium and calculating equipment | |
| CN107329976A (en) | Webpage paging method, device, computer equipment and computer-readable recording medium | |
| CN117216195B (en) | Intelligent paper-making method, system, equipment and storage medium for course examination of universities | |
| CN110245207B (en) | Question bank construction method, question bank construction device and electronic equipment | |
| CN107526589A (en) | Page display method and device | |
| US20120130960A1 (en) | Estimation of errors in attribute values of aggregated databases | |
| CN110322375A (en) | Online interview wire examination method, device, medium and electronic equipment | |
| CN109189437A (en) | Software upgrading reminding method, device, computer equipment and readable storage medium storing program for executing | |
| CN107918922A (en) | Business recommended method and business recommended device | |
| CN108197288A (en) | According to source of houses method for pushing, device, equipment and the medium of user characteristics preference | |
| CN117217209A (en) | Intelligent college examination paper assembling method, system, equipment and storage medium | |
| CN117172427B (en) | Method, system, equipment and medium for assisting college students to select class | |
| CN109086298B (en) | System and method for color selection | |
| CN117670603A (en) | Automatic intelligent college examination arrangement method, system, equipment and storage medium | |
| CN105677671B (en) | A kind of context reasoning method under uncertainty and device | |
| CN117196909B (en) | University course arrangement method, system, equipment and medium based on custom classification |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |