CN114461787A - Test question distribution method and system - Google Patents
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Abstract
The application discloses a test question distribution method, which comprises the following steps: the training data set comprises behavior characteristic data, test question characteristic data and test results of a plurality of samples; the behavior characteristic data comprises statistical data of access behaviors to resources; the test question feature data is used for representing test question marks or evaluation values; combining the training data sets to perform regression fitting calculation according to the behavior characteristic data of the current student and the characteristic data of the test questions to be distributed, and obtaining a test result predicted value corresponding to the current student and the test questions to be distributed; and comparing the test result predicted values of a plurality of test questions to be distributed of the current student, and determining the test questions to be distributed when the test result predicted values are maximum. The application also includes an apparatus for implementing the method. The method and the device solve the problems that learning software in the prior art outputs learning resources when learning repeatedly, is low in efficiency and cannot adapt to the specific conditions of students.
Description
Technical Field
The application relates to the technical field of computers, in particular to a test question distribution method and system in an intelligent learning application scene.
Background
In the current online learning process of recording and broadcasting, all students can learn under a uniformly arranged content and progress frame, and learning feedback is mainly in a mode of spontaneous inquiry of the students. Because individual situations are different and the same time is spent, the learning effect of each student is different.
In the current learning form, efficient feedback and effective tracking are lacked, and the individual learning requirements of students are difficult to meet. Meanwhile, the relation between the content importance degree and the weak points of the student is difficult to accurately evaluate by the student, and the rhythm of learning and reviewing is reasonably arranged. Limitations in the learner's own cognition can lead to an inability to correctly select the appropriate learning resources, resulting in learning inefficiencies. Therefore, there is a need to develop an intelligent learning system that calculates the optimal test question resources for students of different levels during the learning process, thereby helping the students to effectively improve the learning efficiency.
Disclosure of Invention
The application provides a test question distribution method and system, and solves the problems that learning software in the prior art is low in learning resource output efficiency and cannot adapt to the specific conditions of students when learning is repeated.
On one hand, the embodiment of the application provides a test question distribution method, which comprises the following steps:
the training data set comprises behavior characteristic data, test question characteristic data and test results of a plurality of samples; the behavior characteristic data comprises statistical data of access behaviors to resources; the test question feature data is used for representing test question marks or evaluation values;
according to the behavior characteristic data of the current student and the characteristic data of the test questions to be distributed, combining the training data sets to perform regression fitting calculation to obtain a test result predicted value corresponding to the current student and the test questions to be distributed;
and comparing the test result predicted values of a plurality of test questions to be distributed of the current student, and determining the test questions to be distributed when the test result predicted values are maximum.
Preferably, the step of performing a regression fit calculation uses the GBDT + LR model.
Preferably, the behaviour signature data comprises at least one of: duration, number of times, frequency, amount of resources accessed.
Preferably, the test question features comprise at least one of: test question identification, real question proportion, score statistic value, difficulty and importance.
Preferably, the test result includes a result value of the test performed on the sample by using a set of test questions.
In any one embodiment of the first aspect of the present application, preferably, the method further comprises the following steps: and comparing the test result predicted values of a plurality of test questions to be distributed of the current student, and sequencing the test questions to be distributed according to the descending order of the test result predicted values.
Further preferably, the method further comprises the following steps: and in the test questions to be distributed, the test questions to be distributed with the time length between the current time and the last access time of the current student being less than a set threshold value are excluded.
In a second aspect, the present application further provides a test question distribution apparatus, configured to implement the method according to any embodiment of the present application, including:
the system comprises a first module, a second module and a third module, wherein the first module is used for identifying resource access behaviors and generating behavior characteristic data;
the second module is used for combining the training data set to perform regression fitting calculation according to the behavior characteristic data of the current student and the characteristic data of the test questions to be distributed so as to obtain a test result predicted value corresponding to the current student and the test questions to be distributed;
the third module is used for accessing the resources according to the sequence of the test result predicted values to obtain corresponding test questions to be distributed;
a training database for storing the training data set;
the resource comprises at least one of: a knowledge point database, a test question database, and an application program for reading the knowledge point database and/or the test question database.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
the method and the system of the application aim at improving the test score, combine the learning feedback characteristic and the Ebbinghaus memory curve of the student in time, perform joint optimization on the personalized test question distributor, provide a reasonable and efficient personalized learning path for the student, and improve the learning efficiency and the test score.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of an embodiment of the method of the present application;
FIG. 2 is a flow chart of another embodiment of the method of the present application;
fig. 3 shows an embodiment of the device of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flow chart of an embodiment of the method of the present application.
preferably, the behavioral characteristic data comprises at least one of: duration, number of times, frequency, amount of resources accessed. The resource comprises at least one of: a knowledge point database, a test question database, and an application program for reading the knowledge point database and/or the test question database.
Preferably, the test question features comprise at least one of the following types: test question identification, real question proportion, score statistic value, difficulty and importance.
Preferably, the test result includes a result value of the test performed on the sample by using a set of test questions.
It is also noted that the samples in the present application may include current trainees and/or historical trainees. The related data of the sample can also be set sample data special for training.
102, combining the training data set to perform regression fitting calculation according to the behavior characteristic data of the current student and the characteristic data of the test questions to be distributed to obtain a test result predicted value corresponding to the current student and the test questions to be distributed;
preferably, the step of performing a regression fit calculation uses the GBDT + LR model.
And 103, comparing the test result predicted values of a plurality of test questions to be distributed of the current student, and determining the test questions to be distributed when the test result predicted values are maximum.
In this embodiment, it is preferable that the method further includes the steps of: and comparing the test result predicted values of a plurality of test questions to be distributed of the current student, and sequencing the test questions to be distributed according to the descending order of the test result predicted values.
Further preferably, the method further comprises the following steps: in the test questions to be distributed, excluding the test questions to be distributed, wherein the time length between the current time and the last access time of the current student is less than a set threshold value; or, conversely, in the to-be-distributed test questions, the to-be-distributed test questions with the time length between the current time and the current student last direction time being greater than the set threshold value are excluded.
Further preferably, in the to-be-distributed test questions, the to-be-distributed test questions with the duration between the current time and the current student last access time being less than a first set threshold value are excluded; and in the test questions to be distributed, excluding the test questions to be distributed, wherein the time length between the current time and the current student last azimuth time is greater than a second set threshold value. Wherein the second set threshold is greater than the first set threshold.
Fig. 2 is a flow chart of another embodiment of the method of the present application.
Preferably, in the training data set, the trainees are grouped according to different test question feature data. In each group, the basic background of the trainee in each group is basically guaranteed to be the same. Different students are shown with different subjects according to proportion in the experimental group to construct training data, the test results of the students are counted to be used as prediction data, and the GBDT + LR model modified by LOSS is used for regression fitting.
firstly, clustering questions, and extracting relevant characteristics of question cluster, including types and characteristic values of test questions; the type of the test question feature is as described in step 101.
the behavior feature data, as described in step 101.
in step 203, the candidate test question list is screened according to the current progress, and the test questions which do not meet the requirements are filtered by combining the Ebingois memory curve and the time.
Step 203A, determining the range of the test questions to be distributed according to the current learning progress of the student;
step 203B, further determining the range of the test questions to be distributed according to the current access time of the student.
In the test questions to be distributed, excluding the test questions to be distributed, wherein the time length between the current time and the last access time of the current student is less than a set threshold value; or, conversely, in the to-be-distributed test questions, the to-be-distributed test questions with the time length between the current time and the current student last direction time being greater than the set threshold value are excluded.
Further, or, optionally, in the test questions to be distributed, the test questions with any test question feature value within the set range are excluded.
For example, filtering the questions according to the business rules, wherein the questions cannot be repeatedly displayed within a certain time threshold; for another example, the problem with low difficulty or low real problem ratio limits the maximum display times; for another example, when the temperature is new, the non-displayed item cannot be displayed.
the corresponding set sample characteristic value ranges of a plurality of training data sets used for performance prediction of the current trainees are the same.
The training database includes sample feature values that include data representing sample features, such as region, age, gender, academic history or academic effort. In a training data set, the sample characteristic values are counted, samples are classified according to the sample characteristic values, and the proportion of the set sample characteristic value range in all the samples is obtained. For example, the proportion of samples from a set area to the total samples is set, and the proportion of samples in an age range to the total samples is set.
The current trainee, as one of the samples, is also described by a sample characteristic value. The current trainee behavior feature data can also be used for updating the training database.
The training data sets are clustered to form a plurality of training data sets. In each training data set, the proportion of the set sample characteristic value range in all samples of each training data set is the same. For example, in the first training data set, the proportion of samples within the age range to all samples in the first training data set is set to 20%, and in the second training data set, the proportion of samples within the age range to all samples in the second training data set is also set to 20%.
The set sample feature value range includes 1 or more sample feature types, and for each 1 sample feature type, includes 1 or more sample feature value ranges. For example, in addition to the age characteristics, the first training data set also includes samples from a set area, accounting for 30% of all samples in the first training data set; the second training data set also contains samples from a predetermined area, and the percentage of the samples in the second training data set is 30%.
And each grouped training data set is used for predicting the test questions to be distributed corresponding to at least one set test question characteristic value range. That is, each training data set includes test question feature data including 1 or more test question feature types corresponding to the plurality of samples, and each test question feature type includes 1 or more test question feature value ranges. Each test question feature type and the range of the test question feature values are preset.
In step 205, the degree of improvement of the test score by each question is predicted by combining the learning progress of the student, the learning behavior characteristics and the characteristics of the test questions.
And step 206, comparing the test result predicted values of the plurality of test questions to be distributed of the current student, and sequencing the test questions to be distributed according to the descending order of the test result predicted values.
For example, the degrees of improving the test scores are sorted from large to small, and then the candidate topics are selected in sequence and sent to the trainee.
Fig. 3 shows an embodiment of the apparatus of the present application.
In a second aspect, the present application further provides a test question distribution apparatus, configured to implement the method according to any embodiment of the present application, including a first module 31, a second module 32, a third module 33, a training database 34, and a resource database 35.
The first module is used for identifying resource access behaviors and generating behavior characteristic data. For example, in a smart learning software or internet online learning application system, a first module is associated with the user application module 36, through which resource access behavior is identified and behavior feature data is generated in response to user operations acting on the user application.
It should be noted that, during the learning process, any user, including the current learner or the historical learner, accesses the knowledge point database or the test question database through the audio/video playing module and the text or graphic display module with the user operation interface. The resource comprises at least one of: a knowledge point database, a test question database, and an application program for reading the knowledge point database and/or the test question database. The resource database is a medium for storing or running the resources, and the first module is triggered by the access of any user to the resources.
For example, the system divides the nodes of the learning path into five types of stage test, chapter test, knowledge point video, knowledge point graphics and text and knowledge point test question test, and the overall optimization target is to improve the score of the chapter test. Marking the knowledge point video and knowledge point data through a label system, and determining characteristic values representing learning sequence, importance degree, belonging knowledge clusters and the like; marking the test questions, and determining the characteristic values of representing importance degree, examination frequency, difficulty, knowledge point relevance, knowledge point coverage rate and the like.
And in the learning process of the student, the first module extracts a specific characteristic of the learning behavior of the student and uses the specific characteristic as an input for performance prediction of the second module. For example, the content to be learned is predicted to be a knowledge point test question test, and furthermore, the behavior characteristics of the test questions made by the trainee can also be used as historical data to enter a training data set to be used as the input of the next prediction.
The second module is used for combining the training data set to perform regression fitting calculation according to the behavior characteristic data of the current student and the characteristic data of the test questions to be distributed so as to obtain a test result predicted value corresponding to the current student and the test questions to be distributed;
a training database for storing a training data set; the training data set comprises behavior characteristic data, test question characteristic data and test results of a plurality of samples. The test result comprises a result value for testing the sample by using a set test question set. For example, in the intelligent learning software or the internet online learning application system, all test questions are grouped into stage tests, chapter tests, and the like, and each test is applied to one test question set.
Preferably, the second module is further configured to collect basic information related to the trainee after the trainee starts learning, including information such as learning goal, learning time, current level, and personal characteristics. The second module combines the student basic information, the learning behavior characteristics and the Einbinghaus memory curve, in the running process of the user application program for realizing learning, aiming at the current student, on one hand, the resources are called, the mastering degree is timely evaluated in a mode of knowledge point test question testing, chapter testing and the like, sample data is generated, and a training database is updated; on the other hand, candidate contents in the test questions to be distributed in the resource database are predicted and scored according to the current trainees.
The third module is used for accessing the resources according to the size sequence of the test result predicted values to obtain corresponding test questions to be distributed; the third module is associated with the user application program and responds to the test question access request of the user to form the personalized test question distributor.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application therefore also proposes a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of the embodiments of the present application.
Further, the present application also proposes an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any of the embodiments of the present application.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, or may be loaded onto a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A test question distribution method, comprising the steps of:
the training data set comprises behavior characteristic data, test question characteristic data and test results of a plurality of samples; the behavior characteristic data comprises statistical data of access behaviors to resources; the test question feature data is data for representing test question marks or evaluation values;
combining the training data sets to perform regression fitting calculation according to the behavior characteristic data of the current student and the characteristic data of the test questions to be distributed, and obtaining a test result predicted value corresponding to the current student and the test questions to be distributed;
and comparing the test result predicted values of a plurality of test questions to be distributed of the current student, and determining the test questions to be distributed when the test result predicted values are maximum.
2. The method of claim 1,
the step of performing a regression fit calculation uses the GBDT + LR model.
3. The method of claim 1,
the behavioral characteristic data includes at least one of: duration, number of times, frequency, amount of resources accessed.
4. The method of claim 1,
the test question features include at least one of: test question identification, real question proportion, score statistic value, difficulty and importance.
5. The method of claim 1,
the test result comprises a result value for testing the sample by using a set test question set.
6. The method of claim 1, further comprising the step of:
and comparing the test result predicted values of a plurality of test questions to be distributed of the current student, and sequencing the test questions to be distributed according to the descending order of the test result predicted values.
7. The method of claim 6, further comprising the step of:
and in the test questions to be distributed, the test questions to be distributed with the time length between the current time and the last access time of the current student being less than a set threshold value are excluded.
8. A test question distribution apparatus for implementing the method according to any one of claims 1 to 7, comprising:
the system comprises a first module, a second module and a third module, wherein the first module is used for identifying resource access behaviors and generating behavior characteristic data;
the second module is used for combining the training data set to perform regression fitting calculation according to the behavior characteristic data of the current student and the characteristic data of the test questions to be distributed so as to obtain a test result predicted value corresponding to the current student and the test questions to be distributed;
the third module is used for accessing the resources according to the sequence of the test result predicted values to obtain corresponding test questions to be distributed;
a training database for storing the training data set;
the resource comprises at least one of: a knowledge point database, a test question database, and an application program for reading the knowledge point database and/or the test question database.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method of any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method according to any of claims 1 to 7 when executing the computer program.
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| US20200126126A1 (en) * | 2018-10-19 | 2020-04-23 | Cerebri AI Inc. | Customer journey management engine |
| CN112669006A (en) * | 2020-12-28 | 2021-04-16 | 广东国粒教育技术有限公司 | Intelligent paper grouping method based on student knowledge point diagnosis |
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| CN108171358A (en) * | 2017-11-27 | 2018-06-15 | 科大讯飞股份有限公司 | Score prediction method and device, storage medium and electronic device |
| US20200126126A1 (en) * | 2018-10-19 | 2020-04-23 | Cerebri AI Inc. | Customer journey management engine |
| CN110472060A (en) * | 2019-07-05 | 2019-11-19 | 平安国际智慧城市科技股份有限公司 | Topic method for pushing, device, computer equipment and storage medium |
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