CN114529436A - Knowledge point mastery degree evaluation method, device and medium - Google Patents
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Abstract
The invention provides a knowledge point mastery degree evaluation method, which comprises the following steps: s1, classifying the acquired questions according to the knowledge points; s2, acquiring the total number of questions under each knowledge point and the number of answers of the user; s3, calculating the answer correct rate Rkj _ self of the user under the first knowledge point; s4, calculating the answer correct rate Rkj _ avg of other users under the first knowledge point; s5, calculating the mastery degree Kgradp of the current knowledge point of the user according to the answer correct rate Rkj _ self of the user and the answer correct rates Rkj _ avg of other users: kgrasp sig mod (Rkj _ self-Rkj _ avg)
The value of the Sigmod function is (0, 1) and monotonically increases. The invention calculates the mastery degree of the knowledge points of the user according to the answer correct rate of the knowledge points of the user and the answer correct rate of other users, and the inventionThe clear calculation mode of the user mastery degree can well evaluate the knowledge point mastery degree of the user.
Description
Technical Field
The invention relates to the technical field of education, in particular to a knowledge point mastery degree evaluation method, a knowledge point mastery degree evaluation device and a knowledge point mastery degree evaluation medium.
Background
In the learning process of students, in order to accurately find weak knowledge points of users, a diagnosis question bank is generally required to be manufactured for testing, and then the weak knowledge points of the users are judged according to a test result. However, because there is no pertinence, a diagnosis question bank with very large data size and very accuracy needs to be created, and a user needs to make a large number of questions, so that the cost for creating the diagnosis question bank is high, the burden of the user is heavy, and the popularization of the auxiliary learning device is not facilitated. Therefore, there is a need for a method for identifying the degree of knowledge points that can reduce the cost of creating a diagnostic question bank and that does not require a large number of test questions for the user.
The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the material described in this section is not prior art to the claims in this application and is not admitted to be prior art by inclusion in this section.
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides a knowledge point mastery degree evaluation method, which comprises the following steps of:
s1, classifying the acquired questions according to the knowledge points;
s2, acquiring the total number of questions under each knowledge point and the number of answers of the user;
s3, calculating the answer correct rate Rkj _ self of the user under the first knowledge point;
s4, calculating the answer correct rate Rkj _ avg of other users under the first knowledge point;
s5, calculating the mastery degree Kgradp of the current knowledge point of the user according to the answer correct rate Rkj _ self of the user and the answer correct rates Rkj _ avg of other users:
Kgrasp＝sig mod(Rkj_self-Rkj_avg)
the value of the Sigmod function is (0, 1) and monotonically increases.
Specifically, the step S3 specifically includes:
the answer accuracy rate Rkj _ self of the user;
wherein Rkj _ self is the answer accuracy of the j-th knowledge point of the user, and Rkj _ self _ old is the calculation result of the last Rkj _ self of the current Rkj _ self; w1 and w2 are weighting coefficients; nrightj is the number of completely correct questions answered at the jth knowledge point, Nhalfj is the number of partially correct questions answered at the jth knowledge point, and Nallj is the total number of answered questions at the jth knowledge point.
Specifically, the step S4 specifically includes: the answer accuracy rate Rkj _ avg of other users;
in the above formula, Rkj _ avg is the answer accuracy of the j-th knowledge point of other users, Rkj _ avg _ old is the calculation result of the previous Rkj _ avg of the current calculation of Rkj _ avg, and W3 and W4 are weighting coefficients; prightj is the number of completely correct questions answered at the jth knowledge point, Phalfj is the number of partially correct questions answered at the jth knowledge point, and Pallj is the total number of answered questions at the jth knowledge point.
In particular, wherein w1> w 2.
In particular, wherein w3> w 4.
In a second aspect, another embodiment of the present invention discloses a knowledge point mastery degree evaluation device, which includes the following units:
the knowledge point classification unit is used for classifying the acquired questions according to the knowledge points;
the question acquisition unit is used for acquiring the total number of questions under each knowledge point and the number of answers of the user;
the user answer correct rate calculating unit is used for calculating the answer correct rate Rkj _ self of the user under the first knowledge point;
the other user answer correct rate calculating unit is used for calculating the answer correct rates Rkj _ avg of other users under the first knowledge point;
and the knowledge point mastery degree calculating unit is used for calculating the mastery degree Kgradp of the current knowledge point of the user according to the answer correct rate Rkj _ self of the user and the answer correct rates Rkj _ avg of other users:
Kgrasp＝sig mod(Rkj_self-Rkj_avg)
the value of the Sigmod function is (0, 1) and monotonically increases.
Specifically, the answer accuracy of the user Rkj _ self;
wherein Rkj _ self is the answer accuracy of the j-th knowledge point of the user, and Rkj _ self _ old is the calculation result of the last Rkj _ self of the current Rkj _ self; w1 and w2 are weighting coefficients; nrightj is the number of completely correct questions answered at the jth knowledge point, Nhalfj is the number of partially correct questions answered at the jth knowledge point, and Nallj is the total number of answered questions at the jth knowledge point.
Specifically, the answer accuracy rate Rkj _ avg of other users;
in the above formula, Rkj _ avg is the answer accuracy of the j-th knowledge point of other users, Rkj _ avg _ old is the calculation result of the previous Rkj _ avg of the current calculation of Rkj _ avg, and W3 and W4 are weighting coefficients; prightj is the number of completely correct questions answered at the jth knowledge point, Phalfj is the number of partially correct questions answered at the jth knowledge point, and Pallj is the total number of answered questions at the jth knowledge point.
In particular, w1> w2, w3> w 4.
In a third aspect, another embodiment of the present invention discloses a nonvolatile memory that stores instructions for implementing the above-described knowledge point mastery degree evaluation method when executed by a processor.
According to the invention, the knowledge point mastery degree of the user is calculated according to the answer correct rate of the knowledge points of the user and the answer correct rates of other users, and according to the answer correct rates of the knowledge points of the user and the answer correct rates of other users.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram provided by an embodiment of the present invention;
fig. 3 is a schematic diagram provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
Example one
Referring to fig. 1, the present embodiment discloses a knowledge point mastery degree evaluation method, which includes the following steps:
s1, classifying the acquired questions according to the knowledge points;
the embodiment classifies the questions according to the knowledge points, maps knowledge point labels to each question, and classifies the questions according to the knowledge points according to the question labels;
for example: item 1, Pythagorean theorem; topic 2, unary equation of once;
the method specifically comprises the steps of mapping knowledge point labels to each topic, and automatically realizing the mapping, such as analyzing the content of the topic, extracting main keywords in the content, and confirming the knowledge point labels according to the keywords;
specifically, the questions obtained in this embodiment may be obtained in batch directly through the third party data;
one mode of obtaining the question of this embodiment is to adopt the acquisition terminal to obtain the question, the acquisition terminal can be scanning equipment, scanning equipment is used for scanning the examination paper or the examination question, follows obtain the question in the examination paper or the examination question. For example, the collection terminal may be an operation system question collection terminal, an examination paper question collection terminal, a dot matrix paper question collection terminal, a handwriting board question collection terminal, or the like.
Specifically, the knowledge point mastery degree evaluation method of the embodiment can be operated on a cloud or a server;
the server collects corresponding answer data from the N collecting terminals, all the answer data are reported to the cloud, and the collected questions are classified according to knowledge points.
The answer data of this embodiment includes the question and the corresponding answer information of the user, and the answer information includes, but is not limited to, the correctness of the answer.
The server records the number Qi of the topics acquired by each acquisition terminal (Qi represents the number of the topics acquired by the ith acquisition terminal). The total number Q of questions can be obtained by the accumulation of QiallThe following formula shows:
wherein N represents the number of acquisition terminals.
S2, acquiring the total number of questions under each knowledge point and the number of answers of the user;
after the obtained questions are classified by the knowledge points, calculating the total number of the corresponding questions under each knowledge point, wherein the total number of the questions is further divided into the total number of the user question answers and the total number of the total answers;
s3, calculating the answer correct rate Rkj _ self of the user under the first knowledge point;
w1+w2＝1
wherein, Rkj _ self is the answer accuracy of the j-th knowledge point of the user, and Rkj _ self _ old is the calculation result of the last Rkj _ self of the current Rkj _ self. w1 and w2 are weighting coefficients, and w1> w2, which indicates that the latest calculated correct rate weight should be higher than the historical correct rate weight. Nrightj is the number of completely correct questions answered at the jth knowledge point, Nhalfj is the number of partially correct questions answered at the jth knowledge point, and Nallj is the total number of answered questions at the jth knowledge point.
S4, calculating the answer correct rate Rkj _ avg of other users under the first knowledge point;
w3+w4＝1
in the above formula, Rkj _ avg is the answer accuracy of the j-th knowledge point of another user, and Rkj _ avg _ old is the calculation result of the previous Rkj _ avg of the current calculation of Rkj _ avg. W3 and W4 are weighting coefficients, and W3> W4, which indicates that the latest calculated correct rate weight should be higher than the historical correct rate weight. Prightj is the number of completely correct questions answered at the jth knowledge point, Phalfj is the number of partially correct questions answered at the jth knowledge point, and Pallj is the total number of answered questions at the jth knowledge point.
Specifically, w1 ═ w 3.
Specifically, the other users of the present embodiment may be all users or users other than the user.
S5, calculating the mastery degree Kgradp of the current knowledge point of the user according to the answer correct rate Rkj _ self of the user and the answer correct rates Rkj _ avg of other users:
Kgrasp＝sig mod(Rkj_self-Rkj_avg)
the value of the Sigmod function is (0, 1) and monotonically increases. The expression of the above formula means that, with the answer data of the total user as a reference, if the answer accuracy of the user at the corresponding knowledge point is higher than the average accuracy of the total user, the mastery degree value is larger, otherwise, if the answer accuracy is higher than the average accuracy of the total user, the mastery degree value of the knowledge point is smaller.
After the evaluation calculation of the knowledge point mastery degree is completed, the knowledge point mastery degree can be further graded (such as excellent, good, poor and the like) according to the calculation result. The classification can be used for dividing the distribution range of the mastery degree of the knowledge points according to the experience values of professional scientific research experts.
According to the embodiment, the knowledge point mastery degree of the user is calculated according to the answer correct rate of the knowledge points of the user and the answer correct rates of other users, and according to the answer correct rate of the knowledge points of the user and the answer correct rates of other users.
Example two
Referring to fig. 2, the present embodiment discloses a knowledge point mastery degree evaluation device, which includes the following units:
the knowledge point classification unit is used for classifying the acquired questions according to the knowledge points;
the embodiment classifies the questions according to the knowledge points, maps knowledge point labels to each question, and classifies the questions according to the knowledge points according to the question labels;
for example: item 1, Pythagorean theorem; topic 2, unary equation of once;
the method specifically comprises the steps of mapping knowledge point labels to each topic, and automatically realizing the mapping, such as analyzing the content of the topic, extracting main keywords in the content, and confirming the knowledge point labels according to the keywords;
specifically, the questions obtained in this embodiment may be obtained in batch directly through the third party data;
one mode of obtaining questions in this embodiment is to adopt a collection terminal to obtain the questions, where the collection terminal may be a scanning device, and the scanning device is configured to scan test papers or test questions and obtain the questions from the test papers or test questions. For example, the acquisition terminal may be an operation system question acquisition terminal, an examination paper question acquisition terminal, a dot matrix paper question acquisition terminal, a handwriting board question acquisition terminal, and the like.
Specifically, the knowledge point mastery degree evaluation method of the embodiment can be operated on a cloud or a server;
the server collects corresponding answer data from the N collecting terminals, all the answer data are reported to the cloud, and the collected questions are classified according to knowledge points.
The answer data of this embodiment includes the question and the corresponding answer information of the user, and the answer information includes, but is not limited to, the correctness of the answer.
The server records the number Qi of the topics acquired by each acquisition terminal (Qi represents the number of the topics acquired by the ith acquisition terminal). The total number Q of questions can be obtained by the accumulation of QiallThe following formula shows:
wherein N represents the number of acquisition terminals.
The question acquisition unit is used for acquiring the total number of questions under each knowledge point and the number of answers of the user;
after the obtained questions are classified by the knowledge points, calculating the total number of the corresponding questions under each knowledge point, wherein the total number of the questions is further divided into the total number of the user question answers and the total number of the total answers;
the user answer correct rate calculating unit is used for calculating the answer correct rate Rkj _ self of the user under the first knowledge point;
w1+w2＝1
wherein, Rkj _ self is the answer accuracy of the j-th knowledge point of the user, and Rkj _ self _ old is the calculation result of the last Rkj _ self of the current Rkj _ self. w1 and w2 are weighting coefficients, and w1> w2, which indicates that the latest calculated correct rate weight should be higher than the historical correct rate weight. Nrightj is the number of completely correct questions answered at the jth knowledge point, Nhalfj is the number of partially correct questions answered at the jth knowledge point, and Nallj is the total number of answered questions at the jth knowledge point.
The other user answer correct rate calculating unit is used for calculating the answer correct rates Rkj _ avg of other users under the first knowledge point;
w3+w4＝1
in the above formula, Rkj _ avg is the answer accuracy of the j-th knowledge point of another user, and Rkj _ avg _ old is the calculation result of the previous Rkj _ avg of the current calculation of Rkj _ avg. W3 and W4 are weighting coefficients, and W3> W4, which indicates that the latest calculated correct rate weight should be higher than the historical correct rate weight. Prightj is the number of completely correct questions answered at the jth knowledge point, Phalfj is the number of partially correct questions answered at the jth knowledge point, and Pallj is the total number of answered questions at the jth knowledge point.
Specifically, w1 ═ w 3.
And the knowledge point mastery degree calculating unit is used for calculating the mastery degree Kgradp of the current knowledge point of the user according to the answer correct rate Rkj _ self of the user and the answer correct rates Rkj _ avg of other users:
Kgrasp＝sig mod(Rkj_self-Rkj_avg)
the value of the Sigmod function is (0, 1) and monotonically increases. The expression of the above formula means that, with the answer data of the total user as a reference, if the answer accuracy of the user at the corresponding knowledge point is higher than the average accuracy of the total user, the mastery degree value is larger, otherwise, if the answer accuracy is higher than the average accuracy of the total user, the mastery degree value of the knowledge point is smaller.
After the evaluation calculation of the knowledge point mastery degree is completed, the knowledge point mastery degree can be further graded (such as excellent, good, poor and the like) according to the calculation result. The classification can be based on the experience of professional scientific research experts to divide the distribution range of knowledge point mastery.
According to the embodiment, the knowledge point mastery degree of the user is calculated according to the answer correct rate of the knowledge points of the user and the answer correct rates of other users, and according to the answer correct rate of the knowledge points of the user and the answer correct rates of other users.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic structural view of a knowledge point grasp degree evaluation apparatus of the present embodiment. The knowledge point mastery degree evaluating device 20 of this embodiment includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. The processor 21 realizes the steps in the above-described method embodiments when executing the computer program. Alternatively, the processor 21 implements the functions of the modules/units in the above-described device embodiments when executing the computer program.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 22 and executed by the processor 21 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing a specific function for describing an execution process of the computer program in the knowledge point mastery level evaluating device 20. For example, the computer program may be divided into the modules in the second embodiment, and for the specific functions of the modules, reference is made to the working process of the apparatus in the foregoing embodiment, which is not described herein again.
The knowledge point mastery degree evaluation apparatus 20 may include, but is not limited to, a processor 21 and a memory 22. It will be understood by those skilled in the art that the schematic diagram is merely an example of the knowledge point mastery level evaluation device 20, and does not constitute a limitation to the knowledge point mastery level evaluation device 20, and may include more or less components than those shown, or some components may be combined, or different components, for example, the knowledge point mastery level evaluation device 20 may further include an input-output device, a network access device, a bus, or the like.
The Processor 21 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 21 is a control center of the knowledge point grasp degree evaluation apparatus 20, and various interfaces and lines are used to connect the respective parts of the entire knowledge point grasp degree evaluation apparatus 20.
The memory 22 may be used to store the computer programs and/or modules, and the processor 21 may implement various functions of the knowledge point mastery level evaluation apparatus 20 by executing or executing the computer programs and/or modules stored in the memory 22 and calling data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory 22 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The module/unit integrated with the knowledge point grasp degree evaluation device 20 may be stored in a computer-readable storage medium if it is realized in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer readable storage medium, and when the computer program is executed by the processor 21, the steps of the method embodiments described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A knowledge point mastery degree evaluation method comprises the following steps:
s1, classifying the acquired questions according to the knowledge points;
s2, acquiring the total number of questions under each knowledge point and the number of answers of the user;
s3, calculating the answer correct rate Rkj _ self of the user under the first knowledge point;
s4, calculating the answer correct rate Rkj _ avg of other users under the first knowledge point;
s5, calculating the mastery degree Kgradp of the current knowledge point of the user according to the answer correct rate Rkj _ self of the user and the answer correct rates Rkj _ avg of other users:
Kgrasp＝sig mod(Rkj_self-Rkj_avg)
the value of the Sigmod function is (0, 1) and monotonically increases.
2. The method according to claim 1, wherein the step S3 specifically comprises:
the answer accuracy rate Rkj _ self of the user;
wherein Rkj _ self is the answer accuracy of the j-th knowledge point of the user, and Rkj _ self _ old is the calculation result of the last Rkj _ self of the current Rkj _ self; w1 and w2 are weighting coefficients; nrightj is the number of completely correct questions answered at the jth knowledge point, Nhalfj is the number of partially correct questions answered at the jth knowledge point, and Nallj is the total number of answered questions at the jth knowledge point.
3. The method according to claim 1, wherein the step S4 specifically comprises: the answer accuracy rate Rkj _ avg of other users;
in the above formula, Rkj _ avg is the answer accuracy of the j-th knowledge point of other users, Rkj _ avg _ old is the calculation result of the previous Rkj _ avg of the current calculation of Rkj _ avg, and W3 and W4 are weighting coefficients; prightj is the number of completely correct questions answered at the jth knowledge point, Phalfj is the number of partially correct questions answered at the jth knowledge point, and Pallj is the total number of answered questions at the jth knowledge point.
4. The method of claim 2, wherein w1> w 2.
5. The method of claim 3, wherein w3> w 4.
6. A knowledge point mastery degree evaluation device includes the following units:
the knowledge point classification unit is used for classifying the acquired questions according to the knowledge points;
the question acquisition unit is used for acquiring the total number of questions under each knowledge point and the number of answers of the user;
the user answer correct rate calculating unit is used for calculating the answer correct rate Rkj _ self of the user under the first knowledge point;
the other user answer correct rate calculating unit is used for calculating the answer correct rates Rkj _ avg of other users under the first knowledge point;
and the knowledge point mastery degree calculating unit is used for calculating the mastery degree Kgradp of the current knowledge point of the user according to the answer correct rate Rkj _ self of the user and the answer correct rates Rkj _ avg of other users:
Kgrasp＝sig mod(Rkj_self-Rkj_avg)
the value of the Sigmod function is (0, 1) and monotonically increases.
7. The apparatus of claim 6, the user's answer correct rate Rkj _ self;
the Rkj _ self is the answer accuracy of the j knowledge point of the user, and the Rkj _ self _ old is the calculation result of the previous Rkj _ self of the current Rkj _ self; w1 and w2 are weighting coefficients; nrightj is the number of completely correct questions answered at the jth knowledge point, Nhalfj is the number of partially correct questions answered at the jth knowledge point, and Nallj is the total number of answered questions at the jth knowledge point.
8. The apparatus of claim 7, the answer correct rate Rkj _ avg of other users;
in the above formula, Rkj _ avg is the answer accuracy of the j-th knowledge point of other users, Rkj _ avg _ old is the calculation result of the previous Rkj _ avg of the current calculation of Rkj _ avg, and W3 and W4 are weighting coefficients; prightj is the number of completely correct questions answered at the jth knowledge point, Phalfj is the number of partially correct questions answered at the jth knowledge point, and Pallj is the total number of answered questions at the jth knowledge point.
9. The device of claim 8, wherein w1> w2, w3> w 4.
10. A non-volatile memory storing instructions for implementing the knowledge point mastery degree evaluation method of any one of claims 1 to 5 when executed by a processor.
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