CN112419113A - Foundation learning effect analysis method - Google Patents

Foundation learning effect analysis method Download PDF

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CN112419113A
CN112419113A CN202011398295.4A CN202011398295A CN112419113A CN 112419113 A CN112419113 A CN 112419113A CN 202011398295 A CN202011398295 A CN 202011398295A CN 112419113 A CN112419113 A CN 112419113A
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许昭慧
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Shanghai Squirrel Classroom Artificial Intelligence Technology Co Ltd
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Abstract

The invention discloses a foundation-based learning effect method, which is used for solving the problem that no better learning effect analysis method exists at present to evaluate the foundation-based learning effect. The method comprises the following steps: acquiring the understanding degree of a target learning user to the target basic knowledge; calculating the interest degree value of the target learning user on the target basic knowledge according to the understanding degree and a preset interest degree algorithm; calculating the learning effect score value of the target learning user on the target basic knowledge according to the interest value of the target learning user on the target basic knowledge and a preset learning effect score algorithm; and grading the learning effect of the basic knowledge of the target learning user according to the learning effect score of the target learning user on the target basic knowledge and a preset grading strategy. The method can improve the accuracy of the evaluation of the foundation-hitting learning effect.

Description

Foundation learning effect analysis method
Technical Field
The invention relates to the technical field of data analysis, in particular to a foundation learning effect analysis method.
Background
The foundation learning is a root-tracing and source-tracing learning mode. The foundation learning is carried out, the learning foundation is designed as the name implies, the basic knowledge of the lower level is not firmly mastered, and the knowledge points of the higher level can be understood, learned or learned insignificantly. This is as if covering a house, and the foundation of the skyscraper must be the firmest and most important. If the foundation is not solid, the building is not covered, and the building is easy to collapse even if the building is covered, so that the foundation is more and more important to learn. However, the existing foundation learning method has no effective learning effect analysis method, so that the foundation learning method and the learning effect cannot be evaluated, and the learning quality of a learning user is poor.
Disclosure of Invention
The invention provides a method for analyzing the learning effect of foundation construction, which is used for solving the problem that no better method for analyzing the learning effect exists at present and evaluating the learning effect of the foundation construction. The invention provides a foundation learning effect analysis method, which is used for evaluating the teaching quality of basic knowledge from the self perspective of a target learning user and improving the accuracy of foundation learning effect evaluation by calculating the interest degree of the target learning user in the basic knowledge.
The invention provides a foundation-based learning effect analysis method, which comprises the following steps:
acquiring the understanding degree of a target learning user to the target basic knowledge;
calculating the interest degree value of the target learning user to the target basic knowledge according to the understanding degree and a preset interest degree algorithm;
calculating the learning effect rating value of the target learning user on the target basic knowledge according to the interest value of the target learning user on the target basic knowledge and a preset learning effect rating algorithm;
and grading the basic knowledge learning effect of the target learning user according to the learning effect score value of the target learning user on the target basic knowledge and a preset grading strategy.
In one embodiment, the ranking the learning effect of the basic knowledge of the target learning user according to the learning effect score value of the target learning user on the target basic knowledge and a preset ranking strategy includes:
judging whether the learning effect score of the target learning user on the target basic knowledge reaches a preset score or not;
if the learning effect score value of the target learning user on the target basic knowledge reaches the preset score value, grading the basic knowledge learning effect of the target learning user to be a first level;
if the learning effect score value of the target learning user on the target basic knowledge does not reach the preset score value, calculating a score difference value between the preset score value and the learning effect score value of the target learning user on the target basic knowledge;
judging whether the grading difference is smaller than or equal to a preset difference threshold value;
if the grading difference is smaller than or equal to a preset difference threshold value, grading the basic knowledge learning effect of the target learning user as a second level;
if the grading difference is larger than a preset difference threshold, grading the basic knowledge learning effect of the target learning user to be a third level;
and the learning effects corresponding to the first level, the second level and the third level are sequentially deteriorated.
In one embodiment, the preset interestingness algorithm is:
Figure BDA0002816151700000021
wherein k is the interest value of the target learning user to the target basic knowledge, N is the number of the key words of the knowledge points in the target basic knowledge, I is the number of the knowledge points in the target basic knowledge, iJ represents the number of the attribute components in the ith knowledge point, and SijAttribute score, Q, for the jth attribute of the ith knowledge pointijA correction coefficient of an attribute score of a jth attribute of an ith knowledge point, exp is expressed as a natural constant, M is the number of knowledge point keywords understood by the target learning user, RqThe q-th knowledge point keyword understood by the target learning user is the weighted value of the corresponding content in all the contents of the target basic knowledge, A is the cleverness of the target learning user, and theta1The ratio of the clever degree of the target learning user in the final evaluation result, B the understanding degree of the target learning user to the target basic knowledge, theta2The proportion of the understanding degree of the target basic knowledge of the target learning user in the final evaluation result, TqThe holding strength index of the q-th knowledge point key word corresponding content understood by the target learning user is shown, and T is the target basisThe generalization coefficient of knowledge, omega, is the proportion of the high-order knowledge points of the target basic knowledge;
wherein, M, B, TqA is obtained in the step of collecting the understanding degree of the target learning user on the target basic knowledge after the target learning user learns the target basic knowledge N, I, Sij、iJ、Qij、Rq、θ1、θ2T and omega are preset values.
In one embodiment, SijThe value range is [0.3,0.8 ]]Exp is 2.72, theta1Value of 0.4, theta2A value of 0.3, wherein SijThe value increases with the weight of the ith knowledge point content in all the target basic knowledge contents.
In one embodiment, the preset learning effect scoring algorithm is:
Figure BDA0002816151700000031
wherein U is a learning effect score value of the target learning user on the target basic knowledge, t is a learning duration of the target learning user on contents corresponding to the understood knowledge point keywords, e is a natural constant, X is a difficulty index of the target basic knowledge, delta is a probability index of super contents in the target basic knowledge, and t is1A total duration for the target learning user to learn the target basic knowledge, and b is a current learning strength of the target learning user.
In one embodiment, e is 2.72 and δ is 0.1.
In one embodiment, before the collecting the degree of understanding of the target basic knowledge by the target learning user, the method further includes:
and acquiring target basic knowledge, and extracting knowledge point keywords from the target basic knowledge.
According to the method for analyzing the foundation learning effect, the target learning user can evaluate the teaching quality of the target basic knowledge from the perspective of the target learning user by calculating the interest degree of the target learning user, artificial subjective factors can be considered according to the interest degree of the target learning user as an evaluation standard, the final evaluation result is more humanized rather than mechanized, namely more practical, furthermore, the foundation learning effect is confirmed by calculating the learning effect evaluation value of the target learning user on the target basic knowledge, the foundation learning effect can be simultaneously evaluated according to two aspects of the internal influence factor of the target basic knowledge and the self influence factor of the target learning user, and the evaluation result is more accurate.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flowchart of a first embodiment of a method for analyzing the learning effect of a groundwork according to the present invention;
fig. 2 is a flowchart of step S104.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Fig. 1 is a flowchart of an embodiment of a method for analyzing the learning effect of a groundwork according to the present invention. As shown in fig. 1, the method comprises the following steps S101-S104:
s101: acquiring the understanding degree of a target learning user to the target basic knowledge;
in this embodiment, the degree of understanding of the target learning user on the target basic knowledge includes: the target learning user can learn the learning mastery degree of the target basic knowledge based on the knowledge points, and the target learning user can learn the learning mastery degree of the target basic knowledge based on the knowledge points.
In this embodiment, before step S101, it is preferable that: target basic knowledge is obtained, knowledge point keywords are extracted from the target basic knowledge, and one target basic knowledge can comprise a plurality of keywords.
S102: calculating the interest degree value of the target learning user to the target basic knowledge according to the understanding degree and a preset interest degree algorithm;
in this embodiment, the preset interestingness algorithm is as follows:
Figure BDA0002816151700000051
wherein k is the interest value of the target learning user to the target basic knowledge, N is the number of the key words of the knowledge points in the target basic knowledge, I is the number of the knowledge points in the target basic knowledge, iJ represents the number of the attribute components in the ith knowledge point, and SijThe attribute score of the jth attribute of the ith knowledge point is in the value range of [0.3, 0.8%]The value increases along with the weight of the ith knowledge point content in all the target basic knowledge contents, QijThe correction coefficient of the attribute score of the j attribute of the i knowledge point is exp, which is expressed as a natural constant and takes a value of 2.72, M is the number of knowledge point keywords understood by the target learning user, and R isqThe q-th knowledge point keyword understood by the target learning user is the weighted value of the corresponding content in all the contents of the target basic knowledge, A is the cleverness of the target learning user, and theta1The ratio of the clever degree of the target learning user in the final evaluation result is 0.4, and B is the ratio of the target learning user to the targetDegree of understanding of the underlying knowledge, θ2The understanding degree of the target learning user to the target basic knowledge accounts for the final evaluation result, the value is 0.3, and T isqAnd (3) obtaining a holding strength index of the q-th knowledge point keyword corresponding to the content understood by the target learning user, wherein T is a popular coefficient of the target basic knowledge, and omega is the proportion of the high-order knowledge points of the target basic knowledge. Of the above formulas, M, B, TqA learning the target basic knowledge for the target learning user, obtained in step S101, N, I, Sij、iJ、Qij、Rq、θ1、θ2T and omega are preset values.
S103: calculating the learning effect rating value of the target learning user on the target basic knowledge according to the interest value of the target learning user on the target basic knowledge and a preset learning effect rating algorithm;
in this embodiment, the preset learning effect scoring algorithm is as follows:
Figure BDA0002816151700000061
wherein U is a learning effect score value of the target learning user on the target basic knowledge, t is a learning duration of the target learning user on contents corresponding to the understood knowledge point keywords, e is a natural constant and takes a value of 2.72, X is a difficulty index of the target basic knowledge, delta is a probability index of super-class contents in the target basic knowledge and takes a value of 0.1, t is a probability index of the super-class contents in the target basic knowledge1A total duration for the target learning user to learn the target basic knowledge, and b is a current learning strength of the target learning user.
S104: and grading the basic knowledge learning effect of the target learning user according to the learning effect score value of the target learning user on the target basic knowledge and a preset grading strategy.
In this embodiment, as shown in fig. 2, the step S104 preferably includes the following steps:
s201: judging whether the learning effect score of the target learning user on the target basic knowledge reaches a preset score or not; if yes, executing step S202, otherwise executing step S203;
s202, grading the basic knowledge learning effect of the target learning user into a first level;
in this embodiment, the learning effect score of the target learning user on the target basic knowledge is compared with a preset score, and when the learning effect score of the target learning user on the target basic knowledge is greater than or equal to the preset score, it can be determined that the ground learning effect is excellent.
S203, calculating a grading difference value between the preset grading value and the learning effect grading value of the target learning user on the target basic knowledge;
s204: judging whether the grading difference is smaller than or equal to a preset difference threshold value; if yes, executing step S205, otherwise executing step S206;
s205: ranking the basic knowledge learning effect of the target learning user as a second level;
in this embodiment, when the learning effect score of the target learning user on the target basic knowledge is smaller than a preset score, a difference between the former and the latter is further calculated, whether the difference is within a preset range is determined, and if so, the ground learning effect is determined to be good.
S206: ranking the basic knowledge learning effect of the target learning user to a third level;
in this embodiment, if the difference between the preset score and the learning effect score of the target learning user on the target basic knowledge is not within the preset range, it is determined that the ground learning effect is poor.
According to the method for analyzing the foundation learning effect, the target learning user can evaluate the teaching quality of the target basic knowledge from the perspective of the target learning user by calculating the interest degree of the target learning user, artificial subjective factors can be considered according to the interest degree of the target learning user as an evaluation standard, the final evaluation result is more humanized rather than mechanized, namely more practical, furthermore, the foundation learning effect is confirmed by calculating the learning effect evaluation value of the target learning user on the target basic knowledge, the foundation learning effect can be simultaneously evaluated according to two aspects of the internal influence factor of the target basic knowledge and the self influence factor of the target learning user, and the evaluation result is more accurate.
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, optical storage, and the like) having computer-usable program code embodied therein.
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, 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 flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (7)

1. A foundation-based learning effect analysis method is characterized by comprising the following steps:
acquiring the understanding degree of a target learning user to the target basic knowledge;
calculating the interest degree value of the target learning user to the target basic knowledge according to the understanding degree and a preset interest degree algorithm;
calculating the learning effect rating value of the target learning user on the target basic knowledge according to the interest value of the target learning user on the target basic knowledge and a preset learning effect rating algorithm;
and grading the basic knowledge learning effect of the target learning user according to the learning effect score value of the target learning user on the target basic knowledge and a preset grading strategy.
2. The method for analyzing the effect of learning foundation according to claim 1, wherein the step of ranking the learning effect of the basic knowledge of the target learning user according to the learning effect score value of the target learning user on the target basic knowledge and a preset ranking strategy comprises the steps of:
judging whether the learning effect score of the target learning user on the target basic knowledge reaches a preset score or not;
if the learning effect score value of the target learning user on the target basic knowledge reaches the preset score value, grading the basic knowledge learning effect of the target learning user to be a first level;
if the learning effect score value of the target learning user on the target basic knowledge does not reach the preset score value, calculating a score difference value between the preset score value and the learning effect score value of the target learning user on the target basic knowledge;
judging whether the grading difference is smaller than or equal to a preset difference threshold value;
if the grading difference is smaller than or equal to a preset difference threshold value, grading the basic knowledge learning effect of the target learning user as a second level;
if the grading difference is larger than a preset difference threshold, grading the basic knowledge learning effect of the target learning user to be a third level;
and the learning effects corresponding to the first level, the second level and the third level are sequentially deteriorated.
3. The method for analyzing effects of learning based on foundation according to claim 1 or 2, wherein the preset interestingness algorithm is:
Figure FDA0002816151690000021
wherein k is the interest value of the target learning user to the target basic knowledge, N is the number of the key words of the knowledge points in the target basic knowledge, I is the number of the knowledge points in the target basic knowledge, iJ represents the number of the attribute components in the ith knowledge point, and SijAttribute score, Q, for the jth attribute of the ith knowledge pointijA correction coefficient of an attribute score of a jth attribute of an ith knowledge point, exp is expressed as a natural constant, M is the number of knowledge point keywords understood by the target learning user, RqFor learning said objectThe q-th knowledge point key word understood by the user corresponds to the weight value of the content in all the contents of the target basic knowledge, A is the clever degree of the target learning user, and theta1The ratio of the clever degree of the target learning user in the final evaluation result, B the understanding degree of the target learning user to the target basic knowledge, theta2The proportion of the understanding degree of the target basic knowledge of the target learning user in the final evaluation result, TqThe holding strength index of the q-th knowledge point keyword understood by the target learning user is given, T is a popular coefficient of the target basic knowledge, and omega is the proportion of the high-order knowledge points of the target basic knowledge;
wherein, M, B, TqA is obtained in the step of collecting the understanding degree of the target learning user on the target basic knowledge after the target learning user learns the target basic knowledge N, I, Sij、iJ、Qij、Rq、θ1、θ2T and omega are preset values.
4. The groundbreaking learning effect analysis method of claim 3, wherein S isijThe value range is [0.3,0.8 ]]Exp is 2.72, theta1Value of 0.4, theta2A value of 0.3, wherein SijThe value increases with the weight of the ith knowledge point content in all the target basic knowledge contents.
5. The groundbreaking learning effect analysis method according to claim 3, wherein the preset learning effect scoring algorithm is:
Figure FDA0002816151690000022
wherein U is the learning effect score value of the target learning user on the target basic knowledge, t is the learning duration of the target learning user on the content corresponding to the understood knowledge point keywords, and e isNatural constant, X is difficulty index of the target basic knowledge, delta is probability index of super content in the target basic knowledge, t1A total duration for the target learning user to learn the target basic knowledge, and b is a current learning strength of the target learning user.
6. The method for analyzing the foundation-based learning effect according to claim 5, wherein the value of e is 2.72, and the value of δ is 0.1.
7. The method for analyzing effects of groundwork learning according to claim 1, wherein before the collecting the degree of understanding of the target learning user about the target basic knowledge, the method further comprises:
and acquiring target basic knowledge, and extracting knowledge point keywords from the target basic knowledge.
CN202011398295.4A 2020-12-03 2020-12-03 Foundation learning effect analysis method Pending CN112419113A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114049669A (en) * 2021-11-15 2022-02-15 海信集团控股股份有限公司 Method and device for determining learning effect

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114049669A (en) * 2021-11-15 2022-02-15 海信集团控股股份有限公司 Method and device for determining learning effect

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