CN112269936A - User subject learning state analysis method, system and storage medium - Google Patents
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Abstract
The invention provides a method, a system and a storage medium for analyzing the learning state of a user subject, wherein the method comprises the following steps: acquiring learning data of a target user for a target subject in a unit period; calculating a target heat value of the target user to the target subject in each unit period according to the learning data; calculating a target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period; acquiring the average learning times of all users in a target subject in a statistical period and the learning times of the target user in the target subject in the statistical period; and calculating a target preference value of the target user for the target subject according to the target interest value, the average learning times and the learning times. The invention can quickly calculate the real-time preference state of the user, realizes the dynamic evolution of the subject learning state, and provides scientific data support for parents and teachers to know the performance in real time and further check for missing and filling.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to a method and a system for analyzing a user subject learning state and a storage medium.
Background
In the education industry, how to effectively identify the learning state of the user is a big problem in the industry, and it is also concerned by parents and teachers that students are in the learning stage and have insufficient preferences for which subjects. The traditional subject state evaluation method is generally a static evaluation method, the static evaluation method realizes evaluation of the skill level of a student by simple statistics of test results and time, but the quantitative evaluation method of the skill level only considers the final test results and ignores analysis and utilization of training conditions before final test, the skill level of the student is often determined only by the final test results, and the dynamic evolution characteristic of the skill level is not described.
The above method has two defects, on one hand, the test period is too long, and parents or even teachers can not track the learning condition in real time after the examination is finished or even at the end of the school period when acquiring the subject preference. On the other hand, the obtained conclusion is very rough, parents cannot obtain accurate preference data from the detail state of the study of students, the potential of dominant subjects cannot be quickly mined, and the omission of non-preference subjects cannot be easily checked.
Disclosure of Invention
The invention aims to provide a method, a system and a storage medium for analyzing the subject learning state of a user, which can quickly calculate the real-time preference state of the user, realize the dynamic evolution of the subject learning state and provide scientific data support for parents and teachers to know the achievement in real time and further check for omission.
The technical scheme provided by the invention is as follows:
the invention provides a method for analyzing the learning state of a user subject, which comprises the following steps:
acquiring learning data of a target user for a target subject in a unit period, wherein the learning data comprises knowledge points of the target subject and learning time of each knowledge point in different learning modes;
calculating a target heat value of the target user to the target subject in each unit period according to the learning data;
calculating a target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period;
acquiring the average learning times of all users in the target subject in a statistical period and the learning times of the target user in the statistical period;
and calculating a target preference value of the target user for the target subject according to the target interest value, the average learning times and the learning times, and analyzing the learning state of the target user for the target subject according to the target preference value.
Further, calculating a target heat value of the target user for the target subject in each unit period according to the learning data includes:
analyzing the hierarchical relationship of the knowledge points of the target subject according to the incidence relationship among the knowledge points;
calculating the target heat value according to the knowledge points of the first level under the target subject and the learning times of different learning modes of all the knowledge points
Wherein N is the total number of knowledge points of the first level under the target subject, ciIs the effective learning time of the ith knowledge point of the first hierarchy in the unit period, wiIs the weight of the ith knowledge point.
Further, the step of obtaining the learning data of the target user for the target subject in the unit period includes:
acquiring learning data of a target user for a target subject in a unit period, wherein the learning data comprises knowledge points of the target subject and learning time of each knowledge point in different learning modes;
and when any learning time is longer than or equal to the preset time length, marking the corresponding learning as effective learning, and recording the effective learning time, wherein the effective learning time is the learning time length or the learning times.
Further, calculating the target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period comprises the following steps:
acquiring the heat value of each unit period in the statistical period;
calculating the target interest value h according to the heat value of each unit period in the statistical period,
wherein t is the time interval from a certain unit period in the statistical period to the current time,
the heat value of the unit period corresponding to the time interval t is shown, e is a natural constant, and omega is an attenuation coefficient.
Further, calculating a target preference value of the target user for the target subject according to the target interest value, the average learning frequency and the learning frequency, and analyzing the learning state of the target user for the target subject according to the target preference value comprises the steps of:
calculating a target preference value pre of the target user for the target subject according to the target interest value, the average learning times and the learning times,
wherein C learns the number of times of learning of the target subject for the target user,
learning the average learning times of the target subject for all users, wherein m and n are fixed parameters, and n is greater than 0;
and analyzing the learning state of the target user for the target subject according to the target preference value.
The invention also provides an analysis system for the user subject learning state, which comprises the following steps:
the data acquisition module is used for acquiring learning data of a target user for a target subject in a unit period, wherein the learning data comprises knowledge points of the target subject and learning time of each knowledge point in different learning modes;
the heat value analysis module is in communication connection with the data acquisition module and is used for calculating a target heat value of the target user to the target subject in each unit period according to the learning data;
the interest value analysis module is in communication connection with the heat value analysis module and is used for calculating a target interest value of the target user for the target subject according to the heat value of each unit period in a statistical period;
the data acquisition module is further used for acquiring the average learning times of the target subject in a statistical period of all users and the learning times of the target subject in the statistical period of the target user;
the preference value analysis module is in communication connection with the data acquisition module and the interest value analysis module and is used for calculating a target preference value of the target user for the target subject according to the target interest value, the average learning times and the learning times;
and analyzing the learning state of the target user for the target subject according to the target preference value.
Further, the method also comprises the following steps:
the initialization module is used for starting a monitoring thread and awakening the IC when the intelligent terminal is initialized;
and the thread control module is in communication connection with the initialization module and is used for controlling the monitoring thread to enter a dormant state if no wake-up event is generated.
Further, the method also comprises the following steps:
the heat value analysis module is used for analyzing the hierarchical relationship of the knowledge points of the target subject according to the incidence relationship among the knowledge points; calculating the target heat value according to the knowledge points of the first level under the target subject and the learning times of different learning modes of all the knowledge points
Wherein N is the total number of knowledge points of the first level under the target subject, ciIs the effective learning time of the ith knowledge point of the first hierarchy in the unit period, wiIs the weight of the ith knowledge point.
Further, the method also comprises the following steps:
the interest value analysis module acquires the heat value of each unit period in the statistical period; calculating the target interest value h according to the heat value of each unit period in the statistical period,
wherein t is the time interval from a certain unit period in the statistical period to the current time,
the heat value of the unit period corresponding to the time interval t is shown, e is a natural constant, and omega is an attenuation coefficient.
Further, the method also comprises the following steps:
the preference value analysis module calculates a target preference value pre of the target user for the target subject according to the target interest value, the average learning times and the learning times,
wherein C learns the number of times of learning of the target subject for the target user,
learning the average learning times of the target subject for all users, wherein m and n are fixed parameters, and n is greater than 0, and analyzing the learning state of the target user for the target subject according to the target preference value.
The invention also provides a storage medium, in which at least one instruction is stored, where the instruction is loaded and executed by a processor to implement the operation performed by the method for analyzing a learning state of a user subject.
By the method, the system and the storage medium for analyzing the user subject learning state, the real-time preference state of the user can be quickly calculated, the dynamic evolution of the subject learning state is realized, and scientific data support is provided for parents and teachers to know the achievement in real time and further check for missing and filling up the deficiency.
Drawings
The above features, technical features, advantages and implementations of a terminal device testing method, a smart watch and a system will be further described in the following preferred embodiments in a clearly understandable manner with reference to the accompanying drawings.
FIG. 1 is a flow diagram of one embodiment of a method for analyzing a learning state of a user subject of the present invention;
FIG. 2 is a flow chart of another embodiment of a method for analyzing a learning status of a user subject of the present invention;
FIG. 3 is a flow chart of another embodiment of a method for analyzing a learning status of a user subject of the present invention;
fig. 4 is a schematic structural diagram of an embodiment of a system for analyzing a user subject learning state according to the present invention.
Reference numerals:
100 user subject learning state analysis system
110 data acquisition module
120 heat value analysis module
130 value of interest analysis module
140 preference value analysis module
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
For the sake of simplicity, the drawings only schematically show the parts relevant to the present invention, and they do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically illustrated or only labeled. In this document, "one" means not only "only one" but also a case of "more than one".
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
In addition, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not intended to indicate or imply relative importance.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
An embodiment of the present invention, referring to fig. 1, is a method for analyzing a learning state of a user subject, including:
s100, learning data of a target user for a target subject in a unit period are obtained, wherein the learning data comprise knowledge points of the target subject and learning time of each knowledge point in different learning modes;
s200, calculating a target heat value of the target user to the target subject in each unit period according to the learning data;
s300, calculating a target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period;
s400, acquiring the average learning times of the target subject in a statistical period of all users and the learning times of the target subject in the statistical period of the target user;
s500, calculating a target preference value of the target user for the target subject according to the target interest value, the average learning frequency and the learning frequency, and analyzing the learning state of the target user for the target subject according to the target preference value.
Specifically, in this embodiment, on the premise of user permission, the learning data of the user is obtained through the smart terminal (including but not limited to an educational tablet terminal), where the learning data includes: the user can do questions, watch tutorials, use assistants, APP applications and other information dimensions. And judging the preference expression of the user for a certain subject from dimensions such as the use duration of data, the subject selection, the subject making accuracy and the like.
Firstly, acquiring the data in real time, and acquiring the learning data of the target user for the target subject in a unit period. It should be noted that, in order to protect privacy and security, if the learning data of the user is not approved by the user, any data of the user is only calculated and processed on the background of the terminal, and is not specifically displayed. The unit period may be one hour or one day, or two hours or two days, and the actual period length may be freely set by the user. In this embodiment, the preference of a certain target user for a certain target subject is taken as an example for analysis, different users or different subjects are different only in the acquired learning data, and the analysis methods are the same.
The learning data includes knowledge points of the target subject and learning time of different learning modes of the knowledge points. For example, when the target subject is a Chinese language, the learning data includes which knowledge point, such as poems, words or literary languages, of the target user to learn the specific subject of the Chinese language, and since a certain upper and lower hierarchical relationship exists between the knowledge points, such as poems including ancient poems, modern poems, and the like, the ancient poems including seven-language poems, five-language poems, and the like, the association relationship between different knowledge points is also obtained at the same time, so as to analyze the relationship between different knowledge points.
In addition, users with different knowledge points may learn in different learning manners, for example, the users may watch a learning video to learn, and also may do exercises related to the knowledge points, or discuss the knowledge points with students through live broadcasting, and the like, and the efficiency of different learning manners and the user preference are different, so that learning time for learning in different learning manners is obtained respectively.
And then analyzing and weighting the acquired learning data, calculating a target heat value of the target user to the target subject in each unit period according to the learning data, and extracting effective characteristics. For example, when the unit period is one day, the target heat value of the target user for the target subject every day is calculated respectively.
The target heat value in the unit period calculated by the learning data can already represent the interest in the unit period to a certain extent, but because the interest is changed along with the change of time, the relevance degree of the interest is weakened as the time is longer, that is, the learning data in the unit period closer to the current time interval can reflect the learning preference of the user, so the current interest value obtained by the calculation model is obtained by the attenuation of the target heat value in each unit period in the statistical period, that is, the statistical period is greater than the unit period, and the statistical period comprises a plurality of unit periods. And calculating the target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period, for example, when the unit period is one day and the statistical period is 30 days, calculating the target interest value of the target user for the target subject according to the heat value of each day of a certain 30 days.
The preference is usually the reflection of the use interest value of a user for a certain object, but the interest value cannot be directly used, the comprehensive popularity of the object needs to be considered, and the long-term occupation of the subjects with high popularity in the preference list is avoided. Therefore, the average use times of the subjects are used for constraint, only subjects exceeding the average use times can be positively preferred, and the preference value is relatively attenuated.
Therefore, the average learning times of all the users in the target subject in the statistical period and the learning times of the target user in the target subject in the statistical period are obtained. All the data of statistics should be from the same platform or multiple platforms, part of the data cannot be from platform a, and the other part of the data is from platform B. And finally, calculating a target preference value of the target user for the target subject according to the target interest value, the average learning frequency and the learning frequency.
And analyzing the learning state of the target user for the target subject according to the target preference value, wherein the higher the target preference value is, the higher the learning frequency of the target user for the target subject is. Conversely, the smaller the target preference value is, the less the target user prefers to learn the target subject, and the lower the learning frequency is.
The student subject preference identification method provided by the invention based on the big data statistical method is combined with the operation data of the student user on the learning terminal to analyze the learning state, thereby realizing the real-time output of the subject preference state and providing scientific data support for parents, teachers and even students to promote the subsequent learning.
In another embodiment of the present invention, as shown in fig. 2, a method for analyzing a learning status of a user subject includes:
s100, learning data of a target user for a target subject in a unit period are obtained, wherein the learning data comprise knowledge points of the target subject and learning time of each knowledge point in different learning modes;
s210, analyzing the hierarchical relationship of the knowledge points of the target subject according to the incidence relationship among the knowledge points;
s220, calculating the target heat value according to the knowledge points of the first level under the target subject and the learning times of different learning modes of all the knowledge points
Wherein N is the total number of knowledge points of the first level under the target subject, ciIs the effective learning time of the ith knowledge point of the first hierarchy in the unit period, wiThe weight of the ith knowledge point;
s300, calculating a target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period;
s400, acquiring the average learning times of the target subject in a statistical period of all users and the learning times of the target subject in the statistical period of the target user;
s500, calculating a target preference value of the target user for the target subject according to the target interest value, the average learning times and the learning times.
Specifically, in this embodiment, learning data of the target user for the target subject in the unit period is acquired. In particular, the learning data includes knowledge points of the target subject and learning times of different learning methods for the knowledge points. For example, when the target subject is a Chinese language, the learning data includes which knowledge point the target user learns the target subject Chinese language, such as poems, words, or Chinese languages, and in addition, the users with different knowledge points may learn in different learning manners, for example, the users may learn by watching a learning video, may also do exercises related to the knowledge point, or discuss the knowledge point with the same student by live broadcasting, and therefore, the efficiency of different learning manners and the user preference are different, and therefore, learning time for learning in different learning manners is obtained respectively.
And then analyzing and weighting the acquired learning data, and analyzing the hierarchical relationship of each knowledge point of the target subject according to the incidence relationship among the knowledge points, wherein the purpose is mainly to analyze the influence factor of the target heat value of the target subject and avoid repeated calculation of the same learning data, for example, poetry of a target user learning Libai belongs to learning of ancient poetry of the knowledge points, learning of poetry of the knowledge points and learning of poetry of the Tang Dynasty of the knowledge points, so that the hierarchical relationship among all the knowledge points needs to be analyzed. And then selecting a knowledge point of a first level under the target department, namely the knowledge point has no concept of the knowledge point included in the previous level. The division of the hierarchical relationship depends on the division of the target subject influence factor by the user, for example, the target user is divided by the age, etc. In addition, the target user can also choose to acquire knowledge points of a certain level or multiple levels for analysis, and the learning data is not necessarily limited to the learning data of the first level.
Calculating a target heat value according to the knowledge points of the first level under the target subject and the learning times of different learning modes of all the knowledge points
Wherein N is the total number of knowledge points of the first level under the target subject, ciIs the effective learning time of the ith knowledge point of the first hierarchy in the unit period, wiThe weight of the ith knowledge point can be set by a user independently. The calculation method of the target heat value is to analyze data of N influence factors of a target subject, such as the subject-making behavior of students, the course-watching behavior, and the like as evaluation elements, and actually, not only can calculate the target heat value of the target subject, but also can calculate the heat value of any object, for example, the heat value of a certain knowledge point can be calculated through learning data of the knowledge point at a level next to the certain knowledge point.
And calculating the target heat value of the target user to the target subject in each unit period according to the learning data by the calculating method, and extracting effective characteristics. For example, when the unit period is one day, the target heat value of the target user for the target subject every day is calculated respectively.
The target heat value in the unit period calculated by the learning data may already indicate the interest in the unit period to some extent, but since the interest should be changed with the change of time, the correlation degree of the interest is weakened as the time is longer. And calculating the target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period, for example, when the unit period is one day and the statistical period is 30 days, calculating the target interest value of the target user for the target subject according to the heat value of each day of a certain 30 days.
And acquiring the average learning times of all the users in the target subject in the statistical period and the learning times of the target user in the statistical period. All the data of statistics should be from the same platform or multiple platforms, part of the data cannot be from platform a, and the other part of the data is from platform B. And finally, calculating a target preference value of the target user for the target subject according to the target interest value, the average learning frequency and the learning frequency.
The larger the target preference value is, the higher the learning frequency of the target user to the target subject is, and the more the target user prefers the target subject. Conversely, the smaller the target preference value, the less the target user balance prefers to learn the target subject. Therefore, the subsequent learning plan of the target user can be planned according to the target preference value, and the learning achievement of the target subject of the target user can be combined.
The method uses a plurality of user behavior characteristics, such as a problem solving process, a video learning process, a data searching process and other platform active interactive behaviors. The problems of inaccurate calculation results, poor generalization capability and the like caused by a single characteristic model are solved, the real-time preference state of a user can be quickly calculated, and the dynamic evolution of the subject learning state is realized.
And the preference factors are extracted by combining the actual learning behaviors of the user, so that the used learning state judgment is more scientific and reliable. The method is suitable for inputting high-dimensional samples, can effectively operate on a large data set, is suitable for parallel computing, and is high in computing efficiency.
Preferably, in another embodiment of the present application, the step S100 of acquiring learning data of a target user for a target subject in a unit period, where the learning data includes knowledge points of the target subject and learning time of each knowledge point in different learning manners includes:
s110, learning data of a target user for a target subject in a unit period are obtained, wherein the learning data comprise knowledge points of the target subject and learning time of each knowledge point in different learning modes;
and S120, when any learning time is longer than or equal to the preset time length, marking the corresponding learning as effective learning, and recording the effective learning time, wherein the effective learning time is the learning time length or the learning times.
Specifically, in this embodiment, learning data of the target user for the target subject in the unit period is obtained, the learning data generated by the user may not be learning in some cases, for example, when the user searches for some information, the user may be closed after just opening a video, but the user may also record the learning data as the learning data of the user, so that identification of effective learning is performed. And identifying the learning duration of the user traffic in each item of acquired learning data, marking the corresponding learning as effective learning only when the learning time is greater than or equal to a preset time length, and recording the effective learning time.
In addition, the number of times of statistically valid learning may be counted, and then the heat value may be calculated based on the number of times in the above-described manner. On the other hand, however, the learning time length of each learning may be different, so that the learning time length for directly and statistically effective learning may be selected.
The invention judges effective learning based on the learning time of each learning, and further screens effective characteristics, so that the data obtained by analysis is more effective and reliable.
In another embodiment of the present invention, as shown in fig. 3, a method for analyzing a learning status of a user subject includes:
s100, learning data of a target user for a target subject in a unit period are obtained, wherein the learning data comprise knowledge points of the target subject and learning time of each knowledge point in different learning modes;
s200, calculating a target heat value of the target user to the target subject in each unit period according to the learning data;
s310, acquiring the heat value of each unit period in the statistical period;
s320, calculating the target interest value h according to the heat value of each unit period in the statistical period,
wherein t is the time interval from a certain unit period in the statistical period to the current time,
the heat value of the corresponding unit period when the time interval is t, e is a natural constant, and omega is an attenuation coefficient;
s400, acquiring the average learning times of the target subject in a statistical period of all users and the learning times of the target subject in the statistical period of the target user;
s500, calculating a target preference value of the target user for the target subject according to the target interest value, the average learning times and the learning times.
Specifically, in this embodiment, learning data of the target user for the target subject in the unit period is acquired. In particular, the learning data includes knowledge points of the target subject and learning times of different learning methods for the knowledge points. For example, when the target subject is a Chinese language, the learning data includes which knowledge point the target user learns the target subject Chinese language, such as poems, words, or Chinese languages, and in addition, the users with different knowledge points may learn in different learning manners, for example, the users may learn by watching a learning video, may also do exercises related to the knowledge point, or discuss the knowledge point with the same student by live broadcasting, and therefore, the efficiency of different learning manners and the user preference are different, and therefore, learning time for learning in different learning manners is obtained respectively.
And then analyzing and weighting the acquired learning data, calculating a target heat value of the target user to the target subject in each unit period according to the learning data, and extracting effective characteristics. For example, when the unit period is one day, the target heat value of the target user for the target subject every day is calculated respectively.
The target heat value in the unit period calculated by the learning data may already indicate the interest in the unit period to some extent, but since the interest should be changed with the change of time, the correlation degree of the interest is weakened as the time is longer. And calculating the target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period, for example, when the unit period is one day and the statistical period is 30 days, calculating the target interest value of the target user for the target subject according to the heat value of each day of a certain 30 days.
Calculating a target interest value h according to the heat value of each unit period in the statistical period,
wherein t is the time interval between a certain unit period in the statistical period and the current time, the longer the time is, the smaller the influence on the final target interest value is,
when the time interval is t, the heat value of the corresponding unit period, namely the heat value of each unit period in the statistical period, is e, ω is a natural constant, ω is an attenuation coefficient, and ω takes a value (0, 1).
And acquiring the average learning times of all the users in the target subject in the statistical period and the learning times of the target user in the statistical period. All the data of statistics should be from the same platform or multiple platforms, part of the data cannot be from platform a, and the other part of the data is from platform B. And finally, calculating a target preference value of the target user for the target subject according to the target interest value, the average learning frequency and the learning frequency.
In the application, the learning state change is combined, the influence of the interval between the time generated by the learning data and the current time on the user preference degree is considered, and the dynamic attenuation model is used, so that the preference tracking is more accurate, the current latest user preference can be more accurately expressed, the learning state of the user is known and mastered in real time, and the follow-up corresponding measures are taken conveniently.
In another embodiment of the present invention, a method for analyzing a learning status of a user subject includes:
s100, learning data of a target user for a target subject in a unit period are obtained, wherein the learning data comprise knowledge points of the target subject and learning time of each knowledge point in different learning modes;
s200, calculating a target heat value of the target user to the target subject in each unit period according to the learning data;
s300, calculating a target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period;
s400, acquiring the average learning times of the target subject in a statistical period of all users and the learning times of the target subject in the statistical period of the target user;
s510, calculating a target preference value pre of the target user for the target subject according to the target interest value, the average learning times and the learning times,
wherein C learns the number of times of learning of the target subject for the target user,
learning the average number of learning for the target subject for all users; and analyzing the learning state of the target user for the target subject according to the target preference value.
Specifically, in this embodiment, learning data of the target user for the target subject in the unit period is acquired. In particular, the learning data includes knowledge points of the target subject and learning times of different learning methods for the knowledge points. For example, when the target subject is a Chinese language, the learning data includes which knowledge point the target user learns the target subject Chinese language, such as poems, words, or Chinese languages, and in addition, the users with different knowledge points may learn in different learning manners, for example, the users may learn by watching a learning video, may also do exercises related to the knowledge point, or discuss the knowledge point with the same student by live broadcasting, and therefore, the efficiency of different learning manners and the user preference are different, and therefore, learning time for learning in different learning manners is obtained respectively.
And then analyzing and weighting the acquired learning data, calculating a target heat value of the target user to the target subject in each unit period according to the learning data, and extracting effective characteristics. For example, when the unit period is one day, the target heat value of the target user for the target subject every day is calculated respectively.
The target heat value in the unit period calculated by the learning data may already indicate the interest in the unit period to some extent, but since the interest should be changed with the change of time, the correlation degree of the interest is weakened as the time is longer. And calculating the target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period, for example, when the unit period is one day and the statistical period is 30 days, calculating the target interest value of the target user for the target subject according to the heat value of each day of a certain 30 days.
And acquiring the average learning times of all the users in the target subject in the statistical period and the learning times of the target user in the statistical period. All the data of statistics should be from the same platform or multiple platforms, part of the data cannot be from platform a, and the other part of the data is from platform B.
Finally, according to the target interest value, the average learning frequency and the learning frequency, calculating a target preference value pre of the target user for the target subject,
wherein C is the number of learning times that the target user learns the target subject,
learning the average number of learning times of the target subject for all users, m and n being fixed parameters and arbitrary numbers, whereinAnd n is greater than 0, the denominator in the calculation formula is prevented from being 0, and the ratio of m to n tends to 1, so that the influence of the values of m and n on the target preference value is reduced, and the target preference value more obviously indicates the preference of the user to the subject. The larger the target preference value is, the higher the learning frequency of the target user to the target subject is, and the more the target user prefers the target subject. Conversely, the smaller the target preference value, the less the target user prefers to learn the target subject.
According to the calculation mode, the learning data of different users for different subjects are selected, and then the corresponding preference values can be calculated.
The preference degree analysis is carried out based on the average learning times of all users, the average use times of the subjects are used for constraint, the influence of the popularity of the subjects is removed, only the subjects exceeding the average use times can carry out positive preference, otherwise, the preference values are relatively attenuated, and the analysis result is more reliable.
The invention discloses a calculation method of user subject preference degree based on big data and a decay model, which uses a plurality of user behavior characteristics, such as a problem solving process, a video learning process, a data searching process and other platform active interaction behaviors, and uses a time decay model. The method solves the problems of inaccurate calculation result, poor generalization capability and the like caused by a single characteristic model, can quickly calculate the real-time preference state of the user, and realizes the dynamic evolution of the subject learning state. The result of the invention provides scientific data support for parents and teachers to know the performance in real time and further check for missing and filling up the gap.
In an embodiment of the present invention, as shown in fig. 4, a system 100 for analyzing a learning status of a user subject includes:
the data acquisition module 110 is configured to acquire learning data of a target user for a target subject in a unit period, where the learning data includes knowledge points of the target subject and learning time of each knowledge point in different learning manners;
a heat value analysis module 120, communicatively connected to the data acquisition module 110, configured to calculate a target heat value of the target user for the target subject in each unit period according to the learning data;
an interest value analyzing module 130, communicatively connected to the heat value analyzing module 120, configured to calculate a target interest value of the target user for the target subject according to the heat values of each unit period in a statistical period;
the data obtaining module 110 is further configured to obtain the average learning times of the target subject in a statistical period of all users, and the learning times of the target subject in the statistical period of the target user;
a preference value analyzing module 140, communicatively connected to the data obtaining module 110 and the interest value analyzing module 130, configured to calculate a target preference value of the target user for the target subject according to the target interest value, the average learning frequency, and the learning frequency, and analyze a learning state of the target user for the target subject according to the target preference value.
Further comprising:
the heat value analysis module 120 analyzes the hierarchical relationship of the knowledge points of the target subject according to the association relationship between the knowledge points; calculating the target heat value according to the knowledge points of the first level under the target subject and the learning times of different learning modes of all the knowledge points
Wherein N is the total number of knowledge points of the first level under the target subject, ciIs the effective learning time of the ith knowledge point of the first hierarchy in the unit period, wiIs the weight of the ith knowledge point.
Further comprising:
the interest value analysis module 130 obtains the heat value of each unit period in the statistical period; calculating the target interest value h according to the heat value of each unit period in the statistical period,
wherein t is the time interval from a certain unit period in the statistical period to the current time,
the heat value of the unit period corresponding to the time interval t is shown, e is a natural constant, and omega is an attenuation coefficient.
The preference value analysis module 140 calculates a target preference value pre of the target user for the target subject according to the target interest value, the average learning times and the learning times,
wherein C learns the number of times of learning of the target subject for the target user,
learning the average learning times of the target subject for all users, wherein m and n are fixed parameters, and n is greater than 0, and analyzing the learning state of the target user for the target subject according to the target preference value.
Specifically, the functions of the modules in this embodiment have been described in detail in the corresponding method embodiments, and therefore, the description thereof is omitted.
Based on the same inventive concept, the embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements all or part of the method steps of the above method.
The present invention can implement all or part of the processes of the above methods, and can also be implemented by using a computer program to instruct related hardware, where the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above method embodiments can 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 computer program code, recording medium, U.S. 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 media, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.
Based on the same inventive concept, an embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program running on the processor, and the processor executes the computer program to implement all or part of the method steps in the method.
The Processor 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, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center of the computer device and the various interfaces and lines connecting the various parts of the overall computer device.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the computer device by executing or executing the computer programs and/or modules stored in the memory, as well as by invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (e.g., a sound playing function, an image playing function, etc.); the storage data area may store data (e.g., audio data, video data, etc.) created according to the use of the cellular phone. In addition, the memory 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.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, server, 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 has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), servers 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 (10)
1. A method for analyzing the learning state of a user subject is characterized by comprising the following steps:
acquiring learning data of a target user for a target subject in a unit period, wherein the learning data comprises knowledge points of the target subject and learning time of each knowledge point in different learning modes;
calculating a target heat value of the target user to the target subject in each unit period according to the learning data;
calculating a target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period;
acquiring the average learning times of all users in the target subject in a statistical period and the learning times of the target user in the statistical period;
calculating a target preference value of the target user for the target subject according to the target interest value, the average learning times and the learning times, and analyzing the learning state of the target user for the target subject according to the target preference value.
2. The method of claim 1, wherein calculating the target heat value of the target user for the target subject in each unit period according to the learning data comprises:
analyzing the hierarchical relationship of the knowledge points of the target subject according to the incidence relationship among the knowledge points;
calculating the target heat value according to the knowledge points of the first level under the target subject and the learning times of different learning modes of all the knowledge points
Wherein N is the total number of knowledge points of the first level under the target subject, ciIs the effective learning time of the ith knowledge point of the first hierarchy in the unit period, wiIs the weight of the ith knowledge point.
3. The method for analyzing the learning status of the user subject according to claim 2, wherein the step of obtaining the learning data of the target user for the target subject in the unit period comprises the steps of:
acquiring learning data of a target user for a target subject in a unit period, wherein the learning data comprises knowledge points of the target subject and learning time of each knowledge point in different learning modes;
and when any learning time is longer than or equal to the preset time length, marking the corresponding learning as effective learning, and recording the effective learning time, wherein the effective learning time is the learning time length or the learning times.
4. The method for analyzing the learning status of the user subject according to claim 1, wherein the step of calculating the target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period comprises the steps of:
acquiring the heat value of each unit period in the statistical period;
according to the statistical periodCalculating the target interest value h by the heat value of each unit period,
wherein t is the time interval from a certain unit period in the statistical period to the current time,
the heat value of the unit period corresponding to the time interval t is shown, e is a natural constant, and omega is an attenuation coefficient.
5. The method for analyzing the learning status of the user subject according to claim 1, wherein the step of calculating the target preference value of the target user for the target subject according to the target interest value, the average learning number and the learning number, and the step of analyzing the learning status of the target user for the target subject according to the target preference value comprises the steps of:
calculating a target preference value pre of the target user for the target subject according to the target interest value, the average learning times and the learning times,
wherein C learns the number of times of learning of the target subject for the target user,
learning the average learning times of the target subject for all users, wherein m and n are fixed parameters;
and analyzing the learning state of the target user for the target subject according to the target preference value.
6. A system for analyzing a learning status of a user subject, comprising:
the data acquisition module is used for acquiring learning data of a target user for a target subject in a unit period, wherein the learning data comprises knowledge points of the target subject and learning time of each knowledge point in different learning modes;
the heat value analysis module is in communication connection with the data acquisition module and is used for calculating a target heat value of the target user to the target subject in each unit period according to the learning data;
the interest value analysis module is in communication connection with the heat value analysis module and is used for calculating a target interest value of the target user for the target subject according to the heat value of each unit period in a statistical period;
the data acquisition module is further used for acquiring the average learning times of the target subject in a statistical period of all users and the learning times of the target subject in the statistical period of the target user;
and the preference value analysis module is in communication connection with the data acquisition module and the interest value analysis module and is used for calculating a target preference value of the target user for the target subject according to the target interest value, the average learning times and the learning times and analyzing the learning state of the target user for the target subject according to the target preference value.
7. The system for analyzing the learning status of the user subject of claim 6, wherein the heat value analysis module analyzes the hierarchical relationship of the knowledge points of the target subject according to the association relationship between the knowledge points; calculating the target heat value according to the knowledge points of the first level under the target subject and the learning times of different learning modes of all the knowledge points
Wherein N is the total number of knowledge points of the first level under the target subject, ciIs the effective learning time of the ith knowledge point of the first hierarchy in the unit period, wiAs the weight of the ith knowledge point。
8. The system of claim 6, wherein the interest value analysis module obtains a heat value of each unit period in the statistical period; calculating the target interest value h according to the heat value of each unit period in the statistical period,
wherein t is the time interval from a certain unit period in the statistical period to the current time,
the heat value of the unit period corresponding to the time interval t is shown, e is a natural constant, and omega is an attenuation coefficient.
9. The system of analyzing the learning status of a user subject according to claim 6, wherein the preference value analyzing module calculates a target preference value pre of the target user for the target subject according to the target interest value, the average learning number and the learning number,
wherein C learns the number of times of learning of the target subject for the target user,
and learning the average learning times of the target subject for all users, wherein m and n are fixed parameters, and analyzing the learning state of the target user for the target subject according to the target preference value.
10. A storage medium having stored therein at least one instruction, which is loaded and executed by a processor to implement a method of analyzing a user's subject learning state according to any one of claims 1 to 5.
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