CN112053091A - Data processing method and system based on learning operation - Google Patents

Data processing method and system based on learning operation Download PDF

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Publication number
CN112053091A
CN112053091A CN202011043239.9A CN202011043239A CN112053091A CN 112053091 A CN112053091 A CN 112053091A CN 202011043239 A CN202011043239 A CN 202011043239A CN 112053091 A CN112053091 A CN 112053091A
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learning
experience value
current
data
historical
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王枫
马镇筠
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Beijing Love Theory Technology Co ltd
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Beijing Love Theory Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063112—Skill-based matching of a person or a group to a task
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063116—Schedule adjustment for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G06Q50/205—Education administration or guidance

Abstract

The embodiment of the application provides a data processing method and a data processing system based on learning operation, which relate to the field of education, and the method comprises the following steps: acquiring current operation data; calculating according to the current operation data to obtain a learning state conversion experience value; calculating according to the learning state conversion experience value and historical operation data to obtain a learning effect evaluation value; and calculating according to the learning state conversion experience value and the learning effect evaluation value to obtain the current learning experience value. Therefore, by implementing the implementation mode, the data processing can be carried out on the learning operation, so that the final learning result is displayed in the form of an experience value, and the learning result of the user is effectively embodied.

Description

Data processing method and system based on learning operation
Technical Field
The application relates to the field of education, in particular to a data processing method and system based on learning operation.
Background
With the rapid development of the internet industry, more and more software appears in the visual field of people, wherein education software appears like bamboo shoots in spring after rain. However, in practice, it is found that more or less of the current education software includes a learning scoring system, a learning evaluation system, and the like, and the purpose of the learning scoring system is to quantify the learning result of the user, so as to visually display the learning result of the user. However, the use of this method does not reflect the growth of the user in the learning process, and thus the learning process of the user cannot be effectively evaluated, and the original learning result cannot be effectively reflected.
Disclosure of Invention
An object of the embodiments of the present application is to provide a data processing method and system based on a learning operation, which can perform data processing on the learning operation, so that a final learning result is displayed in the form of an empirical value, thereby effectively reflecting the learning result of a user.
A first aspect of an embodiment of the present application provides a data processing method based on learning operation, where the method includes:
acquiring current operation data;
calculating according to the current operation data to obtain a learning state conversion experience value;
calculating according to the learning state conversion experience value and historical operation data to obtain a learning effect evaluation value;
and calculating according to the learning state conversion experience value and the learning effect evaluation value to obtain a current learning experience value.
In the implementation process, the data processing method based on the learning operation can preferentially acquire the current operation data; then, calculating according to the current operation data to obtain a learning state conversion experience value; then, calculating according to the learning state conversion experience value and historical operation data to obtain a learning effect evaluation value; and finally, calculating according to the learning state conversion experience value and the learning effect evaluation value to obtain the current learning experience value. Therefore, by implementing the implementation mode, the learning effect, the learning progress and the learning speed of the user on the current multiple knowledge points can be determined according to the operation of the user, so that the learning state conversion experience value is obtained, then whether the learning speed is increased or not is judged according to the learning speed of the user, and if the learning effect evaluation value is increased to the learning state conversion experience value, the current learning experience value is obtained. Therefore, by implementing the implementation mode, the learning effect, progress and speed of the user can be comprehensively evaluated, and then the learning efficiency is further evaluated, so that the method can obtain reasonable and accurate learning experience values according to various elements learned by the user at this time, and the learning experience values can effectively embody the learning results of the user.
Further, the current operation data includes current knowledge point data, behavior category data, and behavior tag data in one-to-one correspondence.
In the implementation process, the method can determine that the current operation data of the user comprises a plurality of operation subdata, and each operation subdata corresponds to one knowledge point data, one behavior type and one label data; specifically, the behavior type may include a video watching type and a question making type, and the tag data may include viewing duration data and answer right and wrong data. As can be seen, by implementing this embodiment, the actual operation by the user can be converted into the learned state transition empirical value, and the numerical accuracy of the learned state transition empirical value can be improved.
Further, the step of calculating according to the current operation data to obtain a learning state transition experience value includes:
calculating according to the behavior category data and the behavior label data to obtain a current mastering probability corresponding to the current knowledge point data;
acquiring relevant knowledge point data corresponding to the current knowledge point data, relevant mastering probability corresponding to the relevant knowledge point data and the number of historical continuous attack knowledge points;
and calculating according to the current mastering probability, the related mastering probability and the number of the historical continuous attack knowledge points to obtain a learning state conversion experience value.
In the implementation process, the method can calculate the current mastering probability of the user aiming at the current knowledge point data, and can also calculate whether the basic knowledge point of the knowledge point data is consolidated when the user learns the knowledge point data; if the user consolidates the basic knowledge points (namely the related knowledge points) when learning the knowledge point data, the related mastering probability is updated accordingly and is acquired by the system; then, how many related knowledge points exist before the current knowledge point data is further obtained, and the number of the knowledge points is recorded as the number of the continuous attack knowledge points; and finally, carrying out corresponding weighted calculation according to the plurality of numerical values to obtain a final learning state conversion experience value. Therefore, by implementing the implementation mode, a more accurate learning state conversion experience value can be determined according to the learning record and the learning operation of the user, so that the learning state conversion experience value can effectively embody the current learning result of the user.
Further, the step of calculating according to the learning state conversion experience value and the historical operation data to obtain a learning effect evaluation value includes:
calculating according to the historical operation data to obtain a historical average operation experience value;
calculating according to the learning state conversion experience value and the historical average operation experience value to obtain a current average operation experience value;
and calculating according to the learning state conversion experience value, the current average operation experience value and the historical average operation experience value to obtain a learning effect evaluation value.
In the implementation process, the method can calculate the previous average operation experience value and the current average operation experience value, and then calculate the difference value between the previous average operation experience value and the current average operation experience value, so that whether the user has higher learning efficiency or learning effect in the learning operation process can be determined, and the learning efficiency or learning effect is digitized to obtain the learning effect evaluation value. Therefore, by implementing the implementation mode, the numerical parameters about the learning effect of the user can be acquired, so that the finally acquired current learning experience value has a deeper meaning, and the acquisition of the current learning experience value is more valuable.
Further, the step of calculating according to the learning state conversion experience value and the learning effect evaluation value to obtain a current learning experience value includes:
acquiring a historical learning experience value, an experience value increase coefficient corresponding to the historical learning experience value and an evaluation value increase coefficient corresponding to the historical learning experience value;
and calculating according to the historical learning experience value, the experience value increase coefficient, the learning state conversion experience value, the evaluation value increase coefficient and the learning effect evaluation value to obtain the current learning experience value.
In the implementation process, the current learning level can be determined according to the historical learning experience value, then the experience value increase coefficient and the evaluation value increase coefficient corresponding to the learning level are further determined according to the learning level, and then calculation is performed by combining multiple parameters such as the historical learning experience value, so as to obtain the final current learning experience value. Therefore, by implementing the implementation mode, the repeated learning operation of the user can be converted into the accumulated experience value, so that the learning result of the user can be effectively reflected by the experience value, and the comparison of the learning result of the user with other people is facilitated, so that the user can objectively judge the learning progress and the learning result of the user.
A second aspect of the embodiments of the present application provides a data processing system based on learning operation, including:
the learning operation-based data processing system includes:
an acquisition unit configured to acquire current operation data;
the first calculation unit is used for calculating according to the current operation data to obtain a learning state conversion experience value;
the second calculation unit is used for calculating according to the learning state conversion experience value and historical operation data to obtain a learning effect evaluation value;
and the third calculating unit is used for calculating according to the learning state conversion experience value and the learning effect evaluation value to obtain a current learning experience value.
In the implementation process, the data processing system may acquire the current operation data through the acquisition unit; calculating according to the current operation data through a first calculating unit to obtain a learning state conversion experience value; calculating according to the learning state conversion experience value and the historical operation data through a second calculating unit to obtain a learning effect evaluation value; and calculating according to the learning state conversion experience value and the learning effect evaluation value through a third calculating unit to obtain a current learning experience value. Therefore, by implementing the implementation mode, the learning effect, the learning progress and the learning speed of the user on the current knowledge points can be determined according to the operation of the user, so that a learning state conversion experience value is obtained, whether the learning speed is increased or not is judged according to the learning speed of the user, if the learning effect evaluation value is increased to the learning state conversion experience value, the current learning experience value is obtained, and accordingly, by implementing the implementation mode, the learning effect, the learning progress and the learning speed of the user can be comprehensively evaluated, and then the learning efficiency is further evaluated, so that the method can obtain reasonable and accurate learning experience values according to various elements learned by the user at this time, and the learning experience values can effectively embody the learning results of the user.
Further, the current operation data includes current knowledge point data, behavior category data, and behavior tag data in one-to-one correspondence.
In the implementation process, the actual operation of the user can be converted into the learning state conversion experience value, so that the numerical precision of the learning state conversion experience value can be improved.
Further, the first calculation unit includes:
the first calculation subunit is used for calculating according to the behavior category data and the behavior tag data to obtain a current mastering probability corresponding to the current knowledge point data;
the first acquisition subunit is used for acquiring relevant knowledge point data corresponding to the current knowledge point data, relevant mastering probability corresponding to the relevant knowledge point data and the number of historical continuous attack knowledge points;
the first calculating subunit is further configured to calculate according to the current mastering probability, the related mastering probability and the number of the historical continuous attack knowledge points to obtain a learning state conversion experience value.
In the implementation process, the first calculating unit may calculate, by using the first calculating subunit, according to the behavior category data and the behavior tag data, to obtain a current grasping probability corresponding to the current knowledge point data; acquiring relevant knowledge point data corresponding to the current knowledge point data, relevant mastering probability corresponding to the relevant knowledge point data and the number of historical continuous attack knowledge points through a first acquiring subunit; and calculating according to the current mastering probability, the related mastering probability and the number of historical continuous attack knowledge points through a first calculating subunit to obtain a learning state conversion experience value. Therefore, by implementing the implementation mode, a more accurate learning state conversion experience value can be determined according to the learning record and the learning operation of the user, so that the learning state conversion experience value can effectively embody the current learning result of the user.
A third aspect of the embodiments of the present application provides an electronic device, including a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to make the electronic device execute the data processing method based on learning operation according to any one of the first aspect of the embodiments of the present application.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores computer program instructions, and when the computer program instructions are read and executed by a processor, the computer program instructions execute the data processing method based on the learning operation according to any one of the first aspect of the embodiments of the present application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of a data processing method based on learning operation according to an embodiment of the present application;
fig. 2 is a schematic flowchart of another learning operation-based data processing method according to an embodiment of the present application;
FIG. 3 is a block diagram of a data processing system based on learning operations according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of another data processing system based on learning according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a data processing method based on learning operation according to an embodiment of the present application. The method can be applied to online teaching scenes, and particularly applied to the process of quantitatively acquiring the learning condition of the user after the user watches a teaching video or answers a training exercise online. The data processing method based on the learning operation comprises the following steps:
and S101, acquiring current operation data.
In this embodiment, the current operation data includes current knowledge point data, behavior category data, and behavior tag data that correspond one to one.
In this embodiment, the classification of the learning behavior may be performed according to the behavior classification data in the current operation data.
For example, when a user j performs a learning operation in an interface of a data processing system (or a teaching system), the system records operation data (i.e., behavior data) of the user, and classifies the operation data according to a plurality of preset learning behaviors, so that the operation data may correspond to a plurality of behavior tags one by one, thereby forming current knowledge point data Kj ═ { k1j, k2j, …, kij }, behavior category data Xj ═ { x1j, x2j, …, xij }, and behavior tag data Aj ═ a1j, a2j, …, aij }; wherein each of the elements Kj, Xj, and Aj correspond to each other. If the ith operation of the user j on the game interface is a question-making behavior about a k knowledge point, then kij is k; aij ═ 1; making a question and error sequence; if the ith operation of the user j on the game interface is the video watching behavior about the k knowledge point, k is equal to kij; aij ═ 2; xij ═ video viewing duration.
And S102, calculating according to the current operation data to obtain a learning state conversion experience value.
In this embodiment, the learning state transition experience value is used to represent the learning result obtained by the user in the current operation.
And S103, calculating according to the learning state conversion experience value and the historical operation data to obtain a learning effect evaluation value.
In the present embodiment, the learning effect evaluation value is used to represent a promotion effect evaluation value between the current learning operation and the historical learning operation of the user.
And S104, calculating according to the learning state conversion experience value and the learning effect evaluation value to obtain a current learning experience value.
In this embodiment, the current learned experience value is used to represent the accumulated experience value of the user.
In this embodiment, the execution subject of the method may be a computing system such as a computer and a server, and is not limited in this embodiment.
In this embodiment, an execution subject of the method may also be a smart device such as a smart phone and a tablet, which is not limited in this embodiment.
It can be seen that, by implementing the data processing method based on learning operation described in fig. 1, the current operation data can be preferentially acquired; then, calculating according to the current operation data to obtain a learning state conversion experience value; then, calculating according to the learning state conversion experience value and historical operation data to obtain a learning effect evaluation value; and finally, calculating according to the learning state conversion experience value and the learning effect evaluation value to obtain the current learning experience value. Therefore, by implementing the implementation mode, the learning effect, the learning progress and the learning speed of the user on the current multiple knowledge points can be determined according to the operation of the user, so that the learning state conversion experience value is obtained, then whether the learning speed is increased or not is judged according to the learning speed of the user, and if the learning effect evaluation value is increased to the learning state conversion experience value, the current learning experience value is obtained. Therefore, by implementing the implementation mode, the learning effect, progress and speed of the user can be comprehensively evaluated, and then the learning efficiency is further evaluated, so that the method can obtain reasonable and accurate learning experience values according to various elements learned by the user at this time, and the learning experience values can effectively embody the learning results of the user.
Example 2
Referring to fig. 2, fig. 2 is a schematic flowchart of another learning operation-based data processing method according to an embodiment of the present application. The flow chart of the data processing method based on the learning operation described in fig. 2 is improved according to the flow chart of the data processing method based on the learning operation described in fig. 1. The data processing method based on the learning operation comprises the following steps:
s201, obtaining current operation data.
In this embodiment, the current operation data includes current knowledge point data, behavior category data, and behavior tag data that correspond one to one.
S202, calculating according to the behavior category data and the behavior tag data to obtain the current mastering probability corresponding to the current knowledge point data.
In this embodiment, the current mastering probability is a degree of mastering the current knowledge point data by the user.
S203, acquiring relevant knowledge point data corresponding to the current knowledge point data, relevant mastering probability corresponding to the relevant knowledge point data and the number of historical continuous attack knowledge points.
In this embodiment, the number N of all knowledge points learned by the user is extracted.
In this embodiment, the relevant knowledge points are knowledge points relevant to the current knowledge point data in the preset knowledge network. Specifically, after the user learns the current knowledge point, the mastering probability of the current knowledge point is increased, and then the mastering probability of the related knowledge point is changed. For example, when the grasping probability of the knowledge point kij is determined to be updated, the related knowledge point (i.e., the knowledge point whose influence is not negligible) KP ═ { KP1, KP2, …, kpn } (N ≦ N, kij ∈ KP) and the related grasping probability P ═ P1, P2, …, pn } of the related knowledge point, where KP and P correspond to each other.
In the embodiment, the method can extract the number ci-1 of the knowledge points continuously attacking the user j before the ith operation.
And S204, calculating according to the current mastering probability, the related mastering probability and the number of the historical continuous attack knowledge points to obtain a learning state conversion experience value.
In this embodiment, the method may determine the empirical data corresponding to the learning state according to the learning state of the user.
In the embodiment, the knowledge point data kij, the behavior category data xij, the behavior label data aij, the relevant knowledge point KP, the relevant mastering probability P and the number ci-1 of continuous attack knowledge points of the ith operation of the student user j from the operation data transmission module are input. First, the latest grasping probability d ═ f (xi, ai | ph) (kph ═ kij) of the knowledge point data kij is calculated, and then the updated grasping probability of the relevant knowledge point is updated according to the change in the grasping probability of the operation knowledge point: p '{ P1', P2 ', …, pn' }, and the number of successive attack knowledge points ci after the i-th operation. And storing the latest state P' under the relevant knowledge point KP, calculating a learning state conversion experience value according to the data, and outputting the result. Learning state transition empirical value:
s＝g(P'，P)+v*(d-ph)+w*ci
wherein v and w are preset calculation parameters, and this embodiment is not limited at all.
And S205, calculating according to the historical operation data to obtain a historical average operation experience value.
In this embodiment, the historical average operation experience value is an experience value obtained by averaging the increase of each operation before the ith operation of the user j, and is represented by ei-1.
And S206, calculating according to the learning state conversion experience value and the historical average operation experience value to obtain the current average operation experience value.
In this embodiment, the method may calculate the average operation empirical value ei after the ith operation as (ei-1+ s)/i.
And S207, calculating according to the learning state conversion experience value, the current average operation experience value and the historical average operation experience value to obtain a learning effect evaluation value.
In this embodiment, the method may calculate according to the formula:
Vi＝(s-ei-1)/ei
and calculating a learning effect evaluation value.
S208, a historical learning experience value, an experience value increase coefficient corresponding to the historical learning experience value and an evaluation value increase coefficient corresponding to the historical learning experience value are obtained.
In the embodiment, the historical learning experience value EXPi-1 of the student j before the ith operation is recorded.
In this embodiment, the method may determine the level Li-1 at which the historical learned empirical values are located.
In this embodiment, the method may query the corresponding parameters α ═ α i, β ═ β i in different levels in the system according to the historical learned empirical value EXPi-1 and the level Li-1 at which the historical learned empirical value is located. After the ith operation is determined, learning an experience value growth coefficient corresponding to the state conversion experience value s under the grade Li-1; β i is used to determine the evaluation value growth coefficient corresponding to the learning effect evaluation value Vi at the level Li-1 after the ith operation.
S209, calculating according to the historical learning experience value, the experience value increase coefficient, the learning state conversion experience value, the evaluation value increase coefficient and the learning effect evaluation value to obtain the current learning experience value.
In this embodiment, the method may be implemented according to the formula:
EXPi＝EXPi-1+αi*s+βi*Vi
wherein, the current learning experience value is EXPi.
In this embodiment, the method may present the current learning experience value EXPi in the form of an experience bar on the front-end display interface. The accumulation condition of the experience bar is used for reflecting the change of the accumulation degree of learning, and the method can also play a role in stimulating the learning of the user due to the fact that experience addition of continuous attack knowledge points is considered in the calculation process.
As an alternative embodiment, the method may also be used to determine the next best action for the user. Specifically, the learning states KP and P' of the student after the ith operation can be input, then the weak knowledge point kpr which is easiest to attack under the current knowledge network is searched according to a preset weak knowledge point attack model, the attack probability pkr is calculated, and then the weak knowledge point kpr which is easiest to attack, the attack probability pkr and the optimal operation corresponding to the data are output and recommended to the user. So that the user can proceed with the next learning according to the prompt, so that the user can obtain the maximum experience value increase. Wherein, if pkr is more than 0.5, the problem is recommended to be solved and weak knowledge points kpr are recommended to be solved; and if not, recommending to watch the corresponding video and consolidating the weak knowledge points kpr.
As an optional implementation, the method may further include:
determining other users similar to the learning condition of the user;
the average experience value of the other users is calculated.
In this embodiment, this step may be understood as matching, based on the knowledge point set N learned by the user j, other students who have learned the same knowledge point set N (if accurate matching is not possible, 50 students whose number of matched learning is the closest), and obtaining their experience values EA ═ EA1，ea2，…，eam(m.gtoreq.50), where the average empirical value is (ea)1+ea2+…+eam)/m。
As an optional implementation, the method may further include:
and outputting the average empirical value.
In this embodiment, the experience value of the user group similar to the learning condition of the user is displayed on the user interface in the form of an experience bar, and the user students can be promoted to compete with other student users in terms of accumulation amount.
Therefore, by implementing the data processing method based on the learning operation described in fig. 2, the learning effect, progress and speed of the user can be comprehensively evaluated, and then the learning efficiency is further evaluated, so that the method can obtain reasonable and accurate learning experience values according to various elements learned by the user at this time, and the learning experience values can effectively embody the learning results of the user.
Example 3
Referring to fig. 3, fig. 3 is a schematic structural diagram of a data processing system based on learning operation according to an embodiment of the present application. Wherein the learning operation-based data processing system comprises:
an obtaining unit 310, configured to obtain current operation data;
the first calculating unit 320 is configured to calculate according to the current operation data to obtain a learning state conversion experience value;
a second calculating unit 330, configured to perform calculation according to the learning state conversion experience value and the historical operation data to obtain a learning effect evaluation value;
and a third calculating unit 340, configured to calculate according to the learning state conversion experience value and the learning effect evaluation value, to obtain a current learning experience value.
In this embodiment, for the explanation of the data processing system based on the learning operation, reference may be made to the description in embodiment 1 or embodiment 2, and details are not repeated in this embodiment.
Therefore, the data processing system based on the learning operation described in fig. 3 can comprehensively evaluate the learning effect, progress and speed of the user, and then further evaluate the learning efficiency, so that the method can obtain reasonable and accurate learning experience values according to various elements learned by the user at this time, and the learning experience values can effectively embody the learning results of the user.
Example 4
Referring to fig. 4, fig. 4 is a schematic structural diagram of another data processing system based on learning operation according to an embodiment of the present application. The structural diagram of the data processing system based on learning described in fig. 4 is improved from the structural diagram of the data processing system based on learning described in fig. 3. The current operation data comprises current knowledge point data, behavior category data and behavior tag data which are in one-to-one correspondence.
As an alternative embodiment, the first calculation unit 320 includes:
a first calculating subunit 321, configured to perform calculation according to the behavior category data and the behavior tag data, to obtain a current mastering probability corresponding to the current knowledge point data;
a first obtaining subunit 322, configured to obtain relevant knowledge point data corresponding to the current knowledge point data, relevant mastering probability corresponding to the relevant knowledge point data, and the number of historical continuous attack knowledge points;
the first calculating subunit 321 is further configured to calculate according to the current mastering probability, the related mastering probability, and the number of historical continuous attack knowledge points, so as to obtain a learning state conversion experience value.
As an optional implementation manner, the second calculating unit 330 is specifically configured to perform calculation according to historical operation data to obtain a historical average operation experience value; calculating according to the learning state conversion experience value and the historical average operation experience value to obtain a current average operation experience value; and calculating according to the learning state conversion experience value, the current average operation experience value and the historical average operation experience value to obtain a learning effect evaluation value.
As an alternative implementation, the third computing unit 340 may include:
a third acquisition subunit 341 configured to acquire a historical learning experience value, an experience value increase coefficient corresponding to the historical learning experience value, and an evaluation value increase coefficient corresponding to the historical learning experience value;
and a third calculating subunit 342, configured to calculate according to the historical learning experience value, the experience value increase coefficient, the learning state conversion experience value, the evaluation value increase coefficient, and the learning effect evaluation value, so as to obtain a current learning experience value.
In this embodiment, for the explanation of the data processing system based on the learning operation, reference may be made to the description in embodiment 1 or embodiment 2, and details are not repeated in this embodiment.
Therefore, the data processing system based on the learning operation described in fig. 4 can comprehensively evaluate the learning effect, progress and speed of the user, and then further evaluate the learning efficiency, so that the method can obtain reasonable and accurate learning experience values according to various elements learned by the user at this time, and the learning experience values can effectively embody the learning results of the user.
An embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the data processing method based on the learning operation in embodiment 1 or embodiment 2 of the present application.
The embodiment of the present application provides a computer-readable storage medium, which stores computer program instructions, and when the computer program instructions are read and executed by a processor, the computer program instructions execute the data processing method based on the learning operation in any one of embodiment 1 or embodiment 2 of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, system, and method may be implemented in other ways. The apparatus, system embodiments described above are illustrative only, and the flowcharts and block diagrams in the figures, for example, illustrate the architecture, functionality, and operation of possible implementations of apparatus or systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

Claims (10)

1. A method of data processing based on learning operations, the method comprising:
acquiring current operation data;
calculating according to the current operation data to obtain a learning state conversion experience value;
calculating according to the learning state conversion experience value and historical operation data to obtain a learning effect evaluation value;
and calculating according to the learning state conversion experience value and the learning effect evaluation value to obtain a current learning experience value.
2. The learning-operation-based data processing method according to claim 1, wherein the current operation data includes current knowledge point data, behavior category data, and behavior tag data in one-to-one correspondence.
3. The learning-operation-based data processing method according to claim 2, wherein the step of calculating from the current operation data to obtain a learning-state transition empirical value comprises:
calculating according to the behavior category data and the behavior label data to obtain a current mastering probability corresponding to the current knowledge point data;
acquiring relevant knowledge point data corresponding to the current knowledge point data, relevant mastering probability corresponding to the relevant knowledge point data and the number of historical continuous attack knowledge points;
and calculating according to the current mastering probability, the related mastering probability and the number of the historical continuous attack knowledge points to obtain a learning state conversion experience value.
4. The learning-operation-based data processing method according to claim 1, wherein the step of calculating from the learning-state transition experience value and the historical operation data to obtain a learning effect evaluation value comprises:
calculating according to the historical operation data to obtain a historical average operation experience value;
calculating according to the learning state conversion experience value and the historical average operation experience value to obtain a current average operation experience value;
and calculating according to the learning state conversion experience value, the current average operation experience value and the historical average operation experience value to obtain a learning effect evaluation value.
5. The learning-operation-based data processing method according to claim 1, wherein the step of calculating from the learning-state-transition-experience value and the learning-effect evaluation value to obtain a current learning-experience value comprises:
acquiring a historical learning experience value, an experience value increase coefficient corresponding to the historical learning experience value and an evaluation value increase coefficient corresponding to the historical learning experience value;
and calculating according to the historical learning experience value, the experience value increase coefficient, the learning state conversion experience value, the evaluation value increase coefficient and the learning effect evaluation value to obtain the current learning experience value.
6. A learning operation-based data processing system, the learning operation-based data processing system comprising:
an acquisition unit configured to acquire current operation data;
the first calculation unit is used for calculating according to the current operation data to obtain a learning state conversion experience value;
the second calculation unit is used for calculating according to the learning state conversion experience value and historical operation data to obtain a learning effect evaluation value;
and the third calculating unit is used for calculating according to the learning state conversion experience value and the learning effect evaluation value to obtain a current learning experience value.
7. The learning-operation-based data processing system of claim 6, wherein the current operation data comprises one-to-one correspondence of current knowledge point data, behavior category data, and behavior tag data.
8. The learning-operation-based data processing system according to claim 7, wherein the first calculation unit includes:
the first calculation subunit is used for calculating according to the behavior category data and the behavior tag data to obtain a current mastering probability corresponding to the current knowledge point data;
the first acquisition subunit is used for acquiring relevant knowledge point data corresponding to the current knowledge point data, relevant mastering probability corresponding to the relevant knowledge point data and the number of historical continuous attack knowledge points;
the first calculating subunit is further configured to calculate according to the current mastering probability, the related mastering probability and the number of the historical continuous attack knowledge points to obtain a learning state conversion experience value.
9. An electronic device, comprising a memory for storing a computer program and a processor for executing the computer program to cause the electronic device to perform the learning operation-based data processing method of any one of claims 1 to 5.
10. A readable storage medium, in which computer program instructions are stored, which, when read and executed by a processor, perform the learning operation-based data processing method according to any one of claims 1 to 5.
CN202011043239.9A 2020-09-28 2020-09-28 Data processing method and system based on learning operation Pending CN112053091A (en)

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