CN111985582B - Knowledge point mastering degree evaluation method based on learning behaviors - Google Patents
Knowledge point mastering degree evaluation method based on learning behaviors Download PDFInfo
- Publication number
- CN111985582B CN111985582B CN202011035398.4A CN202011035398A CN111985582B CN 111985582 B CN111985582 B CN 111985582B CN 202011035398 A CN202011035398 A CN 202011035398A CN 111985582 B CN111985582 B CN 111985582B
- Authority
- CN
- China
- Prior art keywords
- learning
- target object
- value
- limb
- learning behavior
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
Abstract
The invention discloses a learning behavior-based knowledge point mastery degree evaluation method, which comprises the following steps of: acquiring learning behavior sample data corresponding to a target object, and performing data processing on the acquired learning behavior sample data to obtain a limb action change rate of a learning behavior corresponding to the target object; extracting learning environment information corresponding to the target object, and acquiring the space vector change rate of an interactive object participating in learning behaviors in the learning environment according to the extracted learning environment information; comparing the obtained change rate of the limb actions and the change rate of the space vector with preset standard learning behavior data, and executing evaluation operation of knowledge point mastery degree of the learning behavior corresponding to the target object according to a comparison result; the method achieves the purpose of evaluating the knowledge point mastery degree of the target object through the analysis of the learning behavior of the target object, and improves the objectivity, the accuracy and the convenience of evaluating the knowledge point mastery degree of the target object.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a knowledge point mastery degree evaluation method based on learning behaviors.
Background
With the continuous development and progress of computer technology and the popularization of the internet, the online education system becomes one of the main learning modes selected by more and more users due to the flexibility of time and the convenience of space.
In the existing online education system, the learning ability of students is evaluated according to corresponding knowledge points learned by the students, the knowledge loopholes of the students are accurately scanned, the most appropriate knowledge points are intelligently and adaptively recommended, and the learning efficiency of the students is improved. The measuring dimensionality of the student level is mainly based on the knowledge point capability values of students, and the student level of the students under the knowledge points is tracked and measured through detection of the knowledge point capability values learned by the students. In the processing mode, whether the student does the test question is used as the measuring standard of the student ability value; for example, students do the opposite questions, and the ability value is improved; wrong questions are made, and the capacity value is reduced. The processing mode excessively depends on the condition of the student doing questions, and the matching degree of the evaluated student ability value and the condition of the student doing questions is too high, so that the response to the student ability is excessively sensitive under certain situations, the actual performance of the student in the actual situation is not considered at all, the inclusiveness is not achieved, and the evaluation result is not accurate and objective enough.
Disclosure of Invention
The invention provides a learning behavior-based knowledge point mastery degree evaluation method, and aims to evaluate the mastery degree of knowledge points of a target object by using the learning behavior of the target object.
The invention provides a learning behavior-based knowledge point mastery degree evaluation method, which comprises the following steps of:
acquiring learning behavior sample data corresponding to a target object, and performing data processing on the acquired learning behavior sample data to obtain a limb action change rate of a learning behavior corresponding to the target object;
extracting learning environment information corresponding to the target object, and acquiring the space vector change rate of an interactive object participating in learning behaviors in the learning environment according to the extracted learning environment information;
and comparing the obtained change rate of the limb actions and the change rate of the space vector with preset standard learning behavior data, and executing evaluation operation of knowledge point mastering degree of the learning behavior corresponding to the target object according to a comparison result.
Further, the acquiring learning behavior sample data corresponding to a target object and performing data processing on the acquired learning behavior sample data to obtain a limb action change rate of a learning behavior corresponding to the target object includes:
acquiring learning behavior sample data corresponding to the target object according to the characteristic information which can uniquely determine the target object;
and initially classifying the learning behavior sample data according to a learning behavior type, extracting characteristic parameters of the learning behavior corresponding to the target object from the learning behavior sample data, and calculating the limb action change rate of the learning behavior corresponding to the target object.
Further, the extracting, from the learning behavior sample data, the feature parameter of the learning behavior corresponding to the target object, and calculating the limb movement change rate of the learning behavior corresponding to the target object include:
extracting an initial number value of a limb to be identified of a learning behavior corresponding to the target object, a cosine value of an included angle between each limb of the target object and a horizontal line, a space vector value of each limb of the target object based on the horizontal line before and after unit time and a time tag corresponding to each limb action of the target object from the learning behavior sample data;
and calculating the limb action change rate of the learning behavior corresponding to the target object according to the extracted characteristic parameters.
Further, the calculating, according to the extracted feature parameters, a limb movement change rate of the learning behavior corresponding to the target object includes:
calculating the unit change rate of the angle between each limb of the target object and the horizontal plane according to the extracted characteristic parameters, wherein the unit change rate comprises the following steps:
wherein arctan is an arc tangent function, pi is a circumferential rate, n is an initial number value of the limb to be identified of the target object, and the value is [1,10 ]; u is a cosine value of an included angle between each limb of the target object and a horizontal line, and the value of u is [ -1,1 ];
calculating a spatial displacement value corresponding to the final behavior of each limb of the target object and the initial behavior of the limb, wherein the spatial displacement value comprises the following steps:
wherein t is a time tag, tuA time label corresponding to the cosine value u of the included angle between each limb of the target object and the horizontal line;
the unit time is 1s before, the space vector value of each limb based on the horizontal line;
after the unit time is 1s, the spatial vector value of each limb based on the horizontal line;
calculating the limb action change rate of the learning behavior corresponding to the target object by using a formula (1) according to the calculated angle unit change rate of each limb of the target object and the horizontal plane and the calculated spatial displacement value, and then:
wherein n is the initial number value of the limb to be identified of the target object, and the value of n is [1,10]]；
The calculated change rate of the limb action is obtained.
Further, the extracting learning environment information corresponding to the target object, and obtaining a spatial vector change rate of an interactive object participating in a learning behavior in the learning environment according to the extracted learning environment information includes:
extracting learning environment information corresponding to the target object, and recording state information corresponding to an interactive object participating in learning behaviors of the target object in a learning environment based on the learning environment information;
calculating to obtain the space vector change rate of the interactive object participating in the learning behavior of the target object in the learning environment according to the learning environment information and the recorded state information;
wherein the learning environment information includes: an interactive object in a learning environment that participates in a learning behavior; the state information corresponding to the interactive object participating in the learning behavior comprises: the system comprises an object number corresponding to the interactive object, time required by the interactive object to finish learning behaviors, displacement values of the interactive object which respectively correspond to the upper left corner of a learning environment in different directions by taking the upper left corner of the learning environment as a reference point at a certain moment, and an initial displacement value of the interactive object in the learning environment corresponding to zero moment.
Further, the calculating, according to the learning environment information and the recorded state information, a spatial vector change rate of an interactive object participating in a learning behavior of a target object in the learning environment includes:
calculating a spatial displacement vector value of the interactive object according to the learning environment information and the recorded state information;
calculating a motion angle change value of each limb of the target object at the same time associated with the interactive object according to the extracted characteristic parameters;
and calculating and acquiring the space vector change rate of the interactive object participating in the learning behavior of the target object in the learning environment by using the calculated space displacement vector value of the interactive object and the movement angle change value of each limb of the target object.
Further, the calculating a spatial displacement vector value of the interactive object according to the learning environment information and the recorded state information includes:
calculating a spatial displacement vector value of the interactive object by using the learning environment information and the recorded state information, wherein the following steps are provided:
wherein exp is an exponential function with e as the base, s is the number of the interactive object in the learning environment, and the value range is [0, T]T is the maximum number of the interactive object; t is the time required for completing the learning behavior, and the unit is second; t is tsThe time required for assisting the completion of the learning behavior of the target object for an interactive object with the serial number s in the learning environment is a horizontal displacement value with the left upper corner of the learning environment as a reference point and the direction facing right when the interactive object completes the learning behavior in a matching way, b is a longitudinal displacement value with the left upper corner of the learning environment as a reference point and the direction facing backwards when the interactive object completes the learning behavior in a matching way, c is a vertical displacement value with the left upper corner of the learning environment as a reference point and the direction facing upwards when the interactive object completes the learning behavior in a matching way, and asA transverse displacement value of an interactive object with the serial number s in the learning environment at a certain moment and with the left upper corner of the learning environment as a reference point and the direction towards the right, bsA longitudinal displacement value of an interactive object with the serial number s in the learning environment at a certain moment, which takes the upper left corner of the learning environment as a reference point and has a backward direction, csThe vertical displacement value of the interactive object with the serial number s in the learning environment at a certain moment and with the upper left corner of the learning environment as a reference point and the upward direction，s1、s2、s3Initial lateral, longitudinal, and vertical displacement values of the interactive object in the learning environment at time zero.
Further, the calculating a motion angle variation value of each limb of the target object associated with the interactive object at the same time according to the extracted feature parameters includes:
calculating a motion angle change value of each limb of the target object at the same time associated with the interactive object according to the initial number value of each limb corresponding to the target object in the extracted characteristic parameters, the cosine value of the included angle between each limb of the target object and the horizontal line and the time required by the interactive object participating in the learning behavior to complete the learning behavior of the target object in a cooperative manner, wherein the motion angle change value comprises the following values:
wherein arctan is an arc tangent function, n is an initial number value of the limb to be identified of the target object, and the value of n is [1,10]](ii) a u is the cosine value of the included angle between each limb of the target object and the horizontal line, and the value is [ -1,1]；tsThe time required for assisting the completion of the learning behavior of the target object for the interactive object numbered s in the learning environment.
Further, the calculating and obtaining a spatial vector change rate of the interactive object participating in the learning behavior of the target object in the learning environment by using the calculated spatial displacement vector value of the interactive object and the movement angle change value of each limb of the target object includes:
calculating the space vector change rate of the interactive object according to the motion angle change value and the space vector change rate obtained by calculation, and then:
wherein, Tra (a)s，bs，cs) To the study of the acquisitionThe spatial vector change rate of the interactive object participating in the learning behavior of the target object in the learning environment.
Further, the comparing the obtained change rate of the limb movement and the change rate of the space vector with preset standard learning behavior data, and according to the comparison result, executing an evaluation operation of the knowledge point mastering degree of the learning behavior corresponding to the target object, including steps a1-a 2:
step A1, comparing the obtained change rate of the limb action and the change rate of the space vector with preset standard learning behavior data, and calculating the evaluation value Comp (N) of the mastery degree of the knowledge point corresponding to the target objectd|Ls) Then, there are:
wherein, δ is the final cosine value of the angle between each limb of the standard learning behavior and the horizontal plane, ε is the cosine value of the angle between each interactive object participating in the standard learning behavior and the horizontal plane in the learning environment, and t is the time required for completing the learning behavior, and the unit is second; t is tδThe time t required for completing the learning behavior corresponding to the final cosine value of the angle between each limb of the standard learning behavior and the horizontal plane is deltaεThe time required for completing the learning behavior when the cosine value of the angle between each interactive object and the horizontal plane in the standard learning behavior in the learning environment is epsilon, d is the standard action change rate of the related limbs, n is the initial number value of the limb to be identified of the target object, and the value of the initial number value is [1,10]]，NdThe standard action change rate of the related limbs is d, the corresponding limb initial number value of the target object to be identified is obtained, s is the number corresponding to the interactive object participating in the learning behavior in the learning environment, and the number of s depends on the area of the learning environment; l is the standard displacement value of the object in the learning environment at each moment, LsThe standard displacement value of the object in the learning environment at each moment corresponding to the interactive object with the serial number s is obtained;
step A2, evaluating Comp (N) according to the mastery degree of the knowledge pointsd|Ls) Judging the knowledge point mastering degree of the learning behavior corresponding to the target object;
wherein, when the evaluation value Comp (N) of the knowledge point mastery degreed|Ls) When the value of (a) is not 0, the knowledge point representing the learning behavior corresponding to the target object is mastered by the target object;
when the evaluation value Comp (N) of the knowledge point mastery degreed|Ls) When the value of (2) is 0, the knowledge point representing the learning behavior corresponding to the target object is not grasped by the target object.
The invention relates to a learning behavior-based knowledge point mastery degree evaluation method, which comprises the steps of acquiring learning behavior sample data corresponding to a target object, and performing data processing on the acquired learning behavior sample data to obtain a limb action change rate of a learning behavior corresponding to the target object; extracting learning environment information corresponding to the target object, and acquiring the space vector change rate of an interactive object participating in learning behaviors in the learning environment according to the extracted learning environment information; comparing the obtained change rate of the limb actions and the change rate of the space vector with preset standard learning behavior data, and executing evaluation operation of knowledge point mastery degree of the learning behavior corresponding to the target object according to a comparison result; the method and the device achieve the purpose of evaluating the mastery degree of the knowledge points of the target object by analyzing the learning behavior of the target object, improve the objectivity and the accuracy of evaluating the mastery degree of the knowledge points of the target object, and improve the convenience of evaluation.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described below by means of the accompanying drawings and examples.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart illustrating an embodiment of a learning behavior-based knowledge point mastery level evaluation method according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The invention provides a learning behavior-based knowledge point mastery degree evaluation method, which achieves the purpose of evaluating the knowledge point mastery degree of a target object by analyzing the learning behavior of the target object.
As shown in fig. 1, fig. 1 is a schematic flow chart of an embodiment of a knowledge point mastery level evaluation method based on learning behaviors according to the present invention; the learning behavior-based knowledge point mastery degree evaluation method can be implemented as steps S10-S30 as follows:
step S10, collecting learning behavior sample data corresponding to a target object, and performing data processing on the collected learning behavior sample data to obtain a limb action change rate of the learning behavior corresponding to the target object.
In the embodiment of the invention, when the learning behavior sample data of the target object is acquired, the data acquisition can be performed by using the characteristic information which can uniquely determine the target object.
And step S20, extracting learning environment information corresponding to the target object, and acquiring the space vector change rate of the interactive object participating in the learning behavior in the learning environment according to the extracted learning environment information.
And step S30, comparing the obtained limb action change rate and the space vector change rate with preset standard learning behavior data, and executing the evaluation operation of the knowledge point mastering degree of the learning behavior corresponding to the target object according to the comparison result.
In an embodiment, in step S10 of the embodiment shown in fig. 1, "acquiring learning behavior sample data corresponding to a target object, and performing data processing on the acquired learning behavior sample data to obtain a limb movement change rate of a learning behavior corresponding to the target object", may be implemented according to the following technical means:
acquiring learning behavior sample data corresponding to the target object according to the characteristic information which can uniquely determine the target object; and initially classifying the learning behavior sample data according to a learning behavior type, extracting characteristic parameters of the learning behavior corresponding to the target object from the learning behavior sample data, and calculating the limb action change rate of the learning behavior corresponding to the target object.
In one embodiment, the "extracting feature parameters of the learning behavior corresponding to the target object from the learning behavior sample data, and calculating the limb movement change rate of the learning behavior corresponding to the target object" may be implemented according to the following technical means:
extracting an initial number value of a limb to be identified of a learning behavior corresponding to the target object, a cosine value of an included angle between each limb of the target object and a horizontal line, a space vector value of each limb of the target object based on the horizontal line before and after unit time and a time tag corresponding to each limb action of the target object from the learning behavior sample data; and calculating the limb action change rate of the learning behavior corresponding to the target object according to the extracted characteristic parameters.
In an embodiment, the calculating, according to the extracted feature parameters, a limb movement change rate of the learning behavior corresponding to the target object may be implemented according to the following technical means:
calculating the unit change rate of the angle between each limb of the target object and the horizontal plane according to the extracted characteristic parameters, wherein the unit change rate comprises the following steps:
wherein arctan is an arc tangent function, pi is a circumferential rate, n is an initial number value of the limb to be identified of the target object, and the value is [1,10 ]; u is a cosine value of an included angle between each limb of the target object and a horizontal line, and the value of u is [ -1,1 ];
calculating a spatial displacement value corresponding to the final behavior of each limb of the target object and the initial behavior of the limb, wherein the spatial displacement value comprises the following steps:
wherein t is a time tag, tuA time label corresponding to the cosine value u of the included angle between each limb of the target object and the horizontal line;
the unit time is 1s before, the space vector value of each limb based on the horizontal line;
after the unit time is 1s, the spatial vector value of each limb based on the horizontal line;
calculating the limb action change rate of the learning behavior corresponding to the target object by using a formula (1) according to the calculated angle unit change rate of each limb of the target object and the horizontal plane and the calculated spatial displacement value, and then:
wherein n is the initial number value of the limb to be identified of the target object, and the value of n is [1,10]]；
The calculated change rate of the limb action is obtained.
In an embodiment, in the embodiment of fig. 1, "step S20, extracting learning environment information corresponding to the target object, and obtaining a spatial vector change rate of an interactive object participating in a learning behavior in a learning environment according to the extracted learning environment information" may be implemented according to the following technical means:
extracting learning environment information corresponding to the target object, and recording state information corresponding to an interactive object participating in learning behaviors of the target object in a learning environment based on the learning environment information;
calculating to obtain the space vector change rate of the interactive object participating in the learning behavior of the target object in the learning environment according to the learning environment information and the recorded state information;
wherein the learning environment information includes: an interactive object in a learning environment that participates in a learning behavior; the state information corresponding to the interactive object participating in the learning behavior comprises: the system comprises an object number corresponding to the interactive object, time required by the interactive object to finish learning behaviors, displacement values of the interactive object which respectively correspond to the upper left corner of a learning environment in different directions by taking the upper left corner of the learning environment as a reference point at a certain moment, and an initial displacement value of the interactive object in the learning environment corresponding to zero moment.
In an embodiment, the calculating, according to the learning environment information and the recorded state information, a spatial vector change rate of an interactive object participating in a learning behavior of a target object in the learning environment may be implemented according to the following technical means:
calculating a spatial displacement vector value of the interactive object according to the learning environment information and the recorded state information;
calculating a motion angle change value of each limb of the target object at the same time associated with the interactive object according to the extracted characteristic parameters;
and calculating and acquiring the space vector change rate of the interactive object participating in the learning behavior of the target object in the learning environment by using the calculated space displacement vector value of the interactive object and the movement angle change value of each limb of the target object.
In one embodiment, the calculating the spatial displacement vector value of the interactive object according to the learning environment information and the recorded state information may be implemented according to the following technical means:
calculating a spatial displacement vector value of the interactive object by using the learning environment information and the recorded state information, wherein the following steps are provided:
wherein exp is an exponential function with e as the base, s is the number of the interactive object in the learning environment, and the value range is [0, T]T is the maximum number of the interactive object; t is the time required for completing the learning behavior, and the unit is second; t is tsThe time required for assisting the completion of the learning behavior of the target object for an interactive object with the serial number s in the learning environment is a horizontal displacement value with the left upper corner of the learning environment as a reference point and the direction facing right when the interactive object completes the learning behavior in a matching way, b is a longitudinal displacement value with the left upper corner of the learning environment as a reference point and the direction facing backwards when the interactive object completes the learning behavior in a matching way, c is a vertical displacement value with the left upper corner of the learning environment as a reference point and the direction facing upwards when the interactive object completes the learning behavior in a matching way, and asA transverse displacement value of an interactive object with the serial number s in the learning environment at a certain moment and with the left upper corner of the learning environment as a reference point and the direction towards the right, bsA longitudinal displacement value of an interactive object with the serial number s in the learning environment at a certain moment, which takes the upper left corner of the learning environment as a reference point and has a backward direction, csThe vertical displacement value of the interactive object with the serial number s in the learning environment at a certain moment and with the left upper corner of the learning environment as a reference point and the direction upward is S1、s2、s3Initial lateral, longitudinal, and vertical displacement values of the interactive object in the learning environment at time zero.
In an embodiment, the calculating a motion angle variation value of each limb of the target object associated with the interactive object at the same time according to the extracted feature parameters may be implemented according to the following technical means:
calculating a motion angle change value of each limb of the target object at the same time associated with the interactive object according to the initial number value of each limb corresponding to the target object in the extracted characteristic parameters, the cosine value of the included angle between each limb of the target object and the horizontal line and the time required by the interactive object participating in the learning behavior to complete the learning behavior of the target object in a cooperative manner, wherein the motion angle change value comprises the following values:
wherein arctan is an arc tangent function, n is an initial number value of the limb to be identified of the target object, and the value of n is [1,10]](ii) a u is the cosine value of the included angle between each limb of the target object and the horizontal line, and the value is [ -1,1]；tsThe time required for assisting the completion of the learning behavior of the target object for the interactive object numbered s in the learning environment.
In an embodiment, the calculating and obtaining the spatial vector change rate of the interactive object participating in the learning behavior of the target object in the learning environment by using the calculated spatial displacement vector value of the interactive object and the motion angle change value of each limb of the target object may be implemented according to the following technical means:
calculating the space vector change rate of the interactive object according to the motion angle change value and the space vector change rate obtained by calculation, and then:
wherein, Tra (a)s，bs，cs) And obtaining the change rate of the space vector of the interactive object participating in the target object learning behavior in the learning environment.
In an embodiment, in step S30 in the embodiment of fig. 1, comparing the obtained change rate of the limb movement and the change rate of the space vector with preset standard learning behavior data, and performing an evaluation operation of the knowledge point mastering degree of the learning behavior corresponding to the target object according to the comparison result may be implemented as steps a1-a2 described as follows:
step A1, comparing the obtained change rate of the limb action and the change rate of the space vector with preset standard learning behavior data, and calculating the evaluation value Comp (N) of the mastery degree of the knowledge point corresponding to the target objectd|Ls) Then, there are:
wherein, δ is the final cosine value of the angle between each limb of the standard learning behavior and the horizontal plane, ε is the cosine value of the angle between each interactive object participating in the standard learning behavior and the horizontal plane in the learning environment, and t is the time required for completing the learning behavior, and the unit is second; t is tδThe time t required for completing the learning behavior corresponding to the final cosine value of the angle between each limb of the standard learning behavior and the horizontal plane is deltaεThe time required for completing the learning behavior when the cosine value of the angle between each interactive object and the horizontal plane in the standard learning behavior in the learning environment is epsilon, d is the standard action change rate of the related limbs, n is the initial number value of the limb to be identified of the target object, and the value of the initial number value is [1,10]]，NdThe standard action change rate of the related limbs is d, the corresponding limb initial number value of the target object to be identified is obtained, s is the number corresponding to the interactive object participating in the learning behavior in the learning environment, and the number of s depends on the area of the learning environment; l is the standard displacement value of the object in the learning environment at each moment, LsThe standard displacement value of the object in the learning environment at each moment corresponding to the interactive object with the serial number s is obtained;
step A2, evaluating Comp (N) according to the mastery degree of the knowledge pointsd|Ls) Judging the knowledge point mastering degree of the learning behavior corresponding to the target object;
wherein, when the evaluation value Comp (N) of the knowledge point mastery degreed|Ls) When the value of (a) is not 0, the knowledge point representing the learning behavior corresponding to the target object is mastered by the target object;
when the evaluation value Comp (N) of the knowledge point mastery degreed|Ls) When the value of (2) is 0, the knowledge point representing the learning behavior corresponding to the target object is not grasped by the target object.
The invention relates to a learning behavior-based knowledge point mastery degree evaluation method, which comprises the steps of acquiring learning behavior sample data corresponding to a target object, and performing data processing on the acquired learning behavior sample data to obtain a limb action change rate of a learning behavior corresponding to the target object; extracting learning environment information corresponding to the target object, and acquiring the space vector change rate of an interactive object participating in learning behaviors in the learning environment according to the extracted learning environment information; comparing the obtained change rate of the limb actions and the change rate of the space vector with preset standard learning behavior data, and executing evaluation operation of knowledge point mastery degree of the learning behavior corresponding to the target object according to a comparison result; the method and the device achieve the purpose of evaluating the mastery degree of the knowledge points of the target object by analyzing the learning behavior of the target object, improve the objectivity and the accuracy of evaluating the mastery degree of the knowledge points of the target object, and improve the convenience of evaluation. Furthermore, the technical scheme is that the learning behavior of the target object is compared with preset standard learning behavior parameters according to a time label and a collected limb action change rate and a space vector change rate of an object state in a learning environment, and the evaluation operation of the knowledge point mastering degree of the learning behavior is executed.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (7)
1. A knowledge point mastery degree evaluation method based on learning behaviors is characterized by comprising the following steps:
acquiring learning behavior sample data corresponding to a target object, and performing data processing on the acquired learning behavior sample data to obtain a limb action change rate of a learning behavior corresponding to the target object;
extracting learning environment information corresponding to the target object, and acquiring the space vector change rate of an interactive object participating in learning behaviors in the learning environment according to the extracted learning environment information;
comparing the obtained change rate of the limb actions and the change rate of the space vector with preset standard learning behavior data, and executing evaluation operation of knowledge point mastery degree of the learning behavior corresponding to the target object according to a comparison result;
the acquiring learning behavior sample data corresponding to a target object and performing data processing on the acquired learning behavior sample data to obtain the limb action change rate of the learning behavior corresponding to the target object includes:
acquiring learning behavior sample data corresponding to the target object according to the characteristic information which can uniquely determine the target object;
initially classifying the learning behavior sample data according to a learning behavior type, extracting characteristic parameters of the learning behavior corresponding to the target object from the learning behavior sample data, and calculating the limb action change rate of the learning behavior corresponding to the target object;
wherein, the extracting the characteristic parameters of the learning behavior corresponding to the target object from the learning behavior sample data and calculating the limb action change rate of the learning behavior corresponding to the target object include:
extracting an initial number value of a limb to be identified of a learning behavior corresponding to the target object, a cosine value of an included angle between each limb of the target object and a horizontal line, a space vector value of each limb of the target object based on the horizontal line before and after unit time and a time tag corresponding to each limb action of the target object from the learning behavior sample data;
calculating to obtain the limb action change rate of the learning behavior corresponding to the target object according to the extracted characteristic parameters;
wherein, the calculating the limb action change rate of the learning behavior corresponding to the target object according to the extracted characteristic parameters includes:
calculating the unit change rate of the angle between each limb of the target object and the horizontal plane according to the extracted characteristic parameters, wherein the unit change rate comprises the following steps:
wherein arctan is an arc tangent function, pi is a circumferential rate, n is an initial number value of the limb to be identified of the target object, and the value is [1,10 ]; u is a cosine value of an included angle between each limb of the target object and a horizontal line, and the value of u is [ -1,1 ];
calculating a spatial displacement value corresponding to the final behavior of each limb of the target object and the initial behavior of the limb, wherein the spatial displacement value comprises the following steps:
wherein t is a time tag, tuA time label corresponding to the cosine value u of the included angle between each limb of the target object and the horizontal line;
the unit time is 1s before, the space vector value of each limb based on the horizontal line;
after the unit time is 1s, the spatial vector value of each limb based on the horizontal line;
calculating the limb action change rate of the learning behavior corresponding to the target object by using a formula (1) according to the calculated angle unit change rate of each limb of the target object and the horizontal plane and the calculated spatial displacement value, and then:
wherein n is the initial number value of the limb to be identified of the target object, and the value of n is [1,10]]；
The calculated change rate of the limb action is obtained.
2. The learning behavior-based knowledge point mastery degree evaluation method according to claim 1, wherein the extracting learning environment information corresponding to the target object and obtaining a space vector change rate of an interactive object participating in learning behavior in a learning environment according to the extracted learning environment information comprises:
extracting learning environment information corresponding to the target object, and recording state information corresponding to an interactive object participating in learning behaviors of the target object in a learning environment based on the learning environment information;
calculating to obtain the space vector change rate of the interactive object participating in the learning behavior of the target object in the learning environment according to the learning environment information and the recorded state information;
wherein the learning environment information includes: an interactive object in a learning environment that participates in a learning behavior; the state information corresponding to the interactive object participating in the learning behavior comprises: the system comprises an object number corresponding to the interactive object, time required by the interactive object to finish learning behaviors, displacement values of the interactive object which respectively correspond to the upper left corner of a learning environment in different directions by taking the upper left corner of the learning environment as a reference point at a certain moment, and an initial displacement value of the interactive object in the learning environment corresponding to zero moment.
3. The learning behavior-based knowledge point mastery degree evaluation method according to claim 2, wherein the calculating a space vector change rate of an interactive object participating in learning behavior of a target object in the learning environment according to the learning environment information and the recorded state information comprises:
calculating a spatial displacement vector value of the interactive object according to the learning environment information and the recorded state information;
calculating a motion angle change value of each limb of the target object at the same time associated with the interactive object according to the extracted characteristic parameters;
and calculating and acquiring the space vector change rate of the interactive object participating in the learning behavior of the target object in the learning environment by using the calculated space displacement vector value of the interactive object and the movement angle change value of each limb of the target object.
4. The learning behavior-based knowledge point mastery degree evaluation method according to claim 3, wherein the calculating a spatial displacement vector value of the interactive object according to the learning environment information and the recorded state information comprises:
calculating a spatial displacement vector value of the interactive object by using the learning environment information and the recorded state information, wherein the following steps are provided:
wherein exp is an exponential function with e as the base, s is the number of the interactive object in the learning environment, and the value range is [0, T]T is the maximum number of the interactive object; t is the time required for completing the learning behavior, and the unit is second; t is tsThe time required for assisting the completion of the learning behavior of the target object for an interactive object with the serial number s in the learning environment is a horizontal displacement value which takes the upper left corner of the learning environment as a reference point and has a rightward direction when the interactive object completes the learning behavior in a matching way, and b is the time required for the interactive object to learn the learning behavior in the matching wayThe upper left corner of the environment is a datum point and a longitudinal displacement value with a backward direction, c is a vertical displacement value with the upper left corner of the learning environment as a datum point and an upward direction when the interactive object completes the learning behavior in a matching way, asA transverse displacement value of an interactive object with the serial number s in the learning environment at a certain moment and with the left upper corner of the learning environment as a reference point and the direction towards the right, bsA longitudinal displacement value of an interactive object with the serial number s in the learning environment at a certain moment, which takes the upper left corner of the learning environment as a reference point and has a backward direction, csThe vertical displacement value of the interactive object with the serial number s in the learning environment at a certain moment and with the left upper corner of the learning environment as a reference point and the direction upward is S1、s2、s3Initial lateral, longitudinal, and vertical displacement values of the interactive object in the learning environment at time zero.
5. The learning behavior-based knowledge point mastery degree evaluation method according to claim 4, wherein the calculating a motion angle variation value of each limb of the target object associated with the interactive object at the same time according to the extracted feature parameters includes:
calculating a motion angle change value of each limb of the target object at the same time associated with the interactive object according to the initial number value of each limb corresponding to the target object in the extracted characteristic parameters, the cosine value of the included angle between each limb of the target object and the horizontal line and the time required by the interactive object participating in the learning behavior to complete the learning behavior of the target object in a cooperative manner, wherein the motion angle change value comprises the following values:
wherein arctan is an arc tangent function, n is an initial number value of the limb to be identified of the target object, and the value of n is [1,10]](ii) a u is the cosine value of the included angle between each limb of the target object and the horizontal line, and the value is [ -1,1]；tsThe time required for assisting the completion of the learning behavior of the target object for the interactive object numbered s in the learning environment.
6. The learning behavior-based knowledge point mastery degree evaluation method according to claim 5, wherein the calculating and obtaining a spatial vector change rate of the interactive object participating in the learning behavior of the target object in the learning environment by using the calculated spatial displacement vector value of the interactive object and the movement angle change value of each limb of the target object comprises:
calculating the space vector change rate of the interactive object according to the motion angle change value and the space vector change rate obtained by calculation, and then:
wherein, Tra (a)s，bs，cs) And obtaining the change rate of the space vector of the interactive object participating in the target object learning behavior in the learning environment.
7. The learning behavior-based knowledge point mastery degree evaluation method according to claim 6, wherein the comparing the obtained limb movement rate and space vector rate with preset standard learning behavior data and performing the evaluation operation of the knowledge point mastery degree of the learning behavior corresponding to the target object according to the comparison result comprises steps a1-a 2:
step A1, comparing the obtained change rate of the limb action and the change rate of the space vector with preset standard learning behavior data, and calculating the evaluation value Comp (N) of the mastery degree of the knowledge point corresponding to the target objectd|Ls) Then, there are:
wherein, delta is the cosine value of the final angle between each limb and the horizontal plane of the standard learning behavior, and epsilon isCosine values of angles between each interactive object participating in the standard learning behavior and a horizontal plane in the learning environment, wherein t is time required for completing the learning behavior and the unit is second; t is tδThe time t required for completing the learning behavior corresponding to the final cosine value of the angle between each limb of the standard learning behavior and the horizontal plane is deltaεThe time required for completing the learning behavior when the cosine value of the angle between each interactive object and the horizontal plane in the standard learning behavior in the learning environment is epsilon, d is the standard action change rate of the related limbs, n is the initial number value of the limb to be identified of the target object, and the value of the initial number value is [1,10]]，NdThe standard action change rate of the related limbs is d, the corresponding limb initial number value of the target object to be identified is obtained, s is the number corresponding to the interactive object participating in the learning behavior in the learning environment, and the number of s depends on the area of the learning environment; l is the standard displacement value of the object in the learning environment at each moment, LsThe standard displacement value of the object in the learning environment at each moment corresponding to the interactive object with the serial number s is obtained;
step A2, evaluating Comp (N) according to the mastery degree of the knowledge pointsd|Ls) Judging the knowledge point mastering degree of the learning behavior corresponding to the target object;
wherein, when the evaluation value Comp (N) of the knowledge point mastery degreed|Ls) When the value of (a) is not 0, the knowledge point representing the learning behavior corresponding to the target object is mastered by the target object;
when the evaluation value Comp (N) of the knowledge point mastery degreed|Ls) When the value of (2) is 0, the knowledge point representing the learning behavior corresponding to the target object is not grasped by the target object.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011035398.4A CN111985582B (en) | 2020-09-27 | 2020-09-27 | Knowledge point mastering degree evaluation method based on learning behaviors |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011035398.4A CN111985582B (en) | 2020-09-27 | 2020-09-27 | Knowledge point mastering degree evaluation method based on learning behaviors |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111985582A CN111985582A (en) | 2020-11-24 |
| CN111985582B true CN111985582B (en) | 2021-06-01 |
Family
ID=73450282
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011035398.4A Active CN111985582B (en) | 2020-09-27 | 2020-09-27 | Knowledge point mastering degree evaluation method based on learning behaviors |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111985582B (en) |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107292271B (en) * | 2017-06-23 | 2020-02-14 | 北京易真学思教育科技有限公司 | Learning monitoring method and device and electronic equipment |
| CN109214231A (en) * | 2017-06-29 | 2019-01-15 | 深圳泰山体育科技股份有限公司 | Physical education auxiliary system and method based on human body attitude identification |
| US20190087832A1 (en) * | 2017-09-15 | 2019-03-21 | Pearson Education, Inc. | Digital credential field data mapping |
| CN111079113B (en) * | 2019-12-13 | 2023-02-24 | 柳州铁道职业技术学院 | Teaching system with artificial intelligent control and use method thereof |
| CN111258433B (en) * | 2020-03-02 | 2024-04-02 | 上海松鼠课堂人工智能科技有限公司 | Teaching interaction system based on virtual scene |
| CN111523518A (en) * | 2020-06-08 | 2020-08-11 | 上海乂学教育科技有限公司 | Intelligent adaptive learning method and system |
-
2020
- 2020-09-27 CN CN202011035398.4A patent/CN111985582B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111985582A (en) | 2020-11-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109901996B (en) | Auxiliary test method and device, electronic equipment and readable storage medium | |
| CN113392742A (en) | Abnormal action determination method and device, electronic equipment and storage medium | |
| CN107274020B (en) | Learner subject total measured result prediction system and method based on collaborative filtering thought | |
| US20190118226A1 (en) | Sorting system | |
| CN102473301B (en) | Operation analysis device and operation analysis method | |
| US11461584B2 (en) | Discrimination device and machine learning method | |
| CN112949408B (en) | Real-time identification method and system for target fish passing through fish channel | |
| CN109685104B (en) | Determination method and device for recognition model | |
| CN114847932A (en) | Method and device for determining motion prompt and computer readable storage medium | |
| CN110263678A (en) | A kind of face direction determination process and system | |
| CN111985582B (en) | Knowledge point mastering degree evaluation method based on learning behaviors | |
| CN111639194B (en) | Knowledge graph query method and system based on sentence vector | |
| CN110489602B (en) | Knowledge point capability value estimation method, system, device and medium | |
| CN113743572A (en) | Artificial neural network testing method based on fuzzy | |
| CN116704606A (en) | Physicochemical experiment operation behavior identification method, system, device and storage medium | |
| CN114971425B (en) | Database information monitoring method, device, equipment and storage medium | |
| CN112085631B (en) | Intelligent analysis method for student memory curve | |
| Redondo-Cabrera et al. | Pose estimation errors, the ultimate diagnosis | |
| CN112861689A (en) | Searching method and device of coordinate recognition model based on NAS technology | |
| CN113392744A (en) | Dance motion aesthetic feeling confirmation method and device, electronic equipment and storage medium | |
| CN113392743A (en) | Abnormal action detection method, abnormal action detection device, electronic equipment and computer storage medium | |
| Liu et al. | Learning task-aligned local features for visual localization | |
| CN112464815A (en) | Video multi-target tracking method, device and equipment | |
| CN112966702A (en) | Method and apparatus for classifying protein-ligand complex | |
| CN113792104B (en) | Medical data error detection method and device based on artificial intelligence and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PP01 | Preservation of patent right |
Effective date of registration: 20221020 Granted publication date: 20210601 |
|
| PP01 | Preservation of patent right |