CN112085392A - Learning participation degree determining method and device and computer equipment - Google Patents
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Abstract
The invention discloses a learning participation degree determining method, a learning participation degree determining device and computer equipment, wherein the method comprises the following steps: acquiring learning participation evaluation characteristics of a subject participating in a target classroom, wherein the learning participation evaluation characteristics comprise at least two of behavior characteristics, expression characteristics, language communication characteristics and eye characteristics; determining the score and the weight of each learning participation evaluation feature according to a preset evaluation algorithm; and determining the learning participation of the participation subject according to the weight and the score of each learning participation evaluation feature. According to the invention, the learning participation degree of the participating subject can be determined by acquiring the learning participation degree evaluation feature and according to the score and the weight of the learning participation degree evaluation feature of the preset evaluation algorithm, so that the evaluation on the learning participation degree of the students is more scientific and objective, the obtained learning participation degree is more accurate, and meanwhile, a teacher is assisted to know the participation condition of the students in class, so that the students can intervene in time and feel wrong to learn.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a learning participation degree determining method and device and computer equipment.
Background
The learning participation degree is an important index for representing the learning participation condition of the students. Learning participation degree identification is used for researching how to quantitatively evaluate the learning participation condition of students, and the learning participation degree has close relation with self-cognition development of children and important relevance with the development of teaching ability of teachers.
In the related technology, when the teacher evaluates the participation of the students in the classroom through the teaching video, the teacher is easily affected by subjective factors of evaluators, so that the artificial observation and evaluation of the participation of the students in the classroom are not scientific and objective. Therefore, it is desirable to provide a learning participation degree determining method to assist teachers to know the participation situation of students in class, so as to intervene in time, help students to think back about their own learning, and promote their deep participation in the learning process.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defect of poor accuracy of the method for manually determining student participation in the prior art, so as to provide a learning participation determination method, device and computer equipment.
According to a first aspect, an embodiment of the present invention discloses a learning participation degree determining method, including the following steps: acquiring learning participation evaluation characteristics of a subject participating in a target classroom, wherein the learning participation evaluation characteristics comprise at least two of behavior characteristics, expression characteristics, language communication characteristics and eye characteristics; determining the score and the weight of each learning participation evaluation feature according to a preset evaluation algorithm; and determining the learning participation of the participation subject according to the weight and the score of each learning participation evaluation feature.
Optionally, when the learning engagement evaluation feature includes: behavioral or expressive features, the method further comprising: acquiring video data and eye state data of a subject in a target classroom; carrying out structural processing on the video data and the eye state data; obtaining behavior characteristics and expression characteristics of the participating subject according to the video data after the structured processing; or when the learning engagement assessment feature comprises an ocular feature, the method further comprises: acquiring eye state data of a subject in a target classroom; carrying out structuring processing on the eye state data; and obtaining the eye characteristics of the participating subject according to the eye state data after the structured processing.
Optionally, the learning engagement evaluation feature comprises: a language communication feature, the method further comprising: acquiring audio data of a subject in a target classroom; identifying language content of the participating subject from the audio data; and determining the language communication characteristics of the participating main bodies after comparing the language content with a preset database.
Optionally, the determining a score of each learning participation evaluation feature according to a preset evaluation algorithm includes: obtaining an evaluation index of each learning participation evaluation feature; and obtaining the score of each learning participation degree evaluation characteristic according to the attribute characteristic corresponding to the evaluation index and the weight corresponding to the attribute characteristic.
Optionally, determining the weight of each learning participation evaluation feature according to a preset evaluation algorithm includes:
wherein, betaiA weight representing each learning participation evaluation feature; m represents the number of learning participation evaluation features; the value of a is as follows:
ri，krepresenting the numerical values corresponding to the k column and the i row of the fuzzy consistent judgment matrix established according to each learning participation evaluation characteristic, wherein the numerical values represent the corresponding two learning participation evaluation characteristics chiiHexix-kDegree of membership.
Optionally, the determining the learning participation degree of the participant according to the weight and the score of each learning participation degree evaluation feature includes:
wherein y represents the learning participation degree of the subject in the target classroom; chi shapeiA score representing each learning engagement evaluation feature; beta is aiRepresenting the weight corresponding to each learning participation evaluation characteristic,
m represents the number of learning engagement evaluation features.
Optionally, the method further comprises: the learning participation of each participant in each target classroom is stored.
Optionally, the method further comprises: acquiring the learning participation degree of each participating subject in a target classroom; and obtaining the learning participation degree of the target classroom according to the learning participation degree of each participating subject.
Optionally, the method further comprises: acquiring the daily learning participation of the participating main body in the time to be counted; and obtaining the learning participation degree of the participating main body in the time to be counted according to the learning participation degree of the participating main body in the time to be counted every day.
Optionally, the method further comprises: acquiring the learning participation degree of a subject target course; and obtaining the learning participation degree of the subject target course in the preset interval time according to the learning participation degree of the target course.
According to a second aspect, an embodiment of the present invention further discloses a learning participation determining apparatus, including: the first acquisition module is used for acquiring learning participation evaluation characteristics of a subject in a target classroom, wherein the learning participation evaluation characteristics comprise at least two of behavior characteristics, expression characteristics, language communication characteristics and eye characteristics; the first determination module is used for determining the score and the weight of each learning participation evaluation feature according to a preset evaluation algorithm; and the second determination module is used for determining the learning participation of the participation subject according to the weight and the score of each learning participation evaluation feature.
According to a third aspect, an embodiment of the present invention further discloses a computer device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the learning engagement determination method of the first aspect or any one of the optional embodiments of the first aspect.
According to a fourth aspect, an embodiment of the present invention further discloses a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the learning engagement determination method according to the first aspect or any one of the optional embodiments of the first aspect.
The technical scheme of the invention has the following advantages:
according to the learning participation determining method and device, the learning participation evaluation characteristics of the participation main body in the target classroom are obtained, the learning participation evaluation characteristics comprise at least two of behavior characteristics, expression characteristics, language communication characteristics and eye characteristics, the score and the weight of each learning participation evaluation characteristic are determined according to a preset evaluation algorithm, and the learning participation of the participation main body is determined according to the weight and the score of each learning participation evaluation characteristic. According to the method, the learning participation degree of the participating subject is intelligently determined by acquiring the learning participation degree evaluation feature and learning the score and the weight of the learning participation degree evaluation feature according to the preset evaluation algorithm, so that teachers are assisted to know the participation condition of students in class, timely intervention is facilitated, the students are helped to think about learning of themselves, the students are promoted to deeply participate in the learning process, and compared with the method that the participation degree of the students is judged manually and is easily influenced by subjective factors of evaluators, the method is more scientific and objective, and the accuracy is high.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of a learning engagement determination method in an embodiment of the present invention;
fig. 2 is a schematic block diagram of a specific example of the learning engagement determination device in the embodiment of the present invention;
FIG. 3 is a diagram of an embodiment of a computer device.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The embodiment of the invention discloses a learning participation degree determining method, which comprises the following steps as shown in figure 1:
s11: and acquiring learning participation evaluation characteristics of the subject in the target classroom, wherein the learning participation evaluation characteristics comprise at least two of behavior characteristics, expression characteristics, language communication characteristics and eye characteristics.
For example, the target classroom may be a class of a school student, or a lecture of a school or a society, and the target classroom is not particularly limited in the embodiments of the present invention, and may be determined by a person skilled in the art according to actual situations. Correspondingly, the participating subject can be a student in the target classroom or any person in the lecture seat. The embodiment of the invention is described by taking a target classroom as a class of a school student.
The learning engagement evaluation features include at least two of behavior features, expression features, language communication features, and eye features, and all of the learning engagement evaluation features may be considered at the same time in order to improve learning engagement determination. The embodiment of the present invention is described by taking an example in which the learning participation degree evaluation feature includes all the features described above. The learning participation degree evaluation feature can be directly called from a memory, or can be obtained by inputting video data, audio data and eye state data into a preset model for recognition.
S12: and determining the score and the weight of each learning participation evaluation feature according to a preset evaluation algorithm.
For example, the score determined for each learning participation evaluation feature according to the preset evaluation algorithm may be obtained by dividing the sum of the products of the number of occurrences of each learning participation evaluation feature and the preset weight for each learning participation evaluation feature by the sum of the numbers of occurrences of all learning participation evaluation features, or may be obtained by dividing the product of the number of occurrences of each learning participation evaluation feature and the preset weight for each learning participation evaluation feature by the time of one lesson. The weight for determining each learning participation evaluation feature according to the preset evaluation algorithm may be a preset weight for each learning participation evaluation feature in advance, may be determined according to an analytic hierarchy process, or may be determined according to a fuzzy analytic hierarchy process. The method for determining the score and the weight of each learning participation evaluation feature is not limited, and can be selected by a person skilled in the art according to actual conditions.
S13: and determining the learning participation of the participating subject according to the weight and the score of each learning participation evaluation feature.
Illustratively, the learning engagement for determining the engagement subject may be determined as a sum of products of the weight and the score of each learning engagement evaluation feature, or may be determined as a sum of products of the weight and the score of each learning engagement evaluation feature divided by a sum of the scores of the learning engagement evaluation features.
According to the learning participation degree determining method, the learning participation degree evaluation characteristics of the participating subject in the target classroom are obtained, the learning participation degree evaluation characteristics comprise at least two of behavior characteristics, expression characteristics, language communication characteristics and eye characteristics, the score and the weight of each learning participation degree evaluation characteristic are determined according to a preset evaluation algorithm, and the learning participation degree of the participating subject is determined according to the weight and the score of each learning participation degree evaluation characteristic. According to the method, the learning participation degree of the participating subject is intelligently determined by acquiring the learning participation degree evaluation feature and learning the score and the weight of the learning participation degree evaluation feature according to the preset evaluation algorithm, so that teachers are assisted to know the participation condition of students in class, timely intervention is facilitated, the students are helped to think about learning of themselves, the students are promoted to deeply participate in the learning process, and compared with the method that the participation degree of the students is judged manually and is easily influenced by subjective factors of evaluators, the method is more scientific and objective, and the accuracy is high.
As an alternative embodiment of the present invention, when the learning engagement evaluation feature includes: the learning participation degree determining method further includes:
first, video data of a subject in a target classroom is acquired.
The video data illustratively includes all video images of the participating subject in the target classroom, and in one embodiment, a video capture device, such as a camera, is disposed in the classroom, and the video images can be captured by the camera.
Secondly, the video data is subjected to structuring processing.
For example, the video data is structured by inputting the video data into a preset model, performing sequence modeling on the video data through a video structuring algorithm, and performing expression feature recognition and action recognition on a participant in a video.
Thirdly, obtaining the behavior characteristics and the expression characteristics of the participating subject according to the video data after the structured processing.
Illustratively, the behavioral and expressive characteristics of the participant are derived from the structured processed video data, e.g., a video sequence is identified as a word (e.g., a curriculity) that characterizes the expressive or behavioral characteristics.
Or, when the learning engagement evaluation feature includes: the eye feature, the learning participation determination method further includes:
and acquiring eye state data of the subject in the target classroom.
Illustratively, the eye state data includes state information of gazing, jumping, blinking and the like of the participating subject, and in a specific embodiment, the eye state data can be acquired according to an eye tracker arranged on a desktop or an eye tracker similar to glasses worn by the participating subject, and can be freely selected according to a target classroom.
And carrying out structural processing on the eye state data. The structural processing is the same as the structural processing of the video data, namely the eye state data is input into a preset model, the eye state data is subjected to sequence modeling through a structural algorithm, and the eye feature recognition is performed on the participating main body in the video.
And obtaining the eye characteristics of the participating subject according to the eye state data after the structured processing.
Illustratively, the ocular characteristics of the participating subject are derived from the structured ocular state data, e.g., an ocular sequence is identified as a word (e.g., wink) that characterizes the ocular characteristics.
The embodiment of the invention identifies the video data and the eye state data based on artificial intelligence, so that the obtained learning participation evaluation characteristics are more accurate, the learning participation of students is evaluated more objectively, and the accuracy of the learning participation evaluation is improved.
Furthermore, the embodiment of the invention can also identify the identity of the participating subject in the video, and the specific identification mode can be that the characteristics of the participating subject are identified through a preset model and are compared with the personnel in a preset identity database for determination. The structured feature data, the unstructured video data, the eye state data and the corresponding time information of each participating subject are stored at the same time, so that the participation condition of students in class can be conveniently and subsequently called.
As an alternative embodiment of the present invention, the learning engagement evaluation feature includes: the language communication characteristic, the learning participation degree determining method also includes:
first, audio data of a subject in a target classroom is acquired.
Illustratively, the audio data may include only the audio of the participant when speaking, or may include the audio of all persons in the target class. The audio data can be obtained by an audio acquisition device such as a sound pickup and a microphone.
Second, the language content of the participating subjects is identified from the audio data.
For example, recognizing the language content of the participating subject according to the audio data may be to input the audio data into a preset recognition model to obtain a series of english words. In the embodiment of the invention, the voice of the target classroom is compared with the existing class voice model library by utilizing the voiceprint tracking technology, so that a specific speaker is identified.
And thirdly, comparing the language content with a preset database and determining the language communication characteristics of the participating main bodies.
Illustratively, the preset database may be words or phrases related to the target class set in advance. The language communication characteristics comprise that the language communication characteristics are related to the target classroom content and are unrelated to the target classroom content, whether the language content of the participating subject is related to the classroom content or not and the degree is high or low is judged after the language content is compared with a preset database, and the relevancy is positively related to the learning participation.
As an alternative embodiment of the present invention, the step S12 includes:
first, an evaluation index of each learning participation evaluation feature is acquired.
Illustratively, the learning engagement assessment feature may include a behavioral feature, an expressive feature, a language communication feature, and an ocular feature. Each learning engagement assessment feature may include at least one assessment indicator, for example, the performance feature may include at least one of hand-up, forward-tilt, backward-tilt, and table-prone; the expressive features can include at least one of curiosity, boredom, happiness, depression, exhaustion, and confusion; the ocular features may include at least one of gaze, jerk, and blink; the language exchange feature may include at least one of being related to the target classroom content and being unrelated to the target classroom content. The method for obtaining the evaluation index of each learning participation evaluation feature is the same as the method for obtaining each learning participation evaluation feature, and is not described herein again.
For the evaluation index of the behavior characteristics, the condition that hands are lifted and the body is inclined forwards indicates that the learning participation of the student is high, the condition that the body is inclined backwards generally indicates that the learning participation of the student is general, and the condition that the student is lying on the table indicates that the participation of the student to the learning content is low. For the evaluation indexes of the expression characteristics, three expressions of curiosity, happiness and confusion are specified to indicate that the learning participation degree of students is higher, boring, depression and fatigue indicate that the learning participation degree of students is lower, and the expression form of the evaluation indexes of the expression characteristics is shown in the following table 1. Regarding the evaluation index of the eye features, the expression form of the evaluation index of the eye features is shown in the following table 2, the observation is that the observation stays on an observation target for at least 100ms to 200ms, and most information can be obtained and processed only when the observation is watched; the jump is a rapid jump between fixation points, and during the eye jump, because the image moves on the retina too fast and the visual threshold is raised during the eye jump, almost no information is obtained; normal people blink about 15 times per minute, with increased blink frequency often associated with negative emotions (such as stress, anxiety, and fatigue), and decreased blink frequency associated with a more pleasant mental state. For the language communication characteristics, two or more persons of voice are specified to appear in the classroom voice data, the time interval is less than or equal to 1min, the language communication in the classroom is determined, and the classroom voice is compared with the existing class voice model library by utilizing the voiceprint tracking technology, so that a specific communicator is identified; the communication content generated by the voice recognition technology is compared with the built classroom communication database, so that whether the communication content is related to classroom content or not and the degree is high or low, and the correlation degree is positively related to learning participation degree.
Table 1 expression form of expression characteristic evaluation index
TABLE 2 expression form of ocular characteristic evaluation index
And secondly, obtaining the score of each learning participation degree evaluation feature according to the attribute feature corresponding to the evaluation index and the weight corresponding to the attribute feature.
Illustratively, the attribute feature indicates the number of times each evaluation index occurs. The specific value of each learning participation degree evaluation feature obtained according to the attribute feature corresponding to the evaluation index and the weight corresponding to the attribute feature may be:
wherein, χiA score representing each learning participation evaluation feature; f represents the number of evaluation indexes for each learning participation; btAn attribute feature representing the tth evaluation index; alpha is alphatThe weight of the tth evaluation index is represented, and the setting method of the weight of the evaluation index is the same as that of the weight of the learning participation evaluation feature, and is not repeated herein; and n is taken as the class time (unit: minute) of the target class.
As an alternative embodiment of the present invention, the step S12 includes:
wherein, betaiA weight representing each learning participation evaluation feature; m represents the number of learning participation evaluation features; the smaller the value of a, the more important the decision maker is to take the difference of importance between the factors, and in practical application, the smaller the value of a
ri，kRepresenting the numerical values corresponding to the k column and the i row of the fuzzy consistent judgment matrix established according to each learning participation evaluation characteristic, and representing the corresponding two learning participation evaluation characteristics chiiHexix-kOf the degree of membership of, wherein,
χikdenotes xi：χkA value of (d); r iskDenotes χ of k-th linek1+χk2+χk3+χk4Adding the sums; r isiDenotes χ of i-th linei1+χi2+χi3+χi4And (4) adding the sums.
Illustratively, an Analytic Hierarchy Process (AHP) is a qualitative and quantitative combined multi-objective decision analysis method, and its key link is to establish a judgment matrix, which has the following disadvantages: firstly, the consistency index of the judgment matrix is difficult to reach; secondly, judging that the consistency of the consistent matrix is different from the consistency of human decision thinking; thirdly, the consistency of judgment is difficult to achieve by adopting a 1-9 scale method. Based on the above reasons, we will use the fuzzy consistent matrix to introduce into the hierarchical analysis, and determine the evaluation index weight by establishing the fuzzy consistent matrix, i.e. the fuzzy hierarchical analysis method (FAHP), i.e. the above formula. As shown in table 3 below, the scale of the ambiguity analysis method can reach 0.1-0.9.
Table 30.1-0.9 judgment scale
As an alternative embodiment of the present invention, the step S13 includes:
wherein y represents the learning participation degree of the subject in the target classroom; chi shapeiShow each learningA score of the engagement assessment feature; beta is aiRepresenting the weight corresponding to each learning participation evaluation characteristic,
m represents the number of learning engagement evaluation features.
As an optional embodiment of the present invention, the learning engagement degree determination method further includes: the learning participation of each participant in each target classroom is stored.
For example, the calculated learning participation degree of each participating subject in each target classroom may be stored together with the structured feature data, the unstructured video data, the eye state data, and the corresponding time information of each participating subject, or may be stored separately.
The preset evaluation algorithm can be corrected according to the stored learning participation, the stored structured feature data, the stored unstructured video data and the stored eye state data, so that the calculation accuracy of the learning participation is improved, and the calculation is convenient to call in the follow-up evaluation.
As an optional embodiment of the present invention, the learning engagement degree determination method further includes:
first, learning participation of each participating subject in a target classroom is acquired. The learning engagement can be directly called and stored in advance.
And secondly, obtaining the learning participation degree of the target classroom according to the learning participation degree of each participating subject.
Illustratively, the obtaining of the target classroom learning participation according to the learning participation of each participant may specifically be:
wherein, yhRepresenting the learning participation degree of the h participating subject; v represents the number of participating agents in the target classroom;
and representing the average value of the target classroom learning participation.
According to the embodiment of the invention, the students in the target classroom can know the identity and satisfaction of the students in the classroom by analyzing the learning participation degree of the students in the classroom.
As an optional embodiment of the present invention, the learning engagement degree determination method further includes:
firstly, the learning participation degree of a participating subject in the to-be-counted time every day is obtained. The learning participation degree obtaining method refers to the description of the learning participation degree of each participating subject in the target classroom, and is not described herein again.
And secondly, obtaining the learning participation degree of the participating main body in the time to be counted according to the daily learning participation degree of the participating main body in the time to be counted.
For example, the learning engagement of the participating subject in the time to be counted, which is obtained according to the learning engagement of the participating subject in the time to be counted every day, may specifically be:
wherein L isd，jRepresenting the learning engagement of the participating subject on day j of the d course;
represents the average learning participation of the participating subject in the d course within the time interval of T days.
The invention is helpful to know the learning participation of the participating subject in a certain period of time, helps teachers to know the learning state of the students in a certain period of time and helps the students to adjust the state by evaluating the learning participation of the individual students in the period.
As an optional embodiment of the present invention, the learning engagement degree determination method further includes:
first, learning participation in a subject target course is acquired. The learning participation degree obtaining method refers to the description of the learning participation degree of each participating subject in the target classroom, and is not described herein again.
And secondly, obtaining the learning participation degree of the subject target course within the preset interval time according to the learning participation degree of the target course.
For example, the learning participation degree of the subject target course within the preset interval time according to the learning participation degree of the target course may be specifically:
wherein J represents the time to be counted, and the unit is day;
representing the average value of the learning participation degree of the participation subject in the time to be counted; y isjIndicating the learning engagement of the participating subject on day j.
According to the invention, through analyzing the learning participation of students in different courses in a period, the interest of the students in each subject can be known, on one hand, parents can specifically guide the future development direction of children, help teachers and parents to explore the reason of low participation of the subjects of the children in the basic education stage and timely perform strategic intervention, and on the other hand, the students can also help teachers to retrain the teaching design and teaching mode of the course.
The embodiment of the present invention further discloses a learning participation degree determining apparatus, as shown in fig. 2, including:
the first acquisition module 21 is configured to acquire learning participation evaluation features of a subject in a target classroom, where the learning participation evaluation features include at least two of behavior features, expression features, language communication features, and eye features; the specific implementation manner is described in step S11 in the embodiment, and is not described herein again.
The first determining module 22 is configured to determine a score and a weight of each learning participation evaluation feature according to a preset evaluation algorithm; the specific implementation manner is described in step S12 in the embodiment, and is not described herein again.
And a second determining module 23, configured to determine the learning participation of the participating subject according to the weight and the score of each learning participation evaluation feature. The specific implementation manner is described in step S13 in the embodiment, and is not described herein again.
According to the learning participation degree determining device provided by the invention, the learning participation degree evaluation characteristics of the participating subject in the target classroom are obtained, wherein the learning participation degree evaluation characteristics comprise at least two of behavior characteristics, expression characteristics, language communication characteristics and eye characteristics, the score and the weight of each learning participation degree evaluation characteristic are determined according to a preset evaluation algorithm, and the learning participation degree of the participating subject is determined according to the weight and the score of each learning participation degree evaluation characteristic. According to the method, the learning participation degree of the participating subject is intelligently determined by acquiring the learning participation degree evaluation feature and learning the score and the weight of the learning participation degree evaluation feature according to the preset evaluation algorithm, so that teachers are assisted to know the participation condition of students in class, timely intervention is facilitated, the students are helped to think about learning of themselves, the students are promoted to deeply participate in the learning process, and compared with the method that the participation degree of the students is judged manually and is easily influenced by subjective factors of evaluators, the method is more scientific and objective, and the accuracy is high.
As an alternative embodiment of the present invention, when the learning engagement evaluation feature includes: the learning participation degree determining method further includes:
the video data acquisition module is used for acquiring video data and eye state data of the subject in the target classroom; the specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
The first structuralization processing module is used for structuralizing the video data; the specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
And the behavior characteristic and expression characteristic obtaining module is used for obtaining the behavior characteristic and the expression characteristic of the participant according to the video data after the structured processing. The specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
Or, when the learning engagement evaluation feature includes an ocular feature, the learning engagement determination method further includes:
the eye state data acquisition module is used for acquiring eye state data of the subject in the target classroom; the specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
The second structuralization processing module is used for structuralizing the eye state data; the specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
And the eye feature obtaining module is used for obtaining the eye features of the participating main body according to the eye state data after the structuralization processing. The specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
As an alternative embodiment of the present invention, the learning engagement evaluation feature includes: the language communication characteristic, the learning participation degree determining method also includes:
the audio data acquisition module is used for acquiring audio data participating in a subject in a target classroom; the specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
The recognition module is used for recognizing the language content of the participating subject according to the audio data; the specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
And the comparison module is used for determining the language communication characteristics of the participating main bodies after comparing the language contents with a preset database. The specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
As an optional embodiment of the present invention, the first determining module 22 includes:
the evaluation index acquisition module is used for acquiring the evaluation index of each learning participation evaluation feature; the specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
And the score obtaining module is used for obtaining the score of each learning participation evaluation feature according to the attribute feature corresponding to the evaluation index and the weight corresponding to the attribute feature. The specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
As an optional embodiment of the present invention, the first determining module 22 includes:
wherein, betaiA weight representing each learning participation evaluation feature; m represents the number of learning participation evaluation features; the value of a is as follows:
ri，krepresenting the numerical values corresponding to the k column and the i row of the fuzzy consistent judgment matrix established according to each learning participation evaluation characteristic, and representing the corresponding two learning participation evaluation characteristics chiiHexix-kDegree of membership. The specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
As an optional embodiment of the present invention, the second determining module 23 includes:
wherein y represents the learning participation degree of the subject in the target classroom; chi shapeiA score representing each learning engagement evaluation feature; beta is aiRepresenting the weight corresponding to each learning participation evaluation characteristic,
m represents the number of learning engagement evaluation features. The specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
As an optional embodiment of the present invention, the learning engagement determination apparatus further includes: and the storage module is used for storing the learning participation degree of each participating subject in each target classroom. The specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
As an optional embodiment of the present invention, the learning engagement determination apparatus further includes:
the second acquisition module is used for acquiring the learning participation degree of each participating subject in the target classroom; the specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
And the target classroom learning participation degree obtaining module is used for obtaining the target classroom learning participation degree according to the learning participation degree of each participating subject. The specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
As an optional embodiment of the present invention, the learning engagement determination apparatus further includes:
the third acquisition module is used for acquiring the learning participation degree of the participating main body in each day within the time to be counted; the specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
And the learning participation degree obtaining module of the time to be counted is used for obtaining the learning participation degree of the participating main body in the time to be counted according to the daily learning participation degree of the participating main body in the time to be counted. The specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
As an optional embodiment of the present invention, the learning engagement determination apparatus further includes:
the fourth acquisition module is used for acquiring the learning participation degree of the subject target course; the specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
And the learning participation degree obtaining module of the target course is used for obtaining the learning participation degree participating in the main target course within the preset interval time according to the learning participation degree of the target course. The specific implementation manner is shown in the corresponding steps in the embodiments, and is not described herein again.
An embodiment of the present invention further provides a computer device, as shown in fig. 3, the computer device may include a processor 31 and a memory 32, where the processor 31 and the memory 32 may be connected by a bus or in another manner, and fig. 3 takes the example of being connected by a bus as an example.
The processor 31 may be a Central Processing Unit (CPU). The Processor 31 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 32, which is a non-transitory computer-readable storage medium, may be used for storing non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the learning engagement determination method in the embodiment of the present invention (for example, the first obtaining module 21, the first determining module 22, and the second determining module 23 shown in fig. 2). The processor 31 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 32, that is, implements the learning participation determination method in the above-described method embodiments.
The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 31, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, and these remote memories may be connected to the processor 31 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 32 and, when executed by the processor 31, perform a learning engagement determination method as in the embodiment shown in fig. 1.
The details of the computer device can be understood with reference to the corresponding related descriptions and effects in the embodiment shown in fig. 1, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (13)
1. A learning engagement determination method, characterized by comprising the steps of:
acquiring learning participation evaluation characteristics of a subject participating in a target classroom, wherein the learning participation evaluation characteristics comprise at least two of behavior characteristics, expression characteristics, language communication characteristics and eye characteristics;
determining the score and the weight of each learning participation evaluation feature according to a preset evaluation algorithm;
and determining the learning participation of the participation subject according to the weight and the score of each learning participation evaluation feature.
2. The method according to claim 1, wherein when the learning engagement evaluation feature includes a behavioral feature or an expressive feature, the method further comprises:
acquiring video data of a subject in a target classroom;
carrying out structural processing on the video data;
obtaining behavior characteristics and expression characteristics of the participating subject according to the video data after the structured processing; alternatively, the first and second electrodes may be,
when the learning engagement assessment feature comprises an ocular feature, the method further comprises:
acquiring eye state data of a subject in a target classroom;
carrying out structuring processing on the eye state data;
and obtaining the eye characteristics of the participating subject according to the eye state data after the structured processing.
3. The method of claim 1, wherein the learning engagement assessment feature comprises: a language communication feature, the method further comprising:
acquiring audio data of a subject in a target classroom;
identifying language content of the participating subject from the audio data;
and determining the language communication characteristics of the participating main bodies after comparing the language content with a preset database.
4. The method according to claim 1, wherein the determining the score of each learning engagement evaluation feature according to a preset evaluation algorithm comprises:
obtaining an evaluation index of each learning participation evaluation feature;
and obtaining the score of each learning participation degree evaluation characteristic according to the attribute characteristic corresponding to the evaluation index and the weight corresponding to the attribute characteristic.
5. The method according to claim 1, wherein determining the weight of each learning participation evaluation feature according to a preset evaluation algorithm comprises:
wherein, betaiA weight representing each learning participation evaluation feature; m represents the number of learning participation evaluation features; the value of a is as follows:
ri,krepresenting the numerical values corresponding to the k column and the i row of the fuzzy consistent judgment matrix established according to each learning participation evaluation characteristic, wherein the numerical values represent the corresponding two learning participation evaluation characteristics chiiHexix-kDegree of membership.
6. The method of claim 1, wherein determining the learning engagement of the participating subject based on the weight and the score of each learning engagement assessment feature comprises:
wherein y represents the learning participation degree of the subject in the target classroom; chi shapeiA score representing each learning engagement evaluation feature; beta is aiRepresenting the weight corresponding to each learning participation evaluation characteristic,
m represents the number of learning engagement evaluation features.
7. The method of claim 1, further comprising: the learning participation of each participant in each target classroom is stored.
8. The method of claim 7, further comprising:
acquiring the learning participation degree of each participating subject in a target classroom;
and obtaining the learning participation degree of the target classroom according to the learning participation degree of each participating subject.
9. The method of claim 7, further comprising:
acquiring the daily learning participation of the participating main body in the time to be counted;
and obtaining the learning participation degree of the participating main body in the time to be counted according to the learning participation degree of the participating main body in the time to be counted every day.
10. The method of claim 7, further comprising:
acquiring the learning participation degree of a subject target course;
and obtaining the learning participation degree of the subject target course in the preset interval time according to the learning participation degree of the target course.
11. A learning engagement determination apparatus, comprising:
the first acquisition module is used for acquiring learning participation evaluation characteristics of a subject in a target classroom, wherein the learning participation evaluation characteristics comprise at least two of behavior characteristics, expression characteristics, language communication characteristics and eye characteristics;
the first determination module is used for determining the score and the weight of each learning participation evaluation feature according to a preset evaluation algorithm;
and the second determination module is used for determining the learning participation of the participation subject according to the weight and the score of each learning participation evaluation feature.
12. A computer device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the learning engagement determination method of any one of claims 1-10.
13. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the learning engagement determination method according to any one of claims 1 to 10.
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