CN115186014A - Data processing method for educational training - Google Patents

Data processing method for educational training Download PDF

Info

Publication number
CN115186014A
CN115186014A CN202211107383.3A CN202211107383A CN115186014A CN 115186014 A CN115186014 A CN 115186014A CN 202211107383 A CN202211107383 A CN 202211107383A CN 115186014 A CN115186014 A CN 115186014A
Authority
CN
China
Prior art keywords
child
children
degree
hesitation
classroom
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.)
Granted
Application number
CN202211107383.3A
Other languages
Chinese (zh)
Other versions
CN115186014B (en
Inventor
余正浪
余严江
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangsu Juxin Zhonghui Digital Technology Co ltd
Original Assignee
Jiangsu Juxin Zhonghui Digital Technology Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Jiangsu Juxin Zhonghui Digital Technology Co ltd filed Critical Jiangsu Juxin Zhonghui Digital Technology Co ltd
Priority to CN202211107383.3A priority Critical patent/CN115186014B/en
Publication of CN115186014A publication Critical patent/CN115186014A/en
Application granted granted Critical
Publication of CN115186014B publication Critical patent/CN115186014B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2474—Sequence data queries, e.g. querying versioned data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G06Q50/205—Education administration or guidance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2203/00—Indexing scheme relating to G06F3/00 - G06F3/048
    • G06F2203/01—Indexing scheme relating to G06F3/01
    • G06F2203/012—Walk-in-place systems for allowing a user to walk in a virtual environment while constraining him to a given position in the physical environment

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Tourism & Hospitality (AREA)
  • Data Mining & Analysis (AREA)
  • Educational Administration (AREA)
  • Educational Technology (AREA)
  • Strategic Management (AREA)
  • Human Computer Interaction (AREA)
  • Remote Sensing (AREA)
  • Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Marketing (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • General Business, Economics & Management (AREA)
  • General Health & Medical Sciences (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Software Systems (AREA)
  • Computational Linguistics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to the technical field of data processing, in particular to a data processing method for educational training, wherein related electronic equipment is adopted to identify each child in a target classroom, the behavior membership degree corresponding to each child is determined based on the prestige frequency of each child in the classroom, the typical hesitation time of interaction and the hesitation stability degree, the child with the largest behavior membership degree is taken as a representative child in a target time period and is taken as an object for recommending simulation when a young teacher reviews the target time, the quality of a copy can be improved compared with a mode of randomly selecting a simulation object, the young teacher is helped to find the defects when interacting with the child, the training of professional ability is further deepened, the workload of the copy of the young teacher can be reduced compared with a mode of simulating all children one by one, and the copy efficiency is improved.

Description

Data processing method for educational training
Technical Field
The invention relates to the technical field of data processing, in particular to a data processing method for education training.
Background
Preschool education is foundation education for life development, people attach more and more importance to the preschool education, and practical infant teacher training has wide market prospect.
At present, professional training of preschool education is difficult to realize by scene simulation. As most preschool education forms are indoor interactive activities, children are easy to vague for a great amount of education contents depending on children sitting on the ground, and the interactive ability of teachers is tested. Present VR (virtual reality) technique can provide an immersive experience, can guarantee that the teacher looks back, understand the administrative effect of education in-process, discover the problem that its classroom in-process exists, and then improve follow-up classroom quality, but there is effectual data drive mode temporarily, data processing mode promptly, lead to the teacher not to know when looking back that to put into which children's role and experience, be unfavorable for the teacher to discover the problem that its classroom in-process exists, thereby the effect that the teacher utilized the VR technique to look back in class has been reduced.
Disclosure of Invention
In order to solve the problem that the existing data processing mode can not enable a young teacher to not know which role of the child to be placed for experience when the young teacher uses VR technology to review in the classroom, so that the effect of reviewing in the classroom is reduced, the invention aims to provide a data processing method for educational practical training.
The invention discloses a data processing method for educational training, which comprises the following steps:
acquiring the frequent degree of the prestige of each child in a classroom, the typical hesitation time for interaction and the stability degree of the presthesion in the target classroom;
calculating attraction degrees of the classroom to the children according to typical hesitation time and hesitation stability degree of the interaction of the children in the target classroom;
calculating the behavior difference distance between any two children according to the attraction degree, the prestige frequency degree and the delay time sequence of each child in the classroom of the target classroom; classifying the children in the target classroom according to the behavior difference distance to obtain a plurality of types; the delay time sequence is a sequence formed by the interaction delay times of the corresponding children in the class;
for either type: calculating the spatial distance between each child in the type and other children of the type according to the prestige frequency of each child in the type and the prestige state sequence corresponding to the target time period, calculating the behavior membership degree corresponding to each child in the type according to the spatial distance, taking the child with the maximum behavior membership degree as a representative child of the type corresponding to the target time period, and taking the representative child of the type corresponding to the target time period as a recommended simulation object when a young teacher looks back at the target time; the periscopic state sequence corresponding to the target time period is a sequence formed by enough periscopic states corresponding to the acquisition moments of the corresponding children in the target time period.
Further, the method for obtaining the stability degree of the late doubt comprises the following steps:
the degree of hesitation stability is calculated using the following formula:
Figure DEST_PATH_IMAGE001
wherein, the first and the second end of the pipe are connected with each other,
Figure 980145DEST_PATH_IMAGE002
the degree of hesitation stability for a child,
Figure 834968DEST_PATH_IMAGE003
is the typical hesitation time for that child,
Figure 303121DEST_PATH_IMAGE004
in order to find the maximum value of the value,
Figure 432751DEST_PATH_IMAGE005
is the late-suspected time series for that child.
Further, the late suspected time is typically the average of the first 30% of the maximum late suspected times in the late suspected time sequence.
Further, the attraction degree of the classroom to each child is calculated by using the following formula:
Figure 168495DEST_PATH_IMAGE006
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE007
in order to attract a child in a classroom,
Figure 14791DEST_PATH_IMAGE003
represents a typical suspicion time of the child, K represents a suspicion stability degree of the child, tanh () is a hyperbolic tangent function, alpha is a neighborhood correction parameter,
Figure 468775DEST_PATH_IMAGE008
，
Figure 402096DEST_PATH_IMAGE009
is the maximum of the typical hesitation times for children in the eight neighbourhood of the child.
Further, the behavior difference distance between any two children is calculated by using the following formula:
Figure 493811DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 346230DEST_PATH_IMAGE011
being the behavioural difference distance between child a and child B,
Figure 38242DEST_PATH_IMAGE012
in order to the attraction degree of the class to the child a,
Figure 195161DEST_PATH_IMAGE013
to the extent of attraction of the class to the child B,
Figure 452967DEST_PATH_IMAGE014
the frequent degree of the look for the child a,
Figure 476286DEST_PATH_IMAGE015
the frequent degree of the look of the child B,
Figure 406327DEST_PATH_IMAGE016
is the late-suspected time series for child a,
Figure 353555DEST_PATH_IMAGE017
is the late-suspected time series of child B,
Figure 715135DEST_PATH_IMAGE018
the cosine similarity is solved.
Further, the calculating a spatial distance between each child in the category and another child in the category according to the prestige frequency of each child in the category and the prestige status sequence corresponding to the target time period includes:
Figure 987984DEST_PATH_IMAGE019
wherein the content of the first and second substances,
Figure 80355DEST_PATH_IMAGE020
is the spatial distance between the child p and the child q,
Figure 80541DEST_PATH_IMAGE021
for the sequence of states of the child p in the target time period,
Figure 312939DEST_PATH_IMAGE022
for the sequence of the states of the child q in the target time period,
Figure 773002DEST_PATH_IMAGE023
to the extent that the child p is looking at frequently,
Figure 395744DEST_PATH_IMAGE024
the frequent degree of prestige of the children q.
Further, calculating behavior membership corresponding to each child in the type according to the spatial distance, including:
the behavioral membership is calculated using the following formula:
Figure 465200DEST_PATH_IMAGE025
wherein, the first and the second end of the pipe are connected with each other,
Figure 909694DEST_PATH_IMAGE026
the behavior membership of the child sample p,
Figure 789926DEST_PATH_IMAGE027
is the spatial distance between the child sample p and the child sample q,
Figure 211549DEST_PATH_IMAGE028
kth reachable distance for child sample p
Figure 304270DEST_PATH_IMAGE029
The set of child samples covered in, K being the set value.
Furthermore, the DBSCAN algorithm is utilized to classify the children in the target classroom.
Has the advantages that: the method obtains parameters capable of reflecting behaviors, attention and performance of children in an activity classroom, wherein the parameters comprise frequent degree of prestige of each child in the classroom, typical delay time for interaction and delay stability degree; classifying children in the classroom according to the parameters to obtain different types of children; next, analyzing the children included in each type, and selecting representative children in each type as recommended simulation objects in the later period of driving VR, compared with a mode of randomly selecting simulation objects, the method can improve the quality of the reply, help the young students find the defects in the interaction with the children, further deepen the training of professional ability, and improve the VR review effect; compared with a mode of simulating all children one by one, the tray-duplicating work load of the young teachers can be reduced, and the tray-duplicating efficiency is improved.
Drawings
Fig. 1 is a flow chart of a data processing method for educational training of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention.
As shown in fig. 1, the data processing method for educational training of the present embodiment includes:
step 1: and acquiring the frequent degree of the hope of each child in the classroom, the typical delay time for interaction and the delay stability degree in the target classroom.
This embodiment represents the classroom of the study as the target classroom with multiple children in the target area. In the embodiment, two cameras are arranged in a target classroom, wherein the first camera is a common camera for shooting children and is used for acquiring and calculating the head direction of the children; the second is a VR camera, which is set up behind the sitting area of the children seat for the children to simulate the situation of the children watching the attention reduction.
In order to determine the listening and speaking habits of each child in the target classroom from the activity classroom, the listening and speaking modes of each child are counted, and the frequent degree of each child's prestige is calculated
Figure 137359DEST_PATH_IMAGE030
Typical hesitation time of a child to an interaction
Figure 172180DEST_PATH_IMAGE031
And the degree of tardy stability
Figure 35093DEST_PATH_IMAGE002
. The specific acquisition process of the parameters is as follows:
the head posture of each child can be estimated by collecting the facial key points and the three-dimensional Mesh through the first camera, and a more common way is to determine the ear key points by using OpenPose and roughly calculate the head posture by combining the recognition result of the facial key points. The deflection angle A of the head relative to the position of the young teacher can be obtained through conversion of the pose of the camera, and the specific conversion process is not repeated as the existing head deflection angle estimation technology is widely applied. For the head deflection angle of the child, canPolar coordinate space setting tolerance angle interval based on head center of child
Figure 571703DEST_PATH_IMAGE032
To distinguish the state of the child watching the teachers at the time t from the state of looking around
Figure 570883DEST_PATH_IMAGE033
Recording the looking-at state of each child in the whole class
Figure 776605DEST_PATH_IMAGE034
The samples are taken every 5 seconds until time T is reached and the activity class is over. When the child is in the state of watching the young teacher,
Figure 549651DEST_PATH_IMAGE033
=1; when the child does not belong to the state of watching the young teacher, i.e., the state of showing a look-up,
Figure 46492DEST_PATH_IMAGE033
=0, the child belongs to the state of watching the young teacher, namely the child has the head deflection angle in the tolerance angle interval
Figure 415025DEST_PATH_IMAGE032
The child is not in the condition of watching the young teacher, i.e. the head deflection angle of the child is not in the tolerance angle interval
Figure 276802DEST_PATH_IMAGE032
. Tolerance angle interval
Figure 534215DEST_PATH_IMAGE032
Can be set by self when in application.
Counting the frequent degree of observation
Figure 287276DEST_PATH_IMAGE030
: when the state of looking at the child does not belong to the state for 2 times continuously from a certain moment, the state of looking at the child is considered to be ended, and the child continues to pay attention to the content of the activity class of the young teacher. MeterCalculating the time length of the child in the looking-at state, the looking-at frequency
Figure 261048DEST_PATH_IMAGE030
The time that the deflection angle of the head posture exceeds the tolerance angle (namely the time in the state of looking at) accounts for the whole class time, and the greater the frequent degree of looking at, the less interested the children in the activity class information of the current teacher.
Calculating the typical hesitation time of a child to an interaction
Figure 106775DEST_PATH_IMAGE003
: the interaction time interval is designated by the teachers in each class, so that the hesitation time of the children in the activity class is calculated. Each class generally consists of a plurality of interactions, when the child is in a state of being held in sight before the interaction starts, the beginning of each interaction can attract the attention of the child to turn to the young teacher, a plurality of interaction hesitation times exist in the process, and the hesitation times can form an hesitation time sequence
Figure 165998DEST_PATH_IMAGE035
Wherein, in the step (A),
Figure 394854DEST_PATH_IMAGE036
the hesitation time for a child at the first interaction,
Figure 711216DEST_PATH_IMAGE037
the hesitation time of a certain child at the nth interaction, N is the total number of interactions involved in the class.
Typical hesitation times can be calculated for multiple attention shifts of children in an activity classroom
Figure 242691DEST_PATH_IMAGE038
And late suspicion of stability degree K. For children, multiple times of attention inattention are common phenomena, how to attract children is a part of training of preschool education specialties, and the method is also an important link for guaranteeing education quality. There are many times for inattentionThe interactive hesitation phenomenon, several obvious hesitations enough to affect the quality of the current activity class of the child, is calculated by the embodiment
Figure 445003DEST_PATH_IMAGE039
，
Figure 838069DEST_PATH_IMAGE040
Time series for question delay
Figure 786433DEST_PATH_IMAGE041
The average of the first 30% of the maximum late times included in the late times. In this embodiment, the typical interaction time is calculated according to the last 30% of the maximum late time, and as another embodiment, the last 25% or 40% of the maximum late time may be selected to calculate the typical interaction time.
The suspicion stability degree K may represent the stability degree of the suspicion time of the child in the current activity class, which is shown in this embodiment
Figure 347865DEST_PATH_IMAGE042
If the ratio of the typical latency to the maximum interactive latency is smaller, then this is said
Figure 926220DEST_PATH_IMAGE003
And
Figure 122977DEST_PATH_IMAGE043
the difference is large; if the ratio of the typical hesitation time to the maximum interactive hesitation time is large, it is stated
Figure 660269DEST_PATH_IMAGE003
And
Figure 658181DEST_PATH_IMAGE043
the difference is small, and the difference is small,
Figure 655656DEST_PATH_IMAGE003
and can represent the attraction degree of the class to the children. Implementer settings
Figure 639793DEST_PATH_IMAGE003
The threshold value of (2) is set,
Figure 812017DEST_PATH_IMAGE003
below the threshold, the hearing and speaking quality of the child is considered to be not greatly affected, and K =1; for children without hesitation time, K =1 was set.
Therefore, the frequent degree of the hope of each child in the target classroom, the typical delay time for interaction and the delay stability degree can be obtained.
Step 2: and calculating the attraction degree of the classroom to each child according to the typical delay time and the delay stability degree of each child to the interaction in the target classroom.
The shorter the hesitation time, i.e., the less distracting the child is, the more attractive the child is to interact and listen to in the class of activities by the young teacher and the greater the child's attraction to the quality of the class of activities. In this embodiment, the following formula is specifically used to calculate the attraction degree of the classroom to each child:
Figure 793880DEST_PATH_IMAGE006
wherein the content of the first and second substances,
Figure 959544DEST_PATH_IMAGE007
in order to attract a child in a classroom,
Figure 996639DEST_PATH_IMAGE007
the larger the suction, the larger the degree of suction;
Figure 774102DEST_PATH_IMAGE003
indicating the typical suspicion time of the child and K indicating the suspicion stability of the child.
Figure 346773DEST_PATH_IMAGE002
The smaller, the
Figure 560585DEST_PATH_IMAGE043
Compared with
Figure 152103DEST_PATH_IMAGE003
Larger, if K is not considered, but only in order
Figure 269226DEST_PATH_IMAGE003
Since the calculated attraction degree is large as the size of the attraction degree of the child in the class, the attraction degree of the child listening and speaking is corrected by using K as the correction index in this embodiment. tanh () is a hyperbolic tangent function, used here for normalization of parameters.
The method comprises the following steps of calculating the maximum typical hesitation length of eight neighborhoods, wherein alpha is a neighborhood correction parameter, the eight neighborhoods of the positions where children are located need to be referenced in a setting mode, and the eight neighborhoods can be mutually influenced, so that the maximum typical hesitation length of the eight neighborhoods is counted and used as a reference value. When a child is in a certain position, the typical hesitation time of the child at hand is larger than that of the child, and the child is considered to be more concerned about the activity class content of the young teacher than the surrounding child is attracted by other things. In this example
Figure 779842DEST_PATH_IMAGE008
In which
Figure 497262DEST_PATH_IMAGE038
Indicating a typical late-to-doubt time for a child,
Figure 380554DEST_PATH_IMAGE044
is the maximum of the typical hesitation times for children in the eight neighbourhood of the child. When the child is at an edge location, no non-existent neighborhoods are included in the calculation. The degree of attraction of the children to the contents of the teachers' activities in the activities class is obtained through analysis
Figure 601451DEST_PATH_IMAGE007
。
And step 3: calculating the behavior difference distance between any two children according to the attraction degree, the prestige frequency degree and the delay time sequence of each child in the classroom of the target classroom; classifying the children in the target classroom according to the behavior difference distance to obtain a plurality of types; the hesitation time sequence is a sequence formed by the interaction hesitation times of the corresponding children in the class.
The embodiment analyzes the in-class data of the children in the target classroom and determines the difference of the interaction behaviors of the children of different types to the activity classroom. In the embodiment, based on a dbscan algorithm, r and minpts are set for clustering to obtain more clustering clusters, wherein each clustering cluster is a type; wherein the child behavior difference distance function is constructed as follows:
Figure 282968DEST_PATH_IMAGE045
wherein, the first and the second end of the pipe are connected with each other,
Figure 35155DEST_PATH_IMAGE011
being the behavioural difference distance between child a and child B,
Figure 906159DEST_PATH_IMAGE012
to the extent of attraction of the class to the child a,
Figure 168513DEST_PATH_IMAGE013
in order to the attraction degree of the class to the child B,
Figure 847363DEST_PATH_IMAGE014
the frequent degree of the look for the child a,
Figure 336113DEST_PATH_IMAGE015
the frequent degree of the prestige of the child B,
Figure 994496DEST_PATH_IMAGE016
is the late-suspected time series for child a,
Figure 878401DEST_PATH_IMAGE017
is a late-doubt time series for child B,
Figure 777087DEST_PATH_IMAGE018
the cosine similarity is solved. In the embodiment, the cosine similarity distance is used for secondary comparison, and the attraction degree of the children during listening and speaking is determined according to the similarity
Figure 674504DEST_PATH_IMAGE007
Frequent degree of observation
Figure 590375DEST_PATH_IMAGE030
And constructing a behavior vector. The present embodiment utilizes
Figure 905950DEST_PATH_IMAGE046
The calculation is carried out for the purpose of avoiding coincidence of similar attraction degrees caused by different behavior modes when the attraction degrees are determined, so that the final calculation results of the values of different parameters are the same.
Thus, the difference in the degree of attraction to the activity class between any two children can be obtained. According to the mode, the distance comparison is carried out on any two sampled children at present, and the difference distance between any two children is obtained. Therefore, a child group hypothesis space based on psychological behaviors is obtained, and children with different listening and speaking modes are divided into different child groups based on the improved DBSCAN based on the hypothesis space, wherein each child group corresponds to one type. For example, some children can be well attracted when the interaction between the teachers begins, so that the late-to-doubt time is short and the attraction degree value is large, but the attention is often attracted when the children are not in the interaction link, and the children in the listening and speaking modes are classified into one type. In addition, there is also a large attraction value, and children who continuously pay attention to the young teacher are classified into one type. Of course, there may be other listening and speaking modes of children classified as other types. The r and the minpts can be set by themselves during application, and the difference of the r and the minpts during application can cause the difference of the type quantity and the classification result of the later-stage division, but the division of the child types can be realized.
And 4, step 4: for either type: calculating the spatial distance between each child in the type and other children of the type according to the prestige frequency of each child in the type and the prestige state sequence corresponding to the target time period, calculating the behavior membership degree corresponding to each child in the type according to the spatial distance, taking the child with the maximum behavior membership degree as a representative child of the type corresponding to the target time period, and taking the representative child of the type corresponding to the target time period as a recommended simulation object when a young teacher looks back at the target time; the periscopic state sequence corresponding to the target time period is a sequence formed by the periscopic states corresponding to the collecting moments of the corresponding children in the target time period.
For each listening and speaking mode of children, the young teacher can simulate the head direction of the children in a classroom in a VR mode to simulate and experience the behaviors of the children for teaching. This is inefficient, however, and does not allow for a good analysis of the key reference views and a determination of when the view may represent a location where the teacher needs to have increased expertise, resulting in the teacher having to traverse through many times to find the cause. Considering that for a plurality of children belonging to a listening and speaking mode, the young teacher only needs to select one of the most representative children for analysis, so that the present embodiment next selects the child representative in each listening and speaking mode, so that the young teacher only needs to simulate the child representative in each listening and speaking mode to quickly find the reason that the attention of the corresponding type of children is affected when reviewing at the later stage.
This embodiment takes 8 minutes as a time period, which is a sliding sampling window, and the following is calculated for each data update as the time of the activity class changes. This embodiment performs sampling once every 5 seconds, so this embodiment updates the following calculation process every 5 seconds.
For any period of time in an activity classroom, a state space of attention of a child in a certain listening and speaking mode in the period of time is constructed. The present embodiment utilizes the following formula to calculate the spatial distance between two children belonging to the same type at a certain time:
Figure 224805DEST_PATH_IMAGE019
wherein the content of the first and second substances,
Figure 625830DEST_PATH_IMAGE020
is the spatial distance between the child p and the child q,
Figure 65164DEST_PATH_IMAGE021
the child p is in the periscopic state sequence in the period of time, the periscopic state sequence is the set of the periscopic states corresponding to the sampling moments in the period of time,
Figure 484513DEST_PATH_IMAGE022
for the sequence of the states of attentiveness of the child q during this time, since the included attentiveness states of a group of children are not completely similar but may be approximately of the same type, when the attentiveness state of a child belongs to the situation represented by most children,
Figure 459422DEST_PATH_IMAGE047
the state features that can represent attention are similar, so distances are scaled to close distances in the hypothetical space, i.e., the term goes towards 1, whereas it is kept farther away to longer distances.
Figure 767650DEST_PATH_IMAGE030
Can reflect the condition of attention loss of children in a tidy class,
Figure 633844DEST_PATH_IMAGE023
to the extent that the child p is looking at frequently,
Figure 658432DEST_PATH_IMAGE024
the frequent degree of the children q, the times of the children p and q may be different, so that the state space of attention can further reflect the concentration and the ignorant behavior difference of one type of children. In this embodiment, the period of time is recorded as a target time, and when a time period corresponding to the period of time changes, the target time period also changes correspondingly.
For in the state spaceCalculating the Kth reachable distance of each child
Figure 961499DEST_PATH_IMAGE029
Kth reachable distance
Figure 320805DEST_PATH_IMAGE029
I.e. the distance at which a child sample radiates outward in the associated state space until the K-th adjacent sample is covered. In this embodiment, K is 5, and for K which cannot reach 5, calculation is not required, and K is regarded as an isolated sample; for K up to 5, the analysis was continued. When the children training device is used specifically, an implementer can set the K value according to the number of the class children.
At Kth reachable distance of child sample p
Figure 475843DEST_PATH_IMAGE029
In the method, K child samples q can be covered, and all the covered child samples q are constructed into a set in the covered space
Figure 46283DEST_PATH_IMAGE028
. Therefore, the behavior membership degree of the child sample p can be obtained:
Figure 815524DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 475176DEST_PATH_IMAGE026
the behavior membership of the child sample p,
Figure 122320DEST_PATH_IMAGE027
is the spatial distance between the child sample p and the child sample q. When the behaviors of the children are unified with other children, the higher the concentration degree of the concentration characteristics of the children p and the surrounding of the state space of the children p is, the smaller the reachable distance is, and the higher the membership degree is, which means that the behaviors of the children are representative in the time period and have higher reference value. Conversely, low concentration and membershipLower means that the child's behavior is unique over the time period, with less reference value.
The embodiment takes the most representative child in the period of time as the child representative of the pattern to be observed in the period of time. And taking the set of most representative children corresponding to the time period in each mode as the child roles recommended to be simulated when the teacher looks back at the time period in the later period. The children representative corresponding to different time periods is calculated in the embodiment, the roles of the recommended and simulated children are different when the young teacher looks back at different time periods, the membership degree calculation is performed every 5 seconds in the embodiment, and the recommended data is more real-time. As other embodiments, the membership level calculation may be performed at longer intervals to avoid large calculations or large fluctuations in the recommended data.
The embodiment obtains parameters capable of reflecting behaviors, attention and performance of children in an activity classroom, wherein the parameters comprise frequent degree of prestige of each child in the classroom, typical lagged time for interaction and lagged stability degree; classifying children in the classroom according to the parameters to obtain different types of children; next, analyzing the children included in each type, and selecting representative children in each type as recommended simulation objects for driving VR at a later stage, wherein compared with a mode of randomly selecting simulation objects, the method can improve the quality of a reply, help a young teacher find the defects when interacting with the children, and further deepen the training of professional ability; compared with a mode of simulating all children one by one, the tray-duplicating work load of the young teachers can be reduced, and the tray-duplicating efficiency is improved.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present application, and they should be construed as being included in the present application.

Claims (8)

1. A data processing method for educational training, comprising:
acquiring the frequent degree of the prestige of each child in a classroom, the typical hesitation time for interaction and the stability degree of the presthesion in the target classroom;
calculating attraction degrees of the classroom to the children according to typical hesitation time and hesitation stability degree of the interaction of the children in the target classroom;
calculating the behavior difference distance between any two children according to the attraction degree, the prestige frequency degree and the delay time sequence of each child in the classroom of the target classroom; classifying the children in the target classroom according to the behavior difference distance to obtain a plurality of types; the delay time sequence is a sequence formed by the interaction delay times of the corresponding children in the class;
for either type: calculating the spatial distance between each child in the type and other children of the type according to the prestige frequency of each child in the type and the prestige state sequence corresponding to the target time period, calculating the behavior membership degree corresponding to each child in the type according to the spatial distance, taking the child with the maximum behavior membership degree as a representative child of the type corresponding to the target time period, and taking the representative child of the type corresponding to the target time period as a recommended simulation object when a young teacher looks back at the target time; the periscopic state sequence corresponding to the target time period is a sequence formed by the periscopic states corresponding to the acquisition moments of the corresponding children in the target time period.
2. The data processing method for educational training according to claim 1, wherein the method for obtaining the degree of lateness stability comprises:
the degree of hesitation stability is calculated using the following formula:
Figure 916392DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE003
for a certain child's degree of hesitation to stabilize,
Figure 794218DEST_PATH_IMAGE004
is the typical hesitation time for that child,
Figure DEST_PATH_IMAGE005
in order to find the maximum value,
Figure 675586DEST_PATH_IMAGE006
is the late-suspected time series for that child.
3. The data processing method for educational practical training according to claim 2, wherein the typical hesitation time is a mean value of the first 30% of maximum hesitation times among the hesitation times included in the hesitation time series.
4. The data processing method for educational training according to claim 1, wherein the attraction level of a classroom to each child is calculated using the following formula:
Figure 329684DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE009
to the attraction degree of a classroom to a certain child,
Figure 980108DEST_PATH_IMAGE004
represents the typical hesitation time of the child, K represents the hesitation stability degree of the child, tanh () is a hyperbolic tangent function, alpha is a neighborhood correction parameter,
Figure 887890DEST_PATH_IMAGE010
，
Figure DEST_PATH_IMAGE011
is the maximum of the typical hesitation times for children in the eight neighbourhoods of the child.
5. The data processing method for educational training according to claim 1, wherein the behavior difference distance between any two children is calculated using the following formula:
Figure DEST_PATH_IMAGE013
wherein the content of the first and second substances,
Figure 817406DEST_PATH_IMAGE014
being the behavioural difference distance between child a and child B,
Figure DEST_PATH_IMAGE015
to the extent of attraction of the class to the child a,
Figure 101626DEST_PATH_IMAGE016
to the extent of attraction of the class to the child B,
Figure DEST_PATH_IMAGE017
the frequent degree of the look for the child a,
Figure 232655DEST_PATH_IMAGE018
the frequent degree of the look of the child B,
Figure DEST_PATH_IMAGE019
is the late-suspected time series of child a,
Figure 327650DEST_PATH_IMAGE020
is the late-suspected time series of child B,
Figure DEST_PATH_IMAGE021
to find the cosine similarity.
6. The data processing method for educational training according to claim 1, wherein the calculating the spatial distance between each child in the category and other children in the category according to the frequent degree of views of each child in the category and the sequence of views corresponding to the target time period comprises:
Figure 308245DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE023
is the spatial distance between the child p and the child q,
Figure 369391DEST_PATH_IMAGE024
for the sequence of states of the child p in the target time period,
Figure DEST_PATH_IMAGE025
for the sequence of the children's q's state of view within the target time period,
Figure 119041DEST_PATH_IMAGE026
the frequent degree of the look of the child p,
Figure DEST_PATH_IMAGE027
the frequent degree of prestige of the children q.
7. The data processing method for educational training according to claim 6, wherein calculating the behavioral membership corresponding to each child in the category according to the spatial distance comprises:
the behavioral membership is calculated using the following formula:
Figure DEST_PATH_IMAGE029
wherein the content of the first and second substances,
Figure 73353DEST_PATH_IMAGE030
the behavior membership degree of the child sample p,
Figure DEST_PATH_IMAGE031
is the spatial distance between the child sample p and the child sample q,
Figure 72402DEST_PATH_IMAGE032
kth reachable distance for child sample p
Figure DEST_PATH_IMAGE033
The set of child samples covered in, K being the set value.
8. The data processing method for educational training according to claim 1, wherein each child in the target classroom is classified using DBSCAN algorithm.
CN202211107383.3A 2022-09-13 2022-09-13 Data processing method for educational training Active CN115186014B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211107383.3A CN115186014B (en) 2022-09-13 2022-09-13 Data processing method for educational training

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211107383.3A CN115186014B (en) 2022-09-13 2022-09-13 Data processing method for educational training

Publications (2)

Publication Number Publication Date
CN115186014A true CN115186014A (en) 2022-10-14
CN115186014B CN115186014B (en) 2022-12-02

Family

ID=83524489

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211107383.3A Active CN115186014B (en) 2022-09-13 2022-09-13 Data processing method for educational training

Country Status (1)

Country Link
CN (1) CN115186014B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130151834A1 (en) * 2011-12-13 2013-06-13 International Business Machines Corporation Deployment of a Software Image on Multiple Targets with Streaming Technique
CN108389000A (en) * 2018-03-09 2018-08-10 新华网股份有限公司 Processing method, device and the computing device of child attention concentration degree information
CN110070295A (en) * 2019-04-25 2019-07-30 平安科技（深圳）有限公司 The evaluation and analysis method, apparatus and computer equipment of Classroom Teaching
CN110916631A (en) * 2019-12-13 2020-03-27 东南大学 Student classroom learning state evaluation system based on wearable physiological signal monitoring

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130151834A1 (en) * 2011-12-13 2013-06-13 International Business Machines Corporation Deployment of a Software Image on Multiple Targets with Streaming Technique
CN108389000A (en) * 2018-03-09 2018-08-10 新华网股份有限公司 Processing method, device and the computing device of child attention concentration degree information
CN110070295A (en) * 2019-04-25 2019-07-30 平安科技（深圳）有限公司 The evaluation and analysis method, apparatus and computer equipment of Classroom Teaching
CN110916631A (en) * 2019-12-13 2020-03-27 东南大学 Student classroom learning state evaluation system based on wearable physiological signal monitoring

Also Published As

Publication number Publication date
CN115186014B (en) 2022-12-02

Similar Documents

Publication Publication Date Title
Vascon et al. Detecting conversational groups in images and sequences: A robust game-theoretic approach
CN104023230B (en) A kind of non-reference picture quality appraisement method based on gradient relevance
CN109711377B (en) Method for positioning and counting examinees in single-frame image monitored by standardized examination room
CN111898881B (en) Classroom teaching quality assessment method, device, equipment and storage medium
US10986400B2 (en) Compact video representation for video event retrieval and recognition
US9219998B2 (en) Participant grouping for enhanced interactive experience
CN107133894B (en) Online learning grouping method based on complex network theory
CN111915148A (en) Classroom teaching evaluation method and system based on information technology
Alameda-Pineda et al. Finding audio-visual events in informal social gatherings
CN111507467A (en) Neural network model training method and device, computer equipment and storage medium
CN115907507A (en) Classroom behavior detection and learning situation analysis method for students in combined classroom scene
CN113591678B (en) Classroom attention determination method, device, apparatus, storage medium, and program product
CN111259844A (en) Real-time monitoring method for examinees in standardized examination room
CN113190747B (en) Personalized resource recommendation method based on learning style and cognitive level
CN115186014B (en) Data processing method for educational training
CN111311997B (en) Interaction method based on network education resources
CN102945255B (en) Across media various visual angles imperfections label learning method
CN114265954B (en) Graph representation learning method based on position and structure information
CN114926758A (en) Method for analyzing classroom student participation
Yu et al. Measuring acoustics with collaborative multiple agents
CN111738285A (en) Evaluation method for evaluating learning efficiency and improving and optimizing learning efficiency of students
CN115689139A (en) Dormitory distribution method based on DBSCAN clustering algorithm
CN111369124A (en) Image aesthetic prediction method based on self-generation global features and attention
CN115525810B (en) Context-driven large-scale online learning dynamic grouping method
Fields et al. Recognizing scenes by simulating implied social interaction networks

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