CN108876677A - Assessment on teaching effect method and robot system based on big data and artificial intelligence - Google Patents
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Abstract
Assessment on teaching effect method and robot system based on big data and artificial intelligence, including：The teaching efficiency portrait that the teacher to be checked is searched for and obtained from teaching efficiency portrait knowledge base, obtains the value for belonging to all evaluation unit labels of the evaluation unit to be checked from the teaching efficiency of the teacher to be checked portrait.The above method and system are by evaluating the teaching efficiency of teacher based on the teaching efficiency of big data and artificial intelligence portrait, the objectivity and accuracy of teaching portrait and teaching evaluation can be greatly improved in teaching efficiency that is more true and objectively evaluating teacher.
Description
Technical field
The present invention relates to information technology fields, comment more particularly to a kind of teaching efficiency based on big data and artificial intelligence
Valence method and robot system.
Background technique
Existing Assessment on teaching effect is that student scores to teacher when the end of term and formed.
In realizing process of the present invention, at least there are the following problems in the prior art for inventor's discovery：Student is to teacher's
Evaluation depend not only on this teacher say how, additionally depend on the hobby of this student, student likes oneself
Teacher always gives higher evaluation, such as the student having likes male teacher, and some students like teacher female, and some teacher's hobbies are tight
The teacher of lattice, some teachers like loose teacher, and these hobbies are not directly dependent upon with teaching efficiency, and some does not have
It obtains idealized score or there are pernicious revenge phenomenon by the student of teacher's criticism, it is deliberately poor to teacher in Assessment on teaching effect
It comments.Therefore existing Assessment on teaching effect cannot objectively evaluate teaching efficiency, but be influenced by students ' subjective, and result in religion
The accuracy rate for learning effect assessment is low.
Therefore, the existing technology needs to be improved and developed.
Summary of the invention
Based on this, it is necessary to for the defect or deficiency of Assessment on teaching effect in the prior art, provide based on big data with
The Assessment on teaching effect method and robot system of artificial intelligence, subjectivity to solve Assessment on teaching effect is strong, accuracy rate is low
The shortcomings that.
In a first aspect, a kind of Assessment on teaching effect method is provided, the method includes：
Portrait step is obtained, the teaching effect of the teacher to be checked is searched for and obtained from teaching efficiency portrait knowledge base
Fruit portrait；
Evaluation procedure is obtained, is obtained from the teaching efficiency of the teacher to be checked portrait and belongs to described to be checked comment
The value of all evaluation unit labels of valence unit.
Preferably, further include before acquisition portrait step：
Receive query steps, obtains teacher to be checked and evaluation unit to be checked.
Preferably, further include after the acquisition evaluation procedure：
Effect calculates step, obtains the weight for belonging to all evaluation units of the evaluation unit to be checked, will be described
The value that the value of all evaluation unit labels obtains after being weighted and averaged according to the weight of all evaluation units, as described
The teaching efficiency of the evaluation unit of teacher to be checked.
Preferably, further include before acquisition portrait step：
Data step is obtained, obtains teaching process big data, the teaching process big data includes each of each teacher
The corresponding Teaching video recording of evaluation unit；Preferably, there is temporal information, period information in video recording.
Deliberate action step obtains the preset movement conscientiously listened to the teacher, as the first deliberate action；
Effect portrait step, each evaluation unit of each teacher is drawn a portrait as the teaching efficiency of each teacher
One evaluation unit label, all students identified from the corresponding Teaching video recording of each evaluation unit of each teacher
The first deliberate action total duration account for each evaluation unit total duration ratio, the teaching as each teacher
The value of one evaluation unit label of effect portrait, deposit teaching efficiency portrait knowledge base.
Preferably, the evaluation unit includes the course of preset period of time；First deliberate action includes student's new line eye
It eyeball eyes front or/and starts to take notes.
Second aspect provides a kind of Assessment on teaching effect system, the system comprises：
Portrait module is obtained, for searching for and obtaining the religion of the teacher to be checked from teaching efficiency portrait knowledge base
Learn effect portrait；
Evaluation module is obtained, is belonged to for acquisition in drawing a portrait from the teaching efficiency of the teacher to be checked described to be checked
Evaluation unit all evaluation unit labels value.
Preferably, the system also includes：
Receive enquiry module, for obtaining teacher to be checked and evaluation unit to be checked.
Further include after the search evaluation：
Effect computing module will for obtaining the weight for belonging to all evaluation units of the evaluation unit to be checked
The value that the value of all evaluation unit labels obtains after being weighted and averaged according to the weight of all evaluation units, as
The teaching efficiency of the evaluation unit of the teacher to be checked.
Preferably, the system also includes：
Data module is obtained, for obtaining teaching process big data, the teaching process big data includes each teacher
The corresponding Teaching video recording of each evaluation unit；
Deliberate action module, for obtaining the preset movement conscientiously listened to the teacher, as the first deliberate action；
Effect portrait module, for being drawn each evaluation unit of each teacher as the teaching efficiency of each teacher
One evaluation unit label of picture, that identifies from the corresponding Teaching video recording of each evaluation unit of each teacher is all
The total duration of the first deliberate action of student accounts for the ratio of the total duration of each evaluation unit, as each teacher's
The value of one evaluation unit label of teaching efficiency portrait, deposit teaching efficiency portrait knowledge base.
Preferably, the evaluation unit includes the course of preset period of time；First deliberate action includes student's new line eye
It eyeball eyes front or/and starts to take notes.
The third aspect provides a kind of Assessment on teaching effect robot system, and is each configured in the robot system
Assessment on teaching effect system described in two aspects.
The embodiment of the present invention has the following advantages that and beneficial effect：
The Assessment on teaching effect method based on big data and artificial intelligence and system of robot that the embodiment of the present invention provides
System, the evaluation unit label that each evaluation unit of each teacher is drawn a portrait as the teaching efficiency of each teacher,
The first deliberate action of all students identified from the corresponding Teaching video recording of each evaluation unit of each teacher
Total duration accounts for the ratio of the total duration of each evaluation unit, draws the ratio as the teaching efficiency of each teacher
The value of one evaluation unit label of picture, thus by being drawn a portrait based on the teaching efficiency of big data and artificial intelligence come to religion
The teaching efficiency of teacher is evaluated, teaching efficiency that is more true and objectively evaluating teacher, and teaching portrait can be greatly improved
With the objectivity and accuracy of teaching evaluation.
Detailed description of the invention
Fig. 1 is the flow chart for the Assessment on teaching effect method that one embodiment of the present of invention provides；
Fig. 2 is the flow chart for the Assessment on teaching effect method that a preferred embodiment of the present invention provides；
Fig. 3 is the functional block diagram for the Assessment on teaching effect system that one embodiment of the present of invention provides；
Fig. 4 is the functional block diagram for the Assessment on teaching effect system that a preferred embodiment of the present invention provides.
Specific embodiment
Below with reference to embodiment of the present invention, technical solution in the embodiment of the present invention is described in detail.
The embodiment of the present invention provides Assessment on teaching effect method and robot system based on big data and artificial intelligence,
Wherein, big data technology includes the acquisition of teaching process big data, processing technique, and artificial intelligence technology includes identification technology, religion
Learn effect Portrait brand technology.
(1) the Assessment on teaching effect method based on big data and artificial intelligence
As shown in Figure 1, a kind of Assessment on teaching effect method that one embodiment provides, the method includes：
Portrait step S500 is obtained, searches for and obtain the religion of the teacher to be checked from teaching efficiency portrait knowledge base
Learn effect portrait.Preferably, teaching efficiency portrait is a kind of user's portrait.Wherein, user's portrait is artificial intelligence
Core technology.
Evaluation procedure S600 is obtained, acquisition belongs to described to be checked from the teaching efficiency of the teacher to be checked portrait
Evaluation unit all evaluation unit labels value.
The Assessment on teaching effect method from the portrait of teaching efficiency by searching for the evaluation list of teacher to be checked
Member label value, come obtain the teacher to be checked evaluation unit teaching efficiency so that teaching evaluation is to be based on
Teaching efficiency portrait carries out, and teaching efficiency portrait is carried out based on teaching process big data, so that based on this
The teaching evaluation of embodiment can objectively reflect the teaching efficiency in teaching process, and traditional teaching evaluation is only being learned
It is scored at the end of phase by student, so traditional teaching evaluation is on the one hand excessively subjective, on the other hand has ignored teaching process.
1, portrait step is obtained
In a preferred embodiment, acquisition portrait step S500 includes：
S501, searching for from teaching efficiency portrait knowledge base and obtaining the teacher to be checked includes name, number (example
Such as Zhang San, 2018002) teaching efficiency portrait (such as teaching efficiency portrait of Zhang San).
The described acquisition portrait step S500 obtains the portrait of teacher to be checked in knowledge base by drawing a portrait from teaching efficiency,
So that can be carried out based on the objectively portrait to the evaluation of teaching efficiency.
2, evaluation procedure is obtained
In a preferred embodiment, the acquisition evaluation procedure S600 includes：
S601 is obtained every from the teaching efficiency of the teacher to be checked portrait (such as teaching efficiency portrait of Zhang San)
One evaluation unit label (Zhang San, 2018002, higher mathematics, 2018-5-23 to 2018-8-12；Zhang San, 2018002, English
Language, 2018 academic years；Etc.), then therefrom select belong to the evaluation unit to be checked (in example 1, higher mathematics, 2018-
5-23 to 2018-8-12；In example 2, all courses, 2018 year) all evaluation unit labels (in example 1, be Zhang San,
2018002, higher mathematics, 2018-5-23 to 2018-8-12；It is Zhang San, 2018002, higher mathematics, 2018-5- in example 2
23 to 2018-8-12；Zhang San, 2018002, English, 2018 academic years).
S602, from the teaching efficiency portrait for retrieving and obtaining the teacher to be checked in teaching efficiency portrait knowledge base
Belonging to the values of all evaluation unit labels of the evaluation unit to be checked, (in example 1, the teaching efficiency of Zhang San portrait is commented
The value of valence unit label " Zhang San, 2018002, higher mathematics, 2018-5-23 to 2018-8-12 " is 40%；In example 2, Zhang San
Teaching efficiency portrait evaluation unit label " Zhang San, 2018002, higher mathematics, 2018-5-23 to 2018-8-12 " value
It is 40%；The value of evaluation unit label " Zhang San, 2018002, English, 2018 academic years " of the teaching efficiency portrait of Zhang San is
80%).
The acquisition evaluation procedure S600 is by obtaining the evaluation unit of teacher to be checked in drawing a portrait from teaching efficiency
Label value, so that the portrait based on big data and artificial intelligence can be used to objectively evaluate teaching efficiency.
3, after acquisition evaluation procedure
In a preferred embodiment, further include after the acquisition evaluation procedure S600：
Effect calculates step S700, obtains the weight for belonging to all evaluation units of the evaluation unit to be checked, will
The value that the value of all evaluation unit labels obtains after being weighted and averaged according to the weight of all evaluation units, as
The teaching efficiency of the evaluation unit of the teacher to be checked.Then the teaching of the evaluation unit of the teacher to be checked is imitated
Fruit exports to user.
It is calculated in step S700 in effect, the value obtained after the weighted average is higher, then the teacher to be checked
The teaching efficiency of evaluation unit is better.The value obtained after the weighted average is lower, then the evaluation list of the teacher to be checked
The teaching efficiency of member is poorer.By comparing the size of the value obtained after the different weighted averages, it can be determined that it is different it is described to
The relative superior or inferior of the teaching efficiency of the evaluation unit of the teacher of inquiry.For example, after the weighted average of first teacher's A evaluation unit
Obtained value is 70%, and the value obtained after the weighted average of first teacher's B evaluation unit is 30%, second teacher's B evaluation unit
The weighted average after obtained value be 50%, the value obtained after the weighted average of second teacher's C evaluation unit is 10%,
Then teaching efficiency is ordered as first teacher's A evaluation unit > second teacher's B evaluation unit > first teacher's B evaluation unit > to difference from good
Second teacher's C evaluation unit.
Pass through the comprehensive all evaluations for belonging to the evaluation unit to be checked after the acquisition evaluation procedure S600
Weighted average is calculated in the label value of unit, so that it is single not only to evaluate existing evaluation in the portrait
The corresponding teaching efficiency of member, can also evaluate the corresponding teaching of evaluation unit that multiple evaluation units are composed in the portrait
Effect, to improve the use scope of Assessment on teaching effect.
(1) in a further preferred embodiment, effect calculates step S700 and includes：
S701 obtains the corresponding credit of course for all evaluation units for belonging to the evaluation unit to be checked as power
Weight (in example 1, higher mathematics, the course credit of 2018-5-23 to 2018-8-12 are 1 credit, then correspond to evaluation unit "
Three, 2018002, higher mathematics, the weight of 2018-5-23 to 2018-8-12 " are set as 1；In example 2, higher mathematics, 2018-
The course credit of 5-23 to 2018-8-12 is 1 credit, then correspond to evaluation unit " Zhang San, 2018002, higher mathematics, 2018-
The weight of 5-23 to 2018-8-12 " is set as 1；English, the course credit of 2018 academic years are 3 credits, then corresponding evaluation is single
3) weight of first " Zhang San, 2018002, English, 2018 academic years " is set as.
The value of all evaluation unit labels is weighted and averaged by S702 according to the weight of all evaluation units
(in example 1, the value of label is 40% and corresponding weight is 1, and being weighted and averaged is 40% × 1；In example 2, the value of label is distinguished
It is 40%, 80%, corresponding weight is respectively 1,3, is weighted and averaged as (40% × 1+80% × 3)/4=70%).
S703, by the value obtained after the weighted average (in example 1,40%；In example 2,70%), as described to be checked
The teaching efficiency of the evaluation unit of the teacher of inquiry.
4, it obtains before drawing a portrait step
As shown in Fig. 2, in a preferred embodiment, further including before the acquisition portrait step S500：
Data step S100 is obtained, obtains teaching process big data, the teaching process big data includes each teacher
The corresponding Teaching video recording of each evaluation unit；Preferably, the Teaching video recording includes listening to the teacher, testing to student, practicing, remembering
The video recording of classroom instructions process condition such as take down notes, answer a question, reading aloud.
Deliberate action step S200 obtains the preset movement conscientiously listened to the teacher, as the first deliberate action；
Effect portrait step S300, draws each evaluation unit of each teacher as the teaching efficiency of each teacher
One evaluation unit label of picture, that identifies from the corresponding Teaching video recording of each evaluation unit of each teacher is all
The total duration of the first deliberate action of student accounts for the ratio of the total duration of each evaluation unit, as each teacher's
The value of one evaluation unit label of teaching efficiency portrait, deposit teaching efficiency portrait knowledge base.
Receive query steps S400, obtains teacher to be checked and evaluation unit to be checked.
The step of before the acquisition portrait step S500, is identified by the video recording in teaching process, is imparted knowledge to students
The portrait of effect, rather than only carried out with the total marks of the examination of the subjective marking of student or the active of judging panel marking or student
The portrait of teaching efficiency, so that the portrait of the teaching efficiency can objectively reflect the actual effect of teaching process.
(1) in a further preferred embodiment, obtaining data step S100 includes：
S101, obtaining each teacher includes name, number (such as Zhang San, 2018002；Li Si, 2018003；King five,
2018005；Etc.), it is stored in big data repository (such as Hbase).
S102, obtains that each evaluation unit includes course name, the beginning and ending time, (such as higher mathematics, 2018-5-23 were extremely
2018-8-12；English, 2018 academic years；Chemistry, last term in 2017；Chemistry, next term in 2017；The fine arts, last term in 2016
First three week；Etc.), it is stored in big data repository；
S103 obtains each evaluation unit of each teacher (for example, Zhang San, 2018002, higher mathematics, 2018-5-23
To 2018-8-12；Zhang San, 2018002, English, 2018 academic years；Li Si, 2018003, chemistry, last term in 2017；Etc.),
It is stored in big data repository.
S104 obtains the Teaching video recording of each evaluation unit of each teacher (for example, Zhang San is in 2018-5-23 to 2018-
All Teaching video recordings of upper higher mathematics during 8-12；All Teaching video recordings of Zhang San's English on 2018 academic years；Li Si exists
All Teaching video recordings of chemistry on last term in 2017；Etc.), it is stored in big data repository (such as Hdfs).
(2) in a further preferred embodiment, deliberate action step S200 includes：
S201 prompts movement of the user to conscientiously listening to the teacher, and the feature of title, movement including movement is (for example, speech, head
Forward and mouth is dynamic；It records the note, bow and hold pen and write；Etc.), it is preset.
S202 prompts user to the half-hearted movement listened to the teacher, the feature of title, movement including movement (for example, sleep,
It closes one's eyes and the time is more than 1 minute；Play that mobile phone, bowing sees the mobile phone and the time is more than 1 minute；Etc.), it is preset.
S203 receives the input of user, by the set of the preset movement conscientiously listened to the teacher, it is preset it is half-hearted listen to the teacher it is dynamic
The collection complement of a set of work, is added the set of the first deliberate action, and deposit teaching efficiency identifies knowledge base.
(3) in a further preferred embodiment, effect portrait step S300 includes：
S301, read from big data storage system each teacher each evaluation unit (such as Zhang San, 2018002, it is high
Equal mathematics, 2018-5-23 to 2018-8-12；Zhang San, 2018002, English, 2018 academic years；Li Si, 2018003, chemistry, 2017
Last term in year；Etc.).
S302 establishes teaching efficiency portrait (such as teaching efficiency portrait of Zhang San for each teacher；The teaching of Li Si
Effect portrait；Etc.).
S303, the evaluation that each evaluation unit of each teacher is drawn a portrait as the teaching efficiency of each teacher
Unit label is (for example, Zhang San, 2018002, the teaching efficiency picture of higher mathematics, 2018-5-23 to 2018-8-12 as Zhang San
One evaluation unit label of picture；Zhang San, 2018002, English, 2018 academic years as Zhang San teaching efficiency draw a portrait one comment
Valence unit label；The evaluation unit that Li Si, 2018003, chemistry, last term in 2017 draw a portrait as the teaching efficiency of Li Si
Label；Etc.).
S304 knows by face recognition technology from the corresponding Teaching video recording of each evaluation unit of each teacher
Not Chu each student, and its middle school student is encoded.
S305 obtains the set of preset first movement from teaching efficiency identification knowledge base, obtains from the set
The preset set of actions conscientiously listened to the teacher, the preset half-hearted set of actions listened to the teacher.
S306 identifies the movement of each student in the corresponding Teaching video recording of each evaluation unit of each teacher
And matched with each movement in the set of the preset movement conscientiously listened to the teacher (if the preset movement conscientiously listened to the teacher
Contain duration in feature, then needs to carry out in conjunction with respective action in the adjacent video frame in the front and back of the movement of the identification or photo
Matching), obtaining at least one first matching degree (for example, there is 2 movements in the set for the movement conscientiously listened to the teacher, then can obtain 2
First matching degree), if there is first matching degree is greater than or equal to the first preset matching degree, then the movement of the identification is the
One deliberate action, if the first matching degree less than the first preset matching degree, by the movement of the identification with it is preset half-hearted
The movement of each of set of actions listened to the teacher is matched (if containing sometimes in the feature of the preset half-hearted movement listened to the teacher
It is long, then need to be matched in conjunction with respective action in the adjacent video frame in the front and back of the movement of the identification or photo), it obtains extremely
Few second matching degree, if each second matching degree is both less than the second preset matching degree, the movement of the identification is
First deliberate action.For example, Zhang San, 2018002, higher mathematics, 2018-5-23 to 2018-8-12 Teaching video recording video
Or identify each student from left to right, from top to bottom in the photograph collection captured, and by each student in each frame video or
The movement of each photo and speech, record the note, etc. the preset movement conscientiously listened to the teacher matched, have a matching degree example
Such as it is 0.7 with the matching degree of speech greater than the first preset matching degree such as 0.6, then can determine that the movement of the identification is conscientiously to listen
The movement of class.In another example Zhang San, 2018002, English, the video of the Teaching video recording of 2018 academic years or candid photograph photograph collection in from
It is left-to-right, identify each student from top to bottom, and by each student each frame video or each photo movement with
Make a speech, record the note, etc. the preset movement conscientiously listened to the teacher matched, all matching degrees are both less than the first preset matching degree example
Such as 0.6, then the movement of the identification is matched with preset half-hearted movements listened to the teacher such as sleep, object for appreciation mobile phones, all
It is both less than the second preset matching degree such as 0.8 with degree, then the movement of the identification is the first deliberate action.In another example Li Si,
2018003, know from left to right, from top to bottom in chemical, last term in 2017 the video of Teaching video recording or the photograph collection of candid photograph
Each other student, and by each student each frame video or each photo movement and speech, record the note, etc. it is pre-
If the movement conscientiously listened to the teacher matched, all matching degrees are both less than the first preset matching degree such as 0.6, then by the identification
Movement and sleep, play the preset half-hearted movements listened to the teacher such as mobile phone and matched, have a matching degree for example with play mobile phone
Matching degree be 0.82 be greater than the second preset matching degree such as 0.8, then the movement of the identification is not the first deliberate action.
S307, count in the corresponding Teaching video recording of each evaluation unit of each teacher (for example, Zhang San,
2018002, higher mathematics, 2018-5-23 to 2018-8-12 Teaching video recording video or candid photograph photograph collection) in identify
Each student the first deliberate action shared by duration or video frame number or number of pictures (for example, student's tool that number is 001
Identified in the video for having the first deliberate action it is 150 minutes a length of when No. 001 student takes notes, a length of 50 when speech
Minute, a length of 200 minutes during sleep, play mobile phone when it is 1000 minutes a length of, remaining when it is 600 minutes a length of, available described 001
It is 1000 minutes a length of when shared by the first deliberate action of number student) account for the total duration or video frame number of each evaluation unit
Or the ratio (such as 50%) of number of pictures (such as a length of 2000 minutes when the Teaching video recording).
S308, by the corresponding Teaching video recording of each evaluation unit of each teacher (for example, Zhang San, 2018002,
Higher mathematics, 2018-5-23 to 2018-8-12 Teaching video recording video or candid photograph photograph collection) in each student
The ratio addition that duration shared by one deliberate action or video frame number or number of pictures account for the total duration of each evaluation unit is asked
It is average that (for example, sharing 5 students in the Teaching video recording, the ratio is respectively 50%, 20%, 30%, 60%, 40%, then
It is added and is averaging as (50%+20%+30%+60%+40%)/5=40%), the teaching efficiency as each teacher is drawn
One evaluation unit label of picture value (for example, the evaluation unit label that the teaching efficiency of Zhang San is drawn a portrait " Zhang San,
2018002,40%) higher mathematics, 2018-5-23 to 2018-8-12 " value are.
The value of S309, one evaluation unit label of the teaching efficiency portrait of each teacher are stored into teaching effect
Fruit draw a portrait knowledge base (for example, the evaluation unit label that the teaching efficiency of Zhang San is drawn a portrait " Zhang San, 2018002, higher mathematics,
The value of 2018-5-23 to 2018-8-12 " is 40%；Zhang San teaching efficiency portrait evaluation unit label " Zhang San,
2018002, English, 2018 academic years " value be 80%；Li Si teaching efficiency portrait evaluation unit label " Li Si,
2018003, chemistry, last term in 2017 " value be 30%；Etc.).
(4) in a further preferred embodiment, receiving query steps S400 includes：
S401, obtaining teacher to be checked includes name, number (such as Zhang San, 2018002).
S402, obtaining evaluation unit to be checked includes course name, beginning and ending time (example 1, higher mathematics, 2018-5-23
To 2018-8-12；Example 2, all courses, 2018 years).
5, evaluation unit and deliberate action
In a preferred embodiment, the evaluation unit includes the course of preset period of time；First deliberate action
Including student's new line eyes eyes front or/and start to take notes.
(1) in a further preferred embodiment, the course of preset period of time includes：Course name, the time started and
End time or course name, affiliated academic year or course name, affiliated term.
(2) in a further preferred embodiment, the course of preset period of time further includes hidden curriculum, such as is said
Seat, salon, experiment etc..
(3) in a further preferred embodiment, the preset movement conscientiously listened to the teacher further includes preset half-hearted
Movement other than the movement listened to the teacher uses exclusive method in identification, if not the preset half-hearted movement listened to the teacher, then just
It is judged to being the preset movement conscientiously listened to the teacher.
(4) in a further preferred embodiment, the preset movement conscientiously listened to the teacher further includes expression, sound, mouth
The variation such as type, pupil.
The evaluation unit is by covering course and time period, so that evaluation unit can according to need progress
Personalized setting, can be used for the evaluation of the course and hidden curriculum of various type, can also be generalized to and course
Similar occasion is evaluated.The deliberate action can be updated at any time by receiving user setting, so that the reality
Applying example can be using the movement that can judge teaching efficiency；The deliberate action described simultaneously by a variety of movements conscientiously listened to the teacher and
The combination of a variety of half-hearted movements listened to the teacher improves the accuracy and precision that teaching efficiency is judged by movement of listening to the teacher.
(2) based on the teaching efficiency of big data and artificial intelligence portrait system
As shown in figure 3, a kind of Assessment on teaching effect system that one embodiment provides, the system comprises：
Portrait module 500 is obtained, for searching for from teaching efficiency portrait knowledge base and obtaining the teacher to be checked
Teaching efficiency portrait.
Obtain evaluation module 600, for obtained from the teaching efficiency of the teacher to be checked portrait belong to it is described to
The value of all evaluation unit labels of the evaluation unit of inquiry.
The Assessment on teaching effect system has beneficial effect same as Assessment on teaching effect method noted earlier, herein
It repeats no more.
1, portrait module is obtained
In a preferred embodiment, acquisition portrait module 500 includes unit 501.Unit 501 and front institute
It is corresponding to state step S501 described in preferred embodiment, it is no longer repeated herein.Unit 501 is for executing the S501.
The acquisition portrait module 500 has beneficial effect same as acquisition noted earlier portrait step S500, herein
It repeats no more.
2, evaluation module is obtained
In a preferred embodiment, the acquisition evaluation module 600 includes unit 601,602.Unit 601,602
It is corresponded respectively with step S601, S602 described in preferred embodiment noted earlier, it is no longer repeated herein.Unit
601, it 602 is respectively used to execute described S601, S602.
The acquisition evaluation module 600 has beneficial effect same as acquisition evaluation procedure S600 noted earlier, herein
It repeats no more.
3, after acquisition evaluation module
In a preferred embodiment, further include after the acquisition evaluation module 600：
Effect computing module 700, for obtaining the weight for belonging to all evaluation units of the evaluation unit to be checked,
The value obtained after the value of all evaluation unit labels is weighted and averaged according to the weight of all evaluation units is made
For the teaching efficiency of the evaluation unit of the teacher to be checked.
Effect computing unit 700 includes unit 701,702,703 again.Unit 701,702,703 respectively with it is noted earlier excellent
Step S701, S702, S703 described in the embodiment of choosing is corresponded, and it is no longer repeated herein.701,702,703 points of unit
Described S701, S702, S703 Yong Yu not executed.
After the acquisition evaluation module 600 module have with it is same after acquisition evaluation procedure S600 step noted earlier
Beneficial effect, details are not described herein.
4, it obtains before drawing a portrait module
As shown in figure 4, in a preferred embodiment, further including before the acquisition portrait module 500：
Data module 100 is obtained, for obtaining teaching process big data, the teaching process big data includes each teacher
The corresponding Teaching video recording of each evaluation unit；
Deliberate action module 200, for obtaining the preset movement conscientiously listened to the teacher, as the first deliberate action；
Effect portrait module 300, for being imitated each evaluation unit of each teacher as the teaching of each teacher
One evaluation unit label of fruit portrait, is identified from the corresponding Teaching video recording of each evaluation unit of each teacher
The total duration of the first deliberate action of all students accounts for the ratio of the total duration of each evaluation unit, as described each old
The value of one evaluation unit label of the teaching efficiency portrait of teacher, deposit teaching efficiency portrait knowledge base.
Receive enquiry module 400, for obtaining teacher to be checked and evaluation unit to be checked.
The front module for obtaining portrait module 500 have with before acquisitions noted earlier portrait step S500 after step
Same beneficial effect, details are not described herein.
(1) in a further preferred embodiment, obtaining data module 100 includes unit 101,102,103,104.
Unit 101,102,103,104 respectively with step S101, S102, S103, S104 mono- described in preferred embodiment noted earlier
One is corresponding, and it is no longer repeated herein.Unit 101,102,103,104 be respectively used to execute the S101, S102, S103,
S104。
(2) in a further preferred embodiment, deliberate action module 200 includes unit 201,202,203.Unit
201, it 202,203 is corresponded respectively with step S201, S202, S203 described in preferred embodiment noted earlier, herein not
It repeats and repeats.Unit 201,202,203 is respectively used to execute described S201, S202, S203.
(3) in a further preferred embodiment, effect draw a portrait module 300 include again unit 301,302,303,
304,305,306,307,308,309.Unit 301,302,303,304,305,306,307,308,309 respectively with it is noted earlier
Step S301, S302, S303, S304, S305, S306, S307, S308, S309 described in preferred embodiment corresponds,
It is no longer repeated for this.Unit 301,302,303,304,305,306,307,308,309 be respectively used to execute the S301,
S302、S303、S304、S305、S306、S307、S308、S309。
(4) in a further preferred embodiment, receiving enquiry module 400 includes unit 401,402.Unit 401,
402 correspond with step S401, S402 described in preferred embodiment noted earlier respectively, and it is no longer repeated herein.It is single
Member 401,402 is respectively used to execute described S401, S402.
6, evaluation unit and deliberate action
In a preferred embodiment, the evaluation unit includes the course of preset period of time；First deliberate action
Including student's new line eyes eyes front or/and start to take notes.
The beneficial effect of the evaluation unit and deliberate action is as previously described.
(3) the Assessment on teaching effect robot system based on big data and artificial intelligence
A kind of Assessment on teaching effect robot system that one embodiment provides, configured with described in the robot system
Assessment on teaching effect system.
The teaching efficiency portrait robot system has with teaching efficiency noted earlier portrait system similarly beneficial to effect
Fruit, details are not described herein.
The teaching efficiency portrait method and robot system that the embodiment provides imitate the teaching of Kernel-based methods big data
The standard that fruit is drawn a portrait as Assessment on teaching effect, and the teaching efficiency is drawn a portrait and is used for the evaluation of teaching efficiency, to reduce
Or get rid of the subjectivity of the evaluation taking human as judging panel.On the one hand, it can be used for full automatic teaching evaluation；It on the other hand can be with
It draws a portrait or the result of teaching evaluation for assisting judging panel to carry out teaching evaluation, such as by teaching efficiency provided in an embodiment of the present invention
It is supplied to judging panel's reference.
The Assessment on teaching effect method based on big data and artificial intelligence and system of robot that the embodiment of the present invention provides
System, the evaluation unit label that each evaluation unit of each teacher is drawn a portrait as the teaching efficiency of each teacher,
The first deliberate action of all students identified from the corresponding Teaching video recording of each evaluation unit of each teacher
Total duration accounts for the ratio of the total duration of each evaluation unit, draws the ratio as the teaching efficiency of each teacher
The value of one evaluation unit label of picture, thus by being drawn a portrait based on the teaching efficiency of big data and artificial intelligence come to religion
The teaching efficiency of teacher is evaluated, teaching efficiency that is more true and objectively evaluating teacher, and teaching portrait can be greatly improved
With the objectivity and accuracy of teaching evaluation.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (10)
1. a kind of Assessment on teaching effect method, which is characterized in that the method includes：
Portrait step is obtained, is searched for from teaching efficiency portrait knowledge base and the teaching efficiency for obtaining the teacher to be checked is drawn
Picture；
Evaluation procedure is obtained, is obtained from the teaching efficiency of the teacher to be checked portrait and belongs to the evaluation list to be checked
The value of all evaluation unit labels of member.
2. Assessment on teaching effect method according to claim 1, which is characterized in that before the acquisition portrait step also
Including：
Receive query steps, obtains teacher to be checked and evaluation unit to be checked.
3. Assessment on teaching effect method according to claim 1, which is characterized in that after the acquisition evaluation procedure also
Including：
Effect calculates step, obtains the weight for belonging to all evaluation units of the evaluation unit to be checked, will be described all
The value that the value of evaluation unit label obtains after being weighted and averaged according to the weight of all evaluation units, as described to be checked
The teaching efficiency of the evaluation unit of the teacher of inquiry.
4. Assessment on teaching effect method according to any one of claims 1 to 3, which is characterized in that the acquisition portrait
Further include before step：
Data step is obtained, obtains teaching process big data, the teaching process big data includes each evaluation of each teacher
The corresponding Teaching video recording of unit；
Deliberate action step obtains the preset movement conscientiously listened to the teacher, as the first deliberate action；
Effect portrait step, one that each evaluation unit of each teacher is drawn a portrait as the teaching efficiency of each teacher
Evaluation unit label, the of all students identified from the corresponding Teaching video recording of each evaluation unit of each teacher
The total duration of one deliberate action accounts for the ratio of the total duration of each evaluation unit, the teaching efficiency as each teacher
The value of one evaluation unit label of portrait, deposit teaching efficiency portrait knowledge base.
5. Assessment on teaching effect method according to claim 4, which is characterized in that the evaluation unit includes preset period of time
Course；First deliberate action includes student's new line eyes eyes front or/and starts to take notes.
6. a kind of Assessment on teaching effect system, which is characterized in that the system comprises：
Portrait module is obtained, for the teaching effect of the teacher to be checked to be searched for and obtained from teaching efficiency portrait knowledge base
Fruit portrait；
Evaluation module is obtained, belongs to described to be checked comment for obtaining from the teaching efficiency of the teacher to be checked portrait
The value of all evaluation unit labels of valence unit.
7. Assessment on teaching effect system according to claim 6, which is characterized in that the system also includes：
Receive enquiry module, for obtaining teacher to be checked and evaluation unit to be checked.
Further include after the search evaluation：
Effect computing module will be described for obtaining the weight for belonging to all evaluation units of the evaluation unit to be checked
The value that the value of all evaluation unit labels obtains after being weighted and averaged according to the weight of all evaluation units, as described
The teaching efficiency of the evaluation unit of teacher to be checked.
8. Assessment on teaching effect system according to claim 6, which is characterized in that the system also includes：
Data module is obtained, for obtaining teaching process big data, the teaching process big data includes each of each teacher
The corresponding Teaching video recording of evaluation unit；
Deliberate action module, for obtaining the preset movement conscientiously listened to the teacher, as the first deliberate action；
Effect portrait module, for what each evaluation unit of each teacher was drawn a portrait as the teaching efficiency of each teacher
One evaluation unit label, all students identified from the corresponding Teaching video recording of each evaluation unit of each teacher
The first deliberate action total duration account for each evaluation unit total duration ratio, the teaching as each teacher
The value of one evaluation unit label of effect portrait, deposit teaching efficiency portrait knowledge base.
9. Assessment on teaching effect system according to claim 8, which is characterized in that the evaluation unit includes preset period of time
Course；First deliberate action includes student's new line eyes eyes front or/and starts to take notes.
10. a kind of Assessment on teaching effect robot system, which is characterized in that be respectively configured in the robot system just like right
It is required that the described in any item Assessment on teaching effect systems of 6-9.
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| CN109711263A (en) * | 2018-11-29 | 2019-05-03 | 国政通科技有限公司 | Examination system and its processing method |
| CN116757524A (en) * | 2023-05-08 | 2023-09-15 | 广东保伦电子股份有限公司 | Teacher teaching quality evaluation method and device |
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| CN116757524A (en) * | 2023-05-08 | 2023-09-15 | 广东保伦电子股份有限公司 | Teacher teaching quality evaluation method and device |
| CN116757524B (en) * | 2023-05-08 | 2024-02-06 | 广东保伦电子股份有限公司 | Teacher teaching quality evaluation method and device |
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