CN109345156A - A kind of Classroom Teaching system based on machine vision - Google Patents
A kind of Classroom Teaching system based on machine vision Download PDFInfo
- Publication number
- CN109345156A CN109345156A CN201811521207.8A CN201811521207A CN109345156A CN 109345156 A CN109345156 A CN 109345156A CN 201811521207 A CN201811521207 A CN 201811521207A CN 109345156 A CN109345156 A CN 109345156A
- Authority
- CN
- China
- Prior art keywords
- student
- classroom
- information
- classroom teaching
- attention
- 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.)
- Pending
Links
- 239000012141 concentrate Substances 0.000 claims abstract description 10
- 238000011156 evaluation Methods 0.000 claims abstract description 10
- 230000001815 facial effect Effects 0.000 claims abstract description 10
- 238000001514 detection method Methods 0.000 claims abstract description 6
- 238000012545 processing Methods 0.000 claims description 23
- 238000000034 method Methods 0.000 claims description 14
- 239000006185 dispersion Substances 0.000 claims description 4
- 206010057315 Daydreaming Diseases 0.000 claims description 3
- 206010041349 Somnolence Diseases 0.000 claims description 3
- 210000000056 organ Anatomy 0.000 claims description 3
- 230000010355 oscillation Effects 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012854 evaluation process Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000000275 quality assurance Methods 0.000 description 2
- 238000013441 quality evaluation Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- 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
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Educational Administration (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Marketing (AREA)
- Educational Technology (AREA)
- General Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Multimedia (AREA)
- Game Theory and Decision Science (AREA)
- Social Psychology (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Psychiatry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Human Computer Interaction (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
A kind of Classroom Teaching system based on machine vision of the invention, comprising the following steps: student listens to the teacher the image information of state on 1. acquisition classrooms；2. carrying out face recognition to the student that listens to the teacher using system facial recognition modules, obtains student and register information；3. concentrating situation to obtain student's attention of listening to the teacher using attention of the attention of student detection module detection student on classroom concentrates situation information；4. establish Classroom Teaching model, Classroom Teaching average index is calculated, is stored in database, as the foundation of evaluation Classroom Teaching, and then meets the assessment of quality of instruction objective reality repeatability and policy analysis based on this is studied.
Description
Technical field
The present invention relates to a kind of the Classroom Teaching system based on machine vision, in particular to Classroom Teaching
Evaluation method.
Background technique
Students' evaluation becomes the important component of college quality assurance.Although existing teaching quality evaluation refers to
Mark more science and complete, however when evaluating teaching efficiency, existing passiveness is perfunctory to and unwarranted intervention, and evaluation result is caused to go out
Now it is distorted.Lack the effective monitoring to teaching evaluation process at present, conventional teaching effect assessment cannot function as scientificity
Effective foundation of teaching efficiency.
Summary of the invention
The purpose of the present invention is to provide one kind to be based on machine vision Classroom Teaching system, to realize to teaching
Quality objective reality is assessed repeatablely.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of Classroom Teaching system based on machine vision, comprising: listened to the teacher shape using video camera to student on classroom
The image information of state is acquired, and is obtained student using student's facial recognition modules and is registered information, is detected using attention of student
Module obtains student's attention of listening to the teacher and concentrates situation information, establishes Classroom Teaching model, calculates Classroom Teaching
Average index is stored in database, the foundation as evaluation Classroom Teaching.
Further, the video camera acquires the face of student and action message on classroom in real time, and using 15 frames as interval
It stores and handles the collected student's status information of institute, while recording total time of data acquisition and processing (DAP) in entire teaching process
Number, is denoted as n.
Further, the student registers module, passes through the face using Eigenface to school gathered in advance
Portion's information carries out Geometric Modeling, and the model established is stored into database.After student enters classroom, it is mounted on classroom wall
Video camera on wall acquires student's facial information in disengaging classroom in real time, by being matched with database septum reset model；Such as
Fruit detects that student is to enter classroom, then the pupilage information that will match to is stored into preset container, if
Detect that student leaves classroom, then the identity information that will match to is deleted from the container.Finally by the extraction container
Pupilage information acquisition student information of registering.
Further, the student registers information, comprising: attend class the number of student N that registers1；Student's seated position, record is just
Seat is in the number of student N of first three row2；Number of student N absent from duty3。
Further, the attention of student detection module is used to carry out the class state of school on classroom real-time
Detection.By acquiring status information that student in classroom attends class in real time and carrying out image to collected data information every 15 frames
Processing operation.It feels sleepy state according to whether the status information comprehensive descision student of the face's organ for the student that attends class is in fatigue；It is logical
The swing angle for crossing student head judges whether student gazes around, absent minded；Pass through adjacent student's head oscillation
The movement of relative angle and mouth judges whether there is student and is whispering to each other；The angle that is swung up and down by student head and
Whether the time span comprehensive descision student that head is kept is doing the thing unrelated with classroom, for example plays mobile phone.
Further, the student listen to the teacher attention concentrate situation information, comprising: attend class during play mobile phone number of student
N4；The number of student N talked during attending class5；The number of student N of fatigue during attending class6；Dispersion attention during attending class
Number of student N7。
Further, the Classroom Teaching model exports as Classroom Teaching average index, is denoted as Tq；
Mode input is that student registers information and student's attention of listening to the teacher concentrates situation information, and have: i-th image acquisition and processing obtains
Classroom student's absence rate Ari,；I-th image acquisition and processing, what is obtained is seated at the number of student of first three row
Ratio Aqi,；I-th image acquisition and processing, the obtained process middle school student that attend class play the number of student ratio A of mobile phonewi,；I-th image acquisition and processing, the obtained number of student ratio A talked in the process that attends classji,；I-th
Image acquisition and processing, the obtained number of student ratio A tired in the process that attends classpi,；I-th image acquisition and processing,
The number of student ratio A of dispersion attention during what is obtained attend classfi,；Therefore, Classroom Teaching average index TqFor；Formula
In, c1For the weight coefficient of student classroom absence rate；c2The weight coefficient of the number ratio of first three row is seated at for student；c3For
The weight coefficient of the relevant parameter of situation is concentrated to student's attention of listening to the teacher.
Beneficial effects of the present invention: the present invention utilizes machine vision function, using video camera and image analysis equipment to class
The listen to the teacher image information of state of one's parents student is acquired and handles, and obtains that student registers information and student listens to the teacher attention concentration
Situation information establishes Classroom Teaching model, calculates Classroom Teaching average index, is stored in database, Jin Erman
The assessment of sufficient quality of instruction objective reality repeatability and based on this policy analysis research.
Detailed description of the invention
Present invention is further described in detail in the following with reference to the drawings and specific embodiments.
Fig. 1 is present system flow chart.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with attached drawing to the present invention
Technical solution be clearly and completely described, it is clear that described embodiments are some of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Under every other embodiment obtained, shall fall within the protection scope of the present invention.
Students' evaluation becomes the important component of college quality assurance.Although existing teaching quality evaluation refers to
Mark more science and complete, however when evaluating teaching efficiency, existing passiveness is perfunctory to and unwarranted intervention, and evaluation result is caused to go out
Now it is distorted.Lack the effective monitoring to teaching evaluation process at present, conventional teaching effect assessment cannot function as scientificity
Effective foundation of teaching efficiency.
Based on this, a kind of Classroom Teaching system based on machine vision provided in an embodiment of the present invention can be with
Realize the assessment that objective reality repeatability is carried out to Classroom Teaching.
Fig. 1 is present system flow chart.
Referring to Fig.1, a kind of Classroom Teaching system based on machine vision includes following content: step S110,
Use the image information of place student on wide-angle lens camera acquisition classroom.
Specifically, camera acquires student's facial information in classroom in real time, dsp processor stores and locates using 15 frames as interval
The collected student's status information of reason institute, while the total degree of data acquisition and processing (DAP) in entire teaching process is recorded, it is denoted as
n。
Step S120 obtains student and registers information using student's module of registering.
By carrying out Geometric Modeling using facial information of the Eigenface to school gathered in advance, and will be built
Vertical model is stored into database.After student enters classroom, the video camera being mounted on the wall of classroom acquires disengaging religion in real time
Student's facial information of room, by being matched with database septum reset model；If detecting that student is to enter classroom,
The pupilage information that will match to is stored into preset container, if detecting that student leaves classroom, general
The identity information being fitted on is deleted from the container.Finally by registering for the pupilage information acquisition student extracted in the container
Information.
Step S130, using attention of student detection module obtain student listen to the teacher attention concentrate situation information.
Specifically, by acquiring the status information attended class of student in classroom in real time and believing every 15 frames collected data
Breath carries out image processing operations, and processing result is saved in reservoir.Believed according to the state of the face's organ for the student that attends class
Whether breath comprehensive descision student, which is in fatigue, is felt sleepy state；By the swing angle on student head judge student whether east Zhang Xi
It hopes, it is absent minded；Student is judged whether there is by the movement of the relative angle and mouth of adjacent student's head oscillation to exist
It whispers to each other；Whether the time span comprehensive descision student that the angle swung up and down by student head and head are kept is doing
The thing unrelated with classroom, for example play mobile phone.
Step S140 establishes Classroom Teaching model, calculates Classroom Teaching average index.
Specifically, Classroom Teaching model, exports as Classroom Teaching average index, be denoted as Tq；Model is defeated
Enter to register information for student and student's attention of listening to the teacher concentrates situation information, have: i-th image acquisition and processing, obtained classroom
Student's absence rate Ari,；I-th image acquisition and processing, what is obtained is seated at the number of student ratio A of first three rowqi,；
I-th image acquisition and processing, the obtained process middle school student that attend class play the number of student ratio A of mobile phonewi,；I-th figure
Picture acquisition process, the obtained number of student ratio A talked in the process that attends classji,；I-th image acquisition and processing, obtains
To attend class during fatigue number of student ratio Api,；I-th image acquisition and processing, during what is obtained attends class
The number of student ratio A of dispersion attentionfi,；Therefore, Classroom Teaching average index TqFor；In formula, c1It is lacked for student classroom
The weight coefficient of diligent rate；c2The weight coefficient of the number ratio of first three row is seated at for student；c3To listen to the teacher attention collection with student
The weight coefficient of the relevant parameter of middle situation.
By Classroom Teaching average index TqIt is stored in database, the foundation as evaluation Classroom Teaching.
Above content is only to structure of the invention example and explanation, affiliated those skilled in the art couple
Described specific embodiment does various modifications or additions or is substituted in a similar manner, without departing from invention
Structure or beyond the scope defined by this claim, is within the scope of protection of the invention.
Claims (7)
1. a kind of Classroom Teaching system based on machine vision characterized by comprising using video camera to classroom
The listen to the teacher image information of state of upper student is acquired, and student registers module, using Face datection algorithm to disengaging classroom
It is raw to carry out Face datection, and the facial information in the facial information and face database that will test by face recognition algorithms into
Row matching obtains upper class hour in the number of students in classroom, is registered purpose with reaching student, utilizes the acquisition of attention of student detection module
Student's attention of listening to the teacher concentrates situation information, establishes Classroom Teaching model, calculates Classroom Teaching average index,
It is stored in database, the foundation as evaluation Classroom Teaching.
2. a kind of Classroom Teaching system based on machine vision according to claim 1, which is characterized in that institute
It states video camera and acquires the face of student and action message on classroom in real time, and stored and handled collected as interval using 15 frames
Student's status information, while the total degree of data acquisition and processing (DAP) in entire teaching process is recorded, it is denoted as n.
3. a kind of Classroom Teaching system based on machine vision according to claim 1, which is characterized in that institute
Student is stated to register module, by carrying out Geometric Modeling using facial information of the Eigenface to school gathered in advance,
And the model established is stored into database；After student enters classroom, the video camera being mounted on the wall of classroom is adopted in real time
Student's facial information in collection disengaging classroom, by being matched with database septum reset model；If detecting that student is to enter
Classroom, then the pupilage information that will match to is stored into preset container, if detecting that student leaves classroom,
The identity information that so will match to is deleted from the container, finally by the pupilage information acquisition extracted in the container
Raw information of registering.
4. a kind of Classroom Teaching system based on machine vision according to claim 1, which is characterized in that institute
It states student to register information, comprising: attend class the number of student N that registers1；Student's seated position, record are seated at the student people of first three row
Number N2；Number of student N absent from duty3。
5. a kind of Classroom Teaching system based on machine vision according to claim 1, which is characterized in that institute
Attention of student detection module is stated, by acquiring status information that student in classroom attends class in real time and every 15 frames to collected
Data information carries out image processing operations；Whether it is according to the status information comprehensive descision student of the face's organ for the student that attends class
Fatigue is felt sleepy state；Judge whether student gazes around by the swing angle on student head, it is absent minded；By adjacent
The movement of the relative angle and mouth of student's head oscillation judges whether there is student and is whispering to each other；Above and below student head
Whether the time span comprehensive descision student that the angle of swing and head are kept is doing the thing unrelated with classroom, for example plays hand
Machine.
6. a kind of Classroom Teaching system based on machine vision according to claim 1, which is characterized in that institute
State student listen to the teacher attention concentrate situation information, comprising: attend class during play mobile phone number of student N4；It is talked during attending class
Number of student N5；The number of student N of fatigue during attending class6；The number of student N of dispersion attention during attending class7。
7. a kind of Classroom Teaching system based on machine vision according to claim 1, which is characterized in that institute
The output for stating Classroom Teaching model is Classroom Teaching average index, is denoted as Tq；Mode input is registered for student
Information and student listen to the teacher attention concentrate situation information, then have i-th image acquisition and processing, obtained classroom student's absence rate
Ari,；I-th image acquisition and processing, what is obtained is seated at the number of student ratio A of first three rowqi,；
I-th image acquisition and processing, the obtained process middle school student that attend class play the number of student ratio A of mobile phonewi,；I-th figure
Picture acquisition process, the obtained number of student ratio A talked in the process that attends classji,；I-th image acquisition and processing, what is obtained attends class
Tired number of student ratio A in the processpi,；I-th image acquisition and processing, attention point during what is obtained attend class
Scattered number of student ratio Afi,；Therefore, Classroom Teaching average index TqFor；In formula, c1For the power of student classroom absence rate
Weight coefficient；c2The weight coefficient of the number ratio of first three row is seated at for student；c3For with student listen to the teacher attention concentrate situation phase
The weight coefficient of the parameter of pass；By Classroom Teaching average index TqIt is stored in database, as evaluation Classroom Teaching
Foundation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811521207.8A CN109345156A (en) | 2018-12-12 | 2018-12-12 | A kind of Classroom Teaching system based on machine vision |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811521207.8A CN109345156A (en) | 2018-12-12 | 2018-12-12 | A kind of Classroom Teaching system based on machine vision |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109345156A true CN109345156A (en) | 2019-02-15 |
Family
ID=65304222
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811521207.8A Pending CN109345156A (en) | 2018-12-12 | 2018-12-12 | A kind of Classroom Teaching system based on machine vision |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109345156A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110164249A (en) * | 2019-05-22 | 2019-08-23 | 重庆工业职业技术学院 | A kind of computer on-line study supervision auxiliary system |
| CN110675676A (en) * | 2019-10-14 | 2020-01-10 | 江苏食品药品职业技术学院 | WeChat applet-based classroom teaching timely scoring method |
| CN110991943A (en) * | 2019-12-26 | 2020-04-10 | 河南城建学院 | Teaching quality evaluation system based on cloud computing |
| CN111062844A (en) * | 2020-03-17 | 2020-04-24 | 浙江正元智慧科技股份有限公司 | Intelligent control system for smart campus |
| CN111275345A (en) * | 2020-01-22 | 2020-06-12 | 重庆大学 | Classroom informatization evaluation and management system and method based on deep learning |
| CN111402096A (en) * | 2020-04-03 | 2020-07-10 | 广州云从鼎望科技有限公司 | Online teaching quality management method, system, equipment and medium |
| CN111582611A (en) * | 2019-02-18 | 2020-08-25 | 北京入思技术有限公司 | Classroom teaching evaluation method and system based on emotion perception |
| CN111861146A (en) * | 2020-06-29 | 2020-10-30 | 武汉科技大学 | Teaching evaluation and real-time feedback system based on micro-expression recognition |
| CN112465339A (en) * | 2020-11-25 | 2021-03-09 | 宁波阶梯教育科技有限公司 | Teaching quality evaluation method, device and system and readable storage medium |
| CN115002343A (en) * | 2022-05-06 | 2022-09-02 | 重庆工程学院 | Method and system for objectively evaluating classroom performance of student based on machine vision |
| CN116611968A (en) * | 2023-06-02 | 2023-08-18 | 湖南中医药高等专科学校 | Teaching management system based on data association |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105825189A (en) * | 2016-03-21 | 2016-08-03 | 浙江工商大学 | Device for automatically analyzing attendance rate and class concentration degree of college students |
| CN106228293A (en) * | 2016-07-18 | 2016-12-14 | 重庆中科云丛科技有限公司 | teaching evaluation method and system |
| CN107609517A (en) * | 2017-09-15 | 2018-01-19 | 华中科技大学 | A kind of classroom behavior detecting system based on computer vision |
| CN107659370A (en) * | 2017-09-15 | 2018-02-02 | 安阳工学院 | University student classroom total management system and method |
| CN107895244A (en) * | 2017-12-26 | 2018-04-10 | 重庆大争科技有限公司 | Classroom teaching quality assessment method |
| CN107958351A (en) * | 2017-12-26 | 2018-04-24 | 重庆大争科技有限公司 | Teaching quality assessment cloud service platform |
| CN207965910U (en) * | 2018-01-25 | 2018-10-12 | 西安科技大学 | Education Administration Information System based on recognition of face |
-
2018
- 2018-12-12 CN CN201811521207.8A patent/CN109345156A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105825189A (en) * | 2016-03-21 | 2016-08-03 | 浙江工商大学 | Device for automatically analyzing attendance rate and class concentration degree of college students |
| CN106228293A (en) * | 2016-07-18 | 2016-12-14 | 重庆中科云丛科技有限公司 | teaching evaluation method and system |
| CN107609517A (en) * | 2017-09-15 | 2018-01-19 | 华中科技大学 | A kind of classroom behavior detecting system based on computer vision |
| CN107659370A (en) * | 2017-09-15 | 2018-02-02 | 安阳工学院 | University student classroom total management system and method |
| CN107895244A (en) * | 2017-12-26 | 2018-04-10 | 重庆大争科技有限公司 | Classroom teaching quality assessment method |
| CN107958351A (en) * | 2017-12-26 | 2018-04-24 | 重庆大争科技有限公司 | Teaching quality assessment cloud service platform |
| CN207965910U (en) * | 2018-01-25 | 2018-10-12 | 西安科技大学 | Education Administration Information System based on recognition of face |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111582611A (en) * | 2019-02-18 | 2020-08-25 | 北京入思技术有限公司 | Classroom teaching evaluation method and system based on emotion perception |
| CN110164249B (en) * | 2019-05-22 | 2021-11-05 | 重庆工业职业技术学院 | Computer online learning supervision auxiliary system |
| CN110164249A (en) * | 2019-05-22 | 2019-08-23 | 重庆工业职业技术学院 | A kind of computer on-line study supervision auxiliary system |
| CN110675676A (en) * | 2019-10-14 | 2020-01-10 | 江苏食品药品职业技术学院 | WeChat applet-based classroom teaching timely scoring method |
| CN110991943A (en) * | 2019-12-26 | 2020-04-10 | 河南城建学院 | Teaching quality evaluation system based on cloud computing |
| CN110991943B (en) * | 2019-12-26 | 2023-08-01 | 河南城建学院 | Teaching quality evaluation system based on cloud computing |
| CN111275345A (en) * | 2020-01-22 | 2020-06-12 | 重庆大学 | Classroom informatization evaluation and management system and method based on deep learning |
| CN111275345B (en) * | 2020-01-22 | 2023-08-08 | 重庆大学 | Classroom informatization evaluation and management system and method based on deep learning |
| CN111062844A (en) * | 2020-03-17 | 2020-04-24 | 浙江正元智慧科技股份有限公司 | Intelligent control system for smart campus |
| CN111402096A (en) * | 2020-04-03 | 2020-07-10 | 广州云从鼎望科技有限公司 | Online teaching quality management method, system, equipment and medium |
| CN111861146A (en) * | 2020-06-29 | 2020-10-30 | 武汉科技大学 | Teaching evaluation and real-time feedback system based on micro-expression recognition |
| CN112465339A (en) * | 2020-11-25 | 2021-03-09 | 宁波阶梯教育科技有限公司 | Teaching quality evaluation method, device and system and readable storage medium |
| CN115002343A (en) * | 2022-05-06 | 2022-09-02 | 重庆工程学院 | Method and system for objectively evaluating classroom performance of student based on machine vision |
| CN116611968A (en) * | 2023-06-02 | 2023-08-18 | 湖南中医药高等专科学校 | Teaching management system based on data association |
| CN116611968B (en) * | 2023-06-02 | 2024-02-13 | 湖南中医药高等专科学校 | Teaching management system based on data association |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109345156A (en) | A kind of Classroom Teaching system based on machine vision | |
| CN110334697B (en) | Intelligent table with state monitoring function, monitoring system server and monitoring method | |
| JP6892558B2 (en) | Theological assistance method and the theological assistance system that adopts the method | |
| WO2020082971A1 (en) | Real-time classroom monitoring and evaluation system and operation and creation method thereof | |
| CN109657529A (en) | Classroom teaching effect evaluation system based on human facial expression recognition | |
| CN106599881A (en) | Student state determination method, device and system | |
| CN109523441A (en) | A kind of Teaching Management Method and system based on video identification | |
| CN112183238A (en) | Method and system for detecting attention of remote education | |
| CN111008542A (en) | Object concentration analysis method and device, electronic terminal and storage medium | |
| CN206557851U (en) | A kind of situation harvester of listening to the teacher of imparting knowledge to students | |
| CN109583354A (en) | Attention of student detection model based on machine learning | |
| CN110490173B (en) | Intelligent action scoring system based on 3D somatosensory model | |
| CN115170369B (en) | Live course online watching intelligent management system based on mobile internet | |
| CN107958500A (en) | A kind of monitoring system for real border real time information sampling of imparting knowledge to students | |
| CN111931608A (en) | Operation management method and system based on student posture and student face recognition | |
| CN104408781A (en) | Concentration attendance system | |
| CN111402096A (en) | Online teaching quality management method, system, equipment and medium | |
| WO2020118882A1 (en) | Method, apparatus and smart device for monitoring study status | |
| CN113570916A (en) | Multimedia remote teaching auxiliary method, equipment and system | |
| CN109684984A (en) | A kind of expression recognition method suitable for classroom instruction | |
| CN108492231A (en) | The monitoring system of class efficacy information based on students ' behavior data | |
| CN108596159A (en) | A kind of teaching method and system based on Internet education | |
| CN206293006U (en) | A kind of intelligent teaching terminal system | |
| JP2015170033A (en) | Education support device, education support processing program and education support processing method | |
| CN106600289A (en) | Emotion calculation-based intelligent seat system and implementation method therefor |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190215 |