CN110348761A - On-line intelligence incentive teaching system - Google Patents
On-line intelligence incentive teaching system Download PDFInfo
- Publication number
- CN110348761A CN110348761A CN201910652586.2A CN201910652586A CN110348761A CN 110348761 A CN110348761 A CN 110348761A CN 201910652586 A CN201910652586 A CN 201910652586A CN 110348761 A CN110348761 A CN 110348761A
- Authority
- CN
- China
- Prior art keywords
- module
- value
- excitation
- incentive
- user
- 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
- 230000005284 excitation Effects 0.000 claims abstract description 42
- 238000012545 processing Methods 0.000 claims abstract description 27
- 230000000694 effects Effects 0.000 claims abstract description 22
- 238000004088 simulation Methods 0.000 claims abstract description 18
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 12
- 230000035484 reaction time Effects 0.000 claims description 12
- 230000001186 cumulative effect Effects 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 5
- 238000003062 neural network model Methods 0.000 claims description 3
- 238000006467 substitution reaction Methods 0.000 abstract description 4
- 238000012546 transfer Methods 0.000 abstract description 4
- 230000037007 arousal Effects 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 abstract description 3
- 230000007246 mechanism Effects 0.000 description 5
- 238000000034 method Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000012512 characterization method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000008450 motivation Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 238000000151 deposition Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000012795 verification 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
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- 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
- G06Q50/2057—Career enhancement or continuing education service
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B9/00—Simulators for teaching or training purposes
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Educational Administration (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Educational Technology (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The invention discloses a kind of on-line intelligence incentive teaching systems comprising: client port and remote server；Remote server includes information acquisition module, model processing modules, effect simulation module, excitation module, feedback module and database module；Information acquisition module reading client mouth, the learning data for acquiring User；Model processing modules are for generating learning state value S, movement factor value A and Value Factors value V；Effect simulation module is used to prestore learning state value S, movement factor value A and Value Factors value V substitution in algorithm, acquires target fractional BS and target cost time BT；Excitation module is based on BS, and BT and learning data substitution prestore in algorithm, acquire excitation index Ex；Feedback module transfers the feedback material corresponding to Ex and is sent to client port.The present invention helps its clear study of User to be expected, promotes the study sense of accomplishment of User, Optimization Learning arousal effect by the learning state of simulation student.
Description
Technical field
The invention belongs to technical field of data processing, relate in particular to a kind of on-line intelligence incentive teaching system.
Background technique
With advances in technology, on-line intelligence teaching has become a kind of common teaching means.In this field, if
A fixed incentive mechanism is to improve the important link of student's Learning Motive.Incentive mechanism used by existing on-line teaching system
It is the relationship model for extracting excited rules using the historical data of user and motivating activation threshold value.Existing for this energisation mode
Problem is: the regularity of its incentive mechanism is more obvious, and student can be in order to be motivated and progress deliberately after grasping rule
Pseudo-operation, to weaken the effect of study original intention.Continue simultaneously with learning time, if excitation rule cannot be enriched constantly
Then, can also arousal effect be allowed to gradually decrease.Therefore a kind of novel on-line intelligence incentive teaching system, Neng Gouke how to be developed
The above problem is taken, is the direction that those skilled in the art need to study.
Summary of the invention
The object of the present invention is to provide a kind of on-line intelligence incentive teaching systems, by simulating the learning state of student, side
It helps its clear study of User to be expected, promotes the study sense of accomplishment of User, Optimization Learning arousal effect.
The technical scheme adopted is as follows:
A kind of on-line intelligence incentive teaching system comprising: client port and remote server；The client port is used for
Realize Telnet and information exchange of the User to remote server；The remote server includes information acquisition module,
Model processing modules, effect simulation module, excitation module, feedback module and database module；The database module is for depositing
Store up historical data, knowledge point map and the feedback material of User；The knowledge point map is known by knowledge point id, corresponding to this
The weighted value for knowing point id is constituted；The information acquisition module is used for reading client mouth, acquires the study situation of User simultaneously
The learning data that the study situation is subjected to digitized processing, acquires User；The model processing modules link information is adopted
Collection module, learning data for generating information acquisition module substitute into the formula algorithm prestored, generate learning state value S,
Act factor value A and Value Factors value V；The effect simulation module link model processing module is used for model processing modules
Learning state value S, movement factor value A and the Value Factors value V of generation are substituted into the simulation algorithm prestored, are acquired target fractional BS
Time BT is spent with target；The excitation module connection effect analog module and information acquisition module, for by the BS, BT and
Learning data substitutes into the excitation algorithm prestored, acquires excitation index Ex；The feedback module connection excitation module and database
Module, for reading excitation index Ex, and according to the corresponding relationship formula prestored, transfer from database module corresponding to the anti-of Ex
Feedback material is simultaneously sent to client port.
Preferably, in above-mentioned on-line intelligence incentive teaching system: the information acquisition module User collected
Study situation include answer result, Reaction time, knowledge point id.
It is further preferred that in above-mentioned on-line intelligence incentive teaching system: the model processing modules are based on weight equation A=
A1*R1+a2*R2+a3+b1 acquires movement factor value A；The R1 is answer as a result, the R2 is Reaction time, and the a1 is pre-
If answer result weight, the a2 be preset Reaction time weight, the a3 be preset learning objective weighted value, it is described
B1 is the adjustment factor corresponding to User historical data.
It is further preferred that in above-mentioned on-line intelligence incentive teaching system: the model processing modules are based on weight equation S=
a*T(id)eAT(id)+ b*A+c acquires current learning states value S；Described a, b, c are preset model constants；The T (id) is to know
Know the weighted value of point id.
It is further preferred that in above-mentioned on-line intelligence incentive teaching system: the effect simulation module is based on simulation formula: BS
=eV+f acquires target fractional BS, BT=gV+h and acquires target cost time BT；Described e, f, g, h are all based on V input BP mind
The training threshold value generated through network model.
It may further be preferable that in above-mentioned on-line intelligence incentive teaching system: the excitation module is based on calculation formula:Acquire excitation index Ex；The CS is based on the cumulative total score acquired of answer result；The BS is base
In the Reaction time cumulative answer total time acquired.
It may further be preferable that in above-mentioned on-line intelligence incentive teaching system: the feedback material includes excitation backchannel
Sound and excitation feedback animation.
By using above-mentioned technical proposal: with the learning data of information acquisition module acquisition User and being stored in database
In module；With model processing modules based on information acquisition module learning data collected, based in the formula algorithm prestored, it is raw
At learning state value S, the movement factor value A and Value Factors value V for characterizing the User learning ability；With effect simulation module
Learning state value S, movement factor value A and Value Factors value V based on generation acquire characterization with the current learning ability of User
It also is expected to the target fractional BS realized and target spends time BT；BS, BT and current learning data are based on excitation module
Acquire swashing for the ratio for characterizing the current actual learning data of User and target fractional BS and target cost time BT
Encourage index E x；Simultaneously feedback module connection excitation module and database module, for reading excitation index Ex, and according to prestoring
Corresponding relationship formula transfers the feedback material corresponding to the Ex from database module and feeds back to client port.The feedback material
For prompting, its current learning data distance of User reaches target fractional BS and target spends the degree of time BT also poor
How much.
Compared with prior art, the beneficial effects of the present invention are: according to the learning data of User collected, mould
The learning effect that can be obtained with the current learning state of student is drawn up, and the current study mesh using the learning effect as student
Mark.The learning objective is adjusted with the continuous dynamic of students' learning ability, creates the dynamic that a student can achieve always
Target is conducive to the sense of accomplishment of constantly improve student, is formed and the consistent Individual Motivation mechanism of student.Therefore the present invention can be held
Allow student toward the target hard-working that can be contacted continuously, it is bigger in the investment sense of learning process.
Detailed description of the invention
Present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments:
Fig. 1 is the functional block diagram of the invention.
Fig. 2 is the workflow schematic diagram of information acquisition module；
Each appended drawing reference and component names corresponding relationship are as follows:
1, client port；2, remote server；21, information acquisition module；22, model processing modules；23, effect simulation mould
Block；24, excitation module；25, feedback module；26, database module.
Specific embodiment
In order to illustrate more clearly of technical solution of the present invention, it is further described below in conjunction with each embodiment.
As shown in Figure 1 it is 1 structure of the embodiment of the present invention:
A kind of on-line intelligence incentive teaching system comprising: client port 1 and remote server 2；The client port 1
Telnet and information exchange for realizing User to remote server 2；
The remote server 2 includes information acquisition module 21, model processing modules 22, effect simulation module 23, excitation
Module 24, feedback module 25 and database module 26；
The information acquisition module 21 is used for reading client mouth 1, acquires the study situation of User and will learn feelings
The learning data that condition carries out digitized processing, acquires User；The study situation of the User includes answer result, answers
Inscribe time, knowledge point id.The 26 link information acquisition module 21 of database module, the history number for storing User
According to, knowledge point map and feedback material；The knowledge point map by knowledge point id, corresponding to the weighted value of knowledge point id.
The 22 link information acquisition module 21 of model processing modules, the study number for generating information acquisition module 21
In the formula algorithm prestored according to substitution, generate learning state value S, movement factor value A and Value Factors value V.Specifically, described
Model processing modules 22 are based on weight equation A=a1*R1+a2*R2+a3+b and acquire movement factor value A；The R1 is answer knot
Fruit, the R2 are Reaction time, and the a1 is answer result weight, and the a2 is Reaction time weight, and the a3 is knowledge point
Id weighted value, the b are the adjustment factor based on User historical data.And it is based on weight equation S=a*T (id) eAT(id)+
B*A+c acquires current learning states value S；In the formula, described a, b, c are preset model constants.The T (id) is knowledge point
The weighted value of id.
The 23 link model processing module 22 of effect simulation module, the study shape for generating model processing modules 22
When state value S, movement factor value A and Value Factors value V are substituted into the simulation algorithm prestored, are acquired target fractional BS and target cost
Between BT.Specifically: the effect simulation module 23 is based on simulation formula: BS=eV+f acquires target fractional BS, BT=gV+h
It acquires target and spends time BT；Described e, f, g, h are all the training threshold value for generating V input BP neural network model.
The 24 connection effect analog module 23 of excitation module and information acquisition module 21 are used for the BS, BT and
Data are practised to substitute into the excitation algorithm prestored, acquire excitation index Ex.Specifically, the excitation module 24 is based on calculating public
Formula:Acquire excitation index Ex；Wherein, the CS is based on the cumulative total score acquired of answer result；Institute
Stating BS is based on the Reaction time cumulative answer total time acquired.
The feedback module 25 connects excitation module 24 and database module 26, for reading excitation index Ex, and according to
The corresponding relationship formula prestored transfers the feedback material corresponding to Ex from database module 26 and is sent to client port 1.At this
In example: the feedback material includes excitation feedback voice and excitation feedback animation.
In practice, the course of work is as follows:
As shown in Figure 2: information acquisition module 21 acquires answer result, Reaction time and the learning objective of user, and information is adopted
Collect the custom protocol structure in module 21, be made of 3 parts:
header:{version:1.0,target:2}
Content:{ answer result, Reaction time, learning objective id }
check:md5(header+content)
Header designates protocol version and receives target, and 2 indicate algoritic module；Content is specific data combination,
It is separated with comma；Check is the md5 verification combined to header and content data.It can be answer result during this
It is normalized into as the numerical value between one 0~1, wherein 0 indicates complete mistake, 1 indicates completely correct, and intermediate representation is answered from correctly
The distance of case.Group, which installs, to be transferred data to model processing modules 22 after protocol entity and is handled while being stored in database module
26 carry out preservation backup.
Model processing modules 22, will be above-mentioned using the weight equation that big data training obtains after receiving the data of acquisition
Parameter brings weight equation format A=a1*R1+a2*R2+a3+b into and acquires movement factor value A, so that parameter factors be avoided excessively to lead
The calculating of cause is excessively complicated, then, obtains learning state value S according to the movement factor value A and learning objective id.According to study mesh
Id is marked, its corresponding weight T (id) is obtained from knowledge mapping.
In modeling, initial regulated quantity is set are as follows: a=0.8, b=1.25, c=0.Bring A and S into intensified learning modeling
Obtained model, obtain Value Factors V, V a characterization User can achieve under current learning states and study action
Best result.Effect simulation module 23 is based on V as parameter, in conjunction with the big data training of BP neural network model, further
It obtains target fractional BS and target spends time BT.Excitation module 24 receive expection score BS and required time BT can be obtained
Afterwards, it is compared with student's total score CS and total cost time CT, obtains the excitation index between one 0~1, and according to sharp
Index E x is encouraged, selects different degrees of excitation text and picture or voice feedback to user.
The learning data generated when this programme is according to User last round of study more really simulates student and learns institute
It can achieve the effect that, and maintained close ties with the entire learning process of student, it is specific that student is given by the learning effect simulated
Accessible target, and as target is dynamically evolved and adjusted in the continuous improvement of students' learning ability, make student have one can
With the target consistently achieved, constantly improve sense of accomplishment is formed and the more harmonious more benign competitive excitation of system.Compared to rule or
The energisation mode of fixed model, learning objective can obtain Continuous optimization, be formed and the consistent Individual Motivation mechanism of student.
The self efficacy for helping to improve student is bigger in the investment sense of learning process.
The above, only specific embodiments of the present invention, but scope of protection of the present invention is not limited thereto, it is any ripe
The technical staff of art technology is known in technical scope disclosed by the invention, any changes or substitutions that can be easily thought of, should all contain
Lid is within protection scope of the present invention.Protection scope of the present invention is subject to the scope of protection of the claims.
Claims (7)
1. a kind of on-line intelligence incentive teaching system characterized by comprising client port (1) and remote server (2)；Institute
State Telnet and information exchange of the client port (1) for realizing User to remote server (2)；
The remote server (2) includes information acquisition module (21), model processing modules (22), effect simulation module (23),
Excitation module (24), feedback module (25) and database module (26)；
The database module (26) is used to store historical data, knowledge point map and the feedback material of User；It is described to know
Know point map to constitute by knowledge point id, corresponding to the weighted value of knowledge point id；
The information acquisition module (21) for reading client mouth (1), acquire the study situation of User and by the study
The learning data that situation carries out digitized processing, acquires User；
Model processing modules (22) the link information acquisition module (21), the study for generating information acquisition module (21)
Data substitute into the formula algorithm prestored, generate learning state value S, movement factor value A and Value Factors value V；
Effect simulation module (23) the link model processing module (22), the study for generating model processing modules (22)
State value S, movement factor value A and Value Factors value V are substituted into the simulation algorithm prestored, are acquired target fractional BS and target cost
Time BT；
Excitation module (24) the connection effect analog module (23) and information acquisition module (21), for by the BS, BT and
Learning data substitutes into the excitation algorithm prestored, acquires excitation index Ex；
The feedback module (25) connection excitation module (24) and database module (26), for reading excitation index Ex, and root
According to the corresponding relationship formula prestored, the feedback material corresponding to Ex is transferred from database module (26) and is sent to client port
(1)。
2. on-line intelligence incentive teaching system as described in claim 1, it is characterised in that: the information acquisition module (21) is adopted
The study situation of the User of collection includes answer result, Reaction time, knowledge point id.
3. on-line intelligence incentive teaching system as claimed in claim 2, it is characterised in that: the model processing modules (22) are based on
Weight equation A=a1*R1+a2*R2+a3+b1 acquires movement factor value A；The R1 is answer result, when the R2 is answer
Between, the a1 is preset answer result weight, and the a2 is preset Reaction time weight, and the a3 is preset study mesh
Weighted value is marked, the b1 is the adjustment factor corresponding to User historical data.
4. on-line intelligence incentive teaching system as claimed in claim 3, it is characterised in that: the model processing modules (22) are based on
Weight equation S=a*T (id) eAT(id)+ b*A+c acquires current learning states value S；Described a, b, c are preset model constants；Institute
State the weighted value that T (id) is knowledge point id.
5. on-line intelligence incentive teaching system as claimed in claim 4, it is characterised in that: the effect simulation module (23) is based on
Simulation formula: BS=eV+f acquires target fractional BS, BT=gV+h and acquires target cost time BT；Described e, f, g, h are all base
In the training threshold value that V input BP neural network model generates.
6. on-line intelligence incentive teaching system as claimed in claim 5, it is characterised in that: the excitation module (24) is based on calculating
Formula:Acquire excitation index Ex；The CS be based on answer result it is cumulative acquire must
Point；The BS is based on the Reaction time cumulative answer total time acquired.
7. on-line intelligence incentive teaching system as claimed in claim 6, it is characterised in that: the feedback material includes excitation feedback
Voice and excitation feedback animation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910652586.2A CN110348761A (en) | 2019-07-18 | 2019-07-18 | On-line intelligence incentive teaching system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910652586.2A CN110348761A (en) | 2019-07-18 | 2019-07-18 | On-line intelligence incentive teaching system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110348761A true CN110348761A (en) | 2019-10-18 |
Family
ID=68179163
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910652586.2A Pending CN110348761A (en) | 2019-07-18 | 2019-07-18 | On-line intelligence incentive teaching system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110348761A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112365183A (en) * | 2020-11-26 | 2021-02-12 | 江西台德智慧科技有限公司 | Artificial intelligence education method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104616227A (en) * | 2015-02-12 | 2015-05-13 | 俞琳 | Rewarding method for helping students form good habits |
| CN105913706A (en) * | 2016-06-08 | 2016-08-31 | 黄毓钰 | Foreign language teaching framework based on education ecology concept |
| CN106205243A (en) * | 2016-07-11 | 2016-12-07 | 广州伟度计算机科技有限公司 | A kind of teaching system for aided education process and teaching method |
| CN106780226A (en) * | 2017-04-12 | 2017-05-31 | 中山市读书郎电子有限公司 | A kind of customized type learning method with personalization |
| CN107657558A (en) * | 2016-07-26 | 2018-02-02 | 钟苑菁 | Learning progress monitoring system |
| CN109767366A (en) * | 2019-01-08 | 2019-05-17 | 汪胜利 | A kind of computer radar system of real time scan analysis vocabulary memorization effect |
-
2019
- 2019-07-18 CN CN201910652586.2A patent/CN110348761A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104616227A (en) * | 2015-02-12 | 2015-05-13 | 俞琳 | Rewarding method for helping students form good habits |
| CN105913706A (en) * | 2016-06-08 | 2016-08-31 | 黄毓钰 | Foreign language teaching framework based on education ecology concept |
| CN106205243A (en) * | 2016-07-11 | 2016-12-07 | 广州伟度计算机科技有限公司 | A kind of teaching system for aided education process and teaching method |
| CN107657558A (en) * | 2016-07-26 | 2018-02-02 | 钟苑菁 | Learning progress monitoring system |
| CN106780226A (en) * | 2017-04-12 | 2017-05-31 | 中山市读书郎电子有限公司 | A kind of customized type learning method with personalization |
| CN109767366A (en) * | 2019-01-08 | 2019-05-17 | 汪胜利 | A kind of computer radar system of real time scan analysis vocabulary memorization effect |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112365183A (en) * | 2020-11-26 | 2021-02-12 | 江西台德智慧科技有限公司 | Artificial intelligence education method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Towards smart educational recommendations with reinforcement learning in classroom | |
| CN111160409A (en) | Heterogeneous neural network knowledge reorganization method based on common feature learning | |
| CN109903617A (en) | Individualized exercise method and system | |
| CN107993140A (en) | A kind of personal credit's methods of risk assessment and system | |
| CN103971555A (en) | Multi-level automated assessing and training integrated service method and system | |
| CN107203584A (en) | A kind of learning path planing method of knowledge based point target collection | |
| CN109033418A (en) | Intelligent recommendation method for learning content and learning equipment | |
| CN107423551A (en) | For performing the imaging method of medical inspection | |
| CN110070130A (en) | A kind of certificate information recognition methods, transposition, equipment and medium | |
| CN104616229A (en) | Course matching system used for educational training | |
| CN110473438A (en) | A kind of word assistant learning system and method based on quantitative analysis | |
| CN108197080A (en) | A kind of missing values interpolating method based on many algorithms fusion | |
| CN110531849A (en) | Intelligent teaching system based on 5G communication and capable of enhancing reality | |
| CN108876225A (en) | On-line education system and method based on man-machine hybrid intelligent | |
| Bousquet et al. | Using multi-agent systems in a companion modelling approach for agroecosystem management in South-east Asia | |
| CN110348761A (en) | On-line intelligence incentive teaching system | |
| CN110532445A (en) | The cloud transaction system and its method of neural network training pattern are provided | |
| US20220284315A1 (en) | Apparatus and system for training knowledge tracking model based on data augmentation and operation method thereof | |
| Hu et al. | An intelligent testing system embedded with an ant-colony-optimization-based test composition method | |
| De Boer et al. | Innovation in dental education: The “On‐the‐Fly” approach to simultaneous development, implementation and evidence collection | |
| CN109670596A (en) | Non-fully game decision-making method, system and the intelligent body under information environment | |
| CN107493352A (en) | A kind of mthods, systems and devices of tangible machine people intelligent training | |
| CN109300069A (en) | Acquisition methods, device and the electronic equipment of user's learning path model | |
| CN109635869A (en) | On-line study interfering system | |
| Alfaro et al. | Developing LCA techniques for emerging systems: Game theory, agent modeling as prediction tools |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191018 |
|
| RJ01 | Rejection of invention patent application after publication |