CN108765228A - A kind of adaptive private teaching learning method of computer - Google Patents
A kind of adaptive private teaching learning method of computer Download PDFInfo
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- CN108765228A CN108765228A CN201810633105.9A CN201810633105A CN108765228A CN 108765228 A CN108765228 A CN 108765228A CN 201810633105 A CN201810633105 A CN 201810633105A CN 108765228 A CN108765228 A CN 108765228A
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Abstract
A kind of adaptive private teaching learning method of computer, belongs to Internet education field.The method of the present invention step is：1）It is analyzed by the topic situation of doing of a large amount of students, the capacity index of student is generalized into various dimensions initial classification information, while education resource is marked into the indexing parameter that upper respective capabilities examination requires；2）For some student, by acquisition, his initial information generates original plan and initial learning objective, is then constantly learnt according to step.Compared with the existing technology, the method of the present invention considers the factors such as the learning time of student, student ability, learning Content and inner capacities, it is adjusted into Mobile state, and situation is learnt by the student of comprehensive assessment and adaptively works out study plan, recommendation learning Content, have the characteristics that without teacher's participation, individualized learning.
Description
Technical field
The invention belongs to Internet education field, especially a kind of computer self-adapting intelligent teaching method.
Background technology
Existing artificial intelligence is typically carried out to student in the application of education sector also in early stage of development
Recommend corresponding relatively-stationary learning materials again on the basis of rough classification, is often intended only as a kind of nondominant hand of education
Section.
For example, China Patent Publication No. CN107818698 A " it is a kind of based on big data education skill it is personalized from
Adaptive learning system ", it is characterised in that teacher is helped to provide the interaction mode of student and on-line study material.This patented technology
Teacher's a series of mechanization labour in education sector has been liberated to a certain extent, still, it is still desirable to which teacher participates in analysis
Customize the study plan of student.It can not accomplish that computer learns to feed back according to student, it is adaptive to adjust student's study plan.It is this
The improvement for the education sector that mode is brought is inadequate, can only slightly share the workload of teacher, therefore still can not accomplish to each
Student teaches students in accordance with their aptitude, and is truly realized individualized learning.
For another example, " a kind of adaptive teaching interaction systems and method " of China Patent Publication No. CN104966427 A, it is special
Sign is to recommend new topic according to the topic effect of doing of student, repeat.The advantage is that can be done for each student topic track come
Recommend adaptive personalized topic, but this mode it is excessively unilateral think student do wrong topic can according to repeat to do topic come
Reach raising, not accounting for student's topic that does wrong may be caused due to multiple.
Invention content
In view of the above-mentioned deficiencies in the prior art, the object of the present invention is to provide a kind of adaptive private teaching of computer
Learning method.It considers the factors such as the learning time of student, student ability, learning Content and inner capacities, into action
State adjusts, and learns situation by the student of comprehensive assessment and adaptively work out study plan, recommendation learning Content, has nothing
The characteristics of needing teacher's participation, individualized learning.
In order to reach foregoing invention purpose, technical scheme of the present invention is realized as follows：
A kind of adaptive private teaching learning method of computer, based on the stimulus response association theories of learning, Broome taxonomy of educational objectives
The education theories such as method, recent development area, method and step are：
1）It is analyzed by the topic situation of doing of a large amount of students, the capacity index of student is generalized into various dimensions preliminary classification letter
Breath, while education resource is marked into the indexing parameter that upper respective capabilities examination requires；
2）For some student, by acquisition, his initial information generates original plan and initial learning objective, then according to
Lower step is constantly learnt：
Step 1：Student learns according to original plan, and one group of data is obtained according to learning outcome, computer for this group of data and
Historical data calculation knowledge point degree up to standard, evaluates the Multidimensional Comprehensive ability value of student；
Step 2：Next round study is obtained with deep neural network model is inputted together with the initial learning objective ability value set
Teacher's parameter, new learning objective is updated by teacher's parameter and recent development area difference value equation；
Step 3：Waiting gaining knowledge a little and best suit this and examining for next group is selected from knowledge tree by new learning objective information
Observation of eyes target corresponds to course content, generates new study plan and recommends the student；
Step 4：Repeat step 1；At the same time, it is periodically fed back according to the student performance, depth is updated by way of intensified learning
Spend the parameter of neural network model；Education resource label is periodically corrected according to the study situation of overall student by iterative algorithm
Value.
In the adaptive private teaching learning method of above computer, the topic situation of doing to a large amount of students carries out analysis use
K-Means clustering algorithms.
In the adaptive private teaching learning method of above computer, the plan includes in most thin knowledge point and corresponding course
Hold, learning objective includes the multidimensional capacity index for it is expected to reach and learning Content amount.
In the adaptive private teaching learning method of above computer, the data obtained according to learning outcome include seeing to regard
Frequently topic situation, is done.
In the adaptive private teaching learning method of above computer, the recent development area difference value equation is mainly by with very unwise move
Slightly it is calculated：Students ' learning performance is good, then formulates higher ability value target, learn less inner capacities；Student learns effect
Fruit is bad, then formulates the target for but being below a preceding ability more than his ability, learn more inner capacities.
The present invention is as a result of above method step, and compared with prior art, its private teaching learning method of intelligence can needle
Establish personalized Learning Scheme to each student, and for the adaptive regularized learning algorithm plan of the learning effect of every student and
Content.Meanwhile the method for the present invention does topic feedback by student, comes in dynamic optimization adjustment target, knowledge point and corresponding study
Capacity, and the case where inscribed by student, it is reversed to adjust proposed algorithm parameter, so that it preferably be bonded student personal considerations,
Learn and need to learn, only learn can learn can, be truly realized personalization and carry point, reduce students' burden conscientiously.
The present invention will be further described with reference to the accompanying drawings and detailed description.
Description of the drawings
Fig. 1 is the method for the present invention flow chart；
Fig. 2 is the computer interface schematic diagram that user uses this learning system.
Specific implementation mode
Referring to Fig. 1 and Fig. 2, the adaptive private teaching learning method of computer of the present invention, based on the stimulus response association theories of learning,
Education theories, the method and steps such as Broome taxonomy of educational objectives method, recent development area are：
1）Topic situation situation of correcting errors is done by K-Means focusing solutions analysis totality students to obtain student ability various dimensions and initially divide
Category information.Desired value and inner capacities are investigated by the various dimensions ability of mankind teacher's experience initial setting topic.
2）By single student's initial information initialize student's multidimensional capacity index, teacher's parameter, target capability, content and
Inner capacities constitutes the first round original plan and initial learning objective, then follows the steps below continuous study：
Step 1：Student learns according to original plan, obtains learning outcome set, including see video, answer situation etc., pass through
Practise result data calculation knowledge point degree up to standard, students'comprehensive ability index.
Step 2：The initial learning objective ability value set and practical integration capability value are inputted into deep neural network mould
Output obtains teacher's parameter in type, and teacher's parameter is inputted in recent development area difference value equation and obtains the target capability of next one
Index and object content amount.
Step 3：It searches for obtain knowledge point and the class of Optimum Matching using our knowledge tree and updated target capability
Journey generates new study plan and recommends student.
Step 4：Repeat step 1.
Comprehensive student's learning outcome can set minimum function and carry out intensified learning for deep neural network in short term,
Artificial intelligence teacher algorithm is set increasingly to adapt to the student.
The distribution that long-term comprehensive overall student does topic situation can set EM iterative algorithms for education resource content index
It is updated, the tag parameter of learning object repository is enable to obey the distribution situation of overall student.
Recent development area difference value equation is mainly obtained by following policy calculation：Students ' learning performance is good, then formulates higher
Ability value target, learn very few inner capacities；Students ' learning performance is bad, then formulates primary energy before but being below more than his ability
The target of power learns more inner capacities, and the parameter and difference value equation that specific formula for calculation is exported by neural network obtain.
The method of the present invention had both considered the study situation difference between individual students on space scale, it is contemplated that the time
Student difference the different study periods the case where on scale, it is new most that the technology iteration by strengthening machine learning goes out student
Nearly development zone target, in two key indexes --- microcosmic regulation and control are carried out in ability and inner capacities.Then according to structure in pattra leaves
Knowledge tree on this network, which finds next group, to be waited gaining knowledge and a little constitutes a course with knowledge point related content and recommend user.Together
When the parameter index of current education resource is fed back more on the basis of the learning outcome of a large amount of students by EM algorithms
Newly.
Claims (5)
1. the adaptive private teaching learning method of a kind of computer, based on the stimulus response association theories of learning, Broome educational objective point
The education theories such as class method, recent development area, method and step are：
It is analyzed by the topic situation of doing of a large amount of students, the capacity index of student is generalized into various dimensions initial classification information,
Education resource is marked into the indexing parameter that upper respective capabilities examination requires simultaneously；
For some student, by acquisition, his initial information generates original plan and initial learning objective, then according to following
Step is constantly learnt：
Step 1：Student learns according to original plan, and one group of data is obtained according to learning outcome, computer for this group of data and
Historical data calculation knowledge point degree up to standard, evaluates the Multidimensional Comprehensive ability value of student；
Step 2：Next round study is obtained with deep neural network model is inputted together with the initial learning objective ability value set
Teacher's parameter, new learning objective is updated by teacher's parameter and recent development area difference value equation；
Step 3：Waiting gaining knowledge a little and best suit this and examining for next group is selected from knowledge tree by new learning objective information
Observation of eyes target corresponds to course content, generates new study plan and recommends the student；
Step 4：Repeat step 1；At the same time, it is periodically fed back according to the student performance, depth is updated by way of intensified learning
Spend the parameter of neural network model；Education resource label is periodically corrected according to the study situation of overall student by iterative algorithm
Value.
2. the adaptive private teaching learning method of computer according to claim 1, which is characterized in that described to be done to a large amount of students
Topic situation carries out analysis and uses K-Means clustering algorithms.
3. the adaptive private teaching learning method of computer according to claim 1 or claim 2, which is characterized in that the plan includes most
Thin knowledge point and corresponding course content, learning objective include the multidimensional capacity index for it is expected to reach and learning Content amount.
4. the adaptive private teaching learning method of computer according to claim 3, which is characterized in that described to be obtained according to learning outcome
The data taken include seeing video, doing topic situation.
5. the adaptive private teaching learning method of computer according to claim 4, which is characterized in that the recent development area difference
Formula is mainly obtained by following policy calculation：Students ' learning performance is good, then formulates higher ability value target, learn less
Inner capacities；Students ' learning performance is bad, then formulates the target that a preceding ability but is below more than his ability, learns in more
Capacity.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109920288A (en) * | 2019-03-29 | 2019-06-21 | 上海乂学教育科技有限公司 | Adaptive learning task intelligence generating means and computer learning system |
| CN110162713A (en) * | 2019-05-31 | 2019-08-23 | 成都鼎晟数智科技有限公司 | Adaptive learning content recommendation method and system based on convolutional neural networks |
| CN110414628A (en) * | 2019-08-07 | 2019-11-05 | 清华大学深圳研究生院 | A kind of learning process planning and management method and system from wound course |
| CN110569297A (en) * | 2019-09-11 | 2019-12-13 | 上海乂学教育科技有限公司 | Student learning state statistics and evaluation system |
| CN111401525A (en) * | 2020-03-20 | 2020-07-10 | 珠海读书郎网络教育有限公司 | Adaptive learning system and method based on deep learning |
| CN112289103A (en) * | 2020-12-23 | 2021-01-29 | 河南应用技术职业学院 | Artificial intelligence self-adaptation interactive teaching system |
| CN112489507A (en) * | 2020-11-23 | 2021-03-12 | 广西水利电力职业技术学院 | Big data fusion type intelligent teaching method based on VR and holographic projection |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017018940A1 (en) * | 2015-07-27 | 2017-02-02 | National University Of Singapore | Method and system for dynamically optimized courses of learning |
| CN106447558A (en) * | 2016-06-23 | 2017-02-22 | 温州职业技术学院 | guidance of learning method and learning system combining ontology and clustering analysis technology |
| CN108090855A (en) * | 2017-12-20 | 2018-05-29 | 维沃移动通信有限公司 | Method and mobile terminal are recommended in a kind of study plan |
-
2018
- 2018-06-20 CN CN201810633105.9A patent/CN108765228A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017018940A1 (en) * | 2015-07-27 | 2017-02-02 | National University Of Singapore | Method and system for dynamically optimized courses of learning |
| CN106447558A (en) * | 2016-06-23 | 2017-02-22 | 温州职业技术学院 | guidance of learning method and learning system combining ontology and clustering analysis technology |
| CN108090855A (en) * | 2017-12-20 | 2018-05-29 | 维沃移动通信有限公司 | Method and mobile terminal are recommended in a kind of study plan |
Non-Patent Citations (2)
| Title |
|---|
| 曲毅: "基于Web的个性化学习系统的设计", 《计算机工程与设计》 * |
| 陈显玲: "初中数学错误模型研究及其在自动判卷系统中的应用", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109920288A (en) * | 2019-03-29 | 2019-06-21 | 上海乂学教育科技有限公司 | Adaptive learning task intelligence generating means and computer learning system |
| CN110162713A (en) * | 2019-05-31 | 2019-08-23 | 成都鼎晟数智科技有限公司 | Adaptive learning content recommendation method and system based on convolutional neural networks |
| CN110414628A (en) * | 2019-08-07 | 2019-11-05 | 清华大学深圳研究生院 | A kind of learning process planning and management method and system from wound course |
| CN110569297A (en) * | 2019-09-11 | 2019-12-13 | 上海乂学教育科技有限公司 | Student learning state statistics and evaluation system |
| CN111401525A (en) * | 2020-03-20 | 2020-07-10 | 珠海读书郎网络教育有限公司 | Adaptive learning system and method based on deep learning |
| CN112489507A (en) * | 2020-11-23 | 2021-03-12 | 广西水利电力职业技术学院 | Big data fusion type intelligent teaching method based on VR and holographic projection |
| CN112489507B (en) * | 2020-11-23 | 2023-04-11 | 广西水利电力职业技术学院 | Big data fusion type intelligent teaching method based on VR and holographic projection |
| CN112289103A (en) * | 2020-12-23 | 2021-01-29 | 河南应用技术职业学院 | Artificial intelligence self-adaptation interactive teaching system |
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Application publication date: 20181106 |