CN108573051A - Knowledge point collection of illustrative plates based on big data analysis - Google Patents
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Abstract
Knowledge point collection of illustrative plates based on big data analysis, pass through the incidence relation between syllabus typing meta-knoeledge point and meta-knoeledge point, form knowledge point database, acquire test question information, test question information acquisition method includes being manually entered to identify with electron scanning, knowledge-ID is chosen, the content characterized according to knowledge-ID chooses the first meta-knoeledge point from knowledge point database, and there are the meta-knoeledge of incidence relation points to combine with the first meta-knoeledge point；The proportion that each meta-knoeledge point is shared in knowledge-ID in the first meta-knoeledge point, the combination of meta-knoeledge point is inputted, path length of each meta-knoeledge o'clock relative to the first meta-knoeledge point is determined according to proportion；Dependence level and path length according to meta-knoeledge o'clock relative to the first meta-knoeledge point build knowledge mapping.
Description
Technical field
The present invention relates to big data teaching to apply, more particularly to the knowledge point collection of illustrative plates based on big data analysis.
Background technology
Knowledge mapping structure based on big data analysis is widely used in mathematics education field, and more at present is to pass through
Raw wrong topic knowledge point carries out the structure of knowledge mapping, expresses master degree of the student to knowledge point, to help teacher to understand teaching
Situation.And knowledge point is interrelated in imparting knowledge to students, some meta-knoeledge point students the most basic can grasp, and inscribe knowledge point by mistake
The knowledge mapping of structure can not embody being associated between the meta-knoeledge point that student can grasp and wrong topic knowledge point, need teacher voluntarily
Comb the incidence relation between wrong topic knowledge point and each meta-knoeledge point.
Invention content
The purpose of the present invention is to solve disadvantages existing in the prior art, and the knowing based on big data analysis proposed
Know point collection of illustrative plates.
To achieve the goals above, present invention employs following technical solutions：Knowledge point collection of illustrative plates based on big data analysis,
Construction method includes：
S1：By the incidence relation between syllabus typing meta-knoeledge point and meta-knoeledge point, knowledge point database is formed；
S2：Test question information is acquired, test question information acquisition method includes being manually entered to identify with electron scanning；
S3：Knowledge-ID is chosen according to examination question demand, the content characterized according to knowledge-ID is selected from knowledge point database
The first meta-knoeledge point is taken, and there are the meta-knoeledge of incidence relation points to combine with the first meta-knoeledge point；
S4：The proportion that each meta-knoeledge point is shared in knowledge-ID in the first meta-knoeledge point, the combination of meta-knoeledge point is inputted, than
Weight is denoted as X power by the weight that each meta-knoeledge point accounts for contents of test question in examination question, examination question type weight is denoted as Y power, examination question source is weighed
It is denoted as Z power again, examination question score is denoted as A, and proportion is denoted as n, n=A*X*Y*Z；
S5：Path length of each meta-knoeledge o'clock relative to the first meta-knoeledge point is determined according to proportion；
S6：Dependence level and path length according to meta-knoeledge o'clock relative to the first meta-knoeledge point build knowledge mapping.
Preferably, the meta-knoeledge point stored in knowledge point database, including knowledge point ID, knowledge point title, knowledge point
Day is added in sequencing information in same level of path, knowledge point, knowledge point description information, knowledge point.
Preferably, knowledge point database further included the knowledge point syllabus of pre- typing, knowing according to pre- typing in S2
Knowing the extraction of point syllabus, there are the meta-knoeledge of incidence relation point generation meta-knoeledge points to combine with the first meta-knoeledge point, meta-knoeledge point
Combination includes preposition meta-knoeledge point, parallel meta-knoeledge point and the postposition meta-knoeledge of the first meta-knoeledge point in the syllabus of knowledge point
Point.
Preferably, further include adding meta-knoeledge point in S6, adding procedure is：
Obtain meta-knoeledge point to be added, extract meta-knoeledge point to be added knowledge point path and knowledge point to be added in same layer
The sequencing information of grade；
Meta-knoeledge point to be added, which is obtained, according to the knowledge point path of meta-knoeledge point to be added there is association in knowledge point database
The preposition meta-knoeledge point and/or postposition meta-knoeledge point of relationship, are stored in preposition member by meta-knoeledge point to be added by incidence relation and know
After knowledge point or before postposition meta-knoeledge point；
Sequencing information according to knowledge point to be added in same level repaiies information of the meta-knoeledge point to be added in database
Just, revised membership credentials are obtained；
The level in knowledge point database is updated according to revised membership credentials, completes the addition of meta-knoeledge point.
Preferably, further include deleting meta-knoeledge point in S6, deletion process is：
Choose meta-knoeledge point to be deleted, extract meta-knoeledge point to be deleted knowledge point path and knowledge point to be deleted in same layer
Sequencing information；
Determine that there are the preposition members of incidence relation to know with meta-knoeledge point to be deleted according to the knowledge point path of meta-knoeledge point to be deleted
Know point and/or postposition meta-knoeledge point；
The incidence relation between meta-knoeledge point to be deleted and preposition meta-knoeledge point and/or postposition meta-knoeledge point is deleted, storage is deleted
The sequencing information corresponding with meta-knoeledge point to be deleted in knowledge point database；
Sequencing information according to knowledge point to be deleted in same level repaiies information of the meta-knoeledge point to be deleted in database
Just, revised membership credentials are obtained；
The level in knowledge point database is updated according to revised membership credentials, completes the deletion of meta-knoeledge point.
Preferably, further include mobile meta-knoeledge point in S6, moving method is：
Meta-knoeledge point to be moved is obtained, the knowledge point path of meta-knoeledge point to be moved and the purpose of knowledge point to be moved are extracted
Sequencing information；
Determine that there are the preposition members of incidence relation to know with meta-knoeledge point to be moved according to the knowledge point path of meta-knoeledge point to be moved
Know point and/or postposition meta-knoeledge point；
It is chosen before meta-knoeledge point to be moved has incidence relation after movement according to the purpose sequencing information of knowledge point to be moved
Set meta-knoeledge point and/or postposition meta-knoeledge point；
By the knowledge point path of knowledge point to be moved be changed to it is mobile after there are the preposition meta-knoeledge points and/or postposition of incidence relation
Meta-knoeledge point；
Sequencing information according to knowledge point to be moved in same level repaiies information of the meta-knoeledge point to be moved in database
Just, revised membership credentials are obtained；
The level in knowledge point database is updated according to revised membership credentials, completes the movement of meta-knoeledge point.
Preferably, S2 includes that test question information includes contents of test question, examination question score value, examination question type, examination question subject, examination question
Source and examination question score, and examination question ID is stored, test question information corresponding examination question ID is bound into storage.
Preferably, contents of test question X power is determined by examination question score value, X power=knowledge point score value/examination question total score, examination question type power
Weight Y power is divided into Y1, Y2, Y3, Y4, and identification examination question type selects entitled Y1, the entitled Y2 that fills a vacancy, is non-entitled Y3, discussion topic/answer
The weight ratio of entitled Y4, Y1, Y2, Y3, Y4 determines by the affiliated subject of examination question, and examination question source weight is denoted as Z power, be divided into Z1, Z2,
Z3, examination examination question are Z1, and classroom examination question is Z2, and operation examination question is Z3, weight Z1>Z2>Z3, examination question score are denoted as A, and A is examination question
Criteria scores.
Preferably, it is n grades that will take an examination examination question Z1 points according to examination series, including Z11, Z12, Z13 ... ..Z1n.
Preferably, further include being determined according to the relationship between each meta-knoeledge point in syllabus and relying on level in S6.
The beneficial effects of the invention are as follows：1, the incidence relation of each knowledge point in examination question, auxiliary religion are embodied by examination question score
Teacher understands teaching process；
2, different teaching field face progresses can be embodied according to examination question sample, it is widely applicable；
3, there is embodiment to all meta-knoeledge points in examination question, build more accurate, comprehensive knowledge mapping.
Description of the drawings
Fig. 1 is the knowledge point map construction method figure the present invention is based on big data analysis.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.
Referring to Fig.1, the knowledge point collection of illustrative plates based on big data analysis, construction method include：
S1：By the incidence relation between syllabus typing meta-knoeledge point and meta-knoeledge point, knowledge point database is formed；
S2：Test question information is acquired, test question information acquisition method includes being manually entered to identify with electron scanning；
S3：Knowledge-ID is chosen according to examination question demand, the content characterized according to knowledge-ID is selected from knowledge point database
The first meta-knoeledge point is taken, and there are the meta-knoeledge of incidence relation points to combine with the first meta-knoeledge point；
S4：The proportion that each meta-knoeledge point is shared in knowledge-ID in the first meta-knoeledge point, the combination of meta-knoeledge point is inputted, than
Weight is denoted as X power by the weight that each meta-knoeledge point accounts for contents of test question in examination question, examination question type weight is denoted as Y power, examination question source is weighed
It is denoted as Z power again, examination question score is denoted as A, and proportion is denoted as n, n=A*X*Y*Z；
S5：Path length of each meta-knoeledge o'clock relative to the first meta-knoeledge point is determined according to proportion；
S6：Dependence level and path length according to meta-knoeledge o'clock relative to the first meta-knoeledge point build knowledge mapping.Foundation
The knowledge point collection of illustrative plates that examination question score obtains, the lower meta-knoeledge point of score and the first meta-knoeledge point distance are farthest, contribute to teacher
Each meta-knoeledge point is gone and found out what's going in students ' score, while by farthest weak knowledge point, passing through knowledge mapping
It contacts the meta-knoeledge point most easily grasped from student and formulates a series of correlated knowledge point guidance plans, obtain and preferably teach effect
Fruit.
Knowledge point collection of illustrative plates based on big data analysis, the meta-knoeledge point stored in knowledge point database, including knowledge point
Sequencing information in same level of ID, knowledge point title, knowledge point path, knowledge point, knowledge point description information, knowledge point addition
Day.
Knowledge point collection of illustrative plates based on big data analysis, the extraction meta-knoeledge point group in S3 includes constraint rule, constraint rule
Incidence relation cannot be established including knowledge point itself with itself.
Knowledge point collection of illustrative plates based on big data analysis, knowledge point database further included that the knowledge point teaching of pre- typing is big
Guiding principle, according to the knowledge point syllabus extraction of pre- typing, there are the generations of the meta-knoeledge of incidence relation point with the first meta-knoeledge point in S2
Meta-knoeledge point combines, and the combination of meta-knoeledge point includes all meta-knoeledge point meta-knoeledge point combinations in examination question,.
Knowledge point collection of illustrative plates based on big data analysis identifies each proportion parameter when acquiring test question information, generate proportion.
Knowledge point collection of illustrative plates based on big data analysis, further includes addition meta-knoeledge point in S6, and adding procedure is：
Obtain meta-knoeledge point to be added, extract meta-knoeledge point to be added knowledge point path and knowledge point to be added in same layer
The sequencing information of grade；
Meta-knoeledge point to be added, which is obtained, according to the knowledge point path of meta-knoeledge point to be added there is association in knowledge point database
The preposition meta-knoeledge point and/or postposition meta-knoeledge point of relationship, are stored in preposition member by meta-knoeledge point to be added by incidence relation and know
After knowledge point or before postposition meta-knoeledge point；
Sequencing information according to knowledge point to be added in same level repaiies information of the meta-knoeledge point to be added in database
Just, revised membership credentials are obtained；
The level in knowledge point database is updated according to revised membership credentials, completes the addition of meta-knoeledge point.
Knowledge point collection of illustrative plates based on big data analysis further includes deleting meta-knoeledge point in S6, and deletion process is：
Choose meta-knoeledge point to be deleted, extract meta-knoeledge point to be deleted knowledge point path and knowledge point to be deleted in same layer
Sequencing information；
Determine that there are the preposition members of incidence relation to know with meta-knoeledge point to be deleted according to the knowledge point path of meta-knoeledge point to be deleted
Know point and/or postposition meta-knoeledge point；
The incidence relation between meta-knoeledge point to be deleted and preposition meta-knoeledge point and/or postposition meta-knoeledge point is deleted, storage is deleted
The sequencing information corresponding with meta-knoeledge point to be deleted in knowledge point database；
Sequencing information according to knowledge point to be deleted in same level repaiies information of the meta-knoeledge point to be deleted in database
Just, revised membership credentials are obtained；
The level in knowledge point database is updated according to revised membership credentials, completes the deletion of meta-knoeledge point.
Knowledge point collection of illustrative plates based on big data analysis further includes mobile meta-knoeledge point in S6, and moving method is：
Meta-knoeledge point to be moved is obtained, the knowledge point path of meta-knoeledge point to be moved and the purpose of knowledge point to be moved are extracted
Sequencing information；
Determine that there are the preposition members of incidence relation to know with meta-knoeledge point to be moved according to the knowledge point path of meta-knoeledge point to be moved
Know point and/or postposition meta-knoeledge point；
It is chosen before meta-knoeledge point to be moved has incidence relation after movement according to the purpose sequencing information of knowledge point to be moved
Set meta-knoeledge point and/or postposition meta-knoeledge point；
By the knowledge point path of knowledge point to be moved be changed to it is mobile after there are the preposition meta-knoeledge points and/or postposition of incidence relation
Meta-knoeledge point；
Sequencing information according to knowledge point to be moved in same level repaiies information of the meta-knoeledge point to be moved in database
Just, revised membership credentials are obtained；
The level in knowledge point database is updated according to revised membership credentials, completes the movement of meta-knoeledge point.
Knowledge point collection of illustrative plates based on big data analysis, S2 include that test question information includes contents of test question, examination question score value, examination
Type, examination question subject, examination question source and examination question score are inscribed, and stores examination question ID, the corresponding test question informations of examination question ID are tied up
Fixed storage.
Knowledge point collection of illustrative plates based on big data analysis, contents of test question X are weighed and are determined by examination question score value, and X power=knowledge point score value/
Examination question total score, examination question type weight Y power are divided into Y1, Y2, Y3, Y4, identification examination question type select entitled Y1, the entitled Y2 that fills a vacancy,
It is that entitled Y4 is inscribed/answered in non-entitled Y3, discussion, the weight ratio of Y1, Y2, Y3, Y4 are determined by the affiliated subject of examination question, examination question source power
It is denoted as Z power again, is divided into Z1, Z2, Z3, examination examination question is Z1, and classroom examination question is Z2, and operation examination question is Z3, weight Z1>Z2>Z3, examination
Topic score is denoted as A, and A is the criteria scores of examination question, wherein all student's score summations/total people of student in criteria scores=examination question
Number.
Knowledge point collection of illustrative plates based on big data analysis, it is n grades to divide examination examination question Z1 according to examination series, including Z11,
Z12、Z13、.....Z1n。
Knowledge point collection of illustrative plates based on big data analysis further includes in S6, according between each meta-knoeledge point in syllabus
Relationship, which determines, relies on level.
Knowledge point collection of illustrative plates based on big data analysis, knowledge mapping include free collection of illustrative plates, i.e., go out in the combination of meta-knoeledge point
For existing meta-knoeledge point not in examination question range, which is attributed to free collection of illustrative plates by examination question score value X=0.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Any one skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (10)
1. the knowledge point collection of illustrative plates based on big data analysis, which is characterized in that construction method includes：
S1：By the incidence relation between syllabus typing meta-knoeledge point and meta-knoeledge point, knowledge point database is formed；
S2：Test question information is acquired, test question information acquisition method includes being manually entered to identify with electron scanning；
S3：Knowledge-ID is chosen according to examination question demand, the content characterized according to knowledge-ID is selected from knowledge point database
The first meta-knoeledge point is taken, and there are the meta-knoeledge of incidence relation points to combine with the first meta-knoeledge point；
S4：The proportion that each meta-knoeledge point is shared in knowledge-ID in the first meta-knoeledge point, the combination of meta-knoeledge point is inputted, than
Weight is denoted as X power by the weight that each meta-knoeledge point accounts for contents of test question in examination question, examination question type weight is denoted as Y power, examination question source is weighed
It is denoted as Z power again, examination question score is denoted as A, and proportion is denoted as n, n=A*X*Y*Z；
S5：Path length of each meta-knoeledge o'clock relative to the first meta-knoeledge point is determined according to proportion；
S6：Dependence level and path length according to meta-knoeledge o'clock relative to the first meta-knoeledge point build knowledge mapping.
2. the knowledge point collection of illustrative plates according to claim 1 based on big data analysis, which is characterized in that in knowledge point database
The meta-knoeledge point of middle storage includes the sequence letter of knowledge point ID, knowledge point title, knowledge point path, knowledge point in same level
Day is added in breath, knowledge point description information, knowledge point.
3. the knowledge point collection of illustrative plates according to claim 1 based on big data analysis, which is characterized in that knowledge point database is also
Include the knowledge point syllabus of excessively pre- typing, is extracted and the first meta-knoeledge point according to the knowledge point syllabus of pre- typing in S2
There are the meta-knoeledge of incidence relation points to generate the combination of meta-knoeledge point, and the combination of meta-knoeledge point includes first in the syllabus of knowledge point
Preposition meta-knoeledge point, parallel meta-knoeledge point and the postposition meta-knoeledge point of meta-knoeledge point.
4. the knowledge point collection of illustrative plates according to claim 1 or 2 based on big data analysis, which is characterized in that further include in S6
Meta-knoeledge point is added, adding procedure is：
Obtain meta-knoeledge point to be added, extract meta-knoeledge point to be added knowledge point path and knowledge point to be added in same layer
The sequencing information of grade；
Meta-knoeledge point to be added, which is obtained, according to the knowledge point path of meta-knoeledge point to be added there is association in knowledge point database
The preposition meta-knoeledge point and/or postposition meta-knoeledge point of relationship, are stored in preposition member by meta-knoeledge point to be added by incidence relation and know
After knowledge point or before postposition meta-knoeledge point；
Sequencing information according to knowledge point to be added in same level repaiies information of the meta-knoeledge point to be added in database
Just, revised membership credentials are obtained；
The level in knowledge point database is updated according to revised membership credentials, completes the addition of meta-knoeledge point.
5. the knowledge point collection of illustrative plates according to claim 1 or 2 based on big data analysis, which is characterized in that further include in S6
Meta-knoeledge point is deleted, deletion process is：
Choose meta-knoeledge point to be deleted, extract meta-knoeledge point to be deleted knowledge point path and knowledge point to be deleted in same layer
Sequencing information；
Determine that there are the preposition members of incidence relation to know with meta-knoeledge point to be deleted according to the knowledge point path of meta-knoeledge point to be deleted
Know point and/or postposition meta-knoeledge point；
The incidence relation between meta-knoeledge point to be deleted and preposition meta-knoeledge point and/or postposition meta-knoeledge point is deleted, storage is deleted
The sequencing information corresponding with meta-knoeledge point to be deleted in knowledge point database；
Sequencing information according to knowledge point to be deleted in same level repaiies information of the meta-knoeledge point to be deleted in database
Just, revised membership credentials are obtained；
The level in knowledge point database is updated according to revised membership credentials, completes the deletion of meta-knoeledge point.
6. the knowledge point collection of illustrative plates according to claim 1 or 2 based on big data analysis, which is characterized in that further include in S6
Mobile meta-knoeledge point, moving method are：
Meta-knoeledge point to be moved is obtained, the knowledge point path of meta-knoeledge point to be moved and the purpose of knowledge point to be moved are extracted
Sequencing information；
Determine that there are the preposition members of incidence relation to know with meta-knoeledge point to be moved according to the knowledge point path of meta-knoeledge point to be moved
Know point and/or postposition meta-knoeledge point；
It is chosen before meta-knoeledge point to be moved has incidence relation after movement according to the purpose sequencing information of knowledge point to be moved
Set meta-knoeledge point and/or postposition meta-knoeledge point；
By the knowledge point path of knowledge point to be moved be changed to it is mobile after there are the preposition meta-knoeledge points and/or postposition of incidence relation
Meta-knoeledge point；
Sequencing information according to knowledge point to be moved in same level repaiies information of the meta-knoeledge point to be moved in database
Just, revised membership credentials are obtained；
The level in knowledge point database is updated according to revised membership credentials, completes the movement of meta-knoeledge point.
7. the knowledge point collection of illustrative plates according to claim 1 based on big data analysis, which is characterized in that S2 includes examination question
Information includes contents of test question, examination question score value, examination question type, examination question subject, examination question source and examination question score, and stores examination question ID,
Test question information corresponding examination question ID is bound into storage.
8. the knowledge point collection of illustrative plates according to claim 7 based on big data analysis, which is characterized in that contents of test question X power by
Examination question score value determines that X power=knowledge point score value/examination question total score, examination question type weight Y power is divided into Y1, Y2, Y3, Y4, identification examination
Topic type selects entitled Y1, the entitled Y2 that fills a vacancy, is that entitled Y4, the weight ratio of Y1, Y2, Y3, Y4 are inscribed/answered in non-entitled Y3, discussion
It is determined by the affiliated subject of examination question, examination question source weight is denoted as Z power, is divided into Z1, Z2, Z3, and examination examination question is Z1, and classroom examination question is
Z2, operation examination question are Z3, weight Z1>Z2>Z3, examination question score are denoted as A, and A is the criteria scores of examination question.
9. the knowledge point collection of illustrative plates according to claim 8 based on big data analysis, which is characterized in that will according to examination series
It is n grades to take an examination examination question Z1 points, including Z11, Z12, Z13 ... ..Z1n.
10. the knowledge point collection of illustrative plates according to claim 1 based on big data analysis, which is characterized in that further include in S6, according to
It is determined according to the relationship between each meta-knoeledge point in syllabus and relies on level.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110929012A (en) * | 2019-12-03 | 2020-03-27 | 上海无漏教育科技有限公司 | Method and system for automatically generating lecture from question bank |
| CN111813920A (en) * | 2020-07-06 | 2020-10-23 | 龙马智芯（珠海横琴）科技有限公司 | Learning strategy generation method, device, generation equipment and readable storage medium |
| CN112231522A (en) * | 2020-09-24 | 2021-01-15 | 北京奥鹏远程教育中心有限公司 | Online course knowledge tree generation association method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104008453A (en) * | 2014-05-29 | 2014-08-27 | 启秀科技（北京）有限公司 | Vocational ability evaluation simulation system |
| CN104216933A (en) * | 2013-09-29 | 2014-12-17 | 北大方正集团有限公司 | Method and system for obtaining knowledge point covert relationships |
| CN104317825A (en) * | 2014-09-30 | 2015-01-28 | 武汉天量数据技术有限公司 | Method and system for quantitatively analyzing knowledge point |
| EP3072089A1 (en) * | 2013-11-22 | 2016-09-28 | Cambridge Social Science Decision Lab Inc. | Methods, systems, and articles of manufacture for the management and identification of causal knowledge |
| CN106844384A (en) * | 2015-12-04 | 2017-06-13 | 北大方正集团有限公司 | Examination question indexing method and device |
| US20170249399A1 (en) * | 2014-07-16 | 2017-08-31 | Baidu Online Network Technology (Beijing) Co., Ltd | Method And Apparatus For Displaying Recommendation Result |
| CN107784088A (en) * | 2017-09-30 | 2018-03-09 | 杭州博世数据网络有限公司 | The knowledge mapping construction method of knowledge based point annexation |
-
2018
- 2018-04-23 CN CN201810368358.8A patent/CN108573051A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104216933A (en) * | 2013-09-29 | 2014-12-17 | 北大方正集团有限公司 | Method and system for obtaining knowledge point covert relationships |
| EP3051435A1 (en) * | 2013-09-29 | 2016-08-03 | Peking University Founder Group Co., Ltd | Method and system for obtaining a knowledge point implicit relationship |
| EP3072089A1 (en) * | 2013-11-22 | 2016-09-28 | Cambridge Social Science Decision Lab Inc. | Methods, systems, and articles of manufacture for the management and identification of causal knowledge |
| CN104008453A (en) * | 2014-05-29 | 2014-08-27 | 启秀科技（北京）有限公司 | Vocational ability evaluation simulation system |
| US20170249399A1 (en) * | 2014-07-16 | 2017-08-31 | Baidu Online Network Technology (Beijing) Co., Ltd | Method And Apparatus For Displaying Recommendation Result |
| CN104317825A (en) * | 2014-09-30 | 2015-01-28 | 武汉天量数据技术有限公司 | Method and system for quantitatively analyzing knowledge point |
| CN106844384A (en) * | 2015-12-04 | 2017-06-13 | 北大方正集团有限公司 | Examination question indexing method and device |
| CN107784088A (en) * | 2017-09-30 | 2018-03-09 | 杭州博世数据网络有限公司 | The knowledge mapping construction method of knowledge based point annexation |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110929012A (en) * | 2019-12-03 | 2020-03-27 | 上海无漏教育科技有限公司 | Method and system for automatically generating lecture from question bank |
| CN111813920A (en) * | 2020-07-06 | 2020-10-23 | 龙马智芯（珠海横琴）科技有限公司 | Learning strategy generation method, device, generation equipment and readable storage medium |
| CN111813920B (en) * | 2020-07-06 | 2021-04-13 | 龙马智芯（珠海横琴）科技有限公司 | Learning strategy generation method, device, generation equipment and readable storage medium |
| CN112231522A (en) * | 2020-09-24 | 2021-01-15 | 北京奥鹏远程教育中心有限公司 | Online course knowledge tree generation association method |
| CN112231522B (en) * | 2020-09-24 | 2021-09-14 | 北京奥鹏远程教育中心有限公司 | Online course knowledge tree generation association method |
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