CN106447558A - guidance of learning method and learning system combining ontology and clustering analysis technology - Google Patents
guidance of learning method and learning system combining ontology and clustering analysis technology Download PDFInfo
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Abstract
The invention discloses a guidance of learning method and a learning system combining ontology and clustering analysis technology. The method includes the following steps: 1. constructing courses; 2. acquiring user information; 3. constructing a user initial clustering community; 4. formulating a learning plan; 5. conducting user clustering and constructing a community; 6. updating the state of the user and the learning plan; and 7. evaluating the courses and updating resource archive. According to the invention, the method establishes the user community which enables scattered students who have same characteristics and similar behaviors to get together to learn and communicate and addresses the ubiquitous "loneliness" of learning. The learning system includes a P2P community self-organizing module which provides a resource sharing and evaluating interface for users and makes the network teaching resources more plentiful. The method and the learning system provide a personalized course learning plan, recommends object learning knowledge points, object learning resources and learning time lengths to the user, and increases the learning quality and efficiency of the user.
Description
Technical field
The present invention relates to a kind of course for being applied to long-distance educational system leads method, and set up based on the method
System, more particularly to a kind of using ontology establishment lesson structure, using Clustering Analysis Technology structure P2P dynamic society
The course that area interacts leads method and the system based on the method.
Background technology
Apply with computer technology, the continuous development of mechanics of communication and extensively, long-distance education breaches conventional teaching
Physical restriction, is that more people provide abundant education resource and easily academic environment.However, majority long-distance education at present
System has that user's feeling of lonely is strong, learning control function is not complete, study, and then causes a large amount of levels to differ
How the decline of student learning interest, learning quality and efficiency, carry out effectively, targetedly learning to draw to the student in system
Lead into problem demanding prompt solution in long-distance education.
Content of the invention
In view of the shortcomings of the prior art, it is an object of the invention to provide a kind of combine ontology and cluster analyses skill
The course of art leads method and the system based on the method, and to solve user's feeling of lonely, study control ability is not complete, learn nothing
The problems such as sequence.
For achieving the above object, the invention provides following technical scheme：One kind combines ontology and Clustering Analysis Technology
Lead method, including：
Step one, course construction：With ontology, CELTS standard as reference, Give lecture resource；
Step 2, user profile is gathered：Collect essential information, learning information and the interbehavior information of user；
Step 3, user profile is analyzed：With the essential information of user of the Clustering Analysis Technology to collecting in step 2,
Habit information and interbehavior information carry out clearing up, screen, analyze, and set up files on each of customers and learn initial community；
Step 4, user clustering is built with community：According to the files on each of customers that sets up in step 2 by with identical to be levied
User flocks together, and constructs for the exchange between user and the communities of users that recommends, and allows user to enter community resource
Row is evaluated, and generates user feedback value；Then using the interactive information between user, resource service condition, user feedback value, Yong Huxue
Practise fact and enter Mobile state adjustment to community, build more accurate cluster user community；
Step 5, learning plan making：According to the files on each of customers that sets up in the course resources for building in step one, step 3
Personalized study plan is customized for user；
Step 6, User Status are updated with study plan：The behavior being continually changing according to user and the archives being continually changing
Data, return to step four updates the relation between User Status and user and reconstructs study plan, until user completes course learning；
Step 7, course evaluation is updated with resource archives：When user completes course learning, student's course evaluation letter is generated
Breath and the suggestion for learning further, and update the course resources constructed by step one and the relation between course resources.
As a further improvement on the present invention, it is to be divided into not course content that the course resources in above-mentioned steps one build
With atom knowledge point, then for atom knowledge point set up compound collect knowledge point and context relation, and by course learning resource
Under hang under atom knowledge point.
As a further improvement on the present invention, the context relation includes inclusion relation, pre-knowledge relation, the bag
Containing with transitivity between relation and pre-knowledge relation.
As a further improvement on the present invention, the content of the study plan in above-mentioned steps four is that learning time and course are provided
The layout in source and the recommendation of learning object.
As a further improvement on the present invention, the communities of users in the step 4 is to will have like interest, similar row
For user classified by Clustering Analysis Technology, user carries out in communities of users exchanging, learn and course resources are recommended.
As a further improvement on the present invention, in the step 4 communities of users dynamic adjustment be according to user to user
The value of feedback of community is compared with the desired value of default communities of users, is adjusted according to comparative result.
As a further improvement on the present invention, the expection of the user feedback value in the step 4 and default communities of users
The formula of the comparison of value is
Wherein, Score represents the value of feedback of user to user community, and ExpectedScore represents the expection of communities of users
Value, whenFor just responding；IfIt is then zero response；It is otherwise Negative Acknowledgment.
As a further improvement on the present invention, the content of the study plan in above-mentioned steps five for knowledge point recommendation and
Course resources and the layout of study duration.
A kind of system that should be in aforementioned manners that the present invention is provided, including：
User terminal：For user learning, the port of exchange；
Administrator terminal：Port for manager's maintenance system；
Course ontological construction module, is coupled to administrator terminal, for Give lecture resource, and constantly updates course money
Source；
User data acquisition module：User terminal and course ontological construction module is coupled to, for collecting the basic of user
Information, learning information and interbehavior information；
User clustering analysis module：User data acquisition module is coupled to, with user of the Clustering Analysis Technology to collection
Essential information, learning information and interbehavior information carry out clearing up, screen, analyze, set up files on each of customers, build user society
Area；
P2P community self-organizing module：It is coupled to user clustering analysis module, user data acquisition module and course body structure
Modeling block, based on communities of users, organizes the mutual exchange study in each communities of users, and according to user's real-time feedback data, dynamic
Build community；
Study plan generation module：Course ontological construction module, P2P community self-organizing module is coupled to, is used for customizing
Family individualized learning plan, and constantly renewal learning plan.
As a further improvement on the present invention, community management module, for managing the interbehavior in community between user；
User's AC module, for mutually exchanging between user；
Resource recommendation module, for resource recommendation between user；
Study and evaluation and test module, for study evaluation result in community；
Community's adjusting module, for adjusting member's structure of communities of users；
The community management module, user's AC module, resource recommendation module, study and evaluation and test module and community's adjustment mould
Block is parallel with one another.
Beneficial effects of the present invention, set up communities of users, by discrete student according to identical feature, similar behavior group
Being woven in together carries out learning and exchange, solves the study " feeling of lonely " of generally existing.Using Clustering Analysis Technology, it is user spy
Levy collection and divide and new mode is provided, the algorithm complex is low.Including P2P community self-organizing module, provide the user
Resource-sharing is updated and enriches network teaching resource with interface, dynamic is evaluated.Personalized course learning plan is provided, according to
The data that family is continually changing, it is recommended that ownership goal learning knowledge point, target learning object and study duration, improve of student
Practise quality and the learning efficiency.
Description of the drawings
Fig. 1 is the flow chart for leading method of a kind of combination ontology and Clustering Analysis Technology；
Fig. 2 is the functional module structure figure of system.
Specific embodiment
Below in conjunction with the embodiment given by accompanying drawing, the present invention is described in further detail.
With reference to shown in Fig. 1, a kind of combination ontology of the present embodiment leads method with Clustering Analysis Technology, including
Step one, course construction：With ontology, CELTS standard as reference, Give lecture resource；
Specifically：With ontology as analysis course content is instructed, course content is divided into atom knowledge point.Further according to not
Not same-action of the homoatomic knowledge point in learning process, is that mutual relation is set up in atom knowledge point.Pass between atom knowledge point
System includes inclusion relation and pre-knowledge relation, and wherein b is represented with relational expression a ＜ p, b ＞ p comprising a, the relation power between a and b
Value then represents the degree of membership between a and b；Pre-knowledge relation is then for must first grasp d before grasping c, and the pass between c and d
It is that weights then represent the relative percentage contribution with grasp d of c.The relation between atom knowledge point is so divided, is easy to clear atom
Mutual relation between knowledge point, facilitates later stage study plan to customize.Inclusion relation transitivity shows as a ＜ p, p ＜ x, x ＜ b,
Inclusion relation i.e. between different knowledge points with implicit expression.Compound knowledge point is that the atom knowledge point of same area is merged,
Navigation function is played, is that course content sets up index, facilitate user to understand, learn the course set of association area.
Step 2, user profile is gathered：Collect essential information, learning information and the interbehavior information of user；
Specifically：User logs in the essential information for needing register account number and filling in correlation, essential information bag in user terminal
Age, educational background, learned lesson and learning time section etc. is included, user data collection module collects these information by user terminal；
And learning information and interbehavior information that persistent collection user is produced in each module of learning system.
Step 3, user profile is analyzed：With the essential information of user of the Clustering Analysis Technology to collecting in step 2,
Habit information and interbehavior information carry out clearing up, screen, analyze, and set up files on each of customers and learn initial community；
Specifically：Based on different dimensions to the essential information of user, learning information and interbehavior information, carry out sorting out and draw
Point, it is according to the similarity between user to sort out principle, is mutual several features of similarity highest user group labelling, builds
Vertical files on each of customers and the initial community of study.
Step 4, user clustering is built with community：According to the files on each of customers that sets up in step 2 by with identical to be levied
User flocks together, and constructs for the exchange between user and the communities of users that recommends, and allows user to enter community resource
Row is evaluated, and generates user feedback value；Then using the interactive information between user, resource service condition, user feedback value, Yong Huxue
Practise fact and enter Mobile state adjustment to community, build more accurate cluster user community；The more preferable learning and exchange of user is helped, is subtracted
" feeling of lonely " in few learning process, improves user learning interest, makes user more preferably grasp relevant knowledge faster.
Specifically：The dynamic adjustment of communities of users is mainly adjusted according to the evaluation of user to user community.First fixed
An adopted threshold value, then made comparisons with threshold value with the value of feedback of user, judge the response direction of community.When being that society is described for just responding
Area is suitable for user；When for Negative Acknowledgment, explanation community is not suitable for user needs to be adjusted.Its relational expression is：
Wherein, Score represents value of feedback of the user to the project, and ExpectedScore represents system desired value, whenThen for just responding；IfIt is then zero response；It is otherwise Negative Acknowledgment.This method of adjustment is simple, makes user
Obtain more preferable experience.
Step 5, learning plan making：According to the files on each of customers that sets up in the course resources for building in step one, step 3
Personalized study plan is customized for user；
Specifically：It is to find target atoms knowledge point based on files on each of customers that study plan is generated, further according to completing correlation
The learning process of atom knowledge point user, the course resources of layout study duration and study, and recommend relevant knowledge for user
High score user is learning object.Personalized study plan is customized for user, it is ensured that the feasibility of plan, guiding user is according to meter
Study is drawn, improves the learning efficiency of user.
Step 6, User Status are updated with study plan：The behavior being continually changing according to user and the archives being continually changing
Data, return to step four updates the relation between User Status and user and reconstructs study plan, until user completes course learning；
By constantly adjusting, the individualized learning plan of more suitable user is provided, user learning is guided, enables users to be held according to plan
Learn continuously, finally grasp relevant knowledge.
Step 7, course evaluation is updated with resource archives：When user completes course learning, student's course evaluation letter is generated
Breath and the suggestion for learning further, and update the relation between resources bank and resource.Constantly improve curricular system, makes between knowledge point
Relation definitely, helps more users preferably to learn corresponding knowledge.
A kind of system that should be in aforementioned manners of the present embodiment as shown in Figure 2：
Including
User terminal：For user learning, the port of exchange；
Administrator terminal：Port for manager's maintenance system；
Course ontological construction module 301, is coupled to administrator terminal, for setting up knowledge point structure, establishment and management class
Cheng Ziyuan；
User data acquisition module 302：User terminal and course ontological construction module is coupled to, for collecting the base of user
This information, learning information and interbehavior information；
User clustering analysis module 303：User data acquisition module is coupled to, with use of the Clustering Analysis Technology to collection
The essential information at family, learning information and interbehavior information carry out clearing up, screen, analyze, and set up files on each of customers, build at the beginning of user
Begin cluster；
P2P community self-organizing module 304：It is coupled to user clustering analysis module, user data acquisition module and course sheet
Body builds module, organizes the mutual exchange study in each communities of users based on communities of users, and according to user's exchange and learning behavior,
User's real-time feedback data, dynamic construction, adjustment community；
Study plan generation module 305：Course ontological construction module, P2P community self-organizing module is coupled to, for customizing
User individual study plan, and constantly renewal learning plan.
Modular design system, reduces program complexity, makes the simple operations such as programming, debugging and maintenance.With
Family logs in learning system, typing user basic information by service terminal, and completes the preliminary test and appraisal of course, and user terminal includes
Mobile phone port and pc port.User carries out on the subscriber terminal learning, interacts, tests and assesses and P2P exchange activity.The letter of user terminal
Breath is sent to user data collection module 302 by the Internet.User's initial information that user data collection module 302 is collected.
User clustering analysis module 303 is analyzed to information with Clustering Analysis Technology, is set up files on each of customers, and be will have like spy
The user's aggregation that levies, sets up communities of users, and study plan generation module 305 combines files on each of customers and historical data, generates individual character
The study plan of change, guides the learning behavior of user.Exchange of the self-organizing module 304 in P2P community according to user in community
Habit situation and feedback information dynamic construction community.
Used as a kind of improved specific embodiment, the P2P community self-organizing module 304 includes
Community management module, for managing the interbehavior in community between user；
User's AC module, for mutually exchanging between user；
Resource recommendation module, for resource recommendation between user；
Study and evaluation and test module, for study evaluation result in community；
Community's adjusting module, for adjusting member's structure of communities of users；
The community management module, user's AC module, resource recommendation module, study and evaluation and test module and community's adjustment mould
Block is parallel with one another.By the cooperating operation of modules, make the operation of P2P community self-organizing module 304 more smooth, make user
Obtain more preferable interactive experience.
In sum, the invention provides a kind of not only efficiently completed course learning but also can lift Consumer's Experience lead side
Method, and provided the user the channel of communication, it is adaptable to the long-distance education of all kinds of courses.
The above is only the preferred embodiment of the present invention, and protection scope of the present invention is not limited merely to above-mentioned enforcement
Example, all technical schemes for belonging under thinking of the present invention belong to protection scope of the present invention.It should be pointed out that for the art
Those of ordinary skill for, some improvements and modifications without departing from the principles of the present invention, these improvements and modifications
Should be regarded as protection scope of the present invention.
Claims (9)
1. a kind of combination ontology and Clustering Analysis Technology lead method, including：
Step one, course construction：With ontology, CELTS standard as reference, Give lecture resource；
Step 2, user profile is gathered：Collect essential information, learning information and the interbehavior information of user；
Step 3, user profile is analyzed：With the essential information of user of the Clustering Analysis Technology to collecting in step 2, study letter
Breath and interbehavior information carry out clearing up, screen, analyze, and set up files on each of customers and learn initial community；
Step 4, user clustering is built with community：According to the files on each of customers that sets up in step 2 by with identical user to be levied
Flock together, construct for the exchange between user and the communities of users that recommends, and allow user to comment community resource
Valency, generates user feedback value；Then using the interactive information between user, resource service condition, user feedback value, user learning reality
Condition enters Mobile state adjustment to community, builds more accurate cluster user community；
Step 5, learning plan making：Files on each of customers according to setting up in the course resources for building in step one, step 3 is use
The personalized study plan of family customization；
Step 6, User Status are updated with study plan：The behavior being continually changing according to user and the file data being continually changing,
Return to step four updates the relation between User Status and user and reconstructs study plan, until user completes course learning；
Step 7, course evaluation is updated with resource archives：When user completes course learning, generate student's course evaluation information with
The suggestion for learning further, and update the course resources constructed by step one and the relation between course resources.
2. a kind of combination ontology according to claim 1 and Clustering Analysis Technology lead method, it is characterised in that：On
It is that course content is divided into different atom knowledge points to state the course resources in step one and build, then sets up for atom knowledge point
It is combined and collects knowledge point and context relation, and will hangs under course learning resource under atom knowledge point.
3. a kind of combination ontology according to claim 2 and Clustering Analysis Technology lead method, it is characterised in that：Institute
Stating context relation includes inclusion relation, pre-knowledge relation, has transmission between the inclusion relation and pre-knowledge relation
Property.
4. the combination ontology according to claim 1 or 2 or 3 and Clustering Analysis Technology lead method, it is characterised in that：
Communities of users in the step 4 be will have like interest, the user of similar behavior is carried out point by Clustering Analysis Technology
Class, user is carried out in communities of users exchanging, learns to be recommended with course resources.
5. combination ontology according to claim 4 and Clustering Analysis Technology lead method, it is characterised in that：The step
Communities of users dynamic adjustment in rapid four is desired value of the value of feedback according to user to user community with default communities of users
It is compared, is adjusted according to comparative result.
6. combination ontology according to claim 5 and Clustering Analysis Technology lead method, it is characterised in that：The step
User feedback value in rapid four with the formula of the comparison of the desired value of default communities of users is
Wherein, Score represents the value of feedback of user to user community, and ExpectedScore represents the desired value of communities of users, whenFor just responding；IfIt is then zero response；It is otherwise Negative Acknowledgment.
7. combination ontology according to claim 4 and Clustering Analysis Technology lead method, it is characterised in that：Above-mentioned step
The layout of recommendation and course resources and study duration of the content of the study plan in rapid five for knowledge point.
8. a kind of application claim 1 to 7 any one methods described system, it is characterised in that：Including
User terminal：For user learning, the port of exchange；
Administrator terminal：Port for manager's maintenance system；
Course ontological construction module (301), is coupled to administrator terminal, for Give lecture resource, and constantly updates course
Resource；
User data acquisition module (302)：User terminal and course ontological construction module is coupled to, for collecting the basic of user
Information, learning information and interbehavior information；
User clustering analysis module (303)：User data acquisition module is coupled to, with user of the Clustering Analysis Technology to collection
Essential information, learning information and interbehavior information carry out clearing up, screen, analyze, set up files on each of customers, build user society
Area；
P2P community self-organizing module (304)：It is coupled to user clustering analysis module, user data acquisition module and course body
Module is built, based on communities of users, the mutual exchange study in each communities of users is organized, and according to user's real-time feedback data, move
State builds community；
Study plan generation module (305)：Course ontological construction module, P2P community self-organizing module is coupled to, is used for customizing
Family individualized learning plan, and constantly renewal learning plan.
9. a kind of intelligent learning system according to claim 8, it is characterised in that：The P2P community self-organizing module bag
Include
Community management module, for managing the interbehavior in community between user；
User's AC module, for mutually exchanging between user；
Resource recommendation module, for resource recommendation between user；
Study and evaluation and test module, for study evaluation result in community；
Community's adjusting module, for adjusting member's structure of communities of users；
The community management module, user's AC module, resource recommendation module, study and evaluation and test module and community's adjusting module phase
Mutually in parallel.
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| CN110674955A (en) * | 2019-08-12 | 2020-01-10 | 张艳五 | Interesting experience hall management system for adolescent science popularization education |
| CN112052389A (en) * | 2020-08-27 | 2020-12-08 | 安徽聚戎科技信息咨询有限公司 | Knowledge recommendation method based on regional chain |
| CN112084345A (en) * | 2020-09-11 | 2020-12-15 | 浙江工商大学 | Teaching guiding method and system combining body of course and teaching outline |
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