CN106991197A - The study point and learning path of a kind of target drives of knowledge based collection of illustrative plates recommend method - Google Patents
The study point and learning path of a kind of target drives of knowledge based collection of illustrative plates recommend method Download PDFInfo
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Abstract
The present invention is that a kind of study point of target drives of knowledge based collection of illustrative plates is recommended and learning path optimization method, belong to Distributed Calculation and Software Engineering technology crossing domain, main purpose is in order that learner spends minimum time and efforts (it is assumed that time, energy are uniformly distributed, the knowledge that unit interval and energy are obtained is as many) obtain most efficient learning guide, make learner avoid it is unnecessary go study knowledge, only will not or unskilled knowledge on spend time and efforts.It is first depending on the knowledge hierarchy collection of illustrative plates of the corresponding subject of existing resource construction, the learning objective for obtaining the current study condition of learner and finally being realized, and the knowledge point of learner is marked on knowledge mapping and is not gained knowledge a little, and learning each knowledge point learner time and efforts to be spent, the present invention is embodied with weights.Passage path selection algorithm, recommends to need the knowledge point of study and efficient learning strategy to learner.
Description
Technical field
The present invention is that the study point and learning path of a kind of target drives of knowledge based collection of illustrative plates recommend method.It is mainly used in
Learner is set to spend minimum time and efforts（It is assumed that time, energy are uniformly distributed, the unit interval is as the knowledge that energy is obtained
It is many）Most efficient learning guide is obtained, learner is avoided the unnecessary knowledge for going study, only will not or unskilled know
Time and efforts is spent in knowledge, belongs to Distributed Calculation and Software Engineering technology crossing domain.
Background technology
With the development of kownledge economy, today's society proposes higher requirement to the acquisition of knowledge degree of people, intelligence
The suitable study point of selection recommends learner in tutoring system and the recommendation in individualized learning path has become again with optimization
Want problem.At present, on-line study problems faced is that online data are numerous and jumbled, cause learner be difficult to quickly locate it is suitable oneself
Education resource.It is Development of Distance Education qualitative leap to adapt to inquiry learning, and its immediate cause is with computer, telecommunication and cognition
The integrated use of the Knowledge Media of combination of sciences.Adapt to inquiry learning can suitably be learnt according to the feature selecting of learner content and
Learning method is used as recommendation.Learning path refers to the route and sequence of learning activities, is that learner refers in certain learning strategy
Lead down, the sequence according to learning objective and study content to the learning activities of required completion.Learning path be the resource of study,
Method, target, program, evaluation and monitoring etc. are organic into together with, and study content is presented to learner with different strategies.
Knowledge mapping formally proposes on May 17th, 2012 by Google, and its original intention is to improve the energy of search engine
Power, strengthens the search quality and search experience of user.At present, with the continuous hair that intelligent and individual info service is applied
Exhibition, knowledge mapping is widely used in the fields such as intelligent search, intelligent answer, personalized recommendation.Knowledge mapping has become
The strong tools of knowledge are represented with the digraph form of mark, and provide the semanteme of text message.Knowledge mapping is by will be every
Individual project, entity or user are represented as node, and those nodes of interaction between each other are chained up into structure by edge
The figure made.Side between node can represent any relation.Knowledge point is the substantially single of transmission knowledge information in learning activities
Member, single knowledge point should be able to embody knowledge content in itself refuse integrality, the set of knowledge point can guarantee that professional knowledge body
Relation between the global integrality knowledge point of system is the tie for connecting knowledge point, forms scattered knowledge point and is mutually related
The structure of knowledge.The study point and learning path that the present invention proposes a kind of target drives of knowledge based collection of illustrative plates recommend method, root
Efficient leads is provided for learner according to the current study condition and learning objective of learner and learn strategy, it is ensured that learner is on demand
Study.
The content of the invention
Technical problem：It is an object of the invention to provide a kind of study point of target drives of knowledge based collection of illustrative plates and study road
Method is recommended in footpath, for the learning demand and learning objective of learner, is recommended rational study point content to learner and is learnt
Strategy, study-leading person reaches learning objective, helps learner to improve learning efficiency, Optimization Learning effect.
Technical scheme：The present invention is a kind of tactic method, can apply to provide learning guide for learner, contributes to
Solve under Network Study Environment, cognitive overload and study are got lost problem caused by a large amount of education resources.
On a knowledge point collection of illustrative plates, present invention assumes that it is fixed that can be gained knowledge with unit energy under learner's unit interval
, the knowledge point on knowledge mapping is not necessarily independent, and having for the relation present invention definition between knowledge node following has
Five kinds（It is semantic）Relation is as shown in Figure 1：
1. first order relation：It node A must first be learnt could learn node B, i.e. learning knowledge point B to need knowledge point A support.First
Order relation has transitivity, including directly first order relation and indirect first order relation.If can directly learn after learning knowledge point A
Knowledge point B, then both meet directly first order relation.If other knowledge points of study are also needed to after learning knowledge point A to learn
Knowledge point B, then both meet first order relation indirectly;
2. cover relation：The knowledge point that node A is included covers node B, and learned node A can be without removing study node B again;
3. or relation：For final learning objective, study node A and node B can reach learning objective；
4. and relation（Parallel relation）：It is independent between node, is not present with the knowledge point with relation in learning process
Sequencing；
5. necessary node：For final learning objective, the node of study must be removed；
6. free node：For some knowledge hierarchy, free node is the knowledge point useless to this knowledge hierarchy.
Method flow：
1. the study point and learning path of a kind of target drives of knowledge based collection of illustrative plates recommend method, it is characterised in that study point
The step of recommending to optimize with learning path：
Step 1) build an oriented field study point knowledge mapping, inherent knowledge of the science objectively in reacting knowledge system is closed
Connection；
Step 2) set up learner model, recording learning person's knowledge point, do not gain knowledge it is a little and current in knowledge point,
Respectively with red, yellow, blue color mark, and correspondence knowledge point required time is obtained as learner to an imparting weight of not gaining knowledge
With the measurement required efforts；
Step 3) obtain learner study situation.Knowledge mapping is traveled through, by the knowledge point represented on knowledge mapping and learner
Knowledge point in model matches, on knowledge mapping respectively with it is red, yellow, it is blue mark knowledge, do not gain knowledge and currently exist
Gain knowledge a little；
Step 4）Learner's learning objective is obtained, learner's selection knowledge point to be learnt is pointed out；
Step 5) depend on step 4) obtained by result, circulation finds out all of learner's object knowledge point and first gains knowledge a little；
Step 6) for step 5) produce it is all do not gain knowledge a little, there will be or relation knowledge node by learn some know
Know point required time and energy（That is weight）It is ranked up；
Step 7) cover the node of relation for existing, it is assumed that node A cover node B and node C contained by knowledge, judge node B
Whether all it is that learner is to reach the knowledge required for learning objective with node C.If desired, calculate study node A and learn simultaneously
Practise the time and efforts needed for node B and node C；If need not, select the node for needing time and efforts less to be added to
Practise in path；
Step 8）Based on step 7）The result of generation, road is added to greedy algorithm by current optimal node and path every time
In the array of footpath；
Step 9) the complete learning path of output, recommend learner.
Architecture：
Fig. 2 gives a kind of study point of target drives of knowledge based collection of illustrative plates and the architecture of learning path recommendation method,
The current study condition of learner and the learning objective finally to be realized are obtained first, build the knowledge hierarchy figure of corresponding subject
Spectrum, and mark on knowledge mapping the knowledge point of learner and do not gain knowledge a little, and learn each knowledge point and learn
Person's time and efforts to be spent, the present invention is embodied with weights.Passage path selection algorithm, recommends to need to learn to learner
Knowledge point and efficient learning strategy.
Learner model：Multidate information in learner model in essential information and learning process comprising learner, bag
Include history learning record, learning objective and current study course.History learning record includes knowledge point title, learns the knowledge
The time of point and study number of times；Learning objective represents the knowledge point not learnt；Current study course include knowledge point title,
Study schedule.
Beneficial effect：The inventive method proposes the study point and learning path of a kind of target drives of knowledge based collection of illustrative plates
Recommendation method.With the following remarkable advantage：
（1）Reasonable disposition resource, improves the service efficiency of education resource：The reasonable disposition of education resource is China with effective use
Education resource on the important content of Development of Distance Education, network is enriched, and quality is very different, and the target of knowledge based collection of illustrative plates is driven
Dynamic study point is recommended to help learner on demand to learn, it is not necessary to take a significant amount of time and oneself needs is found in the resource of magnanimity
Education resource；
（2）Learning direction is guided for learner, it is to avoid knowledge is got lost：To learner's recommendation, there is provided learn with Optimization Learning path
Efficient strategy, helps learner to set up suitable knowledge hierarchy, learner is targetedly learnt, and improves study effect
Rate；
（3）The study situation of different learners is set up by analysis, learner model is set up, is targetedly different learners
Personalized learning guide is provided.
Brief description of the drawings
Fig. 1 is the displaying for the incidence relation that may contain between node on knowledge mapping.
Fig. 2 is that the study point and learning path of the target drives of knowledge based collection of illustrative plates recommend the architecture of method.
Embodiment
A kind of study point of target drives of knowledge based collection of illustrative plates and the specific embodiment of learning path recommendation method are：
Step 1) builds corresponding oriented study point knowledge mapping, the inherent knowledge of science objectively in reacting knowledge system
Association, all knowledge points is stored with an array knowledgePoint [N], by learning path array bestPath
[P] is stored；
Step 2) sets up learner model, it is assumed that the fixation for the knowledge point that unit energy learner can grasp between unit,
Mark the time required to the weight of each node obtains correspondence knowledge point as learner and require efforts on knowledge mapping
Weigh；
Step 3) obtain learner current study schedule and learning objective, red-label knowledge is used on knowledge mapping,
With Green Marker object knowledge point, the object knowledge point of learner is stored in array target_knowledge [M] inner；
Step 4) depend on step 3) obtained by result, by all first sequence node yellow marks of learner's object knowledge point
Note comes out；
Step 5) is by step 4) produce it is all do not gain knowledge a little, there will be or relation knowledge node by learning the knowledge point
Required time and energy（That is weight）It is ranked up；
Step 6) covers the node of relation for existing, it is assumed that node A cover node B and node C contained by knowledge, judge node B
Whether all it is that learner is to reach the knowledge required for learning objective with node C.If desired, calculate study node A and learn simultaneously
Practise the time and efforts needed for node B and node C；If need not, select the node for needing time and efforts less to be added to
Practise in path；
Step 7）Based on step 6）The result of generation, road is added to greedy algorithm by current optimal node and path every time
In the array of footpath；
Step 8) to be stored in array bestPath [P] inner for the paths of optimizations, and complete learning path is finally exported, recommends
Habit person.
Claims (1)
1. the study point and learning path of a kind of target drives of knowledge based collection of illustrative plates recommend method, it is characterised in that study point is pushed away
Recommend the step of optimizing with learning path：
Step 1) build an oriented field study point knowledge mapping, inherent knowledge of the science objectively in reacting knowledge system is closed
Connection；
Step 2) set up learner model, recording learning person's knowledge point, do not gain knowledge it is a little and current in knowledge point,
Respectively with red, yellow, blue color mark, and correspondence knowledge point required time is obtained as learner to an imparting weight of not gaining knowledge
With the measurement required efforts；
Step 3) obtain learner study situation, travel through knowledge mapping, by the knowledge point represented on knowledge mapping and learner
Knowledge point in model matches, on knowledge mapping respectively with it is red, yellow, it is blue mark knowledge, do not gain knowledge and currently exist
Gain knowledge a little；
Step 4）Learner's learning objective is obtained, learner's selection knowledge point to be learnt is pointed out；
Step 5) depend on step 4) obtained by result, circulation finds out all of learner's object knowledge point and first gains knowledge a little；
Step 6) for step 5) produce it is all do not gain knowledge a little, there will be or relation knowledge node by learn some know
Know point required time and energy（That is weight）It is ranked up；
Step 7) cover the node of relation for existing, it is assumed that node A cover node B and node C contained by knowledge, judge node B
Whether all it is that learner is to reach the knowledge required for learning objective with node C, if desired, calculate study node A and simultaneously
Practise the time and efforts needed for node B and node C；If need not, select the node for needing time and efforts less to be added to
Practise in path；
Step 8）Based on step 7）The result of generation, road is added to greedy algorithm by current optimal node and path every time
In the array of footpath；
Step 9) the complete learning path of output, recommend learner.
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| CN107832410A (en) * | 2017-11-03 | 2018-03-23 | 四川文轩教育科技有限公司 | A kind of recommendation method in knowledge based e-learning path |
| CN108172047A (en) * | 2018-01-19 | 2018-06-15 | 上海理工大学 | A kind of network on-line study individualized resource real-time recommendation method |
| CN109800300A (en) * | 2019-01-08 | 2019-05-24 | 广东小天才科技有限公司 | learning content recommendation method and system |
| CN110197452A (en) * | 2019-06-11 | 2019-09-03 | 合肥明信软件技术有限公司 | A kind of intelligence adaptation on-line study and examination platform based on artificial intelligence technology |
| CN110263179A (en) * | 2019-06-12 | 2019-09-20 | 湖南酷得网络科技有限公司 | Learning path method for pushing, device, computer equipment and storage medium |
| CN110309363A (en) * | 2018-03-02 | 2019-10-08 | 广州润沁教育科技有限公司 | A kind of instructional video segment method of commerce of knowledge based point |
| CN110737776A (en) * | 2019-08-27 | 2020-01-31 | 南京源涂信息技术有限公司 | path learning planning system based on knowledge graph and target ontology |
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| CN112598554A (en) * | 2020-12-25 | 2021-04-02 | 上海高顿教育科技有限公司 | Online learning service engine based on dynamic recommendation learning |
| CN112733035A (en) * | 2021-01-21 | 2021-04-30 | 唐亮 | Knowledge point recommendation method and device based on knowledge graph, storage medium and electronic device |
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| CN107832410A (en) * | 2017-11-03 | 2018-03-23 | 四川文轩教育科技有限公司 | A kind of recommendation method in knowledge based e-learning path |
| CN108172047A (en) * | 2018-01-19 | 2018-06-15 | 上海理工大学 | A kind of network on-line study individualized resource real-time recommendation method |
| CN108172047B (en) * | 2018-01-19 | 2019-11-01 | 上海理工大学 | A kind of network on-line study individualized resource real-time recommendation method |
| CN110309363A (en) * | 2018-03-02 | 2019-10-08 | 广州润沁教育科技有限公司 | A kind of instructional video segment method of commerce of knowledge based point |
| CN109800300A (en) * | 2019-01-08 | 2019-05-24 | 广东小天才科技有限公司 | learning content recommendation method and system |
| WO2020147405A1 (en) * | 2019-01-18 | 2020-07-23 | 平安科技（深圳）有限公司 | Mapping knowledge domain-based knowledge point recommendation method and apparatus, device, and storage medium |
| CN110197452A (en) * | 2019-06-11 | 2019-09-03 | 合肥明信软件技术有限公司 | A kind of intelligence adaptation on-line study and examination platform based on artificial intelligence technology |
| CN110263179A (en) * | 2019-06-12 | 2019-09-20 | 湖南酷得网络科技有限公司 | Learning path method for pushing, device, computer equipment and storage medium |
| CN110737776A (en) * | 2019-08-27 | 2020-01-31 | 南京源涂信息技术有限公司 | path learning planning system based on knowledge graph and target ontology |
| CN111309927A (en) * | 2020-02-17 | 2020-06-19 | 山东大学 | Knowledge graph mining-based personalized learning path recommendation method and system |
| CN111368182A (en) * | 2020-02-17 | 2020-07-03 | 浙江创课网络科技有限公司 | Individualized self-adaptive learning recommendation method based on big data analysis of education platform |
| CN111368182B (en) * | 2020-02-17 | 2023-07-04 | 河北仓澜教育科技集团有限公司 | Personalized self-adaptive learning recommendation method based on education platform big data analysis |
| CN111914162B (en) * | 2020-06-01 | 2023-03-17 | 大连厚仁科技有限公司 | Method for guiding personalized learning scheme based on knowledge graph |
| CN111914162A (en) * | 2020-06-01 | 2020-11-10 | 西安理工大学 | Method for guiding personalized learning scheme based on knowledge graph |
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| CN112733035A (en) * | 2021-01-21 | 2021-04-30 | 唐亮 | Knowledge point recommendation method and device based on knowledge graph, storage medium and electronic device |
| CN114090780A (en) * | 2022-01-20 | 2022-02-25 | 宏龙科技(杭州)有限公司 | Prompt learning-based rapid picture classification method |
| CN115129895A (en) * | 2022-07-05 | 2022-09-30 | 武汉达芬奇教育科技有限公司 | Self-adaptive learning system |
| CN115129895B (en) * | 2022-07-05 | 2023-05-23 | 武汉达芬奇科技有限公司 | Self-adaptive learning system |
| CN117076782A (en) * | 2023-10-13 | 2023-11-17 | 成都华栖云科技有限公司 | Course recommendation method and device for online learning platform, computer equipment and medium |
| CN117076782B (en) * | 2023-10-13 | 2024-01-23 | 成都华栖云科技有限公司 | Course recommendation method and device for online learning platform, computer equipment and medium |
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