A Method for Recommending Network Learning Resources Based on Mind Map

A network learning and recommendation method technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of lack of network learning recommendation methods, lack of automatic generation methods, and lack of learning thinking methods

Active Publication Date: 2016-03-30
XI AN JIAOTONG UNIV
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AI Technical Summary

Problems solved by technology

[0009] According to the above-mentioned novelty search findings, the existing network learning resource recommendation methods have the following three problems: 1. Lack of network learning recommendation methods based on cognitive strategies
2. For types of strategic knowledge, procedural knowledge, and declarative knowledge, there is a lack of specific ways of thinking about learning
3. As a learning tool for cognitive strategies, mind maps support the training of thinking styles, but lack a method automatically generated by machines

Method used

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  • A Method for Recommending Network Learning Resources Based on Mind Map
  • A Method for Recommending Network Learning Resources Based on Mind Map
  • A Method for Recommending Network Learning Resources Based on Mind Map

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Embodiment Construction

[0086] In order to understand the present invention more clearly, the present invention will be further described in detail below in conjunction with the accompanying drawings. What has been described is by way of explanation, not limitation, of the invention.

[0087] see figure 1 , a method for recommending network learning resources based on a mind map, including the following operations:

[0088] 1) Collect the learning behavior logs generated during the interaction between the learner and the network learning system based on the knowledge map, analyze the learning duration in the learning behavior logs, obtain the learning duration threshold, and filter the learning behavior logs with this threshold to obtain valid logs ;

[0089] 2) Construct a learning path network and learning transaction graph collection according to the filtered log data;

[0090] 3) Mining bubble graph or double bubble graph according to external service request, learning path network and learnin...

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Abstract

The invention discloses a thinking-map-based e-learning resource recommendation method, which comprises the following steps of analyzing a learning behavior log of a learner accessing a knowledge-map-based e-learning system, calculating a learning time length threshold value, and preprocessing the learning behavior log; constructing a learning path network and a learning transaction graph set; mining a thinking map (a bubble map or a double bubble map) for a specified knowledge element set on the basis of the learning path network; feeding back a mining result to the learner to realize thinking-map-based e-learning knowledge recommendation. According to the method, cognitive-strategy-based map type knowledge recommendation service can be provided for an e-learner, and the learning efficiency of the e-learner is further improved.

Description

technical field [0001] The invention belongs to the technical field of network learning resources recommendation, and relates to a method for recommending network learning resources based on a mind map. Background technique [0002] With the continuous development of information technology, the form of human knowledge has also undergone tremendous changes. e-Learning has become an important means for people to continue their education and acquire knowledge because of its ability to break through time and space constraints, facilitate resource sharing, and support autonomous learning. The existing e-Learning learning model is mainly based on the theory of behaviorism, which believes that learning is a simple "relocation" process of external knowledge to learners' memory. The cognitive theory holds that learning is a process in which the cognitive subject continuously forms a new cognitive structure through the assimilation and adaptation mechanism, and the internal relevance...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
Inventor 田锋陈妍付雁曾彬郑庆华
Owner XI AN JIAOTONG UNIV
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