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Multivariate intelligence-fused adaptive learning knowledge graph construction method and multivariate intelligence-fused adaptive learning knowledge graph construction system

An adaptive learning and knowledge graph technology, applied in neural learning methods, relational databases, semantic tool creation, etc., can solve problems such as reducing the accuracy of adaptive learning learning path recommendation, lack of accurate basis, and forming mapping of learner characteristics.

Active Publication Date: 2020-07-17
ZHENGZHOU UNIV
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004]In the current adaptive learning system based on knowledge graph, the attribute extraction of knowledge nodes in domain knowledge only focuses on the subject attributes of knowledge points, which cannot be mapped with learner characteristics. The internal connection between learner characteristics and knowledge attributes is ignored, resulting in the lack of accurate basis for the calculation of knowledge point achievement based on knowledge point characteristics and learner characteristics, thereby reducing the accuracy of adaptive learning learning path recommendation

Method used

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  • Multivariate intelligence-fused adaptive learning knowledge graph construction method and multivariate intelligence-fused adaptive learning knowledge graph construction system
  • Multivariate intelligence-fused adaptive learning knowledge graph construction method and multivariate intelligence-fused adaptive learning knowledge graph construction system
  • Multivariate intelligence-fused adaptive learning knowledge graph construction method and multivariate intelligence-fused adaptive learning knowledge graph construction system

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Embodiment 1

[0070] Such as figure 1 As shown, this embodiment provides a method for constructing a knowledge map that integrates the theory of multiple intelligences, including the following steps:

[0071] S1: Extract domain knowledge element entities and build domain knowledge models, wherein the domain knowledge element entities include subject, course, learning object and other element entities;

[0072] Wherein, the subject is composed of a first-level knowledge point, a second-level knowledge point and the knowledge point, and forms a top-down hierarchical relationship;

[0073] The courses are composed of chapters, sections and knowledge points according to different teaching materials of the same subject, and form a hierarchical relationship from top to bottom;

[0074] The learning object is the support for learning tasks and learning activities in the learning process. The learning object presents to the learners learning content suitable for their individual characteristics wi...

Embodiment 2

[0112] Based on the construction method of adaptive learning knowledge map integrating multiple intelligences in the first embodiment, this embodiment provides an adaptive learning knowledge map construction system integrating multiple intelligences, which is used to implement the above method, including domain knowledge model element entity extraction module 11. Domain knowledge model attribute setting module 12, domain knowledge model association relationship building module 13, knowledge graph updating module 14;

[0113] The domain knowledge model element entity extraction module 11 is used to extract the domain knowledge element entity and build the domain knowledge model, wherein the domain knowledge element entity includes disciplines, courses, and learning objects;

[0114] The domain knowledge model attribute setting module 12 is used to set the attributes of the elements of the domain knowledge model, and integrate the attributes used to describe the learner's learnin...

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Abstract

The invention relates to the technical field of knowledge graph construction, and discloses a multivariate intelligence fused adaptive learning knowledge graph construction method and system. The method comprises the steps: S1, extracting domain knowledge element entities; s2, setting attributes of elements of the domain knowledge model, and integrating attributes for describing the learning ability of a learner in a multivariate intelligent theory into attributes of knowledge points in the domain knowledge model; s3, constructing a relationship between elements of the domain knowledge model;and S4, dynamically updating the netty knowledge graph. In the prior art, knowledge node attribute extraction in domain knowledge only pays attention to knowledge point subject attributes; according to the method, the technical problem that when knowledge point achievement degree calculation is carried out based on knowledge point features and learner features, an accurate basis is lacked due to the fact that the internal relation between the learner features and attributes of knowledge objects learned by the learner features is ignored, and therefore the accuracy of self-adaptive learning path recommendation is reduced is solved.

Description

technical field [0001] The present invention relates to the technical field of knowledge graph construction in an adaptive learning system, and more specifically, it relates to a method and system for building an adaptive learning knowledge graph that integrates multiple intelligences. Background technique [0002] Adaptive learning aims to provide different learners with adaptive learning content and learning paths, so as to achieve the purpose of personalized learning. Peter Brusilovsky, an information scientist at the University of Pittsburgh, first proposed the concept of adaptive learning in 1996, and proposed a general model of an adaptive learning system, which mainly includes domain knowledge model, learner model, teaching model, adaptive engine and interface model . [0003] Among them, the domain knowledge model is used to describe the knowledge structure of the subject area, including knowledge points, knowledge point attributes, and connections between knowledge...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F16/28G06F16/23G06K9/62G06N3/08
CPCG06F16/367G06F16/288G06F16/23G06N3/08G06F18/24323
Inventor 王剑
Owner ZHENGZHOU UNIV
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