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Learning cognitive path generation method based on cognitive map

A path generation and map technology, applied in the field of learning and cognitive path generation based on cognitive maps, can solve the problems of single category, poor self-adaptation, and low personalization of cognitive paths

Pending Publication Date: 2019-12-06
杭州奇迹在线科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] (1) Only the acquisition of the knowledge point level is considered, and the learning log in the method can only reflect the single-grained mastery of the knowledge point by students, while the present invention designs cognitive maps of different dimensions, which can effectively analyze learners’ understanding of the knowledge point. The mastery of different granularities and different aspects of the same knowledge point, so the method of the above patent cannot be applied
[0006] (2) The path recommendation method of the above-mentioned patent is of a single category, poorly adaptive, and the recommended cognitive path is low in personalization

Method used

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  • Learning cognitive path generation method based on cognitive map
  • Learning cognitive path generation method based on cognitive map
  • Learning cognitive path generation method based on cognitive map

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0074] A method for generating a learning cognitive path based on a cognitive map is characterized in that it comprises the following steps:

[0075] S1. Construction of the cognitive map data model, which defines the basic elements of the cognitive map. The basic elements of the cognitive map include the cognitive dimension of the course, the knowledge unit of the course and the learning dependency relationship, and the cognition of the course using the RDF data model Representation and organization of dimensions, knowledge units, and learning dependencies among knowledge units;

[0076] S2. Given a specific course, extract the knowledge units in the course from the teaching text resources of the open domain, mine the learning dependencies between the knowledge units, and combine the cognitive ontology graph to generate the cognitive graph of the given course, Use RDF graphs for representation and storage;

[0077] S3. Based on the cognitive map of a given course, collect learners'...

Embodiment 2

[0081] This embodiment describes step S1 in detail, which is specifically as follows:

[0082] S101, the cognitive map can be expressed as a two-tuple

[0083] among them, Represents the cognitive dimension ontology map composed of cognitive dimensions in the cognitive map;

[0084] Represents the knowledge unit data graph formed by the knowledge unit and the learning dependency relationship in the cognitive map;

[0085] S102. For the ontology map of the cognitive dimension Can be expressed as a two-tuple

[0086] among them, It is a collection of cognitive dimensions, which mainly includes six categories: memory, understanding, application, analysis, evaluation, and creation. Each category is divided into several subcategories;

[0087] It is a collection of relationships between cognitive dimensions, including inheritance and or subordination;

[0088] S103. For the knowledge unit data graph Can be expressed as a two-tuple

[0089] among them, Represents the collection of know...

Embodiment 3

[0094] This embodiment describes step S2 in detail, which is specifically as follows:

[0095] S201. Collect part of the text sequence of the course teaching, take the sentence as the basic unit, expressed as Where k represents the total number of sentences that appear in the teaching text sequence; then organize teachers to label each sentence and mark the knowledge unit, otherwise it is marked as a non-knowledge unit;

[0096] S202. Generate a feature vector x corresponding to each sentence through the feature selection module i , Get the characteristic expression of the entire teaching text sequence Where d represents the dimension of each sentence feature vector;

[0097] S203: Given a marked sentence sequence and feature vector;

[0098] Among them, the tags of each sentence marked by the teacher together constitute the tag set of the sentence, expressed as Where y i ∈{+1,-1} indicates whether the sentence is a knowledge unit;

[0099] S204, use classification algorithms (such a...

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Abstract

The invention discloses a learning cognitive path generation method based on a cognitive map. A cognitive learning path is recommended to a learner in combination with the cognitive dimension ontologyand the course knowledge graph; firstly, a cognitive map modeling and constructing method is provided; on the basis, for a learner and a target learning unit are given, firstly, the current cognitivestate of a learner is recognized according to a learning log; a cognitive learning path of the target knowledge unit is generated by combining the cognitive ability of the target knowledge unit; andrecommendation and generation of cognitive paths in personalized learning can be realized, wherein a cognitive dimension ontology diagram is designed, it is guaranteed that learners can analyze and diagnose the learning situation of each knowledge unit in a finer-grained mode, a learning target cognition path is generated based on the course knowledge graph, and the algorithm integrating the cognition dimension, the learning record and the learner cognition attribute can better provide more accurate personalized learning services for the learners.

Description

Technical field [0001] The invention relates to the application of a knowledge map in the learning cognitive process in online education, in particular to a method for generating a learning cognitive path based on the cognitive map. Background technique [0002] With the development of knowledge graph technology, open knowledge graph data sets on the Internet have appeared one after another, covering many fields such as encyclopedia, education, finance, and medical care, and have become important knowledge resources on the Internet. Artificial intelligence technology based on knowledge graphs also provides brand new technical support for smart education, especially personalized navigation services in the learning process of learners. Personalized learning based on knowledge graphs requires both natural language processing and other artificial intelligence technologies to extract knowledge from the unstructured text of the course, as well as building an accurate domain cognitive o...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/35G06Q50/20
CPCG06F16/367G06F16/355G06Q50/205
Inventor 王丞王萌
Owner 杭州奇迹在线科技有限公司
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