Comprehensive knowledge point mastering probability prediction model construction method

A construction method and probability prediction technology, applied in the field of education and teaching system, to achieve the effect of mastering probability accurately
CN110826802AInactive Publication Date: 2020-02-21SHANGHAI SQUIRREL CLASSROOM ARTIFICIAL INTELLIGENCE TECH CO LTD

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANGHAI SQUIRREL CLASSROOM ARTIFICIAL INTELLIGENCE TECH CO LTD
Publication Date
2020-02-21
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention belongs to the technical field of education and teaching systems, and particularly relates to a construction method of a comprehensive knowledge point mastering probability prediction model capable of being applied to an education and teaching system. The construction method comprises the following steps: step 1, inh calculation; step 2, pi calculation; and step 3, model construction. The model constructed by the invention can accurately test the mastering probability of the students on the comprehensive knowledge points based on the test results (mastering / not mastering) of thestudents on the basic knowledge points.
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Description

technical field

[0001] The invention belongs to the technical field of education and teaching systems, and in particular relates to a method for constructing a comprehensive knowledge point mastery probability prediction model that can be applied in the education and teaching system. Background technique

[0002] The knowledge point map relationship commonly used in the existing education and teaching system is a logical relationship, that is, there is a logical relationship between the knowledge points, such as: A->B->C (letters represent different knowledge points) , A is the front of B, A is more basic and simpler than B; C is the back of B, and C is more advanced and complex than B. Therefore, if B has mastered, we can infer that A has mastered; otherwise, B has not mastered, then we can infer that C cannot master either. Therefore, even if the knowledge points are divided into smaller and more detailed ones, the use of the logical relationship map can improve the...

Claims

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