Adaptive learning path recommendation method and system based on D-S evidence theory

Abstract

The invention relates to the technical field of adaptive learning, and discloses an adaptive learning path recommendation method and system based on a D-S evidence theory, and the method comprises thesteps: selecting meta-knowledge points with an entry degree being zero from a unit knowledge point group from a domain knowledge base, building a recommendation queue, and enabling the meta-knowledgepoints to serve as first knowledge points to be stored in the recommendation queue; judging whether the direct subsequent node set of the meta-knowledge points in the recommendation queue is empty ornot; obtaining the knowledge point with the maximum contribution value in the direct subsequent node set; judging whether knowledge points are reachable or not through a D-S evidence theory; and outputting the knowledge points as a recommended path according to the sequence of the knowledge points in the recommendation queue. The technical problems that in the prior art, a learner is usually matched with learning content only based on one-dimensional attributes, or static attribute value matching is adopted, so that the learning path recommendation accuracy of an adaptive learning system is low, and the real-time dynamic learning path recommendation function cannot be achieved are solved.

Description

technical field [0001] The present invention relates to the technical field of adaptive learning, more specifically, it relates to a method and system for recommending an adaptive learning path based on D-S evidence theory. 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 an adaptive learning system model, which mainly includes domain model, student model, pedagogy model, adaptive engine and interface model. [0003] The domain model (Domain Model, DM) mainly describes the structure of subject domain knowledge by defining the attributes of concepts and the relationship between concepts; Information is described, which is the personalized characteristic model of the learner; the pedagogical mo...

AI Technical Summary

Problems solved by technology

However, the prediction of achievement degree is usually only based on the attributes of a certain dimension to match learners and learning content, or it is completely static attribute value matching, the accuracy of achievement degree prediction is low, and it cannot achieve the real-time dynamic learning required by the adaptive learning system Path Recommended Features

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