Self-adaptive learning method and system based on knowledge model and storage medium

Abstract

The invention discloses an adaptive learning method and system based on a knowledge model, and a storage medium. The method comprises the steps: collecting initial learning data, storing the initial learning data in a learning resource database, carrying out the ontology term extraction and marking, and building the knowledge model through an ontology editor; initializing a knowledge space of thelearner according to a pre-measurement result of the learner in the learning system; updating the learning process state data input into the learner in real time, performing DKT model modeling of multiple knowledge points on the learning process state data through a DKT algorithm, feeding back the mastering state of each knowledge point of the learner, and dynamically updating the knowledge spaceof the learner; and the adaptive engine compares the knowledge space of the learner with the knowledge model, matches a learning path corresponding to the learner, and pushes a learning path link to the learner. According to the method, the DKT is tracked through deep learning based on the knowledge model, multi-knowledge-point modeling is effectively carried out, the latest knowledge state of a learner is accurately mastered and updated, and a corresponding learning path is matched.

Description

Technical field [0001] The present invention relates to the technical field of education informatization, in particular to an adaptive learning method, system and storage medium based on a knowledge model. Background technique [0002] With the advent of the Internet and big data era, traditional education methods are becoming more and more difficult to meet the growing individual learning needs of students. Adaptive online learning has gradually become widely used due to its characteristics such as teaching students in accordance with their aptitude and ease of use. An important part of adaptive learning is student ability assessment and learning resource recommendation, which all need to be based on a structured education domain knowledge system. Therefore, constructing an educational knowledge graph to support adaptive learning has become an urgent problem to be solved. [0003] In the process of research and practice of the prior art, the inventor of the present invention fou...

AI Technical Summary

Problems solved by technology

However, using a static model to construct a knowledge graph cannot represent the relevance and dynamic development characteristics of knowledge points. Moreover, IRT and BKT technologies assume that users will not forget a certain knowledge point once they have mastered it, and do not take into account the user's understanding of knowledge points. The forgetfulness of the knowledge point causes the user to perform very well in the future when the topic belongs to the knowledge point, but in reality, the situation of forgetting the knowledge point will occur, resulting in low prediction accuracy and the inability to accurately grasp the learners' latest knowledge points. Deficiencies in the state of knowledge, so that learners cannot be matched with an adaptive learning path

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