Self-adaptive learning feature extraction system based on dynamic learning style information and application

Abstract

The invention relates to a self-adaptive learning feature extraction system based on dynamic learning style information and application, and the system comprises a first information collection modulewhich is used for dynamically collecting and storing the process information of a pre-learning test of a user; The second information acquisition module is used for acquiring and storing learning style self-evaluation information input by the user; and the learning feature generation module is used for carrying out matching processing on the process information and the learning style self-evaluation information to generate a learning style feature value of the user. Compared with the prior art, the method has the advantages that the learning style characteristics of the user can be accuratelyobtained, and then the learning efficiency is improved.

Description

technical field [0001] The invention relates to the field of adaptive learning technology, in particular to an adaptive learning feature extraction system and application based on dynamic learning style information. Background technique [0002] The adaptive learning system contains three models, learner model, domain knowledge model and adaptive engine. The domain knowledge model is based on the nano-level split of the knowledge map, and the weak knowledge points and ability level of the students are detected through the accurate evaluation of the adaptive engine. The learner model is an abstract representation of learner characteristics. An accurate learner model can cluster learners with similar learning characteristics, thereby facilitating the provision of more accurate and personalized learning services. [0003] The traditional adaptive learning system learner model mainly has the following three technical problems: [0004] (1) The learner model of the traditional ...

AI Technical Summary

Problems solved by technology

[0004] (1) The learner model of the traditional adaptive learning system focuses on a single dimension, which only considers what knowledge points students have mastered and the degree of mastery, and ignores the impact of learning styles on learning outcomes;

[0005] (2) The current learning style measurement methods include the explicit acquisition method of statically representing the current characteristics of the learner from a large number of questions answered by the learner, and the implicit acquisition method of real-time dynamic tracking of the learner's state changes. The former cannot track learning in real time learner’s behavior, and correct the change of learning style in a timely manner. The latter lacks the behavioral characteristics of known learners in the early stage of application, and has the problem of “cold start”;

If we only pursue the extraction of universal learner characteristics, we will be separated from the real cognitive process of the learner and ignore the problems that the learner needs to solve in the learning process

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