Behavior pattern-based learning style identification method and device
By constructing a style and behavior vocabulary, calculating keyword similarity and using an improved K-Means algorithm and machine learning algorithm, the subjectivity and adaptability problems of learning style identification are solved, refined learning style identification and personalized learning services are achieved, and learning efficiency is improved.
CN120707346APending Publication Date: 2025-09-26SHANDONG NORMAL UNIV
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Patent Information
- Application Number
- CN202510756653.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-09
- Publication Date
- 2025-09-26
AI Technical Summary
Technical Problem
The existing learning style recognition methods are highly subjective and cannot adapt to changes in learner styles, resulting in ambiguous recognition results. In addition, the automatic methods have different implementation paths, and their accuracy and effects vary.
Method used
Build a style vocabulary and a behavior vocabulary, form a matching set by calculating the similarity of keywords, use weighted feature vectors and improved K-Means algorithm for clustering, and combine machine learning algorithms to predict learning styles.
Benefits of technology
It achieves fine division of learning styles and personalized learning services, improves learning efficiency, and enhances the accuracy and adaptability of learning style identification.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of data processing application, in particular to a learning style recognition method and device based on a behavior pattern. The invention provides a framework capable of carrying out learning style recognition based on learning behaviors of a learner, the framework fully utilizes the universality of big data, the behaviors of the learner in the learning process are collected, the learning style which the learner tends to is given, the learner can select the learning style suitable for himself / herself, the learning efficiency is improved, and the learning experience of the learner is improved. And matched education content and resource suggestions can be provided for the online learning system to realize personalized learning service. In addition, a conventional K-means clustering algorithm is improved, and a K-Means improved algorithm based on behavior preference is provided, so that the clustering result has higher characterization capability for the behavior preference, and the recognition effect of the learning style is enhanced.
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