Knowledge tracking method and system fusing multi-dimensional interaction characteristics of teacher and student intelligence

Through a knowledge tracking system that integrates the multidimensional interaction characteristics of teachers and students, the problem that traditional methods are difficult to fully reflect students' learning situation is solved, and more accurate knowledge mastery evaluation and personalized learning support are achieved.

CN119961439APending Publication Date: 2025-05-09HUAZHONG NORMAL UNIV
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Patent Information

Application Number
CN202411826865.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-12
Publication Date
2025-05-09

AI Technical Summary

Technical Problem

Traditional knowledge tracking methods often can only be evaluated based on a single dimension, which is difficult to fully reflect students' learning situation.

Method used

By integrating the multidimensional interaction characteristics of teachers and students, a knowledge tracking system is designed, which includes a data acquisition module, a feature extraction module, a knowledge tracking model and feedback and adjustment module. It collects and analyzes teacher-student interaction data in real time, extracts multidimensional features, and builds a dynamic knowledge tracking model based on deep learning algorithms.

Benefits of technology

It has achieved a more accurate assessment of students' mastery of knowledge, provided a basis for personalized learning, helped teachers adjust their teaching strategies, and improved teaching quality and efficiency.

✦ Generated by Eureka AI based on patent content.

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Abstract

The system comprises a data acquisition module, a feature extraction module, a knowledge tracking model and a feedback and adjustment module, the data acquisition module collects multiple types of data of interaction between teachers, students and agents in real time, and the data acquisition module determines a data source, designs a structure and performs feedback and adjustment on the knowledge tracking model. Data are collected, stored and preprocessed in multiple modes. The feature extraction module defines feature types, performs data cleaning and conversion, and extracts, selects and constructs feature vectors by adopting multiple methods. And the knowledge tracking model sets a target, prepares data, selects the model, trains, evaluates and deploys. And the feedback and adjustment module collects and evaluates feedback, adjusts a strategy, generates a learning path, forms a feedback loop and evaluates an effect. The system is beneficial to precise teaching and personalized learning.
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Citation Information

Cited By

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