Intelligent learning behavior monitoring and abnormity early warning method and device

Through multimodal data acquisition and interdisciplinary knowledge processing strategies, the problems of single data acquisition, inaccurate analysis and inflexible early warning in the existing technology are solved, and accurate learning behavior analysis and personalized processing strategies are realized, which improves learning effect and motivation.

CN119723639APending Publication Date: 2025-03-28HUBEI UNIV OF EDUCATION +1
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Patent Information

Application Number
CN202411789138.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-06
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

When monitoring students' learning behavior, the data collection is single, the analysis is not accurate enough, and the individual needs of individual differences cannot be met. The abnormal warning lacks flexibility and accuracy, and the processing strategies lack systemicity and targetedness.

Method used

The multimodal data acquisition module is adopted to integrate the learning platform operation records, facial expression data and physiological data, as well as situational perception data, build a behavioral analysis model, realize dynamic abnormal hierarchical warning, and display processing suggestions through various forms based on interdisciplinary knowledge generation and processing strategies.

Benefits of technology

It realizes all-round and multi-dimensional data acquisition, accurately analyzes students' learning behavior, flexibly and accurately identifys abnormal behaviors, provides personalized processing strategies, and improves learning effect and motivation.

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Abstract

The invention provides an intelligent learning behavior monitoring and abnormity early warning method and device, and relates to the technical field of education analysis, and the device architecture comprises a multi-modal data collection module, a behavior analysis module, a dynamic abnormity grading early warning module and a processing measure module. The multi-modal data acquisition unit integrates learning platform operation records, facial expression data and physiological data as well as environment and situation information collected by the situation awareness data acquisition unit, so that omnibearing and multi-dimensional data acquisition is realized; the long-time monitoring and personalized model construction unit uses a plurality of data mining algorithms and LSTM in deep learning to combine with an attention mechanism, accurately mines student behavior rules according to long-term data and constructs a personalized model, and can flexibly and accurately judge abnormal behaviors according to student individual conditions and learning situations. Graded early warning is provided for related personnel in time, deterioration of learning problems is effectively prevented, and timeliness and effectiveness of education intervention are improved.
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