Novel second classroom prediction and early warning optimization method

By integrating information from universities and student registration, and constructing prediction models for individual students and communities, the problem of incomplete data collection in traditional extracurricular management is solved. This enables real-time quantitative assessment and personalized management of student participation and progress, improving prediction accuracy and intervention efficiency.

CN121599802APending Publication Date: 2026-03-03ANHUI UNIVERSITY OF ARCHITECTURE
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Patent Information

Application Number
CN202511745108.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-25
Publication Date
2026-03-03

AI Technical Summary

Technical Problem

Traditional university extracurricular management suffers from incomplete and unreal-time data collection, making it difficult to capture multi-dimensional dynamic data of students, resulting in low accuracy of early warnings, a lack of personalization and adaptability to diverse scenarios, and low intervention efficiency.

Method used

By integrating university information and student registration information through the data collection module, behavioral and temporal characteristics are generated, individual and tribal prediction models for students are constructed, a multi-dimensional evaluation system is established, and transcripts and personalized recommendations are output to achieve intelligent management.

Benefits of technology

It enables real-time quantitative assessment of student participation and progress, improving prediction accuracy and intervention efficiency, and supporting personalized activity recommendations and organizational management.

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Abstract

The invention discloses a novel second classroom prediction and early warning optimization method, which belongs to the technical field of artificial intelligence and machine learning, and comprises the following steps: constructing a three-index system by integrating college basic information, student registration information, behavior data and second classroom activity and growth records; according to the method and the system, student individual and tribe prediction and early warning are realized by using an SVR + CNN-LSTM + Attention model, data-driven dynamic management and intelligent decision making are realized, the technical problems of systematization, quantification and prediction in traditional college second classroom management and real-time evaluation of student participation degree, growth progress and tribe activity quality are solved, data accuracy and timeliness are improved, and the method and the system have good application prospects. According to the method, the privacy compliance is ensured, efficient processing of multi-source heterogeneous data is realized, organization and management standardization and process are realized, and an output module provides a second class report card, credit and growth record early warning, personalized activity recommendation, activity index reports and multi-dimensional data visualization.
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