User data analysis method based on big data
By collecting and analyzing students' learning behavior, grades and online interactive data, a personalized learning model is constructed, and personalized teaching suggestions are provided to teachers, which solves the problem of inaccurately capturing students' differences in the existing technology, and improves teaching effectiveness and learning quality.
CN120387908AInactive Publication Date: 2025-07-29JIANGXI UNIVERSITY OF FINANCE AND ECONOMICS
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Patent Information
- Application Number
- CN202510450227.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-11
- Publication Date
- 2025-07-29
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Technical Problem
In the prior art, there are great differences in learning methods and habits between students, and algorithmic models cannot accurately capture and explain these differences, resulting in the inability to provide students with effective learning support and suggestions.
Method used
By collecting students' online learning platform data, grade management system data and online interactive data, perform data preprocessing and multi-dimensional analysis, build a personalized learning model, and provide personalized teaching suggestions.
Benefits of technology
It can more accurately identify students' individual differences and learning needs, provide targeted and personalized teaching suggestions, and improve teaching effectiveness and learning quality.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a user data analysis method based on big data. The method comprises the following steps: S1, data collection: collecting student data; s2, data preprocessing: carrying out preprocessing operation on the data; s3, multi-dimensional data analysis: carrying out learning behavior analysis, score analysis and online interaction analysis by utilizing the learning data of the students; s4, personalized learning model construction: constructing a personalized learning model according to the learning modes, learning difficulties and weak links of the students; the learning data and the analysis result of the students are fed back to the teacher in time, the personalized learning model provides personalized teaching suggestions for the teacher according to the learning behaviors, scores and online interaction conditions of the students, and the teaching suggestions comprise learning plans, teaching methods and tutoring strategies for specific students. And the effectiveness of the teaching suggestions is evaluated by regularly comparing the learning data of the students with the implementation conditions of the teaching suggestions.
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