An individualized homework precise pushing service system and method based on whole-lesson learning situation portrait

CN122925488APending Publication Date: 2026-10-16SHAANXI DIGITAL CRAFT CLOUD TECHNOLOGY CO LTD +2
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Patent Information

Application Number
CN202611148780.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-07-31
Publication Date
2026-10-16

AI Technical Summary

Technical Problem

[0012](2)克服现有学情建模维度单一、权重固定缺陷,根据小初高认知特点自适应调整数据权重,提升学情画像精准度

Benefits of technology

[0040] Improved profiling accuracy: By adopting multi-dimensional adaptive profiling modeling, the accuracy rate of profiling students' learning situation across all grade levels is ≥90%, which is more than 10% higher than the traditional fixed-weight scheme, accurately identifying students' knowledge gaps.

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Abstract

This invention relates to the interdisciplinary field of computer application technology and educational informatization, and discloses a personalized homework precision push service system and method based on full-grade learning profiles. The system adopts a five-layer architecture (as shown in Figure 1): data acquisition layer, algorithm engine layer, application service layer, front-end interaction layer, and commercial operation layer. The algorithm engine layer includes a five-level tag-based graded question bank module, a dynamic learning profile module, a grade-level push algorithm module, and a closed-loop optimization module. The dynamic learning profile module configures weight coefficients α, β, and γ for four levels: lower elementary, upper elementary, junior high, and senior high school, respectively. The mastery of each knowledge point is calculated using the formula: total profile score = α × academic score + β × classroom score + γ × behavior score, and updated incrementally weekly. The grade-level push algorithm module automatically filters out knowledge points with a mastery ≥ 0.85 based on grade-level differentiated allocation rules and generates customized homework packages. The closed-loop optimization module iteratively optimizes the learning profile based on incorrect question data. The commercial operation layer uses RBAC permission control to divide the system into a free basic version and a paid value-added version, achieving functional and data isolation. This invention solves the combined challenges of unified adaptation across all educational stages, multi-dimensional dynamic profiling, differentiated push notifications for different educational stages, data closed-loop self-optimization, and commercially sustainable operation, significantly improving the accuracy of student profiling and homework efficiency, while reducing the burden of grading for teachers.
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