基于人工智能的学生学业动态预警与精准干预模型构建研究

Research on the Construction of an Artificial Intelligence-Based Dynamic Early Warning and Precise Intervention Model for Student Academic Performance

摘要: 高校预警工作已成为提升人才培养质量的关键举措，其核心目标是从简单的管理向促进学生发展的方向转变。当前，各高校在实践中存在预警的滞后与标准单一、帮扶措施不够精准等问题。本文以多源时序数据为基础，通过特征提取与机器学习实现风险分级，形成“监测–预警–干预–反馈–优化”的闭环。研本文进一步提出数据治理、试点调优和制度固化的实施路径，为独立学院提升学业支持的前瞻性、精准性和协同性提供参考。

Abstract: Early warning systems in higher education have become essential to improving talent development, with their focus shifting from administrative control to student development. However, current practices often suffer from delayed warnings, uniform criteria, and insufficiently targeted support. Using multi-source time-series data, this study integrates feature extraction and machine learning to classify academic risk and establishes a closed loop of “monitoring—warning—intervention—feedback—optimization.” It further proposes an implementation framework encompassing data governance, pilot-based refinement, and institutionalization, offering a reference for independent colleges to enhance the proactiveness, precision, and coordination of academic support.