Contribution Mechanism of Online Learning Behavior Data to the Accuracy of Academic Early Warning and Model Optimization
在线学习行为数据对学业预警准确性的贡献机制及模型优化

摘要

在线学习平台的普及使大规模采集学习行为数据成为可能，为学业预警系统的精准化建设提供了新的数据 基础。然而，现有学业预警模型在特征选取、动态适应与结果解释三个维度上仍存在明显局限，制约了预警准确性 的进一步提升。本文从行为数据类型与预警机制的关联逻辑出发，系统梳理在线学习行为数据对学业预警准确性的 贡献机制，将其归纳为参与度识别、学习路径分析、深层学习判断与多维协同四个层次，并在此基础上提出模型优 化的四条路径：多维特征融合的输入层优化、动态更新机制的引入、可解释性框架的嵌入，以及干预反馈闭环的构建。 研究认为，行为数据的预警价值不在于数据量的简单累积，而在于对数据结构与学习规律之间内在关联的深度挖掘； 模型优化的核心目标不仅是提升预测精度，更在于使预警结果切实服务于教学干预的及时性与针对性，推动学业预 警系统从数据驱动向机制驱动的范式转型。

Abstract

The popularization of online learning platforms has made it possible to collect large-scale learning behavior data, providing a new data foundation for the precise construction of academic early warning systems. However, existing academic early warning models still have obvious limitations in feature selection, dynamic adaptation, and result interpretation, which restrict the further improvement of early warning accuracy. Starting from the logical relationship between behavior data types and early warning mechanisms, this paper systematically reviews the contribution mechanism of online learning behavior data to the accuracy of academic early warning, and summarizes it into four levels: participation identification, learning path analysis, deep learning judgment, and multidimensional collaboration. On this basis, four paths for model optimization are proposed: input layer optimization based on multidimensional feature fusion, introduction of a dynamic updating mechanism, embedding of an interpretability framework, and construction of an intervention feedback loop. The study argues that the early warning value of behavior data lies not in the simple accumulation of data volume, but in the in-depth exploration of the internal relationship between data structures and learning patterns. The core goal of model optimization is not only to improve prediction accuracy, but also to ensure that early warning results effectively serve the timeliness and pertinence of teaching interventions, thereby promoting the paradigm transformation of academic early warning systems from data-driven to mechanism-driven.