随着教育数字化转型步伐的加速推进,教育过程中产生的数据规模呈现爆发式增长态势。在此背景下,如何高效挖掘并运用教育大数据的深层价值,以推动教学质量的持续优化与革新,已成为教育界亟待破解的核心命题。本文聚焦初中数学课后作业数据,创新性地引入基于众数回归统计诊断框架的机器学习技术,深度剖析作业数据中的异常模式与潜在规律,旨在为一线教育工作者提供精细化、智能化的教学反馈机制,助力课堂教学实现“减量不减质、增效不增负”的双重目标。With the acceleration of the digital transformation of education, the scale of data generated in the process of education shows an explosive growth trend. In this context, how to efficiently tap and apply the deep value of educational big data to promote the continuous optimization and innovation of teaching quality has become a core proposition that needs to be solved in the education sector. This paper focuses on junior high school math homework data resources, innovatively introduces machine learning technology based on mode regression statistical diagnosis framework, and deeply analyzes abnormal patterns and potential rules in the homework data, aiming to provide a refined and intelligent teaching feedback mechanism for front-line educators, and help classroom teaching achieve the dual goals of “reducing quantity without reducing quality, increasing efficiency without increasing negative”.
