
info@juzhikan.asia
Overseas Chinese Middle School, Nan'an Fujian Province, 362000;
Abstract: With the continuous deepening of educational informatization, big data technology has provided brand-new technical support for teaching reform in senior high schools. As a personalized teaching carrier that fits the learning situation of students in a school, school-based homework is a key starting point for optimizing the effect of senior high school mathematics teaching and achieving the goal of reducing burden while improving quality. Currently, in the construction and practice of school-based mathematics homework in senior high schools, there are many common problems such as serious homogenization of homework content, low matching degree with students' learning conditions, and lagging evaluation feedback. Based on the core advantages of big data technology and combined with the teaching characteristics of senior high school mathematics, this paper sorts out and analyzes the current implementation status of school-based mathematics homework in senior high schools, and explores the design principles, practical paths and optimization strategies of big data empowering school-based mathematics homework in senior high schools. The aim is to build an accurate, personalized and efficient senior high school mathematics homework system, and help students' personalized development and the improvement of mathematics teaching quality.
Keywords: Big Data; Senior High School Mathematics; School-Based Homework; Homework Design; Teaching Practice
References
[1]Xue Jinfang. Research on personalized homework design of senior high school mathematics based on big data analysis[J]. Journal of Mathematical Problem Solving Research, 2025(27): 23-24.
[2]Li Juan. Optimal design and practice of senior high school school-based mathematics homework in the big data era[J]. Basic Education Reference, 2024(18): 56-58.
[3] Wang Hao. Accurate teaching practice of senior high school mathematics homework under the background of smart education[J]. Journal of Mathematics Learning and Research, 2023(32): 102-104.