Research on Student Learning Behavior Analysis and Precision Teaching Driven by Big Data

Abstract

With the rapid development of big data technology, the education sector has also encountered unprecedented opportunities. Traditional teaching models are gradually shifting toward data-driven personalized instruction, allowing teachers and educational administrators to leverage vast amounts of learning data to gain deep insights into students' learning behaviors, optimize teaching strategies, and implement precise teaching. This paper aims to investigate student learning behavior analysis and precise teaching driven by big data. Through comprehensive analysis of student learning trajectories, online participation, classroom interactions, and assignment data, this study constructs various learning behavior analysis models, revealing students' learning habits and personalized needs, thereby providing teachers with robust evidence for instructional interventions. Additionally, based on these analyses, this paper proposes precise teaching strategies, including personalized learning path planning, dynamic resource recommendations, and real-time feedback mechanisms, further enhancing teaching efficiency and learning outcomes. In the future, with the integrated development of artificial intelligence technology, precise teaching is expected to achieve a high level of automation and intelligence; however, data privacy and ethical issues must also be given adequate attention.