Research on Precision Teaching in College English via Big Data-Driven Learning Diagnosis Systems

Abstract

This study explored the use of big-data learning diagnosis systems in higher education English precision teaching. The traditional one-size-fits-all teaching model is inadequate in the globalized context, while big-data technology offers new reform opportunities. The study selected 120 university English majors as samples and collected multi-dimensional data to build a linear regression model. Results show that online learning duration, homework completion rates, and classroom interaction frequency positively impact final grades. Targeted interventions lead to significant improvements in these areas. However, challenges remain in data security, teacher capability, system integration, and data quality. This study confirms the effectiveness of big-data learning diagnosis systems in enhancing precision teaching and provides valuable insights for future educational reforms.