Research on English Writing Instruction Based on Data-Driven Learning (DDL)

This study focuses on the application of Data-Driven Learning (DDL) in English writing instruction, exploring its educational effectiveness through theoretical construction and empirical analysis. Based on cognitive learning theory, corpus linguistics, and constructivist learning theory, the study constructs a theoretical framework for DDL, with core elements including corpus utilisation, data-driven autonomous learning, and teacher-guided support. Through a 16-week teaching practice involving 30 non-English major second-year students at a certain university, combined with DDL activities such as vocabulary learning, writing strategy analysis, error correction, and personalised feedback, it was found that students showed significant improvements in vocabulary richness, grammar error rates, frequency of logical transition words, and the depth of argumentation in argumentative essays. Survey results indicated that 85% of students acknowledged the positive impact of DDL on their writing abilities, and 75% reported enhanced self-directed learning skills. The study indicates that DDL effectively enhances students' language accuracy, writing structure rationality, and critical thinking skills through authentic corpus input and self-directed exploration. However, implementation should focus on improving students' proficiency in corpus operations and teachers' instructional design capabilities. This research provides empirical evidence and practical guidance for reforms in English writing instruction.