Big Data Analysis Of Common Grammar Errors Among Efl Learners: A Basis For Improving English Writing Instruction Aligned With Sdg 4 (Quality Education)

The increasing availability of digital learner data has opened new possibilities for enhancing the quality of English as a Foreign Language (EFL) instruction. This study investigates recurring grammar error patterns in EFL learners’ writing using a corpus-based, big data-informed approach. Grounded in Error Analysis Theory and Interlanguage Theory, the study analyzes a large digital corpus of learner- written texts to identify systematic grammatical difficulties, including errors in verb tense, article usage, prepositions, subject-verb agreement, and sentence structure. The findings reveal these grammar errors are not random performance mistakes but consistent indicators of learner’s developing interlanguage system. Unlike traditional grammar instruction, tends to look at small scales or small samples like a few essays, maybe one class and limited classroom samples while digital corpora and NLP-assisted analysis enables to see and identify thousands of texts and writings. Big data does not work as a substitute for teachers, but as a tool that strengthens instructional insight through large-scale evidence. To allow us to move from assumptions to evidence and from guessing to knowing. By informing data-driven curriculum design, supporting AI-assisted feedback practices, and enhancing teacher decision-making, the study demonstrates how large-scale learner data analysis can contribute to more effective and inclusive EFL writing instruction. In alignment with Sustainable Development Goal 4 (Quality Education), this research highlights the potential of evidence-based and learner-centered teaching approaches to promote more effective, inclusive, and equitable EFL writing instruction across diverse educational contexts.