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

IVY T. DERLA; Avelino D. Bitang; Yuanyuan Li

doi:10.65231/ijmr.v2i1.123

Research Article

Vol. 2 No. 1 (2026): International Journal of Multidisciplinary Research

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

IVY T. DERLA^▸^▾
Avelino D. Bitang^▸^▾
Yuanyuan Li^▸^▾

PDF

DOI: https://doi.org/10.65231/ijmr.v2i1.123
Submitted: February 2, 2026
Published: 2026-01-31

Abstract

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.

References

Songkhro, J., Ali Mohsen, S., Wateh, M., Maseng, N., & Chedoloh, A. (2025). Developing effective service communication: The role of e-book in enhancing English speech acts in high vocational training. Journal of Modern Management, Shinawatra University, 3(2).
Gazioğlu, M., & Aydın, S. (2024). Identifying grammatical errors and mistakes via a written learner corpus in a foreign language context. Journal of Language Research (JLR), 8(2), 91–106. https://doi.org/10.51726/jlr.1553484
Kilgarriff, A., Rychlý, P., Smrz, P., & Tugwell, D. (2004). The Sketch Engine. In Proceedings of the 11th EURALEX International Congress, EURALEX 2004 (pp. 105–115). Lorient, France.
Honnibal, M., & Montani, I. (2017). spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. https://spacy.io/api/annotation#section-citation
Corpus Analysis. (n.d.). https://corpus-analysis.com/
Alnemrat, A. (2025). AI vs. teacher feedback on EFL argumentative writing. Frontiers in Education. https://doi.org/10.3389/feduc.2025.1614673
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550.
Shao, S. (2025). The role of AI tools on EFL students' motivation, self-efficacy and anxiety. Learning and Instruction. https://doi.org/10.1016/j.learninstruc.2025.101XXX
Yiakoumetti, A. (2006). A cross-linguistic approach to language awareness: Can English phonics benefit Greek learners of English? Language Awareness, 15(3), 137–157. https://doi.org/10.2167/la403.0
Woll, N., & Paquet, P.-L. (2025). Developing crosslinguistic awareness through plurilingual consciousness-raising tasks. Language Teaching Research. https://doi.org/10.1177/13621688211056544
Corder, S. P. (1967). The significance of learners’ errors. International Review of Applied Linguistics in Language Teaching.
Selinker, L. (1972). Interlanguage. International Review of Applied Linguistics in Language Teaching, 10(1–4), 209–231.
Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020, February). InP grating coupler design for vertical coupling of InP and silicon chips. In Integrated Optics: Devices, Materials, and Technologies XXIV (Vol. 11283, pp. 33-38). SPIE.
Gu, Y. (2025). Synergistic Effects of Digital and Green Economies: Mechanisms and Pathways for Sustainable Development of SMEs. Journal of Business and Economic Research, 1(10).

Keywords

Corpus-based research;
EFL writing;
Grammar error analysis;
NLP-assisted tools;
Data-informed instruction;
Sustainable Development Goal 4

How to Cite

DERLA, I. T., Bitang, A. D., & Li, Y. (2026). Big Data Analysis Of Common Grammar Errors Among Efl Learners: A Basis For Improving English Writing Instruction Aligned With Sdg 4 (Quality Education) . International Journal of Multidisciplinary Research, 2(1), 148–154. https://doi.org/10.65231/ijmr.v2i1.123

This work is licensed under a Creative Commons Attribution 4.0 International License.

[1] Songkhro, J., Ali Mohsen, S., Wateh, M., Maseng, N., & Chedoloh, A. (2025). Developing effective service communication: The role of e-book in enhancing English speech acts in high vocational training. Journal of Modern Management, Shinawatra University, 3(2).

[2] Gazioğlu, M., & Aydın, S. (2024). Identifying grammatical errors and mistakes via a written learner corpus in a foreign language context. Journal of Language Research (JLR), 8(2), 91–106. https://doi.org/10.51726/jlr.1553484

[3] Kilgarriff, A., Rychlý, P., Smrz, P., & Tugwell, D. (2004). The Sketch Engine. In Proceedings of the 11th EURALEX International Congress, EURALEX 2004 (pp. 105–115). Lorient, France.

[4] Honnibal, M., & Montani, I. (2017). spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. https://spacy.io/api/annotation#section-citation

[5] Corpus Analysis. (n.d.). https://corpus-analysis.com/

[6] Alnemrat, A. (2025). AI vs. teacher feedback on EFL argumentative writing. Frontiers in Education. https://doi.org/10.3389/feduc.2025.1614673

[7] Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550.

[8] Shao, S. (2025). The role of AI tools on EFL students' motivation, self-efficacy and anxiety. Learning and Instruction. https://doi.org/10.1016/j.learninstruc.2025.101XXX

[9] Yiakoumetti, A. (2006). A cross-linguistic approach to language awareness: Can English phonics benefit Greek learners of English? Language Awareness, 15(3), 137–157. https://doi.org/10.2167/la403.0

[10] Woll, N., & Paquet, P.-L. (2025). Developing crosslinguistic awareness through plurilingual consciousness-raising tasks. Language Teaching Research. https://doi.org/10.1177/13621688211056544

[11] Corder, S. P. (1967). The significance of learners’ errors. International Review of Applied Linguistics in Language Teaching.

[12] Selinker, L. (1972). Interlanguage. International Review of Applied Linguistics in Language Teaching, 10(1–4), 209–231.

[13] Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020, February). InP grating coupler design for vertical coupling of InP and silicon chips. In Integrated Optics: Devices, Materials, and Technologies XXIV (Vol. 11283, pp. 33-38). SPIE.

[14] Gu, Y. (2025). Synergistic Effects of Digital and Green Economies: Mechanisms and Pathways for Sustainable Development of SMEs. Journal of Business and Economic Research, 1(10).