A BIBLIOMETRIC ANALYSIS OF ERROR ANALYSIS IN ARABIC LANGUAGE LEARNING AND ESSAY WRITING AT THE SECONDARY LEVEL

Abstract

This bibliometric analysis investigates research trends in Arabic language learning and essay writing at the secondary level, aiming to understand the academic landscape and its evolution. The primary problem addressed is understanding how contemporary methods, including data-driven and AI-assisted approaches, influence the study of Arabic language acquisition and writing skills. Utilising Scopus Analyzer and VOSviewer software, a dataset of 1,542 documents was analysed to identify trends, popular keywords, research collaborations, and citation networks. Numerical results reveal that terms like “deep learning” and “machine learning” are frequently associated with studies on Arabic essay writing, indicating a shift towards integrating technological solutions in language pedagogy. The analysis also shows significant international collaborations, with leading contributions from countries such as China, the United States, and the United Kingdom, alongside notable input from Middle Eastern and Southeast Asian regions. The findings suggest that while traditional linguistic approaches remain relevant, there is a clear trend towards the adoption of innovative and data-centric methodologies.