A Techno-Linguistic Decoding Errors in Arabic Writing Assessments: A Case Study of Alikhtibar LMS

Moh Ismail; Abdul Hafidz bin Zaid; Luthfi Muhyiddin; Muhammad Alamuddin Ma’ruf

doi:10.29240/jba.v9i1.12426

Authors

Moh Ismail Universitas Darussalam Gontor, Indonesia https://orcid.org/0000-0003-1097-8304
Abdul Hafidz bin Zaid Universitas Darussalam Gontor, Indonesia
Luthfi Muhyiddin Universität Leipzig, Germany
Muhammad Alamuddin Ma’ruf University of Holy Qur’an and Islamic Science, Sudan

DOI:

https://doi.org/10.29240/jba.v9i1.12426

Keywords:

Arabic writing test, Arabic writing errors, Alikhtibar LMS, Error analysis, Writing proficiency

Abstract

This study aims to investigate techno-linguistic decoding errors in Arabic writing assessments within the Alikhtibar Learning Management System (LMS). The integration of technology in Arabic Language Teaching (ALT) has transformed learning and assessment processes by enhancing student engagement and enabling adaptive evaluation. However, assessing Arabic writing proficiency remains challenging due to the language’s complex grammatical system, especially in digital environments. Employing a qualitative descriptive approach, this research analyzed 46 student-produced texts to identify common error types and their pedagogical implications. The findings revealed that preposition misuse was the most frequent error (30 instances), followed by noun agreement errors (19), verb conjugation errors (15), and redundancy (12). These results emphasize the need for context-sensitive assessment tools and targeted instructional strategies. This study is novel in focusing on decoding errors within a practical Arabic LMS platform and in integrating linguistic theory with digital error analysis. It offers valuable insights for evaluating Arabic writing proficiency in the digital era and contributes to the advancement of intelligent, adaptive feedback systems for Arabic language education, with potential applications in instructional design, curriculum development, and NLP-based assessment tools.

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