Adaptive Arabic Language Learning Model Based on Learning Analytics: Responding to the Challenge of Personalization in the Digital Era

Ahmadi Ahmadi

Abstract


The digital era demands innovative approaches to Arabic language instruction that address learners’ diverse needs through personalization. This study aims to develop an adaptive Arabic language learning model based on learning analytics, which adjusts content, difficulty levels, and feedback in real-time based on individual learner data. Employing a Research and Development (R&D) design, the study involved needs analysis, model design, expert validation, limited trials, and model refinement. Sixty students from an Arabic Education Program in Indonesia participated. Data were collected through LMS activity logs, pre- and post-tests, questionnaires, and interviews.

The results indicate that the adaptive model significantly improved students’ reading and writing skills, with average gains of 12.8 and 13.9 points, respectively. Learning analytics revealed common difficulties in grammar and vocabulary retention, which were addressed through automated remedial content. Additionally, 88% of students reported that the instruction was more relevant to their needs, while 92% felt more motivated due to the system’s responsiveness. This study concludes that learning analytics–based adaptive instruction offers a transformative solution in Arabic language education by enhancing both academic performance and learner engagement.


Keywords


Adaptive Arabic Language Learning, Model Based Learning Analytics, Challenge of Personalization, Digital Era

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References


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DOI: https://doi.org/10.17509/bs_jpbsp.v25i2.88030

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