Personalized and Intelligent Agent Based Context-Aware Mobile Learning Framework
Alaa Edein M. Qoussini1, Shaima M. Al-Tabib2
1Alaa Edein M. Qoussini, Department of Humanities & Basic Sciences, English Language Scientific College of Design, Muscat, Oman.
2Shaima M. Al-Tabib, Research Office, National College of Automotive Technology, Muscat, Oman.
Manuscript Received on 29 October 2019 | First Revised Manuscript Received on 08 January 2025 | Second Revised Manuscript Received on 20 February 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025 | PP: 31-40 | Volume-13 Issue-6, March 2025 | Retrieval Number: 100.1/ijrte.D7832118419 | DOI: 10.35940/ijrte.D7832.13060325
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Mobile learning promises the freedom to learn anytime, anywhere. However, effectively integrating personalization within mobile learning environments presents a significant challenge. This research aims to address this challenge by developing a novel framework for personalized content delivery in mobile learning environments (PCDMLE). The proposed PCDMLE framework leverages three key personalization aspects: student preferences, academic level, and assessment unit. By dynamically adapting learning content based on these individual characteristics, the framework aims to enhance student performance and simplify the management of diverse learning needs. To achieve this objective, an in-depth literature review was conducted to identify key personalization aspects within the context of mobile learning. Based on this review, a framework was developed and subsequently validated through an expert review process. A prototype was then developed and evaluated through a six-week experiment. Finally, participant feedback was collected through a survey to assess their evaluation and satisfaction with the framework. The results, derived from both the expert review and participant feedback, demonstrate the framework’s effectiveness in delivering personalized content based on the identified aspects. Furthermore, the findings indicate a positive impact on student performance. This research, therefore, contributes significantly to the advancement of personalized content delivery within the mobile learning domain.
Keywords: Mobile Learning, Personalized Mobile Learning, Context-Aware, Personalized Content-Delivery and Context Aware Content-Delivery Framework.
Scope of the Article: Mobile Computing and Applications