Published March 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

Personalized and Intelligent Agent Based Context-Aware Mobile Learning Framework

Authors/Creators

  • 1. Department of Humanities & Basic Sciences, English Language Scientific College of Design, Muscat, Oman.

Contributors

  • 1. Department of Humanities & Basic Sciences, English Language Scientific College of Design, Muscat, Oman.
  • 2. Research Office, National College of Automotive Technology, Muscat, Oman.

Description

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.

Files

D7832118419.pdf

Files (540.0 kB)

Name Size Download all
md5:7b52a1c88c8720f3722059808edfbe56
540.0 kB Preview Download

Additional details

Identifiers

DOI
10.35940/ijrte.D7832.13060325
EISSN
2277-3878

Dates

Accepted
2025-03-15
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.

References

  • Ciampa, K. (2014). Learning in a Mobile Age: Investigation of Students Motivation. Journal of Computer Assisted Learning, (vol. 30, pp.82-96). DOI: https://doi.org/10.1111/jcal.12036
  • Filhuo, N. F., & Barabosa, E. F. (2013). A Requirement Catalog for Mobile Learning Environment. Coimbra, Portogal: ACM. DOI: https://doi.org/10.1145/2480362.2480599
  • Qoussini, A. E., & Jusoh, Y. (2014). A Review on Personalization and Agent Technology in Mobile Learning. 2014 International Conference on Intelligent Environment (pp. 260-264). Shanghai: IEEE. DOI: https://doi.org/10.1109/IE.2014.49
  • Qoussini, A. E., Jusoh, Y. Y., Murad, M. A., Jabar, B. A., Pa, N. C., & Tabib, S. M. (2016, Feb). Platform and Device Independent Mobile Learning Architecture. Austrailan Journal for Basic and Applied Sciences Research, (Vol. 10, Issue-4, pp.1-9). Retrieved from https://www.ajbasweb.com/old/ajbas/2016/February/1-9.pdf
  • Alepis, E., & Mivou, M. (2014). Extending Mobile Personalization to Students with Special Needs. In Object-Oriented User Interfaces for Personalized Mobile Learning (pp. 47-64). Intelligent Systems Reference Library 64, Springer. DOI: https://doi.org/10.1007/978-3- 642-53851-3_5
  • Braunhofer, M., Elahi, M., Ge, M., & Ricci, F. (2014). Context Dependent Preference Acquisition with Personality-Based Active Learning in Mobile Recommender Systems. P. Zaphiris and A. Ioannou (Eds.), LCT 2014(Part II), (pp. 105-116). DOI: https://doi.org/10.1007/978-3-319-07485-6_11
  • Qoussini, A. e., Jusoh, Y., & Tabib, S. (2015, September). A Review on personalization in Mobile Learning. International Journal of Computer Science Issues, (Vol. 12, Issue-5, pp. 17-26). https://typeset.io/pdf/areview-on-personalization-in-mobile-learning-1d5vf19ph5.pdf
  • NCES. (2012). U.S. Department of Education. Retrieved 2 28, 2016, from National Center for Education Statistics (USA): https://nces.ed.gov/nationsreportcard/achievement.aspx
  • ALharbi, S., & Drew, S. (2014). Mobile Learning System Usage: An Integrated Framework to Measure Students' Behavioural Intention. Science and Information Conference August 27-29 (pp. 906-911). London, UK: www.conference.thesai.org. DOI: https://doi.org/10.1109/SAI.2014.6918294
  • Hassan, M. H., Alhoban, F., & Hourani, M. (2016). Using Mobile technologies for Enhancing Student Academic Experience: University of Jordan Case Study. International Journal Interactive Mobile Technologies IJIM, (Vo. 10, Issue-1). DOI: https://doi.org/10.3991/ijim.v10i1.4809
  • Qoussini, A. E., Jusoh, Y. Y., Murad, M. A., Jabar, M. B., ChePa, N., & Tabib, S. M. (2016). Location Based Context-Aware Mobile Learning Framework. Journal of Applied Sciences Research, (Vol. 12, Issue-11, pp. 1-8). https://www.aensiweb.net/AENSIWEB/jasr/jasr/2016/November/1- 8.pdf
  • Harchay, A., Cheniti-Belcadhi, L., & Braham, R. (2014). A ContextAware Framework to Provide Personalized Mobile Assessment. Interaction Design and Architecture(s) Journal - IxD&A, N. (Vol. 23, Issue-2014, pp. 82-97). DOI: https://doi.org/10.55612/s-5002-023-006
  • Kim, T.-H., & Jin, S.-H. (2015). Development of auditory design guidelines for improving learning on mobile phones. Computers & Education, (Vol. 91, Issue-2015, pp. 60-72). DOI: https://doi.org/10.1016/j.compedu.2015.09.011
  • Lee, J. H. (2014). Synchronous Mobile Learning System to Cope with Slow Network Connection. Y. Luo (Ed.): CDVE 2014 (pp. 171 - 174). Springer International Publishing Switzerland. DOI: https://doi.org/10.1007/978-3-319-10831-5_25
  • Tamilarasan, Dr. P., Karthick, Mr. S., & Anupama, Mrs. C. G. (2019). Mobile Learning Challenges and Capabilities. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 5358–5361). DOI: https://doi.org/10.35940/ijrte.c6883.098319
  • Ishaq, K., Zin, N. A. M., Rosdi, F., Abid, A., & Ali, Q. (2020). Usefulness of Mobile Assisted Language Learning Application. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 3, pp. 518–525). DOI: https://doi.org/10.35940/ijeat.b4549.029320
  • Pandit, R., & Chaturvedi, S. (2020). Mobile Learning: Detrimental or Beneficial? In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 8, pp. 987–989). DOI: https://doi.org/10.35940/ijitee.h6812.069820
  • Efrina, E., Zulmiyetri, & Kusumastuti, G. (2020). Mobile Learning as Teaching Aid and Learning Media for Special Teacher of Deaf Students. In International Journal of Management and Humanities (Vol. 4, Issue 11, pp. 28–30). DOI: https://doi.org/10.35940/ijmh.k1040.0741120