Adaptive Mobile Learning System . Hisham Jameel Bardesi Mohammed A. Razek Deanship of Distance Learning, King Abdulaziz University , Kingdom of Saudi Arabia, P.O. Box email@example.com , firstname.lastname@example.org. Agenda. Introduction Related Work
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Adaptive Mobile Learning System HishamJameelBardesi Mohammed A. Razek Deanship of Distance Learning, King Abdulaziz University, Kingdom of Saudi Arabia, P.O. Box email@example.com, firstname.lastname@example.org
Agenda • Introduction • Related Work • Adaptive Mobile Learning System Architecture • AMLS Overview • Conclusion & Future work • References
Introduction • Mobile learning technology is growing up rapidly and it has a great potential to provide students with learning at anytime and anywhere • However, each individual has his/her single way of learning. • Adaptive mobile learning can personalize courses to meet individual needs.
Research Objectives • To satisfy the third condition, we need answer two questions: • Is this a learning object that the learner needs? • Does it contain information that he can understand?
Related Work • Adaptive presentation and curriculum sequencing; adaptive navigation Brusilovsky1998 . • CITS uses learning-machine techniques to identify the learners’ preferred learning style [Razek 2003]. • Graf et al. [Graf 2006] keeps track students’ behavior, records that students with different learning style preferences proceed differently in the course.
AMLS Architecture Filtering Agent Pedagogical Agent
Filtering Agent • Itis in charge of acquiring knowledge about learners- such as their learning profiles, learning style
Pedagogical Agent • It classifies learning objects based on Anderson learning styles.
AMLS Overview • AMLS is designed and implemented based on the criteria of Deanship of Distance Learning (DDL), at King Abdulazie University (KAU). • The first version of AMLS is currently developed by using Adobe® Flash® Lite™ 3.0 Developer Edition along with Nokia's Web Runtime (WRT). AMLS is implemented on Nokia 5800 XpressMusic
It allows them to enter their interests, such as (name, data of birth, subjects that they prefer)
Conclusion & Future work • The paper presented adaptive mobile learning system. • The system was developed by Adobe® Flash® Lite™ 3.0 Developer Edition along with Nokia's Web Runtime (WRT). • The future work is to use HTML5 with XML to develop a new version from AMLS in order to run it through Iphoneand other mobile platforms.
References • [Graf 2006] Graf S.., and Kinshuk Considering Learning Styles in Learning Management Systems: Investigating the Behavior of Students in an Online Course. First International Workshop on Semantic Media Adaptation and Personalization (SMAP'06) • [Brusilovsky 1998] Brusilovsky, P. 1998. “Adaptive Educational Systems on the World-Wide-Web: A Review of Available Technologies.” Paper presented at the 4th International Conference on Intelligent Tutoring Systems (ITS '98), in San Antonio . • [Razek et al. 2003a] Razek, M.A., C. Frasson, and M. Kaltenbach.2003. “A Context-Based Information Agent for Supporting Intelligent Distance Learning Environments.” Paper presented at the 12th International World Wide Web Conference, 20-24 May, in Budapest.