Enhanced learner model for adaptive mobile learning

Ahmed Al-Hmouz*, Jun Shen, Jun Yan, Rami Al-Hmouz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)

Abstract

Personalisation and learner modelling are becoming more important in the area of mobile learning applications, taking into consideration learners' interests, preferences and contextual information. Students nowadays are able to learn anywhere and at any time. Mobile learning application content is one of several factors within various contexts that play an important role in the success of the adaptation process. The vast amount of data involved in any successful adaptation process creates complexity and poses serious challenges. This paper focuses on how to model the learner and all possible contexts in an extensible way that can be used for personalisation in mobile learning. The enhanced learner modelling structure to be used in a mobile learning system is proposed. The proposed structure provides per-sonalisation by adopting a hybrid approach combining two machine learning techniques.

Original languageEnglish
Title of host publicationiiWAS2010 - 12th International Conference on Information Integration and Web-Based Applications and Services
Pages783-786
Number of pages4
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event12th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2010 - Paris, France
Duration: Nov 8 2010Nov 10 2010

Publication series

NameiiWAS2010 - 12th International Conference on Information Integration and Web-Based Applications and Services

Other

Other12th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2010
Country/TerritoryFrance
CityParis
Period11/8/1011/10/10

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

Fingerprint

Dive into the research topics of 'Enhanced learner model for adaptive mobile learning'. Together they form a unique fingerprint.

Cite this