Context-aware recommender for mobile learners

Rachid Benlamri,Xiaoyun Zhang

doi:10.1186/s13673-014-0012-z

Rachid Benlamri, Xiaoyun Zhang

Open Access

https://doi.org/10.1186/s13673-014-0012-z

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Abstract

AbstractAs mobile technologies become widespread, new challenges are facing the research community to develop lightweight learning services adapted to the learner’s profile, context, and task at hand. This paper attempts to solve some of these challenges by proposing a knowledge-driven recommender for mobile learning on the Semantic Web. The contribution of this work is an approach for context integration and aggregation using an upper ontology space and a unified reasoning mechanism to adapt the learning sequence and the learning content based on the learner’s activity, background, used technology, and surrounding environment. Whenever context change occurs, the system identifies the new contextual features and translates them into new adaptation constraints in the operating environment. The proposed system has been implemented and tested on various mobile devices. The experimental results show many learning scenarios to demonstrate the usefulness of the system in practice.

Highlights

The Semantic Web is an extension of the current web, whereby content is given well defined meaning so that it can be understood and processed by both software agents and humans [1]
Experimental results To illustrate the ontology reasoning mechanisms used in this study, we provide a number of scenarios that demonstrate the various system-centric and learner-centric adaptations
We showed that knowledge embedded in the upper ontology can homogeneously be used to enable a unified reasoning mechanism that operates on facts instantiated by the perceived heterogeneous context elements

Summary

Introduction

The Semantic Web is an extension of the current web, whereby content is given well defined meaning so that it can be understood and processed by both software agents and humans [1]. These efforts would lead to a Semantic Web that has the potential to revolutionize the way learning services available on the web are discovered, adapted, and delivered to mobile users based on their context To achieve this goal, we need to formally describe web-content, and the various system stakeholders including content producers, content consumers, and surrounding context. A solution to this problem is efficient integration of various ontologies into an upper ontology space capable of capturing and modeling information related to the global shared knowledge at a higher semantic level, and simplifying cross-ontology reasoning. Another problem of concern with ontology based approaches is their inappropriateness to reasoning with uncertainty. This problem can be dealt with by integrating various reasoning models that may combine probabilistic, rulebased and logic reasoning techniques [38,39,40]

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Results

Conclusion