Challenges in context-aware mobile language learning: the MASELTOV approach

Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is en-gaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment

A Novel Collaboration Partner Model Based on the Personal Relationships of SNS

In this paper, we describe a novel model for locating appropriate ‘helpers’ for users based on the Chain of Friends (CoF) personal relationship in a SNS system, in order to locate appropriate ‘helpers’ for different users. This model is called SESNMM (Search Engine for Social Networked Mobile Model) and allows individual users, located in remote locations, to participate in a collaborative online community, via our SESNMM-based system. Such typical helpers are willing to help other users solve their tasks/problems and it is intended that both the users and helpers gain knowledge from these interactive online sessions. We have applied this model for inviting PC members of an international conference – namely LTLE 2012. The results showed that our model is very effective for discovering collaboration partners, locating useful helpers, finding users with similar interests in order to create communities for providing future and longer-term helping and teaching exchange

An Interactive Course Analyzer for Improving Learning Styles Support Level

Abstract. Learning management systems (LMSs) contain tons of existing courses but very little attention is paid to how well these courses actually support learners. In online learning, teachers build courses according to their teaching methods that may not fit with students with different learning styles. The harmony between the learning styles that a course supports and the actual learning styles of students can help to magnify the efficiency of the learning process. This paper presents a mechanism for analyzing existing course contents in learning management systems and an interactive tool for allowing teachers to be aware of their course support level for different learning styles of students based on the Felder and Silverman’s learning style model. This tool visualizes the suitability of a course for students ’ learning styles and helps teachers to improve the support level of their courses for diverse learning styles