Abstract
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.
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Gaved, M. et al. (2014). Challenges in Context-Aware Mobile Language Learning: The MASELTOV Approach. In: Kalz, M., Bayyurt, Y., Specht, M. (eds) Mobile as a Mainstream – Towards Future Challenges in Mobile Learning. mLearn 2014. Communications in Computer and Information Science, vol 479. Springer, Cham. https://doi.org/10.1007/978-3-319-13416-1_34
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DOI: https://doi.org/10.1007/978-3-319-13416-1_34
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