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Context-Awareness in Mobile Language Learning

Despite obvious benefits, some challenges exist in the way of utilizing mobile phone technology for language learning. Burston (2014) lists intrusiveness, cost, practical technological constraints and sound pedagogical methodologies as four factors challenging the success of mobile language learning tasks. This paper shows how these challenges can be better addressed in light of recent advancements in mobile phone technology like context aware mobile learning, informed with a sound pedagogical basis for providing content. Since many models presented so far are either a-theoretical or get their theory from fields other than language learning, we show how the Four Strands model (Nation 2001) as an insider model can be fit for purpose, with its related tasks balancing the selected content according to model's focus used in customizing each learners' profile. We also illustrate how the language level of learners, mapped into Common European Framework of Reference (CEFR), can be updated regularly using big data. The data from learners regarding their background knowledge and location is sought every few hours to trace if the user is following the same saved patterns and update the streamlined content when necessary. Yet, the resulting interactions are made possible and fit for the purpose through a novel context-aware framework which enables implementation of all Four Strands in language learning.


Majid Fatahipour    
Islamic Azad University, Parand Branch

Majid Fartahipour is Faculty Member and Assistant Professor at Islamic Azad University, Parand Branch

Mahnaz Ghaseminajm    
Department of Configuration
Huddersfield University, UK, Queensgate, Huddersfield

Mahnaz Ghaseminajm, is a PhD student in Huddersfield University, UK, Queensgate, Huddersfield. She has worked for the Department of Configuration, MCCI, Tehran, Hamrah-Awwal


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