Mobile data-driven language learning: Affordances and learners' perception. (August 2019) - Pérez-Paredes, Pascual; Ordoñana Guillamón, Carlos; Van de Vyver, Julie; Meurice, Alice; Aguado Jiménez, Pilar; Conole, Grainne; Sánchez Hernández, Purificación
Abstract: Data-driven learning (DDL) is a learner-focused approach which promotes language learners' discovery of linguistic patterns of use and meaning by examining extensive samples of attested uses of language. Despite the emergence of mobile-assisted language learning (MALL) and its affordances, i.e. individualization and personalization, the potential of DDL in this context has not been widely explored. This study involved the creation of a mobile language learning app based on freely available natural language processing (NLP) tools, followed by a test of the app to gather the attitudes and perceptions of several groups of language learners across Europe. The results suggest a generally positive evaluation of DDL's instant and personalized feedback and direct access to a variety of tools. Besides, suggestions for improvement were made concerning the design of the tasks, such as the addition of further built-in tools and adaptations to hardware constraints. Analyses also showed a need for specialized learner training, so as to grasp the potential of the feedback provided. This study may be construed as a first step towards creating more fleshed-out tools and further investigating the potential of combining DDL and MALL.