A citizen-centred approach to education in the smart city: incidental language learning for supporting the inclusion of recent migrants

Smart cities are often developed in a top-down approach and designers may see citizens as bits within data flows. A more human-centred perspective would be to consider what the smart city might afford its citizens. A high speed, pervasive network infrastructure offers the opportunity for ubiquitous mobile learning to become a reality. The MASELTOV project sees the smart city as enabling technology enhanced incidental learning: unplanned or unintentional learning that takes place in everyday life, in any place, at any time, with the city itself the context and the prompt for learning episodes. Migrants in particular will benefit: limited in their opportunity to attend formal education yet with a pressing need for language learning to support their integration. Incidental learning services, like smart city planning, need interdisciplinary communication for successful development. We describe the MASELTOV Incidental Learning Framework which will act as a boundary object to facilitate this process.

Creating Coherent Incidental Learning Journeys on Smartphones Using Feedback and Progress Indicators:

Although the motivating role of feedback and progress indicators is understood in formal learning, their role in supporting incidental mobile learning is less well understood. In this paper we argue that well-designed feedback and progress indicators (FPIs) offer guidance and structure that may encourage mobile app users to move from fragmented learning episodes towards a longer term, reflective learning journey. Drawing from relevant literature we consider how FPIs can be used in the EU-funded MASELTOV project which explores how a suite of smartphone apps can support recent immigrants to Europe to become integrated in their new cities. These apps allow learning episodes to be part of daily activities and interactions. The authors discuss what kinds of FPIs should be provided and introduce the SCAMP model which emphasises five types of FPIs-Social, Cognitive, Affective, Motivational and Progress. Finally, the authors provide examples of FPIs that will be used in the MASELTOV project