Creating coherent incidental learning journeys on mobile devices through feedback and progress indicators

Gaved, Mark; Kukulska-Hulme, Agnes; Jones, Ann; Scanlon, Eileen; Dunwell, Ian; Lameras, Petros and Akiki, Oula (2013). Creating coherent incidental learning journeys on mobile devices through feedback and progress indicators. In: 12th World Conference on Mobile and Contextual Learning (mLearn 2013), 22-24 Oct 2013, Doha, Qatar, QScience.com.

DOI: https://doi.org/10.5339/qproc.2013.mlearn.13

URL: http://www.qscience.com/doi/pdf/10.5339/qproc.2013...

Abstract

Timely and appropriate feedback and indicators of progress can motivate learners. Mobile learning poses a challenge to established instructional strategies with respect to delivering feedback and monitoring learner progress, particularly in informal and incidental learning occurring outside of formal structured learning environments. We argue that well-designed and managed feedback and progress indicators can offer guidance and a sense of structure to learners in the absence of a formal curriculum, accreditation or set outcomes. Furthermore, they can encourage casual users of mobile applications to move from fragmented learning episodes towards a more long term and reflective learning journey. In this paper we describe how we are developing feedback and progress indicators for the EU-funded MASELTOV project, which explores how smartphones can support language learning and social inclusion for recent immigrants to Europe. Presenting educational services and materials on mobile devices allows learning episodes to be incorporated into daily activities and schedules, to be accessed at times and in places that suit learners best. Feedback and progress indicators embedded into these services may motivate such an audience to reconceptualise fragmentary, ephemeral educational experiences into a more coherent, sustained learning journey. We describe how feedback and progress indicators have been used successfully in web-based and games-based learning, and our assessment of which types may best support incidental mobile learning and the challenges we face.

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