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Non-distracting Feedback in Artificial Intelligence Supported Learning

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Artificial Intelligence Supported Educational Technologies

Part of the book series: Advances in Analytics for Learning and Teaching ((AALT))

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Abstract

The introduction of sensory information into computer-supported learning results in a manifold of available data to be collected. In this publication we investigate how this “more in data” does not inevitably lead to a “more in distraction” at the interface. This becomes possible by analyzing intentional and non-intentional actions of the user or the environmental conditions to detect – using artificial intelligence – for instance, if the learner is unconcentrated, tired, or over-challenged. Relying on this analysis within a learning session, a user model can therefore be dynamically updated. This offers the possibility for a direct feedback loop to reflect the learner’s status, progress, etc. but also raises novel challenges that need to be addressed, namely, when to intervene, how much feedback needs to be provided, and how to represent the required information in order to reduce the distraction and thus to improve learning success.

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Correspondence to Matthias Wölfel .

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Wölfel, M. (2020). Non-distracting Feedback in Artificial Intelligence Supported Learning. In: Pinkwart, N., Liu, S. (eds) Artificial Intelligence Supported Educational Technologies. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-41099-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-41099-5_2

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