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A Pedagogical Perspective on Big Data and Learning Analytics: A Conceptual Model for Digital Learning Support

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Abstract

The increasing prevalence of learner-centred forms of learning as well as an increase in the number of learners actively participating on a wide range of digital platforms and devices give rise to an ever-increasing stream of learning data. Learning analytics (LA) can enable learners, teachers, and their institutions to better understand and predict learning and performance. However, the pedagogical perspective and matters of learning design have been underrepresented in research thus far. In our paper, we propose a general design framework that includes critical dimensions of LA and assists in creating LA services that support educational practice. On the basis of a two-dimensional framework (individual vs. social, reflection vs. prediction), we then identify four generic approaches to LA aimed at improving learning process and learning outcomes. To demonstrate the application, four use cases are outlined that are based on four previously elaborated generic approaches to LA. Finally, we discuss the validation of the model and close with an outlook on relevant future research.

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Acknowledgements

Funding was provided by Research Grant of the University of St. Gallen.

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  1. University of St. Gallen, St. Gallen, Switzerland

    Sabine Seufert, Christoph Meier, Matthias Soellner & Roman Rietsche

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  1. Sabine Seufert
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Correspondence to Sabine Seufert.

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Seufert, S., Meier, C., Soellner, M. et al. A Pedagogical Perspective on Big Data and Learning Analytics: A Conceptual Model for Digital Learning Support. Tech Know Learn 24, 599–619 (2019). https://doi.org/10.1007/s10758-019-09399-5

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