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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/7951
Title: Digital Learning Projection. Learning performance estimation from multimodal learning experiences
Authors: Di Mitri, Daniele
Keywords: multimodal data
learning analytics
phd project
doctoral consortium
sensors
Issue Date: 1-Jul-2017
Publisher: Springer International Publishing
Citation: Di Mitri, D. (2017). Digital Learning Projection. Learning performance estimation from multimodal learning experiences. In E. André, R. Baker, X. Hu, Ma. M.T. Rodrigo, & B. du Boulay (Eds.), Proceedings of AIED 2017, 18th International Conference on Artificial Intelligence in Education (pp. 609–612). Wuhan, China: Springer International Publishing, 28 June–1 July 2017.
Series/Report no.: 10331;
Abstract: Multiple modalities of the learning process can now be captured on real-time through wearable and contextual sensors. By annotating these multimodal data (the input space) by expert assessments or self-reports (the output space), machine learning models can be trained to predict the learning performance. This can lead to continuous formative assessment and feedback generation, which can be used to personalise and contextualise content, improve awareness and support informed decisions about learning.
URI: http://hdl.handle.net/1820/7951
ISBN: 978-3-319-61425-0
Appears in Collections:1. TELI Publications, books and conference papers

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