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Title: Learning Pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data
Authors: Di Mitri, Daniele
Scheffel, Maren
Drachsler, Hendrik
Börner, Dirk
Ternier, Stefaan
Specht, Marcus
Keywords: Learning Analytics
Wearable Enhanced Learning
Multimodal data
Machine Learning
Issue Date: 11-Mar-2017
Publisher: ACM
Citation: Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S., & Specht, M. (2017). Learning pulse: a machine approach for predicting performance in self-regulated learning using multimodal data. In M. Hatala et al. (Eds.) Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK ’17 (pp. 188–197). New York, NY, USA: ACM Press.
Abstract: Learning Pulse explores whether using a machine learning approach on multimodal data such as heart rate, step count, weather condition and learning activity can be used to predict learning performance in self-regulated learning settings. An experiment was carried out lasting eight weeks involving PhD students as participants, each of them wearing a FitbitHR wristband and having their application on their computer recorded during their learning and working activities throughout the day. A software infrastructure for collecting multimodal learning experiences was implemented. As part of this infrastructure, a Data Processing Application was developed to pre-process, analyse and generate predictions to provide feedback to the users about their learning performance. Data from di fferent sources were stored using the xAPI standard into a cloud-based Learning Record Store. The participants of the experiment were asked to rate their learning experience through an Activity Rating Tool indicating their perceived level of productivity, stress, challenge and abilities. These self-reported performance indicators were used as markers to train a Linear Mixed E ect Model to generate learner-speci c predictions of the learning performance. We discuss the advantages and the limitations of the used approach, highlighting further development points.
ISBN: 978-1-4503-4870-6/17/03
Appears in Collections:1. TELI Publications, books and conference papers

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