Open Universiteit

Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/9821
Title: The Big Five: Addressing Recurrent Multimodal Learning Data Challenges
Authors: Di Mitri, Daniele
Schneider, Jan
Specht, Marcus
Drachsler, Hendrik
Keywords: multimodal learning analytics
wearables
CrossMMLA
sensor-based learning
Issue Date: Mar-2018
Publisher: Society for Learning Analytics Research
Citation: Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018)┬áThe Big Five: Addressing Recurrent Multimodal Learning Data Challenges. In Pardo, A., Bartimote, K., Lynch, G., Buckingham Shum, S., Ferguson, R., Merceron, A., & Ochoa, X. (Eds.). (2018). Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 418-422). Sydney, Australia: Society for Learning Analytics Research.
Abstract: The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback.
URI: http://hdl.handle.net/1820/9821
Appears in Collections:1. TELI Publications, books and conference papers

Files in This Item:
File Description SizeFormat 
CrossMMLA_CR.pdf92.11 kBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons