Current and Future Multimodal Learning Analytics Data Challenges

Current and Future Multimodal Learning Analytics Data Challenges

Publication Type:
paper-conference
Date Issued:
2017
Authors:
Daniel Spikol , Luis P. Prieto , M. J. Rodriguez-Triana , Marcelo Worsley , Xavier Ochoa , Mutlu Cukurova , Bahtijar Vogel , Emanuele Ruffaldi , Ulla Lunde Ringtved
Publisher:
ACM Digital Library
Language:
eng
Page:
518-519
DOI:
10.1145/3027385.3029437
Abstract:

Multimodal Learning Analytics (MMLA) captures, integrates and analyzes learning traces from different sources in order to obtain a more holistic understanding of the learning process, wherever it happens. MMLA leverages the increasingly widespread availability of diverse sensors, high-frequency data collection technologies and sophisticated machine learning and artificial intelligence techniques. The aim of this workshop is twofold: first, to expose participants to, and develop, different multimodal datasets that reflect how MMLA can bring new insights and opportunities to investigate complex learning processes and environments; second, to collaboratively identify a set of grand challenges for further MMLA research, built upon the foundations of previous workshops on the topic.

Keywords:
Multimodal learning analytics datasets challenges