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Current and future multimodal learning analytics data challenges. / Spikol, Daniel; Worsley, Marcelo; Prieto, Luis P.; Ochoa, Xavier; Rodríguez-Triana, M. J.; Cukurova, Mutlu.
LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ACM Association for Computing Machinery, 2017. s. 518-519 (ACM International Conference Proceeding Series).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Spikol, D, Worsley, M, Prieto, LP, Ochoa, X, Rodríguez-Triana, MJ & Cukurova, M 2017,
Current and future multimodal learning analytics data challenges. i
LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ACM Association for Computing Machinery, ACM International Conference Proceeding Series, s. 518-519, 7th International Conference on Learning Analytics and Knowledge, LAK 2017, Vancouver, Canada,
13/03/2017.
https://doi.org/10.1145/3027385.3029437
APA
Spikol, D., Worsley, M., Prieto, L. P., Ochoa, X., Rodríguez-Triana, M. J., & Cukurova, M. (2017).
Current and future multimodal learning analytics data challenges. I
LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data (s. 518-519). ACM Association for Computing Machinery. ACM International Conference Proceeding Series
https://doi.org/10.1145/3027385.3029437
Vancouver
Spikol D, Worsley M, Prieto LP, Ochoa X, Rodríguez-Triana MJ, Cukurova M.
Current and future multimodal learning analytics data challenges. I LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ACM Association for Computing Machinery. 2017. s. 518-519. (ACM International Conference Proceeding Series).
https://doi.org/10.1145/3027385.3029437
Author
Spikol, Daniel ; Worsley, Marcelo ; Prieto, Luis P. ; Ochoa, Xavier ; Rodríguez-Triana, M. J. ; Cukurova, Mutlu. / Current and future multimodal learning analytics data challenges. LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ACM Association for Computing Machinery, 2017. s. 518-519 (ACM International Conference Proceeding Series).
Bibtex
@inproceedings{4b560756911f4fa7beef2d387b4f358e,
title = "Current and future multimodal learning analytics data challenges",
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, highfrequency 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 = "Challenges, Datasets, Multimodal learning analytics",
author = "Daniel Spikol and Marcelo Worsley and Prieto, {Luis P.} and Xavier Ochoa and Rodr{\'i}guez-Triana, {M. J.} and Mutlu Cukurova",
year = "2017",
month = mar,
day = "13",
doi = "10.1145/3027385.3029437",
language = "English",
series = "ACM International Conference Proceeding Series",
pages = "518--519",
booktitle = "LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference",
publisher = "ACM Association for Computing Machinery",
note = "7th International Conference on Learning Analytics and Knowledge, LAK 2017 ; Conference date: 13-03-2017 Through 17-03-2017",
}
RIS
TY - GEN
T1 - Current and future multimodal learning analytics data challenges
AU - Spikol, Daniel
AU - Worsley, Marcelo
AU - Prieto, Luis P.
AU - Ochoa, Xavier
AU - Rodríguez-Triana, M. J.
AU - Cukurova, Mutlu
PY - 2017/3/13
Y1 - 2017/3/13
N2 - 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, highfrequency 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.
AB - 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, highfrequency 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.
KW - Challenges
KW - Datasets
KW - Multimodal learning analytics
UR - http://www.scopus.com/inward/record.url?scp=85016493237&partnerID=8YFLogxK
U2 - 10.1145/3027385.3029437
DO - 10.1145/3027385.3029437
M3 - Article in proceedings
AN - SCOPUS:85016493237
T3 - ACM International Conference Proceeding Series
SP - 518
EP - 519
BT - LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
PB - ACM Association for Computing Machinery
T2 - 7th International Conference on Learning Analytics and Knowledge, LAK 2017
Y2 - 13 March 2017 through 17 March 2017
ER -
