Current and future multimodal learning analytics data challenges

Daniel Spikol; Marcelo Worsley; Luis P. Prieto; Xavier Ochoa; M. J. Rodríguez-Triana; Mutlu Cukurova

doi:10.1145/3027385.3029437

Current and future multimodal learning analytics data challenges

Daniel Spikol, Marcelo Worsley, Luis P. Prieto, Xavier Ochoa, M. J. Rodríguez-Triana, Mutlu Cukurova

Administration, Leadership & Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
Subtitle of host publication	Understanding, Informing and Improving Learning with Data
Publisher	Association for Computing Machinery
Pages	518-519
Number of pages	2
ISBN (Electronic)	9781450348706
DOIs	https://doi.org/10.1145/3027385.3029437
State	Published - Mar 13 2017
Event	7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada Duration: Mar 13 2017 → Mar 17 2017

Publication series

Name	ACM International Conference Proceeding Series

Other

Other	7th International Conference on Learning Analytics and Knowledge, LAK 2017
Country/Territory	Canada
City	Vancouver
Period	3/13/17 → 3/17/17

Keywords

Challenges
Datasets
Multimodal learning analytics

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/3027385.3029437

Cite this

Spikol, D., Worsley, M., Prieto, L. P., Ochoa, X., Rodríguez-Triana, M. J., & Cukurova, M. (2017). Current and future multimodal learning analytics data challenges. In LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data (pp. 518-519). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3027385.3029437

Current and future multimodal learning analytics data challenges. / Spikol, Daniel; Worsley, Marcelo; Prieto, Luis P. et al.
LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, 2017. p. 518-519 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Spikol, D, Worsley, M, Prieto, LP, Ochoa, X, Rodríguez-Triana, MJ & Cukurova, M 2017, Current and future multimodal learning analytics data challenges. in LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 518-519, 7th International Conference on Learning Analytics and Knowledge, LAK 2017, Vancouver, Canada, 3/13/17. https://doi.org/10.1145/3027385.3029437

Spikol D, Worsley M, Prieto LP, Ochoa X, Rodríguez-Triana MJ, Cukurova M. Current and future multimodal learning analytics data challenges. In LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery. 2017. p. 518-519. (ACM International Conference Proceeding Series). doi: 10.1145/3027385.3029437

Spikol, Daniel ; Worsley, Marcelo ; Prieto, Luis P. et al. / 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. Association for Computing Machinery, 2017. pp. 518-519 (ACM International Conference Proceeding Series).

@inproceedings{f5a49beab2aa4f49a8b3ed3092a577b6,

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",

note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 7th International Conference on Learning Analytics and Knowledge, LAK 2017 ; Conference date: 13-03-2017 Through 17-03-2017",

year = "2017",

month = mar,

day = "13",

doi = "10.1145/3027385.3029437",

language = "English (US)",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "518--519",

booktitle = "LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference",

}

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

UR - http://www.scopus.com/inward/citedby.url?scp=85016493237&partnerID=8YFLogxK

U2 - 10.1145/3027385.3029437

DO - 10.1145/3027385.3029437

M3 - Conference contribution

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 - Association for Computing Machinery

T2 - 7th International Conference on Learning Analytics and Knowledge, LAK 2017

Y2 - 13 March 2017 through 17 March 2017

ER -

Current and future multimodal learning analytics data challenges

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this