The Big Five: Addressing Recurrent Multimodal Learning Data Challenges

Daniele Di Mitri; Jan Schneider; Marcus Specht; Hendrik Drachsler

The Big Five: Addressing Recurrent Multimodal Learning Data Challenges

Daniele Di Mitri, Jan Schneider, Marcus Specht, Hendrik Drachsler

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

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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.

Original language	English
Title of host publication	Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge
Subtitle of host publication	Towards User-Centred Learning Analytics
Place of Publication	Syndey, Australia
Publisher	SoLAR
Pages	420-424
Number of pages	5
Volume	2163
Publication status	Published - Mar 2018
Event	The 8th International Learning Analytics & Knowledge Conference - SMC Conference & Function Centre, Sydney, Australia Duration: 5 Mar 2018 → 9 Mar 2018 https://latte-analytics.sydney.edu.au/

Conference

Conference	The 8th International Learning Analytics & Knowledge Conference
Abbreviated title	LAK18
Country/Territory	Australia
City	Sydney
Period	5/03/18 → 9/03/18
Internet address	https://latte-analytics.sydney.edu.au/

Keywords

CrossMMLA
multimodal learning analytics
sensor-based learning
wearables

Access to Document

LAK-18_companion_proceedingsFinal published version, 70.3 MBLicence: CC BY-NC-ND

https://drive.google.com/file/d/1wN-swZRDiWjf9W4kY25YjA4uyxlWHDcy/viewLicence: CC BY-NC-ND

1 Conference Article in proceeding

Multimodal Challenge: Analytics Beyond User-computer Interaction Data
Di Mitri, D., Schneider, J., Specht, M. & Drachsler, H., Mar 2018, Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK'18): Towards User-Centred Learning Analytics. Pardo, A., Bartimote, K., Lynch, G., Buckingham Shum, S., Ferguson, R., Merceron, A. & Ochoa, X. (eds.). Sydney, Australia: SoLAR, p. 364-367 4 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

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Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018). The Big Five: Addressing Recurrent Multimodal Learning Data Challenges. In Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge: Towards User-Centred Learning Analytics (Vol. 2163, pp. 420-424). SoLAR. https://drive.google.com/file/d/1wN-swZRDiWjf9W4kY25YjA4uyxlWHDcy/view

@inproceedings{5e08caa2242a41dcb648d7e3cb9a2d56,

title = "The Big Five: Addressing Recurrent Multimodal Learning Data Challenges",

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.",

keywords = "CrossMMLA, multimodal learning analytics, sensor-based learning, wearables",

author = "{Di Mitri}, Daniele and Jan Schneider and Marcus Specht and Hendrik Drachsler",

year = "2018",

month = mar,

language = "English",

volume = "2163",

pages = "420--424",

booktitle = "Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge",

publisher = "SoLAR",

note = "The 8th International Learning Analytics & Knowledge Conference, LAK18 ; Conference date: 05-03-2018 Through 09-03-2018",

url = "https://latte-analytics.sydney.edu.au/",

}

Di Mitri, D, Schneider, J, Specht, M & Drachsler, H 2018, The Big Five: Addressing Recurrent Multimodal Learning Data Challenges. in Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge: Towards User-Centred Learning Analytics. vol. 2163, SoLAR, Syndey, Australia, pp. 420-424, The 8th International Learning Analytics & Knowledge Conference, Sydney, New South Wales, Australia, 5/03/18. <https://drive.google.com/file/d/1wN-swZRDiWjf9W4kY25YjA4uyxlWHDcy/view>

The Big Five: Addressing Recurrent Multimodal Learning Data Challenges. / Di Mitri, Daniele; Schneider, Jan; Specht, Marcus et al.
Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge: Towards User-Centred Learning Analytics. Vol. 2163 Syndey, Australia: SoLAR, 2018. p. 420-424.

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

TY - GEN

T1 - The Big Five

T2 - The 8th International Learning Analytics & Knowledge Conference

AU - Di Mitri, Daniele

AU - Schneider, Jan

AU - Specht, Marcus

AU - Drachsler, Hendrik

PY - 2018/3

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N2 - 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.

AB - 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.

KW - CrossMMLA

KW - multimodal learning analytics

KW - sensor-based learning

KW - wearables

M3 - Conference Article in proceeding

VL - 2163

SP - 420

EP - 424

BT - Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge

PB - SoLAR

CY - Syndey, Australia

Y2 - 5 March 2018 through 9 March 2018

ER -

The Big Five: Addressing Recurrent Multimodal Learning Data Challenges

Abstract

Conference

Keywords

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Research output

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

Cite this