Multimodal Challenge: Analytics Beyond User-computer Interaction Data

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Abstract

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data?
Original languageEnglish
Title of host publicationCompanion Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK'18)
Subtitle of host publicationTowards User-Centred Learning Analytics
EditorsAbelardo Pardo, Kathryn Bartimote, Grace Lynch, Simon Buckingham Shum, Rebecca Ferguson, Agathe Merceron, Xavier Ochoa
Place of PublicationSydney, Australia
PublisherSoLAR
Pages364-367
Number of pages4
Publication statusPublished - Mar 2018
EventThe 8th International Learning Analytics & Knowledge Conference - SMC Conference & Function Centre, Sydney, Australia
Duration: 5 Mar 20189 Mar 2018
https://latte-analytics.sydney.edu.au/

Conference

ConferenceThe 8th International Learning Analytics & Knowledge Conference
Abbreviated titleLAK18
CountryAustralia
CitySydney
Period5/03/189/03/18
Internet address

Fingerprint

Sensors
Application programming interfaces (API)
Data mining
Learning systems
Visualization
Internet of things
Wearable sensors

Keywords

  • multimodal learning analytics
  • wearables
  • CrossMMLA
  • sensor-based learning

Cite this

Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018). Multimodal Challenge: Analytics Beyond User-computer Interaction Data. In A. Pardo, K. Bartimote, G. Lynch, S. Buckingham Shum, R. Ferguson, A. Merceron, & X. Ochoa (Eds.), Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK'18): Towards User-Centred Learning Analytics (pp. 364-367). Sydney, Australia: SoLAR.
Di Mitri, Daniele ; Schneider, Jan ; Specht, Marcus ; Drachsler, Hendrik. / Multimodal Challenge: Analytics Beyond User-computer Interaction Data. Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK'18): Towards User-Centred Learning Analytics. editor / Abelardo Pardo ; Kathryn Bartimote ; Grace Lynch ; Simon Buckingham Shum ; Rebecca Ferguson ; Agathe Merceron ; Xavier Ochoa. Sydney, Australia : SoLAR, 2018. pp. 364-367
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title = "Multimodal Challenge: Analytics Beyond User-computer Interaction Data",
abstract = "This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data?",
keywords = "multimodal learning analytics, wearables, CrossMMLA, sensor-based learning",
author = "{Di Mitri}, Daniele and Jan Schneider and Marcus Specht and Hendrik Drachsler",
year = "2018",
month = "3",
language = "English",
pages = "364--367",
editor = "Abelardo Pardo and Kathryn Bartimote and Grace Lynch and {Buckingham Shum}, Simon and Rebecca Ferguson and Agathe Merceron and Xavier Ochoa",
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Di Mitri, D, Schneider, J, Specht, M & Drachsler, H 2018, Multimodal Challenge: Analytics Beyond User-computer Interaction Data. in A Pardo, K Bartimote, G Lynch, S Buckingham Shum, R Ferguson, A Merceron & X Ochoa (eds), Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK'18): Towards User-Centred Learning Analytics. SoLAR, Sydney, Australia, pp. 364-367, The 8th International Learning Analytics & Knowledge Conference, Sydney, Australia, 5/03/18.

Multimodal Challenge: Analytics Beyond User-computer Interaction Data. / Di Mitri, Daniele; Schneider, Jan; Specht, Marcus; Drachsler, Hendrik.

Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK'18): Towards User-Centred Learning Analytics. ed. / Abelardo Pardo; Kathryn Bartimote; Grace Lynch; Simon Buckingham Shum; Rebecca Ferguson; Agathe Merceron; Xavier Ochoa. Sydney, Australia : SoLAR, 2018. p. 364-367.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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T1 - Multimodal Challenge: Analytics Beyond User-computer Interaction Data

AU - Di Mitri, Daniele

AU - Schneider, Jan

AU - Specht, Marcus

AU - Drachsler, Hendrik

PY - 2018/3

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N2 - This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data?

AB - This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data?

KW - multimodal learning analytics

KW - wearables

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BT - Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK'18)

A2 - Pardo, Abelardo

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A2 - Merceron, Agathe

A2 - Ochoa, Xavier

PB - SoLAR

CY - Sydney, Australia

ER -

Di Mitri D, Schneider J, Specht M, Drachsler H. Multimodal Challenge: Analytics Beyond User-computer Interaction Data. In Pardo A, Bartimote K, Lynch G, Buckingham Shum S, Ferguson R, Merceron A, Ochoa X, editors, Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK'18): Towards User-Centred Learning Analytics. Sydney, Australia: SoLAR. 2018. p. 364-367