Adaptable smart learning environments supported by multimodal learning analytics

Publikation: Bidrag til tidsskriftKonferenceartikelfagfællebedømt

Standard

Adaptable smart learning environments supported by multimodal learning analytics. / Serrano-Iglesias, Sergio; Spikol, Daniel; Bote-Lorenzo, Miguel L.; Ouhaichi, Hamza; Gómez-Sánchez, Eduardo; Vogel, Bahtijar.

I: CEUR Workshop Proceedings, Bind 3024, 2021, s. 24-30.

Publikation: Bidrag til tidsskriftKonferenceartikelfagfællebedømt

Harvard

Serrano-Iglesias, S, Spikol, D, Bote-Lorenzo, ML, Ouhaichi, H, Gómez-Sánchez, E & Vogel, B 2021, 'Adaptable smart learning environments supported by multimodal learning analytics', CEUR Workshop Proceedings, bind 3024, s. 24-30. <http://ceur-ws.org/Vol-3024/paper4.pdf>

APA

Serrano-Iglesias, S., Spikol, D., Bote-Lorenzo, M. L., Ouhaichi, H., Gómez-Sánchez, E., & Vogel, B. (2021). Adaptable smart learning environments supported by multimodal learning analytics. CEUR Workshop Proceedings, 3024, 24-30. http://ceur-ws.org/Vol-3024/paper4.pdf

Vancouver

Serrano-Iglesias S, Spikol D, Bote-Lorenzo ML, Ouhaichi H, Gómez-Sánchez E, Vogel B. Adaptable smart learning environments supported by multimodal learning analytics. CEUR Workshop Proceedings. 2021;3024:24-30.

Author

Serrano-Iglesias, Sergio ; Spikol, Daniel ; Bote-Lorenzo, Miguel L. ; Ouhaichi, Hamza ; Gómez-Sánchez, Eduardo ; Vogel, Bahtijar. / Adaptable smart learning environments supported by multimodal learning analytics. I: CEUR Workshop Proceedings. 2021 ; Bind 3024. s. 24-30.

Bibtex

@inproceedings{90c2038213094161936ce47f10ba9229,
title = "Adaptable smart learning environments supported by multimodal learning analytics",
abstract = "Smart Learning Environments and Learning Analytics hold promise of providing personalized support to learners according to their individual needs and context. This support can be achieved by collecting and analyzing data from the different learning tools and systems that are involved in the learning experience. This paper presents a first exploration of requirements and considerations for the integration of two systems: MBOX, a Multimodal Learning Analytics system for the physical space (human behavior and learning context), and SCARLETT, an SLE for the support during across-spaces learning situations combining different learning systems. This integration will enable the SLE to have access to a new and wide range of information, notably students' behavior and social interactions in the physical learning context (e.g. classroom). The integration of multimodal data with the data coming from the digital learning environments will result in a more holistic system, therefore producing learning analytics that trigger personalized feedback and learning resources. Such integration and support is illustrated with a learning scenario that helps to discuss how these analytics can be derived and used for the intervention by the SLE.",
keywords = "Across spaces, Learning design, Multimodal learning analytics, Smart learning environments",
author = "Sergio Serrano-Iglesias and Daniel Spikol and Bote-Lorenzo, {Miguel L.} and Hamza Ouhaichi and Eduardo G{\'o}mez-S{\'a}nchez and Bahtijar Vogel",
note = "Publisher Copyright: {\textcopyright} 2021 CEUR-WS. All rights reserved.; LA4SLE Workshop: Learning Analytics for Smart Learning Environments, LA4SLE 2021 ; Conference date: 21-09-2021 Through 21-09-2021",
year = "2021",
language = "English",
volume = "3024",
pages = "24--30",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "ceur workshop proceedings",

}

RIS

TY - GEN

T1 - Adaptable smart learning environments supported by multimodal learning analytics

AU - Serrano-Iglesias, Sergio

AU - Spikol, Daniel

AU - Bote-Lorenzo, Miguel L.

AU - Ouhaichi, Hamza

AU - Gómez-Sánchez, Eduardo

AU - Vogel, Bahtijar

N1 - Publisher Copyright: © 2021 CEUR-WS. All rights reserved.

PY - 2021

Y1 - 2021

N2 - Smart Learning Environments and Learning Analytics hold promise of providing personalized support to learners according to their individual needs and context. This support can be achieved by collecting and analyzing data from the different learning tools and systems that are involved in the learning experience. This paper presents a first exploration of requirements and considerations for the integration of two systems: MBOX, a Multimodal Learning Analytics system for the physical space (human behavior and learning context), and SCARLETT, an SLE for the support during across-spaces learning situations combining different learning systems. This integration will enable the SLE to have access to a new and wide range of information, notably students' behavior and social interactions in the physical learning context (e.g. classroom). The integration of multimodal data with the data coming from the digital learning environments will result in a more holistic system, therefore producing learning analytics that trigger personalized feedback and learning resources. Such integration and support is illustrated with a learning scenario that helps to discuss how these analytics can be derived and used for the intervention by the SLE.

AB - Smart Learning Environments and Learning Analytics hold promise of providing personalized support to learners according to their individual needs and context. This support can be achieved by collecting and analyzing data from the different learning tools and systems that are involved in the learning experience. This paper presents a first exploration of requirements and considerations for the integration of two systems: MBOX, a Multimodal Learning Analytics system for the physical space (human behavior and learning context), and SCARLETT, an SLE for the support during across-spaces learning situations combining different learning systems. This integration will enable the SLE to have access to a new and wide range of information, notably students' behavior and social interactions in the physical learning context (e.g. classroom). The integration of multimodal data with the data coming from the digital learning environments will result in a more holistic system, therefore producing learning analytics that trigger personalized feedback and learning resources. Such integration and support is illustrated with a learning scenario that helps to discuss how these analytics can be derived and used for the intervention by the SLE.

KW - Across spaces

KW - Learning design

KW - Multimodal learning analytics

KW - Smart learning environments

M3 - Conference article

AN - SCOPUS:85120677206

VL - 3024

SP - 24

EP - 30

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - LA4SLE Workshop: Learning Analytics for Smart Learning Environments, LA4SLE 2021

Y2 - 21 September 2021 through 21 September 2021

ER -

ID: 291680858