Adaptable smart learning environments supported by multimodal learning analytics
Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
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 tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
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