TY - GEN
T1 - Role of Multimodal Learning Systems in Technology-Enhanced Learning (TEL)
T2 - Proceedings of the 18th European Conference on Technology Enhanced Learning, ECTEL 2023
AU - Lee, Yoon
AU - Limbu, Bibeg
AU - Rusak, Zoltan
AU - Specht, Marcus
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Technology-enhanced learning systems, specifically multimodal learning technologies, use sensors to collect data from multiple modalities to provide personalized learning support beyond traditional learning settings. However, many studies surrounding such multimodal learning systems mostly focus on technical aspects concerning data collection and exploitation and therefore overlook theoretical and instructional design aspects such as feedback design in multimodal settings. This paper explores multimodal learning systems as a critical part of technology-enhanced learning used for capturing and analyzing the learning process to exploit the collected multimodal data to generate feedback in multimodal settings. By investigating various studies, we aim to reveal the roles of multimodality in technology-enhanced learning across various learning domains. Our scoping review outlines the conceptual landscape of multimodal learning systems, identifies potential gaps, and provides new perspectives on adaptive multimodal system design: intertwining learning data for meaningful insights into learning, designing effective feedback, and implementing them in diverse learning domains.
AB - Technology-enhanced learning systems, specifically multimodal learning technologies, use sensors to collect data from multiple modalities to provide personalized learning support beyond traditional learning settings. However, many studies surrounding such multimodal learning systems mostly focus on technical aspects concerning data collection and exploitation and therefore overlook theoretical and instructional design aspects such as feedback design in multimodal settings. This paper explores multimodal learning systems as a critical part of technology-enhanced learning used for capturing and analyzing the learning process to exploit the collected multimodal data to generate feedback in multimodal settings. By investigating various studies, we aim to reveal the roles of multimodality in technology-enhanced learning across various learning domains. Our scoping review outlines the conceptual landscape of multimodal learning systems, identifies potential gaps, and provides new perspectives on adaptive multimodal system design: intertwining learning data for meaningful insights into learning, designing effective feedback, and implementing them in diverse learning domains.
KW - Learning Domains
KW - Multimodal Learning Analytics (MMLA)
KW - Sensor-based Technology
UR - http://www.scopus.com/inward/record.url?scp=85171974090&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-42682-7_12
DO - 10.1007/978-3-031-42682-7_12
M3 - Conference contribution
AN - SCOPUS:85171974090
SN - 9783031426810
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 164
EP - 182
BT - Responsive and Sustainable Educational Futures - 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings
A2 - Viberg, Olga
A2 - Jivet, Ioana
A2 - Muñoz-Merino, Pedro J.
A2 - Perifanou, Maria
A2 - Papathoma, Tina
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 September 2023 through 8 September 2023
ER -