Malmö University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Conceptual Design of Multimodal Learning Analytics for Spoken Language Acquisition
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-9278-8063
Copenhagen University, Copenhagen, Denmark.ORCID iD: 0000-0001-9454-0793
Copenhagen University, Copenhagen, Denmark.ORCID iD: 0009-0007-4232-2322
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0001-6708-5983
2024 (English)In: Technology Enhanced Learning for Inclusive and Equitable Quality Education: 19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Krems, Austria, September 16–20, 2024, Proceedings, Part II / [ed] Rafael Ferreira Mello; Nikol Rummel; Ioana Jivet; Gerti Pishtari; José A. Ruipérez Valiente, Springer, 2024, p. 144-149Conference paper, Published paper (Refereed)
Abstract [en]

This study details the technical design and evaluation of a Multimodal Learning Analytics (MMLA) system designed to enhance spoken language acquisition in language café settings. Utilizing the MMLA Model for Design and Analysis (MAMDA) framework, we outline the development of a prototype system that integrates AI voice assistance with the collection and analysis of multimodal data, including audio and video. We provide details about the specific technologies and algorithms employed, such as the Arduino Nicla Vision board for participant tracking and deep learning techniques for audio analysis. The implementation of the prototype for real-world language café sessions highlights its potential for providing valuable insights into learning patterns and interaction dynamics. We discuss the system's performance and limitations, paving the way for future refinements and broader applications in education.

Place, publisher, year, edition, pages
Springer, 2024. p. 144-149
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 15160
Keywords [en]
Conversational AI, Language Learning, Multimodal Learning Analytics, Natural Language Processing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-71887DOI: 10.1007/978-3-031-72312-4_19ISI: 001332998900019Scopus ID: 2-s2.0-85205323203ISBN: 978-3-031-72311-7 (print)ISBN: 978-3-031-72312-4 (electronic)OAI: oai:DiVA.org:mau-71887DiVA, id: diva2:1910456
Conference
19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Krems, Austria, September 16–20, 2024
Available from: 2024-11-04 Created: 2024-11-04 Last updated: 2024-11-23Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopusFulltext

Authority records

Ouhaichi, HamzaSpikol, DanielVogel, Bahtijar

Search in DiVA

By author/editor
Ouhaichi, HamzaSpikol, DanielLi, ZaibeiVogel, Bahtijar
By organisation
Department of Computer Science and Media Technology (DVMT)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 36 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf