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Learning Swedish with AI: Exploring Multimodal Learning Analytics in Spoken Language Acquisition
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-9278-8063
Copenhagen University.ORCID iD: 0000-0001-9454-0793
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0001-6708-5983
2024 (English)In: Methodologies and Intelligent Systems for Technology Enhanced Learning, 14th International Conference / [ed] Christothea Herodotou; Sofia Papavlasopoulou; Carlos Santos; Marcelo Milrad; Nuno Otero; Pierpaolo Vittorini; Rosella Gennari; Tania Di Mascio; Marco Temperini; Fernando De la Prieta, Springer Nature, 2024, Vol. 1171, p. 178-189Conference paper, Published paper (Refereed)
Abstract [en]

This study investigates the application of Multimodal Learning Analytics (MMLA) in language practice, specifically within the authentic and dynamic environment of language café settings. The MMLA Model for Design and Analysis (MAMDA), a design science approach, is utilized to systematically explore the requirements for designing the MMLA system. We identify and map three elements: 1) Learning indicators, referring to spoken language learning signs, such as tone, amount and frequency of speech, and pronunciation. 2) Respective modalities and sensors, referring to the format of data to be collected and 3) Analytics models, including NLP models, that can be employed to identify and process the modalities. We propose a conceptual system that utilizes AI voice assistant while simultaneously collecting audio data for MMLA to enhance language learning experiences. The system is meant for providing insights into learning patterns, participant engagement, and the overall effectiveness of language practice strategies. While presenting a novel system showcasing the use of AI and data analytics in a unique educational setting, the study's central focus is to test and critically reflect on MAMDA as a framework for designing and analyzing MMLA systems.

Place, publisher, year, edition, pages
Springer Nature, 2024. Vol. 1171, p. 178-189
Series
Lecture Notes in Networks and Systems (LNNS), ISSN 2367-3389 ; 1171
Keywords [en]
Multimodal Learning Analytics, Spoken Language Acquisition, Design Science Methodology, Generative AI
National Category
Computer Systems Pedagogy
Identifiers
URN: urn:nbn:se:mau:diva-70105DOI: 10.1007/978-3-031-73538-7_16ISI: 001443939300016Scopus ID: 2-s2.0-85218955796Local ID: 10.1007/978-3-031-73538-7_16ISBN: 978-3-031-73537-0 (print)ISBN: 978-3-031-73538-7 (print)OAI: oai:DiVA.org:mau-70105DiVA, id: diva2:1887706
Conference
14th International Conference of Methodologies and Intelligent Systems for Technology Enhanced Learning (MIS4TEL'24), 26 - 28 June 2024, Salamanca, Spain.
Available from: 2024-08-09 Created: 2024-08-09 Last updated: 2025-04-15Bibliographically approved
In thesis
1. A framework for designing and analyzing multimodal learning analytics systems
Open this publication in new window or tab >>A framework for designing and analyzing multimodal learning analytics systems
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The integration of technology in education offers transformative potential, especially with the advent of data-driven approaches that can personalize learning, support educators, and provide valuable insights into the learning process. Multimodal learning analytics (MMLA) holds remarkable promise within this context. By capturing and analyzing data from multiple sources—including video, audio, and digital interactions—MMLA systems offer a holistic view of learning experiences and the ability to tailor interventions in real time. This application has profound implications for understanding and enhancing learning experiences. However, the design of such sophisticated systems poses a significant challenge. Without conventional and field-tested frameworks, MMLA system development often remains self-driven and tailored to specific contexts, limiting both these systems’ broader adoption and full utilization. This thesis proposes a structured framework for designing MMLA systems across diverse educational contexts to address this fundamental challenge. The development of the framework followed a multifaceted methodology. In addition, action design research involving empirical studies, literature reviews, and expert interviews was employed to establish a set of foundational design considerations. The framework was then applied and refined within real-world educational settings. These included applications in the context of a globally distributed classroom and language acquisition environments. This practical application led to refinements that enhanced the framework’s adaptability and user-centric design. This thesis makes three key contributions: (1) a set of design considerations for MMLA systems, (2) a framework offering a structured guide for the design of MMLA systems, and (3) a conceptual system demonstrating the framework’s principles. The implications of this work are significant for researchers and stakeholders in MMLA, providing a foundation for future MMLA system development and ensuring more systematic and conventional design practices. This structured approach paves the way for broader adoption and integration of MMLA, ultimately enhancing educational outcomes and fostering personalized learning environments.

Place, publisher, year, edition, pages
Malmö: Malmö University Press, 2024. p. 83
Series
Studies in Computer Science ; 26
Keywords
Multimodal Learning Analytics, Educational Technology, Smart learning Environments, Internet of Things
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mau:diva-70111 (URN)10.24834/isbn.9789178775217 (DOI)978-91-7877-520-0 (ISBN)978-91-7877-521-7 (ISBN)
Public defence
2024-09-24, Auditorium C, Niagara, auditorium C, Nordenskiöldsgatan 1, Malmö, 09:00 (English)
Opponent
Supervisors
Available from: 2024-08-26 Created: 2024-08-09 Last updated: 2024-09-18Bibliographically approved

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Ouhaichi, HamzaSpikol, DanielVogel, Bahtijar

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