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Towards designing a flexible multimodal learning analytics system
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
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2022. , p. 43
Series
Studies in Computer Science ; 19
National Category
Computer Systems Signal Processing
Identifiers
URN: urn:nbn:se:mau:diva-51502DOI: 10.24834/isbn.9789178772988ISBN: 978-91-7877-297-1 (print)ISBN: 978-91-7877-298-8 (electronic)OAI: oai:DiVA.org:mau-51502DiVA, id: diva2:1658780
Supervisors
Available from: 2022-05-18 Created: 2022-05-17 Last updated: 2022-11-07Bibliographically approved
List of papers
1. Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study
Open this publication in new window or tab >>Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study
2019 (English)In: Software Business: 10th International Conference, ICSOB 2019, Jyväskylä, Finland, November 18–20, 2019, Proceedings / [ed] Sami Hyrynsalmi; Mari Suoranta; Anh Nguyen-Duc; Pasi Tyrväinen; Pekka Abrahamsson, Springer, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Dynamic data and continuously evolving sets of records are essential for a wide variety of today’s data management applications. Such applications range from large, social, content-driven Internet applications, to highly focused data processing verticals like data intensive science, telecommunications and intelligence applications. However, the dynamic and multimodal nature of data makes it challenging to transform it into machine-readable and machine-interpretable forms. In this paper, we report on an action research study that we conducted in collaboration with a multinational company in the embedded systems domain. In our study, and in the context of a real-world industrial application of dynamic data management, we provide insights to data science community and research to guide discussions and future research into dynamic data management in embedded systems. Our study identifies the key challenges in the phases of data collection, data storage and data cleaning that can significantly impact the overall performance of the system.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 370
Keywords
Dynamic data, Embedded systems, Machine learning, Data management, Business outcomes
National Category
Embedded Systems Signal Processing Computer Systems
Identifiers
urn:nbn:se:mau:diva-48312 (URN)10.1007/978-3-030-33742-1_12 (DOI)000611525900012 ()2-s2.0-85076176939 (Scopus ID)978-3-030-33741-4 (ISBN)978-3-030-33742-1 (ISBN)
Conference
10th International Conference, ICSOB 2019, Jyväskylä, Finland, November 18–20, 2019
Available from: 2021-12-21 Created: 2021-12-21 Last updated: 2023-12-14Bibliographically approved
2. MBOX: Designing a Flexible IoT Multimodal Learning Analytics System
Open this publication in new window or tab >>MBOX: Designing a Flexible IoT Multimodal Learning Analytics System
2021 (English)In: IEEE 21st International Conferenceon Advanced Learning TechnologiesICALT 2021 / [ed] Chang, M., Chen, NS., Sampson, DG., Tlili, A., IEEE, 2021, p. 122-126Conference paper, Published paper (Refereed)
Abstract [en]

Multimodal Learning Analytics (MMLA) provides opportunities for understanding and supporting collaborative problem-solving. However, the implementation of MMLA systems is challenging due to the lack of scalable technologies and limited solutions for collecting data from group work. This paper proposes the Multimodal Box (MBOX), an IoT-based system for MMLA, allowing the collection and processing of multimodal data from collaborative learning tasks. MBOX investigates the development and design for an IoT focusing on small group work in real-world settings. Moreover, MBOX promotes adaptation to different learning environments and enables a better scaling of computational resources used within the learning context.

Place, publisher, year, edition, pages
IEEE, 2021
Series
IEEE International Conference on Advanced Learning Technologies, ISSN 2161-3761
Keywords
Multimodal Learning Analytics, CSCL, IoT, Interaction Design, Human Social Signal Processing
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-48140 (URN)10.1109/ICALT52272.2021.00044 (DOI)000719352000038 ()2-s2.0-85114887166 (Scopus ID)978-1-6654-4106-3 (ISBN)
Conference
IEEE 21st International Conference on Advanced Learning Technologies, 12–15 July 2021 Online
Available from: 2021-12-15 Created: 2021-12-15 Last updated: 2024-02-05Bibliographically approved
3. Adaptable Smart Learning Environments supported by Multimodal Learning Analytics
Open this publication in new window or tab >>Adaptable Smart Learning Environments supported by Multimodal Learning Analytics
Show others...
2021 (English)In: Proceedings of the LA4SLE 2021 Workshop: Learning Analytics for Smart Learning Environmentsco-located with the 16th European Conference on Technology Enhanced Learning 2021 (ECTEL 2021) / [ed] Davinia Hernández-Leo, Elise Lavoué, Miguel L. Bote-Lorenzo, Pedro J. Muñoz-Merino, Daniel Spikol, CEUR-WS.org , 2021, p. 24-30Conference paper, Published paper (Refereed)
Abstract [en]

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.

    

Place, publisher, year, edition, pages
CEUR-WS.org, 2021
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mau:diva-48217 (URN)978-3-030-86436-1 (ISBN)
Conference
EC-TEL 2021: Learning Analytics for Smart Learning Environments, September 21, 2021, Bolzano, Italy
Available from: 2021-12-16 Created: 2021-12-16 Last updated: 2024-05-21Bibliographically approved

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Ouhaichi, Hamza

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