Publikationer från Malmö universitet
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Towards designing a flexible multimodal learning analytics system
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).ORCID-id: 0000-0002-9278-8063
2022 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
sted, utgiver, år, opplag, sider
Malmö: Malmö universitet, 2022. , s. 43
Serie
Studies in Computer Science ; 19
HSV kategori
Identifikatorer
URN: urn:nbn:se:mau:diva-51502DOI: 10.24834/isbn.9789178772988ISBN: 978-91-7877-297-1 (tryckt)ISBN: 978-91-7877-298-8 (digital)OAI: oai:DiVA.org:mau-51502DiVA, id: diva2:1658780
Veileder
Tilgjengelig fra: 2022-05-18 Laget: 2022-05-17 Sist oppdatert: 2022-11-07bibliografisk kontrollert
Delarbeid
1. Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study
Åpne denne publikasjonen i ny fane eller vindu >>Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study
2019 (engelsk)Inngår i: 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, 2019Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer, 2019
Serie
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 370
Emneord
Dynamic data, Embedded systems, Machine learning, Data management, Business outcomes
HSV kategori
Identifikatorer
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)
Konferanse
10th International Conference, ICSOB 2019, Jyväskylä, Finland, November 18–20, 2019
Tilgjengelig fra: 2021-12-21 Laget: 2021-12-21 Sist oppdatert: 2023-12-14bibliografisk kontrollert
2. MBOX: Designing a Flexible IoT Multimodal Learning Analytics System
Åpne denne publikasjonen i ny fane eller vindu >>MBOX: Designing a Flexible IoT Multimodal Learning Analytics System
2021 (engelsk)Inngår i: IEEE 21st International Conferenceon Advanced Learning TechnologiesICALT 2021 / [ed] Chang, M., Chen, NS., Sampson, DG., Tlili, A., IEEE, 2021, s. 122-126Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE, 2021
Serie
IEEE International Conference on Advanced Learning Technologies, ISSN 2161-3761
Emneord
Multimodal Learning Analytics, CSCL, IoT, Interaction Design, Human Social Signal Processing
HSV kategori
Identifikatorer
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)
Konferanse
IEEE 21st International Conference on Advanced Learning Technologies, 12–15 July 2021 Online
Tilgjengelig fra: 2021-12-15 Laget: 2021-12-15 Sist oppdatert: 2024-02-05bibliografisk kontrollert
3. Adaptable Smart Learning Environments supported by Multimodal Learning Analytics
Åpne denne publikasjonen i ny fane eller vindu >>Adaptable Smart Learning Environments supported by Multimodal Learning Analytics
Vise andre…
2021 (engelsk)Inngår i: 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, s. 24-30Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

    

sted, utgiver, år, opplag, sider
CEUR-WS.org, 2021
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-48217 (URN)978-3-030-86436-1 (ISBN)
Konferanse
EC-TEL 2021: Learning Analytics for Smart Learning Environments, September 21, 2021, Bolzano, Italy
Tilgjengelig fra: 2021-12-16 Laget: 2021-12-16 Sist oppdatert: 2024-05-21bibliografisk kontrollert

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