Publikationer från Malmö universitet
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  • 1.
    Ouhaichi, Hamza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Towards designing a flexible multimodal learning analytics system2022Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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  • 2.
    Ouhaichi, Hamza
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of TechnologyGothenburgSweden.
    Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study2019Ingår 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, 2019Konferensbidrag (Refereegranskat)
    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.

  • 3.
    Ouhaichi, Hamza
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Spikol, Daniel
    Univ Copenhagen, Dept Sci Educ, Copenhagen, Denmark..
    Vogel, Bahtijar
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    MBOX: Designing a Flexible IoT Multimodal Learning Analytics System2021Ingår i: IEEE 21st International Conferenceon Advanced Learning TechnologiesICALT 2021 / [ed] Chang, M., Chen, NS., Sampson, DG., Tlili, A., IEEE, 2021, s. 122-126Konferensbidrag (Refereegranskat)
    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.

  • 4.
    Ouhaichi, Hamza
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Spikol, Daniel
    Copenhagen University, Copenhagen, Denmark.
    Vogel, Bahtijar
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Research trends in multimodal learning analytics: A systematic mapping study2023Ingår i: Computers and Education: Artificial Intelligence, ISSN 2666-920X, Vol. 4, s. 100136-100136, artikel-id 100136Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    Understanding and improving education are critical goals of learning analytics. However, learning is not always mediated or aided by a digital system that can capture digital traces. Learning in such environments can be studied by recording, processing, and analyzing different signals, including video and audio, so that traces of actors’ actions and interactions are captured. Multimodal Learning Analytics refers to analyzing these signals through the use and integration of these multiple modes. However, a need exists to evaluate how research is conducted in the emerging field of multimodal learning analytics to aid and evaluate how these systems work. With the growth of multimodal learning analytics, research trends and technologies are needed to support its development. We conducted a systematic mapping study based on established systematic literature practices to identify multimodal learning analytics research types, methodologies, and trending research themes. Most mapped papers presented different solutions and used evaluation-based research methods to demonstrate an increasing interest in multimodal learning analytics technologies. In addition, we identified 14 topics under four themes––learning context, learning process, systems and modality, and technologies––that can contribute to the growth of multimodal learning analytics.

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  • 5.
    Ouhaichi, Hamza
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Spikol, Daniel
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Disciplinary literacy and inclusive teaching.
    Vogel, Bahtijar
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Rethinking MMLA: Design Considerations for Multimodal Learning Analytics Systems2023Ingår i: L@S '23: Proceedings of the Tenth ACM Conference on Learning @ Scale, ACM Digital Library, 2023, s. 354-359Konferensbidrag (Refereegranskat)
    Abstract [en]

    Designing MMLA systems is a complex task requiring a wide range of considerations. In this paper, we identify key considerations that are essential for designing MMLA systems. These considerations include data management, human factors, sensors and modalities, learning scenarios, privacy and ethics, interpretation and feedback, and data collection. The implications of these considerations are twofold: 1) The need for flexibility in MMLA systems to adapt to different learning contexts and scales, and 2) The need for a researcher-centered approach to designing MMLA systems. Unfortunately, the sheer number of considerations can lead to a state of "analysis paralysis," where deciding where to begin and how to proceed becomes overwhelming. This synthesis paper asks researchers to rethink the design of MMLA systems and aims to provide guidance for developers and practitioners in the field of MMLA.

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  • 6.
    Serrano Iglesias, Sergio
    et al.
    GSIC-EMIC Research Group, Universidad de Valladolid, Spain.
    Spikol, Daniel
    Departments of Computer Science and Science Education, University of Copenhagen, Denmark.
    Bote Lorenzo, Miguel Luis
    GSIC-EMIC Research Group, Universidad de Valladolid, Spain.
    Ouhaichi, Hamza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Gómez Sánchez, Eduardo
    GSIC-EMIC Research Group, Universidad de Valladolid, Spain.
    Vogel, Bahtijar
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Adaptable Smart Learning Environments supported by Multimodal Learning Analytics2021Ingår 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. 24-30Konferensbidrag (Refereegranskat)
    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.

        

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