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  • 1.
    Ouhaichi, Hamza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    A framework for designing and analyzing multimodal learning analytics systems2024Doctoral 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.

    List of papers
    1. Rethinking MMLA: Design Considerations for Multimodal Learning Analytics Systems
    Open this publication in new window or tab >>Rethinking MMLA: Design Considerations for Multimodal Learning Analytics Systems
    2023 (English)In: L@S '23: Proceedings of the Tenth ACM Conference on Learning @ Scale, ACM Digital Library, 2023, p. 354-359Conference paper, Published paper (Refereed)
    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.

    Place, publisher, year, edition, pages
    ACM Digital Library, 2023
    Keywords
    Multimodal Learning Analytics, System Design, Internet of Things, Scalability
    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:mau:diva-63744 (URN)10.1145/3573051.3596186 (DOI)001125787500048 ()2-s2.0-85167870433 (Scopus ID)9798400700255 (ISBN)
    Conference
    Conference on Learning @ Scale, Copenhagen, Denmark, July 20-22, 2023
    Available from: 2023-11-20 Created: 2023-11-20 Last updated: 2024-09-18Bibliographically approved
    2. Exploring design considerations for multimodal learning analytics systems: an interview study
    Open this publication in new window or tab >>Exploring design considerations for multimodal learning analytics systems: an interview study
    2024 (English)In: Frontiers in Education, E-ISSN 2504-284X, Vol. 9Article in journal (Refereed) Published
    Abstract [en]

    Multimodal Learning Analytics (MMLA) systems integrate diverse data to provide real-time insights into student learning, yet their design faces the challenge of limited established guidelines. This study investigates essential design considerations for MMLA systems during the research and development phase, aiming to enhance their effectiveness in educational settings. A qualitative approach employing semi-structured interviews was conducted with a diverse group of researchers in the MMLA field. Deductive and thematic analysis were used to identify key design considerations, including technology integration, constraints and learning scenarios. The analysis further revealed intersections between various design considerations, both confirming existing themes and highlighting new emergent ones. Based on the findings, the MMLA Design Framework (MDF) was developed to provide a structured approach to guide the design and development of MMLA systems. This framework, along with the identified design considerations, addresses the lack of conventional practices in MMLA design and offers practical insights for practitioners and researchers. The results of this study have the potential to significantly impact both research and educational applications of MMLA systems, paving the way for more effective and informed designs.

    Place, publisher, year, edition, pages
    Frontiers Media S.A., 2024
    Keywords
    multimodal learning analytics, design considerations, data mining in education, MMLA design, educational technology
    National Category
    Computer Systems
    Identifiers
    urn:nbn:se:mau:diva-70101 (URN)10.3389/feduc.2024.1356537 (DOI)2-s2.0-85199900473 (Scopus ID)
    Available from: 2024-08-09 Created: 2024-08-09 Last updated: 2024-08-30Bibliographically approved
    3. Guiding the Integration of Multimodal Learning Analytics in the Glocal Classroom: A Case Study Applying MAMDA
    Open this publication in new window or tab >>Guiding the Integration of Multimodal Learning Analytics in the Glocal Classroom: A Case Study Applying MAMDA
    2024 (English)In: Proceedings of the 16th International Conference on Computer Supported Education - (Volume 1), SciTePress, 2024, Vol. 1Conference paper, Published paper (Refereed)
    Abstract [en]

    This study explores the integration of Multimodal Learning Analytics (MMLA) within the dynamic learning ecosystem of the Glocal Classroom (GC). By employing the MMLA Model for Design and Analysis (MAMDA), our research proposes a conceptual model leveraging the GC’s existing infrastructure into an MMLA system to enrich learning experiences and inform course design. Our methodology involves a case study approach guided by the six phases of MAMDA. Building on previous studies, including a systematic mapping of MMLA research and an investigation into MMLA system design. We seek to employ MMLA insights to comprehensively understand the learning experience, identify issues, and guide improvement strategies. Furthermore, we discuss potential challenges, mainly focusing on privacy and ethical considerations. The result of this work aims to facilitate a responsible and effective implementation of MMLA systems in educational settings.

    Place, publisher, year, edition, pages
    SciTePress, 2024
    Keywords
    Glocal Classroom, Multimodal Learning Analytics, Smart Learning Environment
    National Category
    Computer Systems
    Identifiers
    urn:nbn:se:mau:diva-70104 (URN)10.5220/0012690900003693 (DOI)2-s2.0-85193943037 (Scopus ID)978-989-758-697-2 (ISBN)
    Conference
    16th International Conference on Computer Supported Education CSEDU, May 2 - 4 2024, Angers, France.
    Available from: 2024-08-09 Created: 2024-08-09 Last updated: 2024-09-18Bibliographically approved
    4. Learning Swedish with AI: Exploring Multimodal Learning Analytics in Spoken Language Acquisition
    Open this publication in new window or tab >>Learning Swedish with AI: Exploring Multimodal Learning Analytics in Spoken Language Acquisition
    2024 (English)Conference 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, 2024
    Keywords
    Multimodal Learning Analytics, Spoken Language Acquisition, Design Science Methodology, Generative AI
    National Category
    Computer Systems
    Identifiers
    urn:nbn:se:mau:diva-70105 (URN)
    Conference
    MIS4TEL'24
    Available from: 2024-08-09 Created: 2024-08-09 Last updated: 2024-09-18
    5. Analytics in Glocal Classrooms: Integrating Multimodal Learning Analytics in a Smart Learning Environment
    Open this publication in new window or tab >>Analytics in Glocal Classrooms: Integrating Multimodal Learning Analytics in a Smart Learning Environment
    2024 (English)In: 2024 IEEE International Conference on Advanced Learning Technologies (ICALT), Nicosia, North Cyprus, Cyprus, 2024, IEEE, 2024Conference paper, Published paper (Refereed)
    Abstract [en]

    In the dynamic landscape of digital education, the Glocal Classroom (GC) stands out as a multifaceted smart learning environment. The integration of Multimodal Learning Analytics (MMLA) comes as an intriguing proposition, promising insights into learning dynamics and enhancing educational outcomes. Encountering numerous interdependent considerations involved in the design and integration of MMLA systems, the MMLA design framework (MDF) addresses this challenge, providing a systematic approach. MDF consists of a phased and iterative method for designing MMLA systems. In this study, we delve into the details of the fifth phase, focusing on the development phase. Our primary objective is to assess and refine the applicability of MDF, by taking the integration of MMLA in GC as a use case. We analyze GC's technological infrastructure, evaluating existing hardware, network capabilities, and potential challenges. The central emphasis is on the technical architecture, specifically the hardware components supporting MMLA. By focusing on the technical complexities, the study provides insights into challenges and opportunities associated with MMLA implementation. The outcomes will deepen our understanding of technology in education and refine the MDF model, making it more effective for designing MMLA systems.

    Place, publisher, year, edition, pages
    IEEE, 2024
    Keywords
    Multimodal Learning Analytics, Glocal Classroom, Smart Learning Environment, Learning Dynamics, MMLA Design
    National Category
    Computer Systems
    Identifiers
    urn:nbn:se:mau:diva-70106 (URN)10.1109/ICALT61570.2024.00033 (DOI)001308583600027 ()2-s2.0-85203798128 (Scopus ID)979-8-3503-6205-3 (ISBN)
    Conference
    ICALT 2024 – 24th IEEE International Conference on Advanced Learning Technologies, July 1 – 4 2024, North Nicosia, North Cyprus.
    Available from: 2024-08-09 Created: 2024-08-09 Last updated: 2024-11-08Bibliographically approved
    6. A SYSTEMATIC REVIEW OF MULTIMODAL LEARNING ANALYTICS DESIGN MODELS AND FRAMEWORKS
    Open this publication in new window or tab >>A SYSTEMATIC REVIEW OF MULTIMODAL LEARNING ANALYTICS DESIGN MODELS AND FRAMEWORKS
    2024 (English)In: Proceedings of 16th International Conference on Education and New Learning Technologies, IATED , 2024Conference paper, Published paper (Refereed)
    Abstract [en]

    Multimodal Learning Analytics (MMLA) systems hold immense potential for understanding and shaping learning experiences. However, the lack of standardized design models hinders the consistent and effective development of these systems. This systematic review addresses this gap by identifying and analyzing existing MMLA design models and frameworks. We employed a rigorous search strategy aligned with established guidelines to identify relevant studies published in the past decade. Following a qualitative approach, the review combined narrative synthesis and thematic analysis to extract and synthesize key findings. Our analysis revealed a diverse landscape of MMLA design models and frameworks, varying in their scope (specific learning activities vs. comprehensive MMLA system design), level of detail (high-level guidance vs. specific steps), and development process (theoretical foundations vs. practical experience). Notably, several models addressed key design considerations and core commitments emphasized by recent research (e.g., data privacy, learner agency, inclusive learning environments). More importantly, the aggregation of these identified models suggests promise for the development of a more comprehensive design model. This is because individual models cover distinct areas and aspects with some intersections. The review also identified recurring themes related to factors influencing MMLA system design, including usability, scalability, and ethical considerations. Finally, we provide a discussion on potential strategies for a concrete development, offering valuable insights for researchers, developers, and educators seeking to harness the power of MMLA to improve learning outcomes.

    Place, publisher, year, edition, pages
    IATED, 2024
    Keywords
    Multimodal Learning Analytics (MMLA), Systems Design Frameworks, Systematic Literature Review, Educational Technology
    National Category
    Computer Systems
    Identifiers
    urn:nbn:se:mau:diva-70107 (URN)10.21125/edulearn.2024.1299 (DOI)978-84-09-62938-1 (ISBN)
    Conference
    16th International Conference on Education and New Learning Technologies, 1-3 July 2024, Palma, Spain.
    Available from: 2024-08-09 Created: 2024-08-09 Last updated: 2024-09-18Bibliographically approved
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  • 2.
    Ouhaichi, Hamza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Spikol, Daniel
    University of Copenhagen.
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    A SYSTEMATIC REVIEW OF MULTIMODAL LEARNING ANALYTICS DESIGN MODELS AND FRAMEWORKS2024In: Proceedings of 16th International Conference on Education and New Learning Technologies, IATED , 2024Conference paper (Refereed)
    Abstract [en]

    Multimodal Learning Analytics (MMLA) systems hold immense potential for understanding and shaping learning experiences. However, the lack of standardized design models hinders the consistent and effective development of these systems. This systematic review addresses this gap by identifying and analyzing existing MMLA design models and frameworks. We employed a rigorous search strategy aligned with established guidelines to identify relevant studies published in the past decade. Following a qualitative approach, the review combined narrative synthesis and thematic analysis to extract and synthesize key findings. Our analysis revealed a diverse landscape of MMLA design models and frameworks, varying in their scope (specific learning activities vs. comprehensive MMLA system design), level of detail (high-level guidance vs. specific steps), and development process (theoretical foundations vs. practical experience). Notably, several models addressed key design considerations and core commitments emphasized by recent research (e.g., data privacy, learner agency, inclusive learning environments). More importantly, the aggregation of these identified models suggests promise for the development of a more comprehensive design model. This is because individual models cover distinct areas and aspects with some intersections. The review also identified recurring themes related to factors influencing MMLA system design, including usability, scalability, and ethical considerations. Finally, we provide a discussion on potential strategies for a concrete development, offering valuable insights for researchers, developers, and educators seeking to harness the power of MMLA to improve learning outcomes.

    Download full text (pdf)
    fulltext
  • 3.
    Ouhaichi, Hamza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Spikol, Daniel
    Copenhagen University, Department of Science Education, Copenhagen, Denmark.
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Li, Zaibei
    Copenhagen University, Departement of Computer Science, Copenhagen, Denmark.
    Analytics in Glocal Classrooms: Integrating Multimodal Learning Analytics in a Smart Learning Environment2024In: 2024 IEEE International Conference on Advanced Learning Technologies (ICALT), Nicosia, North Cyprus, Cyprus, 2024, IEEE, 2024Conference paper (Refereed)
    Abstract [en]

    In the dynamic landscape of digital education, the Glocal Classroom (GC) stands out as a multifaceted smart learning environment. The integration of Multimodal Learning Analytics (MMLA) comes as an intriguing proposition, promising insights into learning dynamics and enhancing educational outcomes. Encountering numerous interdependent considerations involved in the design and integration of MMLA systems, the MMLA design framework (MDF) addresses this challenge, providing a systematic approach. MDF consists of a phased and iterative method for designing MMLA systems. In this study, we delve into the details of the fifth phase, focusing on the development phase. Our primary objective is to assess and refine the applicability of MDF, by taking the integration of MMLA in GC as a use case. We analyze GC's technological infrastructure, evaluating existing hardware, network capabilities, and potential challenges. The central emphasis is on the technical architecture, specifically the hardware components supporting MMLA. By focusing on the technical complexities, the study provides insights into challenges and opportunities associated with MMLA implementation. The outcomes will deepen our understanding of technology in education and refine the MDF model, making it more effective for designing MMLA systems.

  • 4.
    Ouhaichi, Hamza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Spikol, Daniel
    Copenhagen University, Copenhagen, Denmark.
    Li, Zaibei
    Copenhagen University, Copenhagen, Denmark.
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Conceptual Design of Multimodal Learning Analytics for Spoken Language Acquisition2024In: 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, 2024, p. 144-149Conference 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.

  • 5.
    Ouhaichi, Hamza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Spikol, Daniel
    Department of Science Education, University of Copenhagen, Copenhagen, Denmark.
    Exploring design considerations for multimodal learning analytics systems: an interview study2024In: Frontiers in Education, E-ISSN 2504-284X, Vol. 9Article in journal (Refereed)
    Abstract [en]

    Multimodal Learning Analytics (MMLA) systems integrate diverse data to provide real-time insights into student learning, yet their design faces the challenge of limited established guidelines. This study investigates essential design considerations for MMLA systems during the research and development phase, aiming to enhance their effectiveness in educational settings. A qualitative approach employing semi-structured interviews was conducted with a diverse group of researchers in the MMLA field. Deductive and thematic analysis were used to identify key design considerations, including technology integration, constraints and learning scenarios. The analysis further revealed intersections between various design considerations, both confirming existing themes and highlighting new emergent ones. Based on the findings, the MMLA Design Framework (MDF) was developed to provide a structured approach to guide the design and development of MMLA systems. This framework, along with the identified design considerations, addresses the lack of conventional practices in MMLA design and offers practical insights for practitioners and researchers. The results of this study have the potential to significantly impact both research and educational applications of MMLA systems, paving the way for more effective and informed designs.

    Download full text (pdf)
    fulltext
  • 6.
    Ouhaichi, Hamza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Spikol, Daniel
    Department of Science Education, Copenhagen University, Copenhagen, Denmark.
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Guiding the Integration of Multimodal Learning Analytics in the Glocal Classroom: A Case Study Applying MAMDA2024In: Proceedings of the 16th International Conference on Computer Supported Education - (Volume 1), SciTePress, 2024, Vol. 1Conference paper (Refereed)
    Abstract [en]

    This study explores the integration of Multimodal Learning Analytics (MMLA) within the dynamic learning ecosystem of the Glocal Classroom (GC). By employing the MMLA Model for Design and Analysis (MAMDA), our research proposes a conceptual model leveraging the GC’s existing infrastructure into an MMLA system to enrich learning experiences and inform course design. Our methodology involves a case study approach guided by the six phases of MAMDA. Building on previous studies, including a systematic mapping of MMLA research and an investigation into MMLA system design. We seek to employ MMLA insights to comprehensively understand the learning experience, identify issues, and guide improvement strategies. Furthermore, we discuss potential challenges, mainly focusing on privacy and ethical considerations. The result of this work aims to facilitate a responsible and effective implementation of MMLA systems in educational settings.

    Download full text (pdf)
    fulltext
  • 7.
    Ouhaichi, Hamza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Spikol, Daniel
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Disciplinary literacy and inclusive teaching.
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Learning Swedish with AI: Exploring Multimodal Learning Analytics in Spoken Language Acquisition2024Conference 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.

  • 8.
    Ouhaichi, Hamza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Spikol, Daniel
    Copenhagen University, Copenhagen, Denmark.
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Research trends in multimodal learning analytics: A systematic mapping study2023In: Computers and Education: Artificial Intelligence, ISSN 2666-920X, Vol. 4, p. 100136-100136, article id 100136Article, review/survey (Refereed)
    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.

    Download full text (pdf)
    fulltext
  • 9.
    Ouhaichi, Hamza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Spikol, Daniel
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Disciplinary literacy and inclusive teaching.
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Rethinking MMLA: Design Considerations for Multimodal Learning Analytics Systems2023In: L@S '23: Proceedings of the Tenth ACM Conference on Learning @ Scale, ACM Digital Library, 2023, p. 354-359Conference paper (Refereed)
    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.

    Download full text (pdf)
    fulltext
  • 10.
    Spikol, Daniel
    et al.
    University of Copenhagen.
    Li, Zaibei
    University of Copenhagen.
    Serrano-Iglesias, Sergio
    Universidad de Valladolid.
    Ouhaichi, Hamza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    MBOX Lightweight Voice Analysis Sensors fro MMLA2022Conference paper (Refereed)
    Abstract [en]

    This abstracts presents MBOX, a scalable and lightweight system that integrates data collection, data analysis and instructive feedback to evaluate participants’ engagement levels of learning activities.

    Download full text (pdf)
    fulltext
  • 11.
    Ouhaichi, Hamza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Towards designing a flexible multimodal learning analytics system2022Licentiate thesis, comprehensive summary (Other academic)
    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: 2024-09-18Bibliographically 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: 2024-09-18Bibliographically approved
    Download full text (pdf)
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  • 12.
    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 University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Gómez Sánchez, Eduardo
    GSIC-EMIC Research Group, Universidad de Valladolid, Spain.
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Adaptable Smart Learning Environments supported by Multimodal Learning Analytics2021In: 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 (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.

        

    Download full text (pdf)
    fulltext
  • 13.
    Ouhaichi, Hamza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Spikol, Daniel
    Univ Copenhagen, Dept Sci Educ, Copenhagen, Denmark..
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    MBOX: Designing a Flexible IoT Multimodal Learning Analytics System2021In: IEEE 21st International Conferenceon Advanced Learning TechnologiesICALT 2021 / [ed] Chang, M., Chen, NS., Sampson, DG., Tlili, A., IEEE, 2021, p. 122-126Conference 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.

  • 14.
    Ouhaichi, Hamza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Bosch, Jan
    Chalmers University of TechnologyGothenburgSweden.
    Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study2019In: 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 (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.

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