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Ouhaichi, H. (2024). A framework for designing and analyzing multimodal learning analytics systems. (Doctoral dissertation). Malmö: Malmö University Press
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
Ouhaichi, H., Spikol, D. & Vogel, B. (2024). A SYSTEMATIC REVIEW OF MULTIMODAL LEARNING ANALYTICS DESIGN MODELS AND FRAMEWORKS. In: Proceedings of 16th International Conference on Education and New Learning Technologies: . Paper presented at 16th International Conference on Education and New Learning Technologies, 1-3 July 2024, Palma, Spain.. IATED
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
Ouhaichi, H., Spikol, D., Vogel, B. & Li, Z. (2024). Analytics in Glocal Classrooms: Integrating Multimodal Learning Analytics in a Smart Learning Environment. In: 2024 IEEE International Conference on Advanced Learning Technologies (ICALT), Nicosia, North Cyprus, Cyprus, 2024: . Paper presented at ICALT 2024 – 24th IEEE International Conference on Advanced Learning Technologies, July 1 – 4 2024, North Nicosia, North Cyprus.. IEEE
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
Series
IEEE International Conference on Advanced Learning Technologies, E-ISSN 2161-3761
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: 2025-02-04Bibliographically approved
Ouhaichi, H., Spikol, D., Li, Z. & Vogel, B. (2024). Conceptual Design of Multimodal Learning Analytics for Spoken Language Acquisition. In: Rafael Ferreira Mello; Nikol Rummel; Ioana Jivet; Gerti Pishtari; José A. Ruipérez Valiente (Ed.), 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. Paper presented at 19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Krems, Austria, September 16–20, 2024 (pp. 144-149). Springer
Open this publication in new window or tab >>Conceptual Design of Multimodal Learning Analytics for Spoken Language Acquisition
2024 (English)In: 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, Springer, 2024, p. 144-149Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer, 2024
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 15160
Keywords
Conversational AI, Language Learning, Multimodal Learning Analytics, Natural Language Processing
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-71887 (URN)10.1007/978-3-031-72312-4_19 (DOI)001332998900019 ()2-s2.0-85205323203 (Scopus ID)978-3-031-72311-7 (ISBN)978-3-031-72312-4 (ISBN)
Conference
19th European Conference on Technology Enhanced Learning, EC-TEL 2024, Krems, Austria, September 16–20, 2024
Available from: 2024-11-04 Created: 2024-11-04 Last updated: 2024-11-23Bibliographically approved
Ouhaichi, H., Vogel, B. & Spikol, D. (2024). Exploring design considerations for multimodal learning analytics systems: an interview study. Frontiers in Education, 9
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)001276320800001 ()2-s2.0-85199900473 (Scopus ID)
Available from: 2024-08-09 Created: 2024-08-09 Last updated: 2024-11-25Bibliographically approved
Ouhaichi, H., Spikol, D. & Vogel, B. (2024). Guiding the Integration of Multimodal Learning Analytics in the Glocal Classroom: A Case Study Applying MAMDA. In: Proceedings of the 16th International Conference on Computer Supported Education - (Volume 1): . Paper presented at 16th International Conference on Computer Supported Education CSEDU, May 2 - 4 2024, Angers, France.. SciTePress, 1
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
Ouhaichi, H., Spikol, D. & Vogel, B. (2024). Learning Swedish with AI: Exploring Multimodal Learning Analytics in Spoken Language Acquisition. In: Christothea Herodotou, Sofia Papavlasopoulou, Carlos Santos, Marcelo Milrad, Nuno Otero, Pierpaolo Vittorini, Rosella Gennari, Tania Di Mascio, Marco Temperini, Fernando De la Prieta (Ed.), Methodologies and Intelligent Systems for Technology Enhanced Learning, 14th International Conference: . Paper presented at 14th International Conference of Methodologies and Intelligent Systems for Technology Enhanced Learning (MIS4TEL'24), 26 - 28 June 2024, Salamanca, Spain. (pp. 178-189). Springer Nature, 1171
Open this publication in new window or tab >>Learning Swedish with AI: Exploring Multimodal Learning Analytics in Spoken Language Acquisition
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
Series
Lecture Notes in Networks and Systems (LNNS), ISSN 2367-3389 ; 1171
Keywords
Multimodal Learning Analytics, Spoken Language Acquisition, Design Science Methodology, Generative AI
National Category
Computer Systems Pedagogy
Identifiers
urn:nbn:se:mau:diva-70105 (URN)
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: 2025-01-09Bibliographically approved
Ouhaichi, H., Spikol, D. & Vogel, B. (2023). Research trends in multimodal learning analytics: A systematic mapping study. Computers and Education: Artificial Intelligence, 4, 100136-100136, Article ID 100136.
Open this publication in new window or tab >>Research trends in multimodal learning analytics: A systematic mapping study
2023 (English)In: Computers and Education: Artificial Intelligence, ISSN 2666-920X, Vol. 4, p. 100136-100136, article id 100136Article, review/survey (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Multimodal learning analytics, Mapping study, Learning technologies, Artificial intelligence
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-64288 (URN)10.1016/j.caeai.2023.100136 (DOI)2-s2.0-85151456109 (Scopus ID)
Available from: 2023-12-12 Created: 2023-12-12 Last updated: 2024-09-18Bibliographically approved
Ouhaichi, H., Spikol, D. & Vogel, B. (2023). Rethinking MMLA: Design Considerations for Multimodal Learning Analytics Systems. In: L@S '23: Proceedings of the Tenth ACM Conference on Learning @ Scale: . Paper presented at Conference on Learning @ Scale, Copenhagen, Denmark, July 20-22, 2023 (pp. 354-359). ACM Digital Library
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: 2025-01-21Bibliographically approved
Spikol, D., Li, Z., Serrano-Iglesias, S., Ouhaichi, H. & Vogel, B. (2022). MBOX Lightweight Voice Analysis Sensors fro MMLA. In: : . Paper presented at CROSSMMLA SLE @ LAK’22: Learning Analytics for Smart Learning Environments Crossing Physical and Virtual Learning Spaces, March 22 2022, Online.. SITE Central Europe (CEUR)
Open this publication in new window or tab >>MBOX Lightweight Voice Analysis Sensors fro MMLA
Show others...
2022 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
SITE Central Europe (CEUR), 2022
Keywords
Audio Analysis, Machine Learning, Multimodal Learning Analytics, IOT
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-70132 (URN)
Conference
CROSSMMLA SLE @ LAK’22: Learning Analytics for Smart Learning Environments Crossing Physical and Virtual Learning Spaces, March 22 2022, Online.
Available from: 2024-08-10 Created: 2024-08-10 Last updated: 2024-09-18Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9278-8063

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