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.

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.

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.

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.