Analytics in Glocal Classrooms: Integrating Multimodal Learning Analytics in a Smart Learning Environment

• Published in: Proceedings (IEEE International Conference on Advanced Learning Technologies) pp. 94 - 96
• Main Authors: Ouhaichi, Hamza, Spikol, Daniel, Vogel, Bahtijar, Li, Zaibei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2024
• Subjects: Complexity theory; Educational technology; Federated learning; Focusing; Glocal Classroom; Hardware; Iterative methods; Learning Dynamics; MMLA Design; Multimodal Learning Analytics; Smart Learning Environment; Systematics
• ISSN: 2161-377X
• DOI: 10.1109/ICALT61570.2024.00033

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.