Multimodal Learning Analytics (MMLA) expands traditional learning analytics into the digital and physical learning environment, using diverse sensors and systems to collect information about education in more real-world environments. Challenges remain in making these technologies practical for capturing data in authentic learning situations. With the advent of readily accessible powerful artificial intelligence that includes multimodal large language models, new opportunities are available. However, few approaches allow access to these technologies, and most systems are developed for specific environments. Recent work has begun to make toolkits with access to collecting data from sensors, processing, and analyzing, yet these tools are challenging to integrate into a system. This paper introduces OpenMMLA, a toolkit approach that provides programming interfaces for harnessing these technologies into an MMLA platform with prebuilt pipelines, including the audio analyzer, indoor positioning, and video frame analyzer, offering multimodal data collection and visualizations and analytics. The paper provides an initial evaluation of the functionalities of the toolkit in data capturing and the implemented pipelines' performances.

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.

Multimodal Learning Analytics (MMLA) is an evolving sector within learning analytics that has become increasingly useful for examining complex learning and collaboration dynamics for group work across all educational levels. The availability of low-cost sensors and affordable computational power allows researchers to investigate different modes of group work. However, the field faces challenges stemming from the complexity and specialization of the systems required for capturing diverse interaction modalities, with commercial systems often being expensive or narrow in scope and researcher-developed systems needing to be more specialized and difficult to deploy. Therefore, more user-friendly, adaptable, affordable, open-source, and easy-to-deploy systems are needed to advance research and application in the MMLA field. The paper presents a field report on the design of mBox that aims to support group work across different contexts. We share the progress of mBox, a low-cost, easy-to-use platform grounded on learning theories to investigate collaborative learning settings. Our approach has been guided by iterative design processes that let us rapidly prototype different solutions for these settings.