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) 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.

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.

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.

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) 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.