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.

The conveniences of remote work are various, but a surge in cyberthreats has heavily affected the optimal processes of organizations. As a result, employees' cybersecurity awareness was jeopardized, prompting organizations to require improvement of cybersecurity processes at all levels. This paper explores which cybersecurity aspects are more relevant and/or relatable for remote working employees. A qualitative approach via interviews is used to collect experiences and perspectives from employees in different organizations. The results show that human factors, such as trust in cybersecurity infrastructure, previous practices, training, security fatigue, and improvements with gamification, are core to supporting the success of a cybersecurity program in a remote work environment.

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.

The traditional way of teaching may no longer be sufficient to cope with current requirements specifically in the Internet of Things (IoT) domain. The case for this paper is related to an introductory programming course on JavaScript for the period of 2016-2018. In this study a multi-method approach for data collection is utilized. Project-Based Learning (PBL), Scrum and rapid prototyping are utilized to support student projects over the three years. Students developed a number of prototypes for various IoT domains related to ongoing research projects within our research center. The results show that students could easily use their JavaScript knowledge for any type of IoT development. PBL, Scrum and rapid prototyping help addressing uncertainties during the projects and balancing the team efforts for learning, development, problem solving and creativity. One of the outcomes of this paper confirms that smaller team sizes of students perform better during the project lifetime. In conclusion, focusing on knowledge increase, teamwork, collaboration, interaction, constant feedback, and adaptability should be considered a priority while exploiting teaching approaches such as PBL, Scrum and rapid prototyping for IoT development.

Proprietary software solutions are extensively deployed through multiple platforms including desktop, web and mobile devices. Such systems are in many cases closed, thus making it difficult for software developers to expand and create additional features to these systems. Recent research in the field of web and mobile technologies indicates that open systems are more successful than those based on proprietary technologies. In this paper, we investigate and analyze existing research related to the notion of open architectures. We conducted a literature survey of the state of the art projects and definitions related to this concept. Our efforts have resulted in a refined definition of the term followed by a conceptual model of an open architecture contextualized within the field of web and mobile software. The model primarily relies on four key characteristics, namely flexibility, evolvability, customizability and extensibility. Furthermore, we present a validation plan based on the GQM approach that further instantiates these characteristics into a set of properties that can serve as metrics.

The use of diverse standards while developing web and mobile technologies brings new challenges when it comes to flexibility, interoperability, customizability and extensibility of the software systems. In addition, such systems in most of the cases are closed, thus make the development and customization process for system designers, developers and end-users a challenging effort. All these developments require further research attention. This work addresses these challenges from open system architecture perspective. The proposed approach is based on practical development efforts, and theoretical research including state of the art projects and definitions related to open architectures that we surveyed. The initial results indicate that a combination of service-oriented approaches with open source components and open standard data formats pave the way towards an open, extensible architecture. The core contribution of this research will be (a) an open architecture model and (b) the developed system itself based on the model, and (c) the benefits of applying open architecture approaches throughout the development processes.