Using design-based research to develop a Mobile Learning Framework for Assessment Feedback

Students’ lack of engagement with their assessment feedback and the lack of dialogue and communication for feedback are some of the issues that affect educational institutions. Despite the affordance that mobile technologies could bring in terms of assessment feedback, research in this area is scarce. The main obstacle for research on mobile learning assessment feedback is the lack of a cohesive and unified mobile learning framework. This paper thus presents a Mobile Learning Framework for Assessment Feedback (MLFAF), developed using a design-based research approach. The framework emerged from the observation of, and reflection upon, the different stages of a research project that investigated the use of a mobile web application for summative and formative assessment feedback. MLFAF can be used as a foundation to study the requirements when developing and implementing wide-scale mobile learning initiatives that underpin longitudinal practices, as opposed to short-term practices. The paper also provides design considerations and implementation guidelines for the use of mobile technology in assessment feedback to increase student engagement and foster dialogic feedback communication channels

The future of technology enhanced active learning – a roadmap

The notion of active learning refers to the active involvement of learner in the learning process, capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learnercontent interaction, is a central aspect of technology-enhanced active learning. In this roadmap, the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap

Using activity-oriented design methods (AODM) to investigatemobile learning

The past few years have witnessed significant interest and developments in researching mobile learning, with a lot of important contributions being made towards understanding and defining mobile learning. However, current research efforts are being redirected towards a new agenda to establish appropriate methods for investigating mobile learning, as this book testifies. This chapter contributes to this research effort by articulating how to adapt Activity-Oriented Design Methods (AODM – see Mwanza, 2002) for use in mobile learning research

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications