Learning Design: reflections on a snapshot of the current landscape

The mounting wealth of open and readily available information and the swift evolution of social, mobile and creative technologies warrant a re-conceptualisation of the role of educators: from providers of knowledge to designers of learning. This need is being addressed by a growing trend of research in Learning Design. Responding to this trend, the Art and Science of Learning Design workshop brought together leading voices in the field and provided a forum for discussing its key issues. It focused on three thematic axes: practices and methods, tools and resources, and theoretical frameworks. This paper reviews some definitions of Learning Design and then summarises the main contributions to the workshop. Drawing upon these, we identify three key challenges for Learning Design that suggest directions for future research

Herding cats: observing live coding in the wild

After a momentous decade of live coding activities, this paper seeks to explore the practice with the aim of situating it in the history of contemporary arts and music. The article introduces several key points of investigation in live coding research and discusses some examples of how live coding practitioners engage with these points in their system design and performances. In the light of the extremely diverse manifestations of live coding activities, the problem of defining the practice is discussed, and the question raised whether live coding will actually be necessary as an independent category

Designing a commutative replicated data type

Commuting operations greatly simplify consistency in distributed systems. This paper focuses on designing for commutativity, a topic neglected previously. We show that the replicas of \emph{any} data type for which concurrent operations commute converges to a correct value, under some simple and standard assumptions. We also show that such a data type supports transactions with very low cost. We identify a number of approaches and techniques to ensure commutativity. We re-use some existing ideas (non-destructive updates coupled with invariant identification), but propose a much more efficient implementation. Furthermore, we propose a new technique, background consensus. We illustrate these ideas with a shared edit buffer data type

Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web

Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun

QDQD-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations

The paper considers a class of multi-agent Markov decision processes (MDPs), in which the network agents respond differently (as manifested by the instantaneous one-stage random costs) to a global controlled state and the control actions of a remote controller. The paper investigates a distributed reinforcement learning setup with no prior information on the global state transition and local agent cost statistics. Specifically, with the agents' objective consisting of minimizing a network-averaged infinite horizon discounted cost, the paper proposes a distributed version of $Q$-learning, $\mathcal{QD}$-learning, in which the network agents collaborate by means of local processing and mutual information exchange over a sparse (possibly stochastic) communication network to achieve the network goal. Under the assumption that each agent is only aware of its local online cost data and the inter-agent communication network is \emph{weakly} connected, the proposed distributed scheme is almost surely (a.s.) shown to yield asymptotically the desired value function and the optimal stationary control policy at each network agent. The analytical techniques developed in the paper to address the mixed time-scale stochastic dynamics of the \emph{consensus + innovations} form, which arise as a result of the proposed interactive distributed scheme, are of independent interest.Comment: Submitted to the IEEE Transactions on Signal Processing, 33 page

Implementation and design of a service-based framework to integrate personal and institutional learning environments

The landscape of teaching and learning has changed in recent years because of the application of Information and Communications technology. Among the most representative innovations in this regard are Learning Management Systems. Despite of their popularity in institutional contexts and the wide set of tools and services that they provide to learners and teachers, they present several issues. Learning Management Systems are linked to an institution and a period of time, and are not adapted to learners' needs. In order to address these problems Personal Learning Environments are defined, but it is clear that these will not replace Learning Management Systems and other institutional contexts. Both types of environment should therefore coexist and interact. This paper presents a service-based framework to facilitate such interoperability. It supports the export of functionalities from the institutional to the personal environment and also the integration within the institution of learning outcomes from personal activities. In order to achieve this in a flexible, extensible and open way, web services and interoperability specifications are used. In addition some interoperability scenarios are posed. The framework has been tested in real learning contexts and the results show that interoperability is possible, and that it benefits learners, teachers and institutions.Peer ReviewedPostprint (author's final draft