The Ambient Horn: Designing a novel audio-based learning experience

The Ambient Horn is a novel handheld device designed to support children learning about habitat distributions and interdependencies in an outdoor woodland environment. The horn was designed to emit non-speech audio sounds representing ecological processes. Both symbolic and arbitrary mappings were used to represent the processes. The sounds are triggered in response to the children’s location in certain parts of the woodland. A main objective was to provoke children into interpreting and reflecting upon the significance of the sounds in the context in which they occur. Our study of the horn being used showed the sounds to be provocative, generating much discussion about what they signified in relation to what the children saw in the woodland. In addition, the children appropriated the horn in creative ways, trying to ‘scoop’ up new sounds as they walked in different parts of the woodland

Characterizing the Landscape of Musical Data on the Web: State of the Art and Challenges

Musical data can be analysed, combined, transformed and exploited for diverse purposes. However, despite the proliferation of digital libraries and repositories for music, infrastructures and tools, such uses of musical data remain scarce. As an initial step to help fill this gap, we present a survey of the landscape of musical data on the Web, available as a Linked Open Dataset: the musoW dataset of catalogued musical resources. We present the dataset and the methodology and criteria for its creation and assessment. We map the identified dimensions and parameters to existing Linked Data vocabularies, present insights gained from SPARQL queries, and identify significant relations between resource features. We present a thematic analysis of the original research questions associated with surveyed resources and identify the extent to which the collected resources are Linked Data-ready

Construction and abstraction: contrasting methods of supporting model building in learning science

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Video Data Visualization System: Semantic Classification And Personalization

We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the edges are the relation between documents and the classes of documents. Finally, we construct the user's profile, based on the interaction with the system, to render the system more adequate to its references.Comment: graphic

Designing Open Educational Resources through Knowledge Maps to enhance Meaningful learning

This paper demonstrates some pedagogical strategies for developing Open Educational Resources (OERs) using the knowledge mapping tool Compendium. It also describes applications of Knowledge Maps to facilitate meaningful learning by focusing on specific OER examples. The study centres on the OpenLearn project, a large scale online environment that makes a selection of higher education learning resources freely available via the internet. OpenLearn, which is supportedby William and Flora Hewlett Foundation, was launched in October 2006 and in the two year period of its existence hasreleased over 8,100 learning hours of the OU's distance learning resources for free access and modification by learnersand educators under the Creative Commons license. OpenLearn also offers three knowledge media tools: Compendium(knowledge mapping software), MSG (instant messaging application with geolocation maps) and FM (web-based videoconferencing application). Compendium is a software tool for visual thinking, used to connect ideas, concepts, arguments, websites and documents. There are numerous examples of OERs that have been developed and delivered by institutions across the world, for example, MIT, Rice, Utah State, Core, Paris Tech, JOCW. They present a wide variety of learning materials in terms of styles as well as differing subject content. Many such offerings are based upon original lecture notes, hand-outs and other related papers used in face-to-face teaching. Openlearn OERs, however, are reconstructed from original self study distance learning materials developed at the Open University and from a vast academic catalogue of materials. Samples of these “units” comprise a variety of formats: text, images, audio and video. In this study, our findings illustratethe benefits of sharing some OER content through knowledge maps, the possibility of condensing high volumes of information,accessing resources in a more attractive way, visualising connections between diverse learning materials, connecting new ideas to familiar references, organising thinking and gaining new insights into subject specific content