Apologize for cross-list posting. ====================================================================== Learning Linked Data Project Call for Comments http://lld.ischool.uw.edu/ The Learning Linked Data Project, a planning activity funded under the IMLS National Leadership Program from October 2011 through September 2012, has taken a first step towards developing a software platform to help instructors, students, and independent learners interpret and create Linked Data. The platform is envisioned to be of use to anyone offering training and education in Linked Data principles and practice, whether in academia or professional settings, in online instruction or in classrooms. As Linked Data is based on data structures of a linguistic nature, the guiding metaphor for the project is that of designing a "language lab" -- a software platform for analyzing and manipulating Linked Data in support of a wide range of pedagogical approaches and expected learning outcomes. The project has prepared a draft "Inventory of Learning Topics", with an analysis of software required for such a platform, and posted it for public review through 15 June 2012 on a blog at: http://lld.ischool.uw.edu/learning/ The document is divided into five short blog pages: -- Understanding Linked Data [2]: "prerequisite" topics, specific to Linked Data, which must be grasped before a learner can meaningfully use software tools. The list of topics is linked to a three-page glossary [9] with definitions of terminology used. -- Searching and Querying Linked Data [3]: just as language learners learn through dialog with native speakers, learners of Linked Data must learn how to pose queries and explore datasets. Tools for doing so include data validators, reasoners, query tools, and Semantic Web search engines. -- Creating and Manipulating RDF Data [4]: In the Linked Data cloud, descriptions of things and descriptions of the vocabularies used to describe those things are all considered "data," so many of the basic tools for editing, mapping, converting, and extracting data may be adapted for different types of data. -- Visualization [5]: Linked Data is conceptually diagrammatic in nature, and graphical tools can help the learner explore the statistical, spatial, or temporal characteristics of datasets by visualizing webs of data at various levels of granularity or by plotting the data to maps or timelines. -- Implementing a Linked Data Application [6]: Simply learning how to interpret and manipulate Linked Data could stop with the topics outlined above. The extent to which a language-lab-like platform for learning Linked Data should encompass tools for building real applications poses questions of scope on which the project would appreciate input. The project envisions the platform as a basis for the development of course modules by people involved in both formal and informal learning environments, so comments about the usefulness of such a platform for particular scenarios would be especially welcome. The comments received will be incorporated into a revised document and final report to be published in September 2012. This report will be used as the basis for a subsequent IMLS project proposal, to be submitted in early 2013, for implementing the platform specified. The partners of the Learning Linked Data Project are the University of Washington, Kent State University, the University of North Carolina, JES & Company, and 3 Round Stones, Inc. The project lead and contact person is Mike Crandall of the University of Washington. [1] http://www.imls.gov/news/national_leadership_grant_announcement.aspx#WA [2] http://lld.ischool.uw.edu/learning/understanding-linked-data/ [3] http://lld.ischool.uw.edu/learning/searching-and-querying-linked-data/ [4] http://lld.ischool.uw.edu/learning/creating-and-manipulating-rdf-data/ [5] http://lld.ischool.uw.edu/learning/visualization/ [6] http://lld.ischool.uw.edu/learning/implementing-a-linked-data-application/ [7] http://lld.ischool.uw.edu/glossary/