- From: Fabien Gandon <Fabien.Gandon@sophia.inria.fr>
- Date: Tue, 29 Aug 2006 16:40:26 +0200
- To: public-grddl-wg <public-grddl-wg@w3.org>
Thanks Harry for your feedback on the wiki scenario. The online version of the file seems to be locked so here is an extract of the scenario including the "history of Michel the lecturer". Fabien. Use case #2 - Wikis and e-learning: The Technical University of Marcilly decided to use wikis to foster knowledge exchanges between lecturers and students. The Technical University of Marcilly (TMU) decided to use wikis to foster knowledge exchanges between lecturers and students. They tested several wikis over the years and they want to experiment with novel ways of structuring the wiki to improve navigation and retrieval. They also want to make it easier to reuse learning objects in different contexts. Ideally TMU wants the information structuring the wiki to be: 1. easy to add, edit, enrich and this should be done at the same time a user edits a page to avoid multiplying interfaces and manipulations. 2. explicit and understandable to machines so that the wiki engine can rely on it to propose related pages, to perform precise search, to generate browsing interfaces, to build dynamic indexes based on customized queries and to provide customized sorting and filtering for them. 3. accessible to other applications to allow integration with other information systems, links or migration to other wiki engines, extension of its functionalities, etc. In this context TMU uses metadata embedded in the wikipages to: * store the results of social tagging on the pages: tags suggested by users are inserted in the page itself and may reuse data from the page (e.g. the authors name) or annotate specific portions of the page (e.g. type a paragraph as a definition, categorize an image); * generate navigation widgets: lists of forward and back links to navigate the wiki, lists of similar pages, list of all pages tagged with a specific topic, view of the clusters of pages, etc. * enrich them with schemata to restructure the wiki (declare equivalent tags, broader/narrower tags, add synonymous labels to existing tags, etc.) and enrich the navigation with these links; * include queries on these metadata in the wikipages to dynamically generate tailored indexes for the different departments, the different years, the different topics, etc. * import learning objects edited in classical word processing application by using the styles of the different sections to extract annotations for each section and recompose new documents (e.g. transform a handout into a web site for practical sessions). Let us consider the case of Michel, a lecturer in engines and thermodynamics. He used the wiki to publish the handouts of his course. He initially tagged each handout with the main concepts it introduces (e.g. "RenewableEnergies", "Ethanol", "Diesel"). In addition, Michel automatically typed each section of the document using predefined styles (e.g. definitions, formula, example, etc.). The next practical session will involve knowledge on classical Diesel engines and Ethanol-based engines. In order to generate a mnemonic card for this session Michel runs a query to extract definitions and formulas of the courses tagged with "Diesel" or "Ethanol". He also uses these tags to generate dynamic "see also" sections at the end of his sections suggesting other sections to read. Students edit the online handouts, to add pointers, to insert comments on part they found difficult to understand, to recall pieces of previous courses useful for understanding a new course, etc. Students also tagged the pages with their own tags to organize their reading and bookmark important parts for them; they use tags to create transversal thematic tracks (e.g. "LiquidFlow"), to give feedback on the content (e.g. "Difficult"), to prioritize reading (e.g. "NiceToKnow", "Vital"). These tags allow them to have transversal navigation and reorganize the content depending on the task they are doing (e.g. preparing an exam, writing a report, running an experiment). These tags are also used by Michel to evaluate the understanding and the shortcomings of his course. Finally the mass of the course material and tags is such that it needs to be reorganized. Using the tag editor Michel groups "Ethanol" and "Methanol" as sub tags of a new tag he calls "Alcohol". Doing so the pages tagged with "Ethanol" or "Methanol" are grouped and accessible through "Alcohol". He repeats this with other tags (e.g. "Alcohol" and "Hydrogen" becomes sub- tags of "NewEngineEnergy"). This reorganizes the wiki seamlessly e.g. suggestion of navigation in the pages automatically propose narrower, broader and brother tags thus when viewing a page tagged with "Ethanol", the system suggest other pages tagged with "Methanol". Later when a student posts his report on an engine using "CopraOil", his new tag can be placed under the existing one "NewEngineEnergy"; he or anyone else can do it and the result will immediately benefit the whole community of the users. Using these tags and their organization, thematic indexes are dynamically generated for the materials of the course and automatically updated. From the technical stand point, TMU designed a wiki that stores its pages directly in XHTML and RDF annotations are used to represent the wiki structure and annotate the wikipages and the objects it contains (images, uploaded files, etc.). The RDF structure allows refactoring the wiki structure by editing the RDF annotations and the RDFS schemas they are based on. RDF annotations are embedded in the wiki pages themselves using the RDFa and microformats. Some of the learning objects can be saved in XML formats and an XSLT stylesheet exploits the styles used for the session to tag the different parts (e.g. definition, exercise, example) and these annotation can then be used to generate new views on this resource (e.g. list of definition, hypertext support for practical sessions, etc.). Using RDFa and GRDDL in wikis The embedded RDF is extracted using a GRDDL XSLT stylesheets available online to provide semantic annotations directly to the application that needs to extract the embedded metadata: * if someone sends a wiki page to someone else the annotations follow it and can be processed by applications of the recipient; * if another application crawls (e.g. the crawler of a search engine) the wiki site it can extract the metadata and reuse them just by applying the same GRDDL transformation; * if a new community of practice of TMU (e.g. the accountants) wants a dedicated index of its working document, it can be embedding the corresponding SPARQL query in a wikipage: the search engine fed with the GRDDL results solves this query and the result is rendered by an XSLT stylesheet and embedded in the page; * if the wiki engine is to be changed, the migration transformations can exploit the embedded metadata; * if a division wants to setup access rules to some documents, they can be based on these metadata merged with others (e.g. only lecturer can access document tagged as "tests"). * if some users are interested in being informed on any new information on a topic (e.g. chemists want to be informed on any new norm for the environment) they can use notification systems monitoring the wiki by querying its metadata (e.g. recurrent SPARQL queries on pages tagged with "environment") -- "It takes both sunshine and rain to make a rainbow." ____________ |__ _ |_ http://www-sop.inria.fr/acacia/personnel/Fabien.Gandon/ | (_||_) INRIA Sophia Antipolis - ph# (33)(0)4 92 38 77 88
Received on Tuesday, 29 August 2006 14:42:59 UTC