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learningObject = document
document = text | graphics | image | audio | video .
text = Word | Powerpoint | HTML | PDF | Postscript | TeX |
ASCII
.
There are three basic modes of use:
The first two modes correspond to database addition and retrieval operations: it is the third mode that makes PIAVEE unique amongst learning object repositories.
Indexing is the process of extracting information from a learning object that will facilitate the process of search and retrieval. In principle, information can be gleaned from a variety of sources, but in practice (in the current implementation) only two sources are utilized: the document itself, and the human reader.
From the document itself, information extracted consists of a word frequency table of all words used in the document. This table can be tailored in a given context by limiting the indices on frequency count, by removing identified stop words, or by searching only for certain thesaurus words. Only text based documents lend themselves to this technique, and these are currently the only objects that can be automatically indexed. Currently text filters exist for Word, RTF, PDF, dvi, TeX, Powerpoint, and Postscript documents.
All documents (including non-text ones) can have indexing terms supplied by the human reader of the document. This is akin to meta-data tagging of the document, although most systems relying upon metadata also impose some constraints upon the style and organization of the metadata. This is not the case currently in PIAVEE, although it is projected that future implementations may allow the optional specification of one or more metadata protocols to guide the reader in this process. Thus even non-text oriented documents such as images and audio can be handled by PIAVEE.
Searching allows the user to enter various search terms to retrieve a document, or set of documents.
Intellectual Property of documents in the system is maintained by keeping track of where the documents have been. Details such as author, URLs, citations, etc., are logged along with each document, and this allows an audit trail of all intellectual input to the document.
We also include access control mechanisms to allow (or disallow) access at various levels:
or others as required.
PIAVEE is an open, easy to use system. It does not require vetting or meta-data tagging of submitted materials. Learning objects can be ephemeral, or persistent, meaning that they may have arbitrary lifetimes within the system. Most repositories require objects to be persistent, that is, once added to the system, they remain there for the indefinite future. PIAVEE, on the other hand, allows the object owner to determine the lifetime of the object, so that objects may be experimented with, altered, or even destroyed. An automatic garbage collection mechanism is used to retrieve and identify broken links, so that others can update their cross references easily.
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