No subject

- Sequentially open a series of URIs for the various locations
questions might be located, suppressing errors for missing or
unavailable sources.  The URIs would either use a format parameter
that specifically parses and builds question objects according to the
hierarchical scheme you outlined above,  or some other format
supported by the API.

for example:

q =3Dquizdata.open("file:///path/to/local/questions/?format=3Dimage_quiz")
q.append(
     quizdata.open("xmpp://quizmaster@school-server/quizname?format=3Dgener=
ic_xml"))

- In the activity do additional filtering and combining of the
resultant lists of questions to form the full question set that
comprises all the sources.
- At that point you could push the discovered questions into a local
database (if that makes sense) along with the additional processing
(retrieving image URLs, etc) needed to present the questions offline.
Or you could just present the questions, retrieving additional
resources as required in an online fashion.

> The focus of ImageQuiz is matching an image or a place in an image to the
> answer to a question. For language learning, I am setting up a question b=
ank
> where the question is an audio clip (e.g. 'ball' spoken in the target
> language), and a set of pictures, one of which shows a ball (or a picture=
 of
> several objects where the answer is to click on the ball). ImageQuiz will
> also support standard flash cards (text question, text answer) plus
> combinations like audio question, text answer. Sadly, speech recognition
> isn't ready to support audio question, audio answer.

At the most basic level, this should be straightforward enough to
accomplish; however, managing the structure and format of multimedia
(image, audio, video, complex/multilingual text, etc.) questions
requires some careful thought to be flexible, simple, and general
enough to be widely useful.

The details will need to be fleshed out; hopefully we can find a
common base so neither of us reinvent too much...

Greg S.
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