Mehrsprachigkeit in Europa: Plurilinguismo in Europa ... - EURAC

Quantity OR quality – an inevitable dilemma in innovative CALL systems?
to the learner, and we would like to ask the learner to indicate the one that does not fi t. It is
possible to generate
⎛n⎞
⎛5
⎞
⎜ ⎟ = ⎜ ⎟ = 10
⎝k
⎠ ⎝3⎠
possibilities to choose a group of three words out of the fi ve synonyms. There are four (in
general k+1) different places where the word that does not fi t can be shown: at the beginning
of the list, somewhere in the middle, or at the end. Since we have about 20,000 different words
for each language in ELDIT we have 20,000 possibilities to randomly assign a word that does not
fi t. Hence, altogether we have
⎛n⎞
⎜ ⎟ ⋅
⎝k
⎠
⎛5⎞
⎝3⎠
( k + 1 ) ⋅ 20 , 000 = ⎜ ⎟ ⋅(
3 + 1)
⋅ 20 , 000 = 10 ⋅ 4 ⋅ 20 , 000 = 800,
000
different possibilities to generate just this quiz.
7. The Exercise Module
For the generation of the exercises we use different kinds of data: defi nitions, examples,
semantic fi elds, collocations, synonyms … The exercise module offers three types of exercises:
gap fi lling, translation and multiple choice exercises:
The gap fi lling exercises provide the largest number of generation possibilities. They can
be fi lled with all kinds of data used within the module; the generator can create a gap-fi lling
exercise out of each sentence or fragment contained in the system.
Translation exercises provide a word or the defi nition of a word. As a translation the corrector
accepts the correct word itself or a synonym of it.
The multiple choices currently can be used to practice synonyms or other types of familiarity
(semantic relations) among words.
For the correction we use the following tools 1 : (1) there is a spell check within the search
engine (Knapp 2004) which checks a word for orthographical errors if it is not realized as a valid
word. (2) On morphological level we use WordManager (ten Hacken and Domenig 1996): each
required and each typed word is sent to WordManager for analysis. The resulting information
is the basis upon which the feedback is generated. (3) In case that the typed word does not
correspond semantically with the required one the semantic correction takes place using
semantic fi elds. In this way possibly existing word relations between the correct and submitted
answer can be explained. (4) Syntactic information is stored and can be reused within the verb
valency module (Abel et al. 2003). (5) Finally collocational issues are examined with the help of
a large set of collocations and free word combinations collected for the dictionary (Abel et al.
2004). (6) In very simple quizzes (e.g. “Which of the following synonyms is quite colloquial?”)
we can even check pragmatic issues, by which we mean errors regarding politeness, style and
register of language and words.
On of the most interesting issues is to combine syntagmatic and paradigmatic relations
between words for feedback in gap fi lling exercises. Synonyms of the correct answer are
considered as correct, if and only if the supplied word forms also a correct collocation with the
surrounding words.
1 Special thanks goes to Alessia Pavanello for having tested the fi rst prototype!
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