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Quantity OR quality – an inevitable dilemma in innovative CALL systems?
rules. Regarding correction diffi culty we order the mistakes according to the following levels:
orthographic, morphological, syntactic and collocational, semantic, and pragmatic errors.
Last, the feedback generation module generates feedback. The details of the feedback
certainly depends on the details of the error analysis, in the simplest case, only “correct” or
“wrong” is answered to the user, in more sophisticated cases a detailed explanation is given
which can be furthermore adapted to the number of trials within the learning session, or to
other user specifi c data.
4.2 Language Resources
We do not want to elaborate and give a general defi nition for the term language resources but
just clarify what we mean with this term in this paper. For the description of our problem and
the solution we found, we must distinguish three different kinds of language resources:
• With text based language resources we mean for instance text corpora which are more or
less encoded in detail with any markup language.
• With structured language resources we mean a pool of structured language data together
with morphological, syntactic of semantic information, e.g. WordManager (ten Hacken and
Domenig 1996) or WordNet (Kunze and Lemnitzer 2002).
• Rule based language resources are a set of mainly language rules that can be extended (by
hand or by trainings) and/or applied to written text data (POS-taggers, Parsers, Grammars
…).
All these language resources can be used both for the generation and the correction of
exercise data. While text based and rule based language resources are already frequently in
use, the use of structured language resources has been proposed quite often in literature (ten
Hacken and Tschichold 2001, Kunze and Lemnitzer 2002), but rarely applied.
5. Problem Description and Solution
Two kinds of exercises can be encountered when reviewing the literature: (1) High-quality
exercises which require large development efforts, since not only the presented data, but
also a lot of background knowledge (solutions, alternative correct answers, wrong answers,
feedback, algorithms for the correction, etc) has to be developed and encoded, and (2) lowquality
exercises that may not include all this knowledge but only the correct solution. This
second kind of exercises usually applies a simple pattern matching approach when correcting
the learner’s answers and provide simple right/wrong-responses.
Another difference between the examined systems can be noted: (1) As mentioned before
high-quality exercise have often been developed only up to a prototypical stage, which means
that some exemplary exercises, maybe out of a very specifi c domain or targeted at a precisely
determined user group, have been developed. Hence, quantity could not play an important role
in system development. (2) For low-quality exercises, however, even entire authoring tools for
language teachers have been developed which allow producing a large number of quizzes in
relatively short time (Holmes 2006).
Summing up these observations one may note that the question quality or quantity seems to
be an inevitable dilemma in nowadays CALL systems. In the ELDIT project as a solution to this
dilemma we propose the pure automatic generation of exercises by reusing existing language
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