|dc.description.abstract||This thesis considers possible criteria for the selection of example
sentences for difficult or unknown words in reading texts for students of German
as a Second Language (GSL). The examples are intended to be provided
within the context of an Intelligent Computer-Aided Language Learning (ICALL) Vocabulary
Learning System, where students can choose
among several explanation options for difficult words. Some of these options (e.g. glosses)
have received a good deal of attention in the ICALL/Second Language (L2) Acquisition
literature; in contrast, literature on examples has been the near exclusive province
The selection of examples is explored from an educational,
L2 teaching point of view: the thesis is intended as a first
exploration of the question of what makes an example helpful to the
L2 student from the perspective of L2 teachers. An important motivation for this work is that
selecting examples from a dictionary or randomly from a corpus has
several drawbacks: first, the number of available dictionary
examples is limited; second, the examples fail to take into account the context
in which the word was encountered; and third, the rationale
and precise principles behind the selection of dictionary examples is usually
less than clear.
Central to this thesis is the hypothesis that a random selection of example
sentences from a suitable corpus can be improved by a guided selection process that takes
into account characteristics of helpful examples.
This is investigated by an empirical study conducted with teachers of L2 German.
The teacher data show that four dimensions are significant criteria amenable to analysis:
(a) reduced syntactic complexity, (b) sentence similarity,
provision of (c) significant co-occurrences and (d) semantically related words.
Models based on these dimensions are developed using logistic regression analysis,
and evaluated through two further empirical studies with teachers and students of L2 German.
The results of the teacher evaluation are encouraging: for the teacher evaluation, they indicate
that, for one of the models, the top-ranked selections perform on the same level as dictionary
examples. In addition, the model provides a ranking of potential examples that roughly
corresponds to that of experienced teachers of L2 German. The student evaluation confirms
and notably improves on the teacher evaluation in that the best-performing model of the
teacher evaluation significantly outperforms both random corpus selections
and dictionary examples (when a penalty for missing entries is included).||en