Abstract
This article explores the question whether or not a user model with an automated adaptive selection of online generated and enriched reading texts from newspapers ensures that readers of different vocabulary levels receive linguistic input that is best suited to their abilities and preferences. In a simulation study, 30 'readers' with a vocabulary size varying from 500 to 15,000 lemmas received three texts aimed at 88% text coverage (relative proportion of lemmas known to the reader) for ten days, to make sure that they understood the texts and could learn new words from them. All texts and words encountered were logged (usage data) to build up a dynamic user model. The study revealed that except for the two lower vocabulary levels (knowledge of 500 and 1000 lemmas) all texts had sufficient text coverage. After 20-25 texts, the number of words encountered for the first time in each text was about 4 to 6% (dependent on low or high vocabulary level), and not yet stable. The number of words learned after 30 texts - two to five words - was quite low. More research with far more texts is needed to find a stable pattern in the number of words learned while reading online newspapers.
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