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A dynamic perspective on second language acquisition

07 October 2010

PhD ceremony: Ms. T. Caspi, 16.15 uur, Academiegebouw, Broerstraat 5, Groningen

Thesis: A dynamic perspective on second language acquisition

Promotor(s): prof. C.L.J. de Bot

Faculty: Arts


The dissertation of Tal Caspi applied Dynamic Systems Theory (DST), a well-established approach to complex natural systems, to second language (L2) development. The dissertation includes two studies, both case studies of advanced L2 (English) development in four learners. The first study focuses on vocabulary knowledge, defined as a continuum of receptive and productive levels. The second concentrates on writing performance in four categories: lexical complexity, lexical accuracy, syntactic complexity, and syntactic accuracy. The studies explore the applicability of principles of DST to these areas. DST posits that development is inherently nonlinear, since it reflects changing interactions between co-developing components of language. These interactions arise from two general factors that apply to development in virtually any domain: a hierarchy or order of emergence, and limited resources available for growth. Due to these factors, interactions between co-developing language components are conditional, and simultaneously supportive and competitive. In the context of the two studies, these DST principles imply that receptive vocabulary knowledge precedes production, and that lexicon precedes syntax and complexity precedes accuracy. Within these orders, vocabulary reception precedes and is conditional for its production, but also competes with this production. Similarly, lexicon is prerequisite for syntax structure; yet the acquisition processes of lexicon and syntax rely on restricted capacities, and therefore compete with each other. The study was able to corroborate these hypotheses by a dynamic mathematical model, which configured interactions deduced from the data variability patterns on the basis of the structural data hierarchy and achieved a good fit with the data.


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