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BEYOND EXPLICIT RULE LEARNING

Automatizing Second Language Morphosyntax

Published online by Cambridge University Press:  01 June 1997

Robert M. DeKeyser
Affiliation:
University of Pittsburgh

Abstract

This study is a fine-grained analysis of extensive empirical data on the automatization of explicitly learned rules of morphosyntax in a second language. Sixty-one subjects were taught four morphosyntactic rules and 32 vocabulary items in an artificial language. After they had reached criterion on a set of metalinguistic tests of grammar and vocabulary, they engaged in systematic, computer-controlled comprehension and production practice for 8 weeks. Comprehension practice consisted of choosing between pictures displayed on the computer screen to match a sentence; production practice consisted of typing the correct sentence corresponding to a picture. All subjects were taught the same rules and then practiced them, and all subjects had the same amount of comprehension and production practice, but which rules were practiced in comprehension and which in production varied between groups. Results show that the learning of morphosyntactic rules is highly skill-specific and that these skills develop very gradually over time, following the same power function learning curve as the acquisition of other cognitive skills. These results are consistent with current skill acquisition theory.

Type
Research Article
Copyright
© 1997 Cambridge University Press

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Footnotes

This study was supported by U.S. Department of Education grant P017A50064. I thank Chris Connors and Jim Rankin for their expert programming and Ben and Philippa Benson-Xu, Jeanine Carlock, Lorien Clemens, Jing-Fu Fan, Kiduk Kim, Jannine Markizon, Jeri and Scott Misler, Yong-Ping Mou, David Novinksi, John Smith, David Steinitz, and Zander Teller for their superb acting performances. Thanks are also due to the experimenters Keiko Iijima, Jannine Markizon, André Mather, Don Peckham, Leonore Rodrigues, Michelle Sadlier, Clay Taylor, Paul Toth, Eugenia Wan, and Bill Williams. I gratefully acknowledge the advice and encouragement from Carol Baker, Alan Juffs, Donald McBurney, Daniel Everett, Robert Henderson, David Malicki, Christina Paulston, Charles Perfetti, and Richmond and Sarah Thomason and the financial support of the University of Pittsburgh Central Research Development Fund for a pilot study as well as that of the Linguistics Department for various expenses. Kathleen Bardovi-Harlig, Nick Ellis, Jan Hulstijn, Donald Peckham, Peter Robinson, and Lynne Yang provided helpful comments on an earlier draft of this paper. Finally, I wish to express my utmost gratitude to Jim Rankin for computational feats far beyond the call of duty.
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