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BEYOND EXPLICIT RULE LEARNING

Automatizing Second LanguageMorphosyntax

Published online by Cambridge University Press:  01 June 1997

Robert M. DeKeyser
Robert M. DeKeyser
Affiliation:
University of Pittsburgh
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Abstract

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

Information

Type
Research Article
Information
Studies in Second Language Acquisition , Volume 19 , Issue 2 , June 1997 , pp. 195 - 221
Copyright
© 1997 Cambridge University Press

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Footnotes

This study was supported by U.S. Department ofEducation grant P017A50064. I thank Chris Connors and Jim Rankin for their expertprogramming 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, JohnSmith, David Steinitz, and Zander Teller for their superb acting performances. Thanks are alsodue to the experimenters Keiko Iijima, Jannine Markizon, André Mather, Don Peckham,Leonore Rodrigues, Michelle Sadlier, Clay Taylor, Paul Toth, Eugenia Wan, and Bill Williams. Igratefully acknowledge the advice and encouragement from Carol Baker, Alan Juffs, DonaldMcBurney, Daniel Everett, Robert Henderson, David Malicki, Christina Paulston, CharlesPerfetti, and Richmond and Sarah Thomason and the financial support of the University ofPittsburgh Central Research Development Fund for a pilot study as well as that of the LinguisticsDepartment for various expenses. Kathleen Bardovi-Harlig, Nick Ellis, Jan Hulstijn, DonaldPeckham, Peter Robinson, and Lynne Yang provided helpful comments on an earlier draft of thispaper. Finally, I wish to express my utmost gratitude to Jim Rankin for computational feats farbeyond the call of duty.