Lots of Selves, some rebellious: Developing the Self Discrepancy Model for language learners

Research output: Contribution to journalArticle

Full text download(s)

Published copy (DOI)

Author(s)

Department/unit(s)

Publication details

JournalSystem
DateAccepted/In press - 31 May 2016
DateE-pub ahead of print (current) - 22 Jun 2016
Volume60
Number of pages14
Pages (from-to)79-92
Early online date22/06/16
Original languageEnglish

Abstract

This article develops a new language learning motivation model, which is, like the
currently dominant model by D€ornyei, based on Higgins’ (1987) Self Discrepancy Theory.
Increasing evidence of ‘non-fit’ of D€ornyei’s model, especially (but not solely) from language learners with English as a first language, let to the author revisiting Higgins’ original, which had more complex delineation of different Selves that adopted by D€ornyei. After critically reviewing the body of literature suggesting ‘non-fit’ of D€ornyei’s model, a model with Higgins’ original delineations of Selves is proposed and adapted to the language learning context, and then applied on novel data from two different learner groups with English as first language: mature university and adolescent school students. The proposed
Self Discrepancy Model for Language Learners contributes to solutions of several problems raised in current discussion of language learner motivation: it provides a better fit of data seemingly incompatible with D€ornyei’s model, especially a learner type labelled ‘rebellious’, offers a better embedding of a range of contextual influences on motivation, and facilitates developmental perspectives on language learner motivation. The empirical data delivers on the first two goals, and offers pathways regarding the last.

Bibliographical note

©2016 Elsevier Ltd. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

    Research areas

  • language learning, motivation, self system, rebellious self

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations