Modeling Individual Differences in Breadth and Depth of L2 Vocabulary Knowledge Development: A SEM Approach

Lee Sunjung (이선정)

Modeling Individual Differences in Breadth and Depth of L2 Vocabulary Knowledge Development: A SEM Approach

Modern English Education
Abbr : MEESO
2017, 18(3), pp.1-24
Publisher : The Modern English Education Society
Research Area : Humanities > English Language and Literature > English Language Teaching
Published : August 31, 2017

Lee Sunjung ¹

¹서울시립대학교

Accredited

ABSTRACT

This study investigates how aptitude, motivation, strategy use, and language processing experience operate in tandem to contribute to breadth and depth of L2 vocabulary knowledge development. Participants included 141 Korean EFL learners in university English courses. The learners completed the Korean version of the Modern Language Aptitude Test, the Attitude/Motivation Test Battery, the Vocabulary Learning Strategies Survey, and a background questionnaire. Vocabulary breadth and depth were measured by the Vocabulary Level Test and the Word Associates Test, respectively. Data were analyzed using structural equation modeling (SEM). The results revealed support for the following model: (a) aptitude positively influenced vocabulary breadth development; (b) motivation positively influenced vocabulary depth development through the mediation of language processing experience; and (c) motivation positively influenced strategy use, but strategy use did not significantly influence vocabulary acquisition. By considering the combined effects of cognitive and motivational factors, this study expands upon previous research conducted with a single focus on individual differences in lexical development. The results also shed light on the role of motivation, which has received inadequate attention in the literature on L2 vocabulary development. The implications of these findings also are discussed in relation to L2 vocabulary instruction.

KEYWORDS

individual differences, L2 vocabulary breadth and depth, SEM

Citation status

* References for papers published after 2023 are currently being built.

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