2,205
Views
4
CrossRef citations to date
0
Altmetric
Articles

Closing the enjoyment gap: heritage language maintenance motivation and reading attitudes among Chinese-American children

ORCID Icon &
Pages 1070-1087 | Received 06 Dec 2019, Accepted 29 Feb 2020, Published online: 24 Mar 2020
 

ABSTRACT

Research demonstrates strong associations between psychosocial factors (motivation, attitudes, beliefs), outside of class behavior, and second language (L2) learning, particularly reading achievement [Briggs and Walter Citation2016. Read On! Extensive Reading and Young Second Language Learners’ Motivation and Attitudes; Masgoret and Gardner Citation2003. “Attitudes, Motivation, and Second Language Learning: A Meta-Analysis of Studies Conducted by Gardner and Associates.” Language Learning 53 (S1): 167–210]. Fewer studies have explored the attitudes of students studying a heritage language (HL). Mandarin Chinese is the second most commonly spoken home language among dual language learner children in the U.S. [Park, Zong, and Batalova Citation2018. Growing Superdiversity among Young US Dual Language Learners and its Implications. Washington, DC: Migration Policy Institute] and increasing numbers of children receive explicit Chinese instruction. Comparatively less is known, however, regarding their attitudes toward Chinese maintenance and reading or the impact on intended effort. The current study addressed language learning attitudes and motivations, language specific reading attitudes, and out-of-school language use in physical and digital environments, among 58 children ages 10–18 enrolled in Chinese school. Findings revealed that ideal self accounted for significant variance in school effort (12%), but less than previously found in other contexts. L1/L2 reading attitudes had a significant negative relationship; the more enjoyable reading in English, the less enjoyable reading in Chinese. Chinese reading activity in digital environments uniquely accounted for significant variance in school effort. Results expand our understanding of motivation variability and underscore the importance of digital environments for young learners.

Acknowledgements

We would like to thank all the participating children and families, and teachers who allowed us into their classrooms. Thank you to Principle Pei Wang for her immense support and enthusiasm and Dr. Jessica Briggs Baffoe-Djan for her continued brilliance.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Sara A. Smith

Sara A. Smith is an Assistant Professor in Foreign Language/ESOL Education at the Department of Teaching and Learning, University of South Florida. She received her MSc in Applied Linguistics and Second Language Acquisition and a Doctorate in Education from the University of Oxford Department of Education. Her research interests include within-population diversity among bilinguals related to social and environmental language use, the role formulaic language in reading and language comprehension for Dual Language Learners, and cognitive and educational implications of bilingualism.

Zhengjie Li

Zhengjie Li received his Ph.D. in Technology in Education and Second Language Acquisition from the Teaching and Learning Department at the University of South Florida in 2019. His research interests include instructional technologies in foreign language education, and use of social media technologies to promote language learning among EFL learners in China.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 339.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.