CHILDES English OdiaMAIN Corpus

Madhavi Gayathri Raman
Materials Development, Testing and Evaluation
The English and Foreign Languages University
gayathri@efluniversity.ac.in
website

Firdaus Shireen
The School of English Language Education
The English and Foreign Languages University
frdsshireen@gmail.com

Participants:	15
Type of Study:	MAIN narratives
Location:	India
Media type:	audio
DOI:	doi:10.21415/R741-Y976

Browsable transcripts

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Link to media folder

Citation information

Shireen, F. (2021). Exploring the macrostructural narrative skills of Odia ESL learners with the help of Multilingual Assessment Instrument for Narratives. Unpublished MA dissertation. EFL-U, Hyderabad, India.

Gagarina, N., Klop, D., Kunnari, S., Tantele, K., Välimaa, T., Bohnacker, U. & Walters, J. (2019). MAIN: Multilingual Assessment Instrument for Narratives - Revised. Materials for use. ZAS Papers in Linguistics, 63. Odia version. Translated and adapted by Nanda S., & Raman, M.G.

In accordance with TalkBank rules, any use of data from this corpus must be accompanied by at least one of the above references. Please send a copy of any published article to the emails given above for the two contributors.

Project Description

This database contains the English transcripts and audio-records from 15 Odia-English bilinguals using Odia and English versions of the Multilingual Assessment Instrument for Narratives (MAIN). which was developed to assess narrative production and comprehension skills of bilingual/multilingual children from 3 to 10 years. The narratives in this corpus were elicited in the online mode as the study was conducted during the COVID-19 pandemic. The offline Odia adaptation of MAIN was developed by Nanda S., & Raman, M.G. (2019). The online Odia version used for eliciting the data in this corpus parallels as closely as possible the offline version. The modified Odia protocols were developed by Shireen, F. and were based on the online Slovak version developed by Kapalkova, S., Bicianova, A., & Nemcova, M. (2021). See the description for the MAIN Project here

Acknowledgements

We would like to thank the team at ZAS: Natalia Gagarina for motivating us to work on MAIN and Alyona Sternharz for patiently going through the transcriptions at various points in time.