Home > Research > Publications & Outputs > Clustering, visualising and elaborating DDL-int...

Electronic data

View graph of relations

Clustering, visualising and elaborating DDL-integrated error correction process in EFL writing activities

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Published

Standard

Clustering, visualising and elaborating DDL-integrated error correction process in EFL writing activities. / Zhu, Fangzhou; Wang, Tingjun.
2024. 122-135 Paper presented at 2024 (20th) ChinaCALL Conference, Beijing, China.

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Harvard

Zhu, F & Wang, T 2024, 'Clustering, visualising and elaborating DDL-integrated error correction process in EFL writing activities', Paper presented at 2024 (20th) ChinaCALL Conference, Beijing, China, 23/08/24 - 25/08/24 pp. 122-135.

APA

Zhu, F., & Wang, T. (2024). Clustering, visualising and elaborating DDL-integrated error correction process in EFL writing activities. 122-135. Paper presented at 2024 (20th) ChinaCALL Conference, Beijing, China.

Vancouver

Author

Bibtex

@conference{dc52c404b9464dc88404ba33f7f4ed80,
title = "Clustering, visualising and elaborating DDL-integrated error correction process in EFL writing activities",
abstract = "The use of language corpora for EFL (English as a foreign language) learning purposes, known as data-driven learning (DDL), has shown effectiveness from many recent studies. Although DDL seems promising, it is barely popularised in Chinese EFL context for distinct reasons, such as deficiency of process investigation and exclusivity of other available consultation resources in DDL research. This study aims to unpack DDL-integrated EFL writing error correction in real world, where common consultation resources can be referred to along with DDL. Fifty-nine participants in a Chinese university completed six writing tasks with follow-up revisions, eleven of which then joined stimulated recalls. TraMineR, a sequence data analysis toolkit, was used to visualise consultation processes for error correction and cluster representative trajection by error types. Retrospective device provided insightful details to further explain sequential data in error correction processes. Results showed DDL played a significant role in error correction activities, though it functioned variously regarding error types. DDL helped participants either retrieve prior knowledge or explore new linguistic knowledge with multiple cognitive strategies. Drawbacks of DDL also raised by participants, indicating the importance of combining other consultation resources in error correction activities for better performance. ",
keywords = "DDL, error correction, EFL writing, sequence analysis",
author = "Fangzhou Zhu and Tingjun Wang",
year = "2024",
month = sep,
day = "20",
language = "English",
pages = "122--135",
note = "2024 (20th) ChinaCALL Conference : International Congress on English Language Education and Applied Linguistics , ChinaCALL2024&ICELEAL 2024 ; Conference date: 23-08-2024 Through 25-08-2024",
url = "https://www.chinacall.org.cn/conference2024/en_index.html",

}

RIS

TY - CONF

T1 - Clustering, visualising and elaborating DDL-integrated error correction process in EFL writing activities

AU - Zhu, Fangzhou

AU - Wang, Tingjun

PY - 2024/9/20

Y1 - 2024/9/20

N2 - The use of language corpora for EFL (English as a foreign language) learning purposes, known as data-driven learning (DDL), has shown effectiveness from many recent studies. Although DDL seems promising, it is barely popularised in Chinese EFL context for distinct reasons, such as deficiency of process investigation and exclusivity of other available consultation resources in DDL research. This study aims to unpack DDL-integrated EFL writing error correction in real world, where common consultation resources can be referred to along with DDL. Fifty-nine participants in a Chinese university completed six writing tasks with follow-up revisions, eleven of which then joined stimulated recalls. TraMineR, a sequence data analysis toolkit, was used to visualise consultation processes for error correction and cluster representative trajection by error types. Retrospective device provided insightful details to further explain sequential data in error correction processes. Results showed DDL played a significant role in error correction activities, though it functioned variously regarding error types. DDL helped participants either retrieve prior knowledge or explore new linguistic knowledge with multiple cognitive strategies. Drawbacks of DDL also raised by participants, indicating the importance of combining other consultation resources in error correction activities for better performance.

AB - The use of language corpora for EFL (English as a foreign language) learning purposes, known as data-driven learning (DDL), has shown effectiveness from many recent studies. Although DDL seems promising, it is barely popularised in Chinese EFL context for distinct reasons, such as deficiency of process investigation and exclusivity of other available consultation resources in DDL research. This study aims to unpack DDL-integrated EFL writing error correction in real world, where common consultation resources can be referred to along with DDL. Fifty-nine participants in a Chinese university completed six writing tasks with follow-up revisions, eleven of which then joined stimulated recalls. TraMineR, a sequence data analysis toolkit, was used to visualise consultation processes for error correction and cluster representative trajection by error types. Retrospective device provided insightful details to further explain sequential data in error correction processes. Results showed DDL played a significant role in error correction activities, though it functioned variously regarding error types. DDL helped participants either retrieve prior knowledge or explore new linguistic knowledge with multiple cognitive strategies. Drawbacks of DDL also raised by participants, indicating the importance of combining other consultation resources in error correction activities for better performance.

KW - DDL

KW - error correction

KW - EFL writing

KW - sequence analysis

M3 - Conference paper

SP - 122

EP - 135

T2 - 2024 (20th) ChinaCALL Conference

Y2 - 23 August 2024 through 25 August 2024

ER -