Investigating the Impact of Teacher Feedback on Content Revisions in EFL Students’ Writing by the Automated Tracking Approach

Gary Cheng; Mike Hin Leung Chui; Bernie Chun Nam Mak

doi:10.1007/978-3-030-92836-0_31

Investigating the Impact of Teacher Feedback on Content Revisions in EFL Students’ Writing by the Automated Tracking Approach

Gary Cheng^*, Mike Hin Leung Chui, Bernie Chun Nam Mak

^*Corresponding author for this work

School of Continuing Education

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Teacher feedback plays an important role in motivating students who learn English as a Foreign Language (EFL) to revise and improve their writing. Investigating how EFL students revise their writing in response to various types of teacher feedback has long received research attention. However, such investigation is human dependent, which requires considerable time and effort, limiting its application to a wide range of EFL writing classes in practice. To address this issue, this study was designed to adopt a systematic and automated approach to investigate the impact of different types of teacher feedback on the content revisions of EFL students at a larger scale. A total of 114 EFL undergraduate students and 3 experienced English teachers from three universities participated in this study. The writing draft and final writing of students and the written feedback from teachers were collected as data and were entered into an automated tracking system for analysis. The analytical results indicate that constructive criticism and imperative appeared to be most effective in triggering content revisions, advice could have a comparable effect, and that question was least effective. The automated analysis would be helpful to teachers in making informed adjustments to their feedback and instructional strategies to better support student revisions.

Original language	English
Title of host publication	Emerging Technologies for Education - 6th International Symposium, SETE 2021, Revised Selected Papers
Editors	Weijia Jia, Yong Tang, Raymond S. Lee, Michael Herzog, Hui Zhang, Tianyong Hao, Tian Wang
Publisher	Springer Cham
Pages	355-363
Number of pages	9
Edition	1st
ISBN (Electronic)	9783030928360
ISBN (Print)	9783030928353
DOIs	https://doi.org/10.1007/978-3-030-92836-0_31
Publication status	Published - 11 Nov 2021
Event	6th International Symposium on Emerging Technologies for Education, SETE 2021 = 第六届新兴教育技术国际研讨会 - Zhuhai, China Duration: 11 Nov 2021 → 12 Nov 2021 https://sete2021.uic.edu.cn/program_agenda.htm (Conference program) https://sete2021.uic.edu.cn/cn/sete1111.pdf (Conference manual) https://link.springer.com/book/10.1007/978-3-030-92836-0 (Conference proceedings)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Symposium on Emerging Technologies for Education, SETE 2021 = 第六届新兴教育技术国际研讨会
Country/Territory	China
City	Zhuhai
Period	11/11/21 → 12/11/21
Internet address	https://sete2021.uic.edu.cn/program_agenda.htm (Conference program) https://sete2021.uic.edu.cn/cn/sete1111.pdf (Conference manual) https://link.springer.com/book/10.1007/978-3-030-92836-0 (Conference proceedings)

Scopus Subject Areas

Theoretical Computer Science
General Computer Science

User-Defined Keywords

Automated tracking
Content revision
Second language writing
Teacher feedback

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-92836-0_31

Cite this

Cheng, G., Chui, M. H. L., & Mak, B. C. N. (2021). Investigating the Impact of Teacher Feedback on Content Revisions in EFL Students’ Writing by the Automated Tracking Approach. In W. Jia, Y. Tang, R. S. Lee, M. Herzog, H. Zhang, T. Hao, & T. Wang (Eds.), Emerging Technologies for Education - 6th International Symposium, SETE 2021, Revised Selected Papers (1st ed., pp. 355-363). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Springer Cham. https://doi.org/10.1007/978-3-030-92836-0_31

Cheng, Gary ; Chui, Mike Hin Leung ; Mak, Bernie Chun Nam. / Investigating the Impact of Teacher Feedback on Content Revisions in EFL Students’ Writing by the Automated Tracking Approach. Emerging Technologies for Education - 6th International Symposium, SETE 2021, Revised Selected Papers. editor / Weijia Jia ; Yong Tang ; Raymond S. Lee ; Michael Herzog ; Hui Zhang ; Tianyong Hao ; Tian Wang. 1st. ed. Springer Cham, 2021. pp. 355-363 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Investigating the Impact of Teacher Feedback on Content Revisions in EFL Students{\textquoteright} Writing by the Automated Tracking Approach",

abstract = "Teacher feedback plays an important role in motivating students who learn English as a Foreign Language (EFL) to revise and improve their writing. Investigating how EFL students revise their writing in response to various types of teacher feedback has long received research attention. However, such investigation is human dependent, which requires considerable time and effort, limiting its application to a wide range of EFL writing classes in practice. To address this issue, this study was designed to adopt a systematic and automated approach to investigate the impact of different types of teacher feedback on the content revisions of EFL students at a larger scale. A total of 114 EFL undergraduate students and 3 experienced English teachers from three universities participated in this study. The writing draft and final writing of students and the written feedback from teachers were collected as data and were entered into an automated tracking system for analysis. The analytical results indicate that constructive criticism and imperative appeared to be most effective in triggering content revisions, advice could have a comparable effect, and that question was least effective. The automated analysis would be helpful to teachers in making informed adjustments to their feedback and instructional strategies to better support student revisions.",

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booktitle = "Emerging Technologies for Education - 6th International Symposium, SETE 2021, Revised Selected Papers",

edition = "1st",

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Cheng, G, Chui, MHL & Mak, BCN 2021, Investigating the Impact of Teacher Feedback on Content Revisions in EFL Students’ Writing by the Automated Tracking Approach. in W Jia, Y Tang, RS Lee, M Herzog, H Zhang, T Hao & T Wang (eds), Emerging Technologies for Education - 6th International Symposium, SETE 2021, Revised Selected Papers. 1st edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Cham, pp. 355-363, 6th International Symposium on Emerging Technologies for Education, SETE 2021 = 第六届新兴教育技术国际研讨会, Zhuhai, China, 11/11/21. https://doi.org/10.1007/978-3-030-92836-0_31

Investigating the Impact of Teacher Feedback on Content Revisions in EFL Students’ Writing by the Automated Tracking Approach. / Cheng, Gary; Chui, Mike Hin Leung; Mak, Bernie Chun Nam.
Emerging Technologies for Education - 6th International Symposium, SETE 2021, Revised Selected Papers. ed. / Weijia Jia; Yong Tang; Raymond S. Lee; Michael Herzog; Hui Zhang; Tianyong Hao; Tian Wang. 1st. ed. Springer Cham, 2021. p. 355-363 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Investigating the Impact of Teacher Feedback on Content Revisions in EFL Students’ Writing by the Automated Tracking Approach

AU - Cheng, Gary

AU - Chui, Mike Hin Leung

AU - Mak, Bernie Chun Nam

PY - 2021/11/11

Y1 - 2021/11/11

N2 - Teacher feedback plays an important role in motivating students who learn English as a Foreign Language (EFL) to revise and improve their writing. Investigating how EFL students revise their writing in response to various types of teacher feedback has long received research attention. However, such investigation is human dependent, which requires considerable time and effort, limiting its application to a wide range of EFL writing classes in practice. To address this issue, this study was designed to adopt a systematic and automated approach to investigate the impact of different types of teacher feedback on the content revisions of EFL students at a larger scale. A total of 114 EFL undergraduate students and 3 experienced English teachers from three universities participated in this study. The writing draft and final writing of students and the written feedback from teachers were collected as data and were entered into an automated tracking system for analysis. The analytical results indicate that constructive criticism and imperative appeared to be most effective in triggering content revisions, advice could have a comparable effect, and that question was least effective. The automated analysis would be helpful to teachers in making informed adjustments to their feedback and instructional strategies to better support student revisions.

AB - Teacher feedback plays an important role in motivating students who learn English as a Foreign Language (EFL) to revise and improve their writing. Investigating how EFL students revise their writing in response to various types of teacher feedback has long received research attention. However, such investigation is human dependent, which requires considerable time and effort, limiting its application to a wide range of EFL writing classes in practice. To address this issue, this study was designed to adopt a systematic and automated approach to investigate the impact of different types of teacher feedback on the content revisions of EFL students at a larger scale. A total of 114 EFL undergraduate students and 3 experienced English teachers from three universities participated in this study. The writing draft and final writing of students and the written feedback from teachers were collected as data and were entered into an automated tracking system for analysis. The analytical results indicate that constructive criticism and imperative appeared to be most effective in triggering content revisions, advice could have a comparable effect, and that question was least effective. The automated analysis would be helpful to teachers in making informed adjustments to their feedback and instructional strategies to better support student revisions.

KW - Automated tracking

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Cheng G, Chui MHL, Mak BCN. Investigating the Impact of Teacher Feedback on Content Revisions in EFL Students’ Writing by the Automated Tracking Approach. In Jia W, Tang Y, Lee RS, Herzog M, Zhang H, Hao T, Wang T, editors, Emerging Technologies for Education - 6th International Symposium, SETE 2021, Revised Selected Papers. 1st ed. Springer Cham. 2021. p. 355-363. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-92836-0_31

Investigating the Impact of Teacher Feedback on Content Revisions in EFL Students’ Writing by the Automated Tracking Approach

Abstract

Publication series

Conference

Scopus Subject Areas

User-Defined Keywords

UN SDGs

Access to Document

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