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        <title>A Dataset for Investigating the Impact of Feedback on Student Revision Outcome</title>
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    <abstract>We present an annotation scheme and a dataset of teacher feedback provided for texts written by non-native speakers of English. The dataset consists of student-written sentences in their original and revised versions with teacher feedback provided for the errors. Feedback appears both in the form of open-ended comments and error category tags. We focus on a specific error type, namely linking adverbial (e.g. however, moreover) errors. The dataset has been annotated for two aspects: (i) revision outcome establishing whether the re-written student sentence was correct and (ii) directness, indicating whether teachers provided explicitly the correction in their feedback. This dataset allows for studies around the characteristics of teacher feedback and how these influence students’ revision outcome. We describe the data preparation process and we present initial statistical investigations regarding the effect of different feedback characteristics on revision outcome. These show that open-ended comments and mitigating expressions appear in a higher proportion of successful revisions than unsuccessful ones, while directness and metalinguistic terms have no effect. Given that the use of this type of data is relatively unexplored in natural language processing (NLP) applications, we also report some observations and challenges when working with feedback data.</abstract>
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%0 Conference Proceedings
%T A Dataset for Investigating the Impact of Feedback on Student Revision Outcome
%A Pilan, Ildiko
%A Lee, John
%A Yeung, Chak Yan
%A Webster, Jonathan
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F pilan-etal-2020-dataset
%X We present an annotation scheme and a dataset of teacher feedback provided for texts written by non-native speakers of English. The dataset consists of student-written sentences in their original and revised versions with teacher feedback provided for the errors. Feedback appears both in the form of open-ended comments and error category tags. We focus on a specific error type, namely linking adverbial (e.g. however, moreover) errors. The dataset has been annotated for two aspects: (i) revision outcome establishing whether the re-written student sentence was correct and (ii) directness, indicating whether teachers provided explicitly the correction in their feedback. This dataset allows for studies around the characteristics of teacher feedback and how these influence students’ revision outcome. We describe the data preparation process and we present initial statistical investigations regarding the effect of different feedback characteristics on revision outcome. These show that open-ended comments and mitigating expressions appear in a higher proportion of successful revisions than unsuccessful ones, while directness and metalinguistic terms have no effect. Given that the use of this type of data is relatively unexplored in natural language processing (NLP) applications, we also report some observations and challenges when working with feedback data.
%U https://aclanthology.org/2020.lrec-1.41
%P 332-339

Markdown (Informal)

[A Dataset for Investigating the Impact of Feedback on Student Revision Outcome](https://aclanthology.org/2020.lrec-1.41) (Pilan et al., LREC 2020)

• A Dataset for Investigating the Impact of Feedback on Student Revision Outcome (Pilan et al., LREC 2020)

ACL

• Ildiko Pilan, John Lee, Chak Yan Yeung, and Jonathan Webster. 2020. A Dataset for Investigating the Impact of Feedback on Student Revision Outcome. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 332–339, Marseille, France. European Language Resources Association.