A Dataset for Investigating the Impact of Feedback on Student Revision Outcome

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationLREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
PublisherEuropean Language Resources Association (ELRA)
Pages332–339
ISBN (Electronic)9791095546344
Publication statusPublished - May 2020

Publication series

NameLREC - International Conference on Language Resources and Evaluation, Conference Proceedings

Conference

Title12th Conference on Language Resources and Evaluation (LREC 2020)
Location
PlaceFrance
CityMarseille
Period11 - 16 May 2020

Link(s)

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.

Research Area(s)

  • corrective feedback, hedging, error correction, Intelligent Computer-Assisted Language Learning

Citation Format(s)

A Dataset for Investigating the Impact of Feedback on Student Revision Outcome. / Pilán, Ildikó; Lee, John; Yeung, Chak Yan; Webster, Jonathan.

LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings. European Language Resources Association (ELRA), 2020. p. 332–339 (LREC - International Conference on Language Resources and Evaluation, Conference Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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