@inproceedings{velentzas-etal-2024-logging,
title = "Logging Keystrokes in Writing by {E}nglish Learners",
author = "Velentzas, Georgios and
Caines, Andrew and
Borgo, Rita and
Pacquetet, Erin and
Hamilton, Clive and
Arnold, Taylor and
Nicholls, Diane and
Buttery, Paula and
Gaillat, Thomas and
Ballier, Nicolas and
Yannakoudakis, Helen",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.938",
pages = "10725--10746",
abstract = "Essay writing is a skill commonly taught and practised in schools. The ability to write a fluent and persuasive essay is often a major component of formal assessment. In natural language processing and education technology we may work with essays in their final form, for example to carry out automated assessment or grammatical error correction. In this work we collect and analyse data representing the essay writing process from start to finish, by recording every key stroke from multiple writers participating in our study. We describe our data collection methodology, the characteristics of the resulting dataset, and the assignment of proficiency levels to the texts. We discuss the ways the keystroke data can be used {--} for instance seeking to identify patterns in the keystrokes which might act as features in automated assessment or may enable further advancements in writing assistance {--} and the writing support technology which could be built with such information, if we can detect when writers are struggling to compose a section of their essay and offer appropriate intervention. We frame this work in the context of English language learning, but we note that keystroke logging is relevant more broadly to text authoring scenarios as well as cognitive or linguistic analyses of the writing process.",
}
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<abstract>Essay writing is a skill commonly taught and practised in schools. The ability to write a fluent and persuasive essay is often a major component of formal assessment. In natural language processing and education technology we may work with essays in their final form, for example to carry out automated assessment or grammatical error correction. In this work we collect and analyse data representing the essay writing process from start to finish, by recording every key stroke from multiple writers participating in our study. We describe our data collection methodology, the characteristics of the resulting dataset, and the assignment of proficiency levels to the texts. We discuss the ways the keystroke data can be used – for instance seeking to identify patterns in the keystrokes which might act as features in automated assessment or may enable further advancements in writing assistance – and the writing support technology which could be built with such information, if we can detect when writers are struggling to compose a section of their essay and offer appropriate intervention. We frame this work in the context of English language learning, but we note that keystroke logging is relevant more broadly to text authoring scenarios as well as cognitive or linguistic analyses of the writing process.</abstract>
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%0 Conference Proceedings
%T Logging Keystrokes in Writing by English Learners
%A Velentzas, Georgios
%A Caines, Andrew
%A Borgo, Rita
%A Pacquetet, Erin
%A Hamilton, Clive
%A Arnold, Taylor
%A Nicholls, Diane
%A Buttery, Paula
%A Gaillat, Thomas
%A Ballier, Nicolas
%A Yannakoudakis, Helen
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F velentzas-etal-2024-logging
%X Essay writing is a skill commonly taught and practised in schools. The ability to write a fluent and persuasive essay is often a major component of formal assessment. In natural language processing and education technology we may work with essays in their final form, for example to carry out automated assessment or grammatical error correction. In this work we collect and analyse data representing the essay writing process from start to finish, by recording every key stroke from multiple writers participating in our study. We describe our data collection methodology, the characteristics of the resulting dataset, and the assignment of proficiency levels to the texts. We discuss the ways the keystroke data can be used – for instance seeking to identify patterns in the keystrokes which might act as features in automated assessment or may enable further advancements in writing assistance – and the writing support technology which could be built with such information, if we can detect when writers are struggling to compose a section of their essay and offer appropriate intervention. We frame this work in the context of English language learning, but we note that keystroke logging is relevant more broadly to text authoring scenarios as well as cognitive or linguistic analyses of the writing process.
%U https://aclanthology.org/2024.lrec-main.938
%P 10725-10746
Markdown (Informal)
[Logging Keystrokes in Writing by English Learners](https://aclanthology.org/2024.lrec-main.938) (Velentzas et al., LREC-COLING 2024)
ACL
- Georgios Velentzas, Andrew Caines, Rita Borgo, Erin Pacquetet, Clive Hamilton, Taylor Arnold, Diane Nicholls, Paula Buttery, Thomas Gaillat, Nicolas Ballier, and Helen Yannakoudakis. 2024. Logging Keystrokes in Writing by English Learners. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 10725–10746, Torino, Italia. ELRA and ICCL.