@inproceedings{da-corte-baptista-2024-enhancing-writing,
title = "Enhancing Writing Proficiency Classification in Developmental Education: The Quest for Accuracy",
author = "Da Corte, Miguel and
Baptista, Jorge",
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.542",
pages = "6134--6143",
abstract = "Developmental Education (DevEd) courses align students{'} college-readiness skills with higher education literacy demands. These courses often use automated assessment tools like Accuplacer for student placement. Existing literature raises concerns about these exams{'} accuracy and placement precision due to their narrow representation of the writing process. These concerns warrant further attention within the domain of automatic placement systems, particularly in the establishment of a reference corpus of annotated essays for these systems{'} machine/deep learning. This study aims at an enhanced annotation procedure to assess college students{'} writing patterns more accurately. It examines the efficacy of machine-learning-based DevEd placement, contrasting Accuplacer{'}s classification of 100 college-intending students{'} essays into two levels (Level 1 and 2) against that of 6 human raters. The classification task encompassed the assessment of the 6 textual criteria currently used by Accuplacer: mechanical conventions, sentence variety {\&} style, idea development {\&} support, organization {\&} structure, purpose {\&} focus, and critical thinking. Results revealed low inter-rater agreement, both on the individual criteria and the overall classification, suggesting human assessment of writing proficiency can be inconsistent in this context. To achieve a more accurate determination of writing proficiency and improve DevEd placement, more robust classification methods are thus required.",
}
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<abstract>Developmental Education (DevEd) courses align students’ college-readiness skills with higher education literacy demands. These courses often use automated assessment tools like Accuplacer for student placement. Existing literature raises concerns about these exams’ accuracy and placement precision due to their narrow representation of the writing process. These concerns warrant further attention within the domain of automatic placement systems, particularly in the establishment of a reference corpus of annotated essays for these systems’ machine/deep learning. This study aims at an enhanced annotation procedure to assess college students’ writing patterns more accurately. It examines the efficacy of machine-learning-based DevEd placement, contrasting Accuplacer’s classification of 100 college-intending students’ essays into two levels (Level 1 and 2) against that of 6 human raters. The classification task encompassed the assessment of the 6 textual criteria currently used by Accuplacer: mechanical conventions, sentence variety & style, idea development & support, organization & structure, purpose & focus, and critical thinking. Results revealed low inter-rater agreement, both on the individual criteria and the overall classification, suggesting human assessment of writing proficiency can be inconsistent in this context. To achieve a more accurate determination of writing proficiency and improve DevEd placement, more robust classification methods are thus required.</abstract>
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%0 Conference Proceedings
%T Enhancing Writing Proficiency Classification in Developmental Education: The Quest for Accuracy
%A Da Corte, Miguel
%A Baptista, Jorge
%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 da-corte-baptista-2024-enhancing-writing
%X Developmental Education (DevEd) courses align students’ college-readiness skills with higher education literacy demands. These courses often use automated assessment tools like Accuplacer for student placement. Existing literature raises concerns about these exams’ accuracy and placement precision due to their narrow representation of the writing process. These concerns warrant further attention within the domain of automatic placement systems, particularly in the establishment of a reference corpus of annotated essays for these systems’ machine/deep learning. This study aims at an enhanced annotation procedure to assess college students’ writing patterns more accurately. It examines the efficacy of machine-learning-based DevEd placement, contrasting Accuplacer’s classification of 100 college-intending students’ essays into two levels (Level 1 and 2) against that of 6 human raters. The classification task encompassed the assessment of the 6 textual criteria currently used by Accuplacer: mechanical conventions, sentence variety & style, idea development & support, organization & structure, purpose & focus, and critical thinking. Results revealed low inter-rater agreement, both on the individual criteria and the overall classification, suggesting human assessment of writing proficiency can be inconsistent in this context. To achieve a more accurate determination of writing proficiency and improve DevEd placement, more robust classification methods are thus required.
%U https://aclanthology.org/2024.lrec-main.542
%P 6134-6143
Markdown (Informal)
[Enhancing Writing Proficiency Classification in Developmental Education: The Quest for Accuracy](https://aclanthology.org/2024.lrec-main.542) (Da Corte & Baptista, LREC-COLING 2024)
ACL