Publicação
Enhancing writing proficiency classification in developmental education: the quest for accuracy
| datacite.subject.sdg | 04:Educação de Qualidade | |
| datacite.subject.sdg | 10:Reduzir as Desigualdades | |
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| dc.contributor.author | Da Corte, Miguel | |
| dc.contributor.author | Baptista, Jorge | |
| dc.date.accessioned | 2026-05-15T15:14:37Z | |
| dc.date.available | 2026-05-15T15:14:37Z | |
| dc.date.issued | 2024 | |
| dc.description.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. | eng |
| dc.description.sponsorship | 1010094837, HORIZON CL2-2022-TRANSFORMATIONS-01-07 | |
| dc.identifier.isbn | 978-249381410-4 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/28980 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | European Language Resources Association (ELRA) | |
| dc.relation | Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | Developmental education | |
| dc.subject | Machine learning | |
| dc.subject | Natural language processing | |
| dc.subject | Corpus annotation methods | |
| dc.title | Enhancing writing proficiency classification in developmental education: the quest for accuracy | eng |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.awardNumber | UIDB/50021/2020 | |
| oaire.awardTitle | Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT | |
| oaire.citation.endPage | 6143 | |
| oaire.citation.startPage | 6134 | |
| oaire.citation.title | 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Da Corte | |
| person.familyName | Baptista | |
| person.givenName | Miguel | |
| person.givenName | Jorge | |
| person.identifier.ciencia-id | 7010-5366-22C5 | |
| person.identifier.orcid | 0000-0001-8782-8377 | |
| person.identifier.orcid | 0000-0003-4603-4364 | |
| person.identifier.rid | H-7699-2013 | |
| person.identifier.scopus-author-id | 14035269500 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
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