Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance

Autores
Cascallar, Eduardo; Musso, Mariel Fernanda
Año de publicación
2008
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This research describes the application of a neural networks approach in the prediction of readiness for reading upon entry to primary education. Machine-learning techniques used offer an iterative methodology that is capable of discovering complex relationships and interactions in the inputs and outcomes. The approach maximized classification accuracy, and was able to model various outcome patterns from the over 700 students studied. Results based on hypotheses of student characteristics using these predictive modeling achieved a total accuracy of 98% in the identification of "students-below-readiness-threshold". The presentation explains the processes and the stream analysis technique utilized, and explores various alternative models.
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
Fil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; Argentina
XXIX International Congress of Psychology
Berlín
Alemania
International Union of Psychological Science
Materia
Prediction
Reading
Performance
Classification
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/238563
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/238563
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Classificatory stream analysis in the prediction of expected reading readiness: understanding student performanceCascallar, EduardoMusso, Mariel FernandaPredictionReadingPerformanceClassificationhttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5This research describes the application of a neural networks approach in the prediction of readiness for reading upon entry to primary education. Machine-learning techniques used offer an iterative methodology that is capable of discovering complex relationships and interactions in the inputs and outcomes. The approach maximized classification accuracy, and was able to model various outcome patterns from the over 700 students studied. Results based on hypotheses of student characteristics using these predictive modeling achieved a total accuracy of 98% in the identification of "students-below-readiness-threshold". The presentation explains the processes and the stream analysis technique utilized, and explores various alternative models.Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; BélgicaFil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; ArgentinaXXIX International Congress of PsychologyBerlínAlemaniaInternational Union of Psychological ScienceWiley2008info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoJournalhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/238563Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance; XXIX International Congress of Psychology; Berlín; Alemania; 2008; 242-2420020-75941464-066XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/epdf/10.1080/00207594.2008.10108484info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/toc/1464066x/2008/43/3-4Internacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:17:00Zoai:ri.conicet.gov.ar:11336/238563instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:17:00.767CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance
title Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance
spellingShingle Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance
Cascallar, Eduardo
Prediction
Reading
Performance
Classification
title_short Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance
title_full Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance
title_fullStr Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance
title_full_unstemmed Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance
title_sort Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance
dc.creator.none.fl_str_mv Cascallar, Eduardo
Musso, Mariel Fernanda
author Cascallar, Eduardo
author_facet Cascallar, Eduardo
Musso, Mariel Fernanda
author_role author
author2 Musso, Mariel Fernanda
author2_role author
dc.subject.none.fl_str_mv Prediction
Reading
Performance
Classification
topic Prediction
Reading
Performance
Classification
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.1
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv This research describes the application of a neural networks approach in the prediction of readiness for reading upon entry to primary education. Machine-learning techniques used offer an iterative methodology that is capable of discovering complex relationships and interactions in the inputs and outcomes. The approach maximized classification accuracy, and was able to model various outcome patterns from the over 700 students studied. Results based on hypotheses of student characteristics using these predictive modeling achieved a total accuracy of 98% in the identification of "students-below-readiness-threshold". The presentation explains the processes and the stream analysis technique utilized, and explores various alternative models.
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
Fil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; Argentina
XXIX International Congress of Psychology
Berlín
Alemania
International Union of Psychological Science
description This research describes the application of a neural networks approach in the prediction of readiness for reading upon entry to primary education. Machine-learning techniques used offer an iterative methodology that is capable of discovering complex relationships and interactions in the inputs and outcomes. The approach maximized classification accuracy, and was able to model various outcome patterns from the over 700 students studied. Results based on hypotheses of student characteristics using these predictive modeling achieved a total accuracy of 98% in the identification of "students-below-readiness-threshold". The presentation explains the processes and the stream analysis technique utilized, and explores various alternative models.
publishDate 2008
dc.date.none.fl_str_mv 2008
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Congreso
Journal
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/238563
Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance; XXIX International Congress of Psychology; Berlín; Alemania; 2008; 242-242
0020-7594
1464-066X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/238563
identifier_str_mv Classificatory stream analysis in the prediction of expected reading readiness: understanding student performance; XXIX International Congress of Psychology; Berlín; Alemania; 2008; 242-242
0020-7594
1464-066X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/epdf/10.1080/00207594.2008.10108484
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/toc/1464066x/2008/43/3-4
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.coverage.none.fl_str_mv Internacional
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844614119224770560
score 13.070432

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