Predicting academic success of engineering students in technical drawing from visualization test scores
Autor(a) principal: | |
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Data de Publicação: | 2002 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/221015 |
Resumo: | While observing the difficulties of first-year engineering students toward learning technical drawing, taking into account the progressively reduced work time with them, and recognizing the importance of spatial aptitude in the engineering profession, we feel the necessity to improve the teaching methodologies in this subject. In our opinion, in order to effectively plan the didactic process, it is necessary to detect as early as possible those students who require more attention and support. This study proposes an investigation of a visualization psychometric test that could facilitate an early diagnosis concerning the academic performance of technical drawing students. To this end, a computerized version of the Mental Cutting Test (MCT) was carried out on a sample of Brazilian engineering students from the Paulista State University at Guaratingueta Campus (UNESP) and from the Polytechnic School of Sao Paulo University (EPUSP). The test was analyzed by the Item Response Theory, with the Rasch model, a measurement model with optimal properties in order to estimate the level in spatial aptitude of the examinees. The results suggest that MCT can be useful in detecting those students with different performance levels in technical drawing. |
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Repositório Institucional da UNESP |
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Predicting academic success of engineering students in technical drawing from visualization test scoresRasch modelSpatial aptitudeTechnical drawingWhile observing the difficulties of first-year engineering students toward learning technical drawing, taking into account the progressively reduced work time with them, and recognizing the importance of spatial aptitude in the engineering profession, we feel the necessity to improve the teaching methodologies in this subject. In our opinion, in order to effectively plan the didactic process, it is necessary to detect as early as possible those students who require more attention and support. This study proposes an investigation of a visualization psychometric test that could facilitate an early diagnosis concerning the academic performance of technical drawing students. To this end, a computerized version of the Mental Cutting Test (MCT) was carried out on a sample of Brazilian engineering students from the Paulista State University at Guaratingueta Campus (UNESP) and from the Polytechnic School of Sao Paulo University (EPUSP). The test was analyzed by the Item Response Theory, with the Rasch model, a measurement model with optimal properties in order to estimate the level in spatial aptitude of the examinees. The results suggest that MCT can be useful in detecting those students with different performance levels in technical drawing.Psychology Faculty Salamanca University, Avda. de la Merced, 109-131Mechanics Dept. Engineering Faculty Paulista State University, Av. Ariberto Pereira da Cunha, 333Mechanics Dept. Engineering Faculty Paulista State University, Av. Ariberto Pereira da Cunha, 333Salamanca UniversityUniversidade Estadual Paulista (UNESP)Adanez, Gerardo PrietoVelasco, Angela Dias [UNESP]2022-04-28T19:08:42Z2022-04-28T19:08:42Z2002-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article99-109Journal for Geometry and Graphics, v. 6, n. 1, p. 99-109, 2002.1433-8157http://hdl.handle.net/11449/2210152-s2.0-85042015555Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal for Geometry and Graphicsinfo:eu-repo/semantics/openAccess2022-04-28T19:08:42Zoai:repositorio.unesp.br:11449/221015Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:08:42Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Predicting academic success of engineering students in technical drawing from visualization test scores |
title |
Predicting academic success of engineering students in technical drawing from visualization test scores |
spellingShingle |
Predicting academic success of engineering students in technical drawing from visualization test scores Adanez, Gerardo Prieto Rasch model Spatial aptitude Technical drawing |
title_short |
Predicting academic success of engineering students in technical drawing from visualization test scores |
title_full |
Predicting academic success of engineering students in technical drawing from visualization test scores |
title_fullStr |
Predicting academic success of engineering students in technical drawing from visualization test scores |
title_full_unstemmed |
Predicting academic success of engineering students in technical drawing from visualization test scores |
title_sort |
Predicting academic success of engineering students in technical drawing from visualization test scores |
author |
Adanez, Gerardo Prieto |
author_facet |
Adanez, Gerardo Prieto Velasco, Angela Dias [UNESP] |
author_role |
author |
author2 |
Velasco, Angela Dias [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Salamanca University Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Adanez, Gerardo Prieto Velasco, Angela Dias [UNESP] |
dc.subject.por.fl_str_mv |
Rasch model Spatial aptitude Technical drawing |
topic |
Rasch model Spatial aptitude Technical drawing |
description |
While observing the difficulties of first-year engineering students toward learning technical drawing, taking into account the progressively reduced work time with them, and recognizing the importance of spatial aptitude in the engineering profession, we feel the necessity to improve the teaching methodologies in this subject. In our opinion, in order to effectively plan the didactic process, it is necessary to detect as early as possible those students who require more attention and support. This study proposes an investigation of a visualization psychometric test that could facilitate an early diagnosis concerning the academic performance of technical drawing students. To this end, a computerized version of the Mental Cutting Test (MCT) was carried out on a sample of Brazilian engineering students from the Paulista State University at Guaratingueta Campus (UNESP) and from the Polytechnic School of Sao Paulo University (EPUSP). The test was analyzed by the Item Response Theory, with the Rasch model, a measurement model with optimal properties in order to estimate the level in spatial aptitude of the examinees. The results suggest that MCT can be useful in detecting those students with different performance levels in technical drawing. |
publishDate |
2002 |
dc.date.none.fl_str_mv |
2002-01-01 2022-04-28T19:08:42Z 2022-04-28T19:08:42Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Journal for Geometry and Graphics, v. 6, n. 1, p. 99-109, 2002. 1433-8157 http://hdl.handle.net/11449/221015 2-s2.0-85042015555 |
identifier_str_mv |
Journal for Geometry and Graphics, v. 6, n. 1, p. 99-109, 2002. 1433-8157 2-s2.0-85042015555 |
url |
http://hdl.handle.net/11449/221015 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal for Geometry and Graphics |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
99-109 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803649886842781696 |