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An assessment of the weight of the experimental component in physics and chemistry classes

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Resumo:ExperimentalworkplaysacentralroleinPhysicsandChemistryteach- ing. However, the use of experimental work depends on the perception that teacher has about the gains in terms of the students’ motivation and learning. Thus, this study aims to evaluate the weight of the experimental component in the chemistry teaching focusing on four topics, i.e., material resources, teaching methodologies, learning achievements, and teacher engagement. For this purpose, a questionnaire was developed and applied to a cohort comprising 129 Physics and Chemistry teachers of both genders, aged between 26 and 60 years old. The questionnaire consists of two sections, the first of which contains general questions, whereas the second contains information on the topics mentioned above. Mathematical-logical programs are presented, considering the teachers’ opinions in terms of Best and Worst-case Scenarios, complemented with a computer approach based on artificial neural networks. The model was trained and tested with real data exhibiting an overall accuracy of 91.5%.
Autores principais:Figueiredo, M.
Outros Autores:Esteves, M.; Chaves, Humberto; Neves, J.; Vicente, H.
Assunto:Experimental work Thermodynamics Entropy Knowledge representation and reasoning Logic programming Artificial neural networks Decision support system
Ano:2022
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso restrito
Instituição associada:Instituto Politécnico de Beja
Idioma:inglês
Origem:Repositório Institucional do IPBeja
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author Figueiredo, M.
author2 Esteves, M.
Chaves, Humberto
Neves, J.
Vicente, H.
author2_role author
author
author
author
author_facet Figueiredo, M.
Esteves, M.
Chaves, Humberto
Neves, J.
Vicente, H.
author_role author
country_str PT
creators_json_txt [{\"Person.name\":\"Figueiredo, M.\"},{\"Person.name\":\"Esteves, M.\"},{\"Person.name\":\"Chaves, Humberto\"},{\"Person.name\":\"Neves, J.\"},{\"Person.name\":\"Vicente, H.\"}]
datacite.creators.creator.creatorName.fl_str_mv Figueiredo, M.
Esteves, M.
Chaves, Humberto
Neves, J.
Vicente, H.
datacite.date.Accepted.fl_str_mv 2022-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2022-12-13T13:03:54Z
datacite.date.embargoed.fl_str_mv 2022-12-13T13:03:54Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_16ec
datacite.subjects.subject.fl_str_mv Experimental work
Thermodynamics
Entropy
Knowledge representation and reasoning
Logic programming
Artificial neural networks
Decision support system
datacite.titles.title.fl_str_mv An assessment of the weight of the experimental component in physics and chemistry classes
dc.creator.none.fl_str_mv Figueiredo, M.
Esteves, M.
Chaves, Humberto
Neves, J.
Vicente, H.
dc.date.Accepted.fl_str_mv 2022-01-01T00:00:00Z
dc.date.available.fl_str_mv 2022-12-13T13:03:54Z
dc.date.embargoed.fl_str_mv 2022-12-13T13:03:54Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12207/5684
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv Springer
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.subject.none.fl_str_mv Experimental work
Thermodynamics
Entropy
Knowledge representation and reasoning
Logic programming
Artificial neural networks
Decision support system
dc.title.fl_str_mv An assessment of the weight of the experimental component in physics and chemistry classes
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_5794
description ExperimentalworkplaysacentralroleinPhysicsandChemistryteach- ing. However, the use of experimental work depends on the perception that teacher has about the gains in terms of the students’ motivation and learning. Thus, this study aims to evaluate the weight of the experimental component in the chemistry teaching focusing on four topics, i.e., material resources, teaching methodologies, learning achievements, and teacher engagement. For this purpose, a questionnaire was developed and applied to a cohort comprising 129 Physics and Chemistry teachers of both genders, aged between 26 and 60 years old. The questionnaire consists of two sections, the first of which contains general questions, whereas the second contains information on the topics mentioned above. Mathematical-logical programs are presented, considering the teachers’ opinions in terms of Best and Worst-case Scenarios, complemented with a computer approach based on artificial neural networks. The model was trained and tested with real data exhibiting an overall accuracy of 91.5%.
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identifier.url.fl_str_mv https://hdl.handle.net/20.500.12207/5684
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institution Instituto Politécnico de Beja
instname_str Instituto Politécnico de Beja
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organization_str_mv urn:organizationAcronym:ipb
person_str_mv Figueiredo, M.
Esteves, M.
Chaves, Humberto
Neves, J.
Vicente, H.
publishDate 2022
publisher.none.fl_str_mv Springer
reponame_str Repositório Institucional do IPBeja
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spelling porExperimentalworkplaysacentralroleinPhysicsandChemistryteach- ing. However, the use of experimental work depends on the perception that teacher has about the gains in terms of the students’ motivation and learning. Thus, this study aims to evaluate the weight of the experimental component in the chemistry teaching focusing on four topics, i.e., material resources, teaching methodologies, learning achievements, and teacher engagement. For this purpose, a questionnaire was developed and applied to a cohort comprising 129 Physics and Chemistry teachers of both genders, aged between 26 and 60 years old. The questionnaire consists of two sections, the first of which contains general questions, whereas the second contains information on the topics mentioned above. Mathematical-logical programs are presented, considering the teachers’ opinions in terms of Best and Worst-case Scenarios, complemented with a computer approach based on artificial neural networks. The model was trained and tested with real data exhibiting an overall accuracy of 91.5%.application/pdfengSpringerporAn assessment of the weight of the experimental component in physics and chemistry classesFigueiredo, M.Esteves, M.Chaves, HumbertoNeves, J.Vicente, H.Handlehttps://hdl.handle.net/20.500.12207/5684ISBNIsPartOf978-3-030-92666-3DOIIsPartOfhttps://doi.org/10.1007/978-3-030-92666-3_292022-12-13T13:03:54Z2022-01-01T00:00:00Z2022-01http://purl.org/coar/access_right/c_16ecrestricted accessporExperimental workporThermodynamicsporEntropyporKnowledge representation and reasoningporLogic programmingporArtificial neural networksporDecision support system432697 byteshttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://repositorio.ipbeja.pt/bitstreams/33bcd43a-e32d-4003-bff8-1f74dc58c980/downloadother research producthttp://purl.org/coar/resource_type/c_5794conference paper
spellingShingle An assessment of the weight of the experimental component in physics and chemistry classes
Figueiredo, M.
Experimental work
Thermodynamics
Entropy
Knowledge representation and reasoning
Logic programming
Artificial neural networks
Decision support system
status SINGLETON
subject.fl_str_mv Experimental work
Thermodynamics
Entropy
Knowledge representation and reasoning
Logic programming
Artificial neural networks
Decision support system
title An assessment of the weight of the experimental component in physics and chemistry classes
title_full An assessment of the weight of the experimental component in physics and chemistry classes
title_fullStr An assessment of the weight of the experimental component in physics and chemistry classes
title_full_unstemmed An assessment of the weight of the experimental component in physics and chemistry classes
title_short An assessment of the weight of the experimental component in physics and chemistry classes
title_sort An assessment of the weight of the experimental component in physics and chemistry classes
topic Experimental work
Thermodynamics
Entropy
Knowledge representation and reasoning
Logic programming
Artificial neural networks
Decision support system
topic_facet Experimental work
Thermodynamics
Entropy
Knowledge representation and reasoning
Logic programming
Artificial neural networks
Decision support system
url https://hdl.handle.net/20.500.12207/5684
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