Lab Classes in Chemistry Learning - An Artificial Intelligence View

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dc.contributor.authorFigueiredo, Margarida
dc.contributor.authorEsteves, M. Lurdes
dc.contributor.authorNeves, José
dc.contributor.authorVicente, Henrique
dc.contributor.editorde la Puerta, José Gaviria
dc.contributor.editorFerreira, Iván García
dc.contributor.editorBringas, Pablo Garcia
dc.contributor.editorKlett, Fanny
dc.contributor.editorAbraham, Ajith
dc.contributor.editorde Carvalho, André C.P.L.F.
dc.contributor.editorHerrero, Álvaro
dc.contributor.editorBaruque, Bruno
dc.contributor.editorQuintián, Héctor
dc.contributor.editorCorchado, Emilio
dc.date.accessioned2014-07-22T16:28:03Z
dc.date.available2014-07-22T16:28:03Z
dc.date.issued2014-06-30
dc.description.abstractThe teaching methodology used in lab classes in Chemistry Learning was studied for a cohort of 702 students in the 10th grade of Portuguese Secondary Schools. The k-Means Clustering Method, with k values ranging between 2 (two) and 4 (four), was used in order to segment the data. Decision Trees were used for the development of explanatory models of the segmentation. The results obtained showed that the majority of the answerers considered that experimentation is central on Chemistry learning. The results also showed that the significance of research in Chemistry learning is strongly dependent on the students’ involvement in lab work.por
dc.identifier.authoremailmtf@uevora.pt
dc.identifier.authoremailm4233@alunos.uevora.pt
dc.identifier.authoremailjneves@di.uminho.pt
dc.identifier.authoremailhvicente@uevora.pt
dc.identifier.capituloLab Classes in Chemistry Learning – An Artificial Intelligence View
dc.identifier.citationFigueiredo, M., Esteves, M.L., Neves, J. & Vicente, H., Lab Classes in Chemistry Learning – An Artificial Intelligence View. In J.G. Puerta, I.G. Ferreira, P.G. Bringas, F. Klett, A. Abraham, A.C. Carvalho, Á. Herrero, B. Baruque, H. Quintián & E. Corchado Eds., International Joint Conference SOCO’14 – CISIS’14 – ICEUTE’14, Advances in Intelligent Systems and Computing, Vol. 299, pp. 565–575, Springer International Publishing, Cham, Switzerland, 2014.por
dc.identifier.doi10.1007/978-3-319-07995-0_56
dc.identifier.edicaoSpringer International Publishing
dc.identifier.isbn978-3-319-07994-3
dc.identifier.issn2194-5357
dc.identifier.locationCham, Switzerland
dc.identifier.numpag11
dc.identifier.scientificarea229por
dc.identifier.sharewithDepartamento de Químicapor
dc.identifier.urihttp://hdl.handle.net/10174/11351
dc.identifier.volume299
dc.language.isoengpor
dc.publisherSpringer International Publishingpor
dc.rightsopenAccesspor
dc.subjectArtificial Intelligencepor
dc.subjectChemistry Learningpor
dc.subjectDecision Treespor
dc.subjectk-Meanspor
dc.subjectLab Classespor
dc.subjectLab Workpor
dc.titleLab Classes in Chemistry Learning - An Artificial Intelligence Viewpor
dc.typebookPartpor
degois.publication.firstPage565por
degois.publication.lastPage575por
degois.publication.locationCham, Switzerlandpor
degois.publication.titleInternational Joint Conference SOCO’14 – CISIS’14 – ICEUTE’14, Advances in Intelligent Systems and Computingpor
degois.publication.volume299por

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