FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2185755013> ?p ?o ?g. }

• W2185755013 abstract "This work aims to analyze how 24 high school in-service teachers understand covariation, within the context of Vitruvian Man. This concept was explored in an informal way during a teaching intervention mode, by applying tasks in which teachers observed the height and arm span of students. They had to describe both variables, and answer whether it was possible to say that the height and arm span measurements were equal. They were also asked to construct the scatter plot with and without drawing the linear function. Just after the construction of the scatter plot and the linear function, teachers considered that the measurements of both variables were close. We believe that at the end of the set of tasks, the understanding of covariation in this group of teachers was improved, leading us to think that proposals such as these can aid the training of math teachers for teaching this topic in schools. TEACHING COVARIATION It is common in our daily life that we encounter various pieces of information presented in the media through tables, graphs and statistical measurements, such as, for example, the results of election polls and market, financial indicators, as well as situations referring to the association between two or more variables (height and arm span measurements, the number of study hours and grade obtained, plant growth and the amount of water deposited). The reading and interpretation of these information’s statistics require more and more from citizens’ contextual understanding and knowledge, which enable them to evaluate the data critically and reason over their conclusions, allowing us to consider that an individual is statistically literate (Gal, 2002). We highlight the fact that in situations involving the association between two variables, if the citizen domains the concept of covariation, he can check with competence the structure and the intensity of this bivariate correlation. Therefore, we consider that it is important to approach this concept at school; however, according to Peck and Gould (2005), Cazorla (2006), and Contreras, Batanero, Diaz, and Fernandes (2011), among other researchers, there are still gaps in math teachers’ training regarding conceptual and statistical aspects. Reflecting on the need for effective action to assist teachers’ training in statistics teaching, particularly covariation, we have elaborated and applied to a group of 24 high school math teachers a set of tasks to introduce this concept in an informal approach (without calculating the covariation and Pearson’s correlation), using Leonardo da Vinci’s Vitruvian Man figure. From the results of the teaching intervention in this article, we aim to analyze the understanding covariation of these teachers in the solution of the proposed tasks. According to Moritz (2004), covariation can be classified into three types: logical covariation, numerical covariation and statistical covariation. Logic covariation is defined by the logic variables involved, and can be classified as true or false; numerical covariation can be expressed by an equation involving the variables that have assumed real values, while maintaining the correlation in one value when related to another; and the statistical covariation that involves the correlation between two random variables, which vary over a numerical scale. We emphasize that this research deals only with statistical covariation." @default.
• W2185755013 created "2016-06-24" @default.
• W2185755013 creator A5010793125 @default.
• W2185755013 creator A5014287428 @default.
• W2185755013 creator A5021481217 @default.
• W2185755013 date "2014-01-01" @default.
• W2185755013 modified "2023-10-16" @default.
• W2185755013 title "ANALYSIS OF TEACHERS' UNDERSTANDING OF COVARIATION IN THE VITRUVIAN MAN CONTEXT" @default.
• W2185755013 cites W1484398556 @default.
• W2185755013 cites W155678322 @default.
• W2185755013 cites W2103710075 @default.
• W2185755013 cites W2134098812 @default.
• W2185755013 cites W2159166504 @default.
• W2185755013 cites W2223726079 @default.
• W2185755013 cites W2314764823 @default.
• W2185755013 cites W2973576818 @default.
• W2185755013 hasPublicationYear "2014" @default.
• W2185755013 type Work @default.
• W2185755013 sameAs 2185755013 @default.
• W2185755013 citedByCount "0" @default.
• W2185755013 crossrefType "journal-article" @default.
• W2185755013 hasAuthorship W2185755013A5010793125 @default.
• W2185755013 hasAuthorship W2185755013A5014287428 @default.
• W2185755013 hasAuthorship W2185755013A5021481217 @default.
• W2185755013 hasConcept C105795698 @default.
• W2185755013 hasConcept C138885662 @default.
• W2185755013 hasConcept C14036430 @default.
• W2185755013 hasConcept C145420912 @default.
• W2185755013 hasConcept C15744967 @default.
• W2185755013 hasConcept C166957645 @default.
• W2185755013 hasConcept C167651023 @default.
• W2185755013 hasConcept C177264268 @default.
• W2185755013 hasConcept C199360897 @default.
• W2185755013 hasConcept C205649164 @default.
• W2185755013 hasConcept C2779343474 @default.
• W2185755013 hasConcept C2780801425 @default.
• W2185755013 hasConcept C33923547 @default.
• W2185755013 hasConcept C41008148 @default.
• W2185755013 hasConcept C41895202 @default.
• W2185755013 hasConcept C554936623 @default.
• W2185755013 hasConcept C78458016 @default.
• W2185755013 hasConcept C86803240 @default.
• W2185755013 hasConceptScore W2185755013C105795698 @default.
• W2185755013 hasConceptScore W2185755013C138885662 @default.
• W2185755013 hasConceptScore W2185755013C14036430 @default.
• W2185755013 hasConceptScore W2185755013C145420912 @default.
• W2185755013 hasConceptScore W2185755013C15744967 @default.
• W2185755013 hasConceptScore W2185755013C166957645 @default.
• W2185755013 hasConceptScore W2185755013C167651023 @default.
• W2185755013 hasConceptScore W2185755013C177264268 @default.
• W2185755013 hasConceptScore W2185755013C199360897 @default.
• W2185755013 hasConceptScore W2185755013C205649164 @default.
• W2185755013 hasConceptScore W2185755013C2779343474 @default.
• W2185755013 hasConceptScore W2185755013C2780801425 @default.
• W2185755013 hasConceptScore W2185755013C33923547 @default.
• W2185755013 hasConceptScore W2185755013C41008148 @default.
• W2185755013 hasConceptScore W2185755013C41895202 @default.
• W2185755013 hasConceptScore W2185755013C554936623 @default.
• W2185755013 hasConceptScore W2185755013C78458016 @default.
• W2185755013 hasConceptScore W2185755013C86803240 @default.
• W2185755013 hasLocation W21857550131 @default.
• W2185755013 hasOpenAccess W2185755013 @default.
• W2185755013 hasPrimaryLocation W21857550131 @default.
• W2185755013 hasRelatedWork W196989192 @default.
• W2185755013 hasRelatedWork W2008766881 @default.
• W2185755013 hasRelatedWork W2039294537 @default.
• W2185755013 hasRelatedWork W2050385571 @default.
• W2185755013 hasRelatedWork W2068996946 @default.
• W2185755013 hasRelatedWork W2077993893 @default.
• W2185755013 hasRelatedWork W2121874913 @default.
• W2185755013 hasRelatedWork W2159312652 @default.
• W2185755013 hasRelatedWork W2201549145 @default.
• W2185755013 hasRelatedWork W2215275176 @default.
• W2185755013 hasRelatedWork W2598710730 @default.
• W2185755013 hasRelatedWork W2890430090 @default.
• W2185755013 hasRelatedWork W3036224572 @default.
• W2185755013 hasRelatedWork W3132850849 @default.
• W2185755013 hasRelatedWork W3214713035 @default.
• W2185755013 hasRelatedWork W85308786 @default.
• W2185755013 hasRelatedWork W1995114285 @default.
• W2185755013 hasRelatedWork W2185983247 @default.
• W2185755013 hasRelatedWork W2188601581 @default.
• W2185755013 hasRelatedWork W2598988217 @default.
• W2185755013 isParatext "false" @default.
• W2185755013 isRetracted "false" @default.
• W2185755013 magId "2185755013" @default.
• W2185755013 workType "article" @default.