Reference : THE IMPACT OF LANGUAGE BACKGROUND ON BASIC MATH COMPETENCE
Scientific Presentations in Universities or Research Centers : Scientific presentation in universities or research centers
Social & behavioral sciences, psychology : Neurosciences & behavior
Educational Sciences
http://hdl.handle.net/10993/31767
THE IMPACT OF LANGUAGE BACKGROUND ON BASIC MATH COMPETENCE
English
Poncin, Alexandre mailto [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Education, Culture, Cognition and Society (ECCS) >]
Van Rinsveld, Amandine mailto [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Education, Culture, Cognition and Society (ECCS)]
Schiltz, Christine mailto [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Education, Culture, Cognition and Society (ECCS) >]
18-Feb-2016
National
Number Group Meeting
18-02-2016 to 19-02-2016
Belval
Luxembourg
[en] Transcoding ; Number ; Language
[en] German number word system inverts units and tens compared to the Arabic notation. This is not the case in French, which is more transparent regarding the Arabic number code. Evidence indicates that the linguistic structure of number words can facilitate or impede numerical development (Zuber, Pixner, & Moeller, 2009). Moreover, in transcoding tasks more mistakes are made in non-transparent compared to transparent languages (Imbo, Vanden Bulcke, De Brauwer, & Fias, 2014). We used a new paradigm of transcoding task in which 28 French-speaking (FR) and 19 German-speaking (GE) 4th grade children had to listen two digits numbers. The new thing was that we manipulate the order of appearance of the units and the tens of the number in three conditions: Units-First (UF), Tens-First (TF) and Simultaneous (S). Then, the subjects had to choose the heard number among four numbers presented on the computer screen. Results sows that GE are globally slower than FR (F(1,45) = 3.95, p = .053). The largest difference was observed for the TF: (t(45) = -3.729, p = .001). Moreover, when the order of the number appearance was congruent with the number word system, the transcoding was faster in both languages. For GE the S condition was slower than TF condition (F(2,36) = 6.918, p = .008) and than UF condition (F(2,36) = 6.918, p = .003.). For FR, the TF was faster than S (F(2,54) = 69.419, p < .001) and UF (F(2,54) = 69.419, p < .001). All these data indicate that language structure qualitatively impacts on basic numerical tasks.
Researchers
http://hdl.handle.net/10993/31767
FnR ; FNR9161107 > Alexandre Poncin > NUMLANGDev > The Impact Of Language Background On Basic Math Competence > 15/03/2015 > 14/03/2018 > 2014

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