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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Charles, Sophie
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document
Éléments de mesure de la compétence de visualisation spatiale d’étudiants ingénieurs en mécanique
Abstract
This study is concerned with spatial visualisation and its possible inferences as a necessary ability in French engineering education, and is completed as part of a French research programme, which aims at better understanding how multi-purpose 3-D modelling software is used by learners at different levels of schooling. Spatial visualisation is one of the components of spatial ability (McGee, 1979; Tartre, 1990; Uttal, Meadow, Tipton, Hand, Alden, Warren & Newcombe, 2013), which predicts choices and success in Science, Technology, Engineering, and Maths disciplines and professions (Wai, Lubinski & Benbow, 2009). Spatial visualisation can be further developed into mental rotation and mental transformation (Kersh & Cook, 1979, as cited in Tartre, 1990). In order to evaluate the spatial visualisation skills of first-year students in a French engineering school specialised in Mechanics, the Revised Purdue Spatial Visualization Tests: Visualization of Rotations (Yoon, 2011), the Mental Rotation Test (Vandenberg & Kuse, 1978), which both aim at measuring mental rotation, and the Mental Cutting T est (CEEB, 1939), which aims at measuring mental transformation, were administered to around 135 freshmen in September 2018. In the French Grande Ecole system, engineering students are recruited after a two-year preparation, which is equivalent to the first two years of a degree course. This data collection was completed with a selection of demographics and academic assessment scores. An analysis of variance revealed these spatial tests are significant predictors of the students’ success in mechanism analysis, Algorithmics and Applied mathematics. Given the malleability of spatial skills (Uttal et al., 2013), these findings open the way for remedial courses, which could increase students’ learning and success in engineering courses (Sorby, 2005).