Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2019Éléments de mesure de la compétence de visualisation spatiale d’étudiants ingénieurs en mécaniquecitations

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Chart of shared publication
Peyret, Nicolas
1 / 2 shared
Jeannin, Laurent
1 / 3 shared
Rivière, Alain
1 / 1 shared
Jaillet, Alain
1 / 1 shared
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2019

Co-Authors (by relevance)

  • Peyret, Nicolas
  • Jeannin, Laurent
  • Rivière, Alain
  • Jaillet, Alain
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document

Éléments de mesure de la compétence de visualisation spatiale d’étudiants ingénieurs en mécanique

  • Peyret, Nicolas
  • Jeannin, Laurent
  • Charles, Sophie
  • Rivière, Alain
  • Jaillet, Alain
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).

Topics
  • impedance spectroscopy