Jung Stefanie, Meinhardt Anna, Braeuning David, Roesch Stephanie, Cornu Véronique, Pazouki Tahereh, Schiltz Christine, Lonnemann Jan, Moeller Korbinian
Leibniz-Institut für Wissensmedien, Tübingen, Germany.
Department of Psychology, Eberhard Karls University Tübingen, Tübingen, Germany.
Front Psychol. 2020 May 20;11:871. doi: 10.3389/fpsyg.2020.00871. eCollection 2020.
Visual-spatial abilities (VSA) are considered a building block of early numerical development. They are intuitively acquired in early childhood and differentiate in further development. However, when children enter school, there already are considerable individual differences in children's visual-spatial and numerical abilities. To better understand this diversity, it is necessary to empirically evaluate the development as well as the latent structure of early VSA as proposed by the 2 by 2 taxonomy of Newcombe and Shipley (2015). In the present study, we report on a tablet-based assessment of VSA using the digital application (app) MaGrid in kindergarten children aged 4-6 years. We investigated whether the visual-spatial tasks implemented in MaGrid are sensitive to replicate previously observed age differences in VSA and thus a hierarchical development of VSA. Additionally, we evaluated whether the selected tasks conform to the taxonomy of VSA by Newcombe and Shipley (2015) applying a confirmatory factor analysis (CFA) approach. Our results indicated that the hierarchical development of VSA can be measured using MaGrid. Furthermore, the CFA substantiated the hypothesized factor structure of VSA in line with the dimensions proposed in the taxonomy of Newcombe and Shipley (2015). Taken together, the present results advance our knowledge to the (hierarchical) development as well as the latent structure of early VSA in kindergarten children.
视觉空间能力（VSA）被认为是早期数字发展的基石。它们在幼儿期通过直觉获得，并在进一步发展过程中分化。然而，当儿童进入学校时，他们的视觉空间和数字能力已经存在相当大的个体差异。为了更好地理解这种多样性，有必要根据纽科姆和希普利（2015年）的2×2分类法，对早期VSA的发展以及潜在结构进行实证评估。在本研究中，我们报告了一项针对4至6岁幼儿园儿童的基于平板电脑的VSA评估，使用数字应用程序（app）MaGrid。我们研究了MaGrid中实施的视觉空间任务是否能够灵敏地重现先前观察到的VSA年龄差异，从而反映VSA的分层发展。此外，我们通过应用验证性因素分析（CFA）方法，评估了所选任务是否符合纽科姆和希普利（2015年）提出的VSA分类法。我们的结果表明，可以使用MaGrid测量VSA的分层发展。此外，CFA证实了VSA的假设因素结构，与纽科姆和希普利（2015年）分类法中提出的维度一致。综上所述，目前的结果推进了我们对幼儿园儿童早期VSA的（分层）发展以及潜在结构的认识。
