No abstract

No abstract

No abstract

In principle, neural networks and other algorithms of machine learning can perfectly map unique relations of any complexity between input and output variables. We investigate whether using multi-layer neural networks allows improving personality assessments by constructing short-tests that are more efficient. Personality data for N = 3,498 participants from Germany, the US and the UK was collected using the International Personality Item Pool-300-item-version (IPIP-300 or IPIP-NEO), the Big Five Inventory (BFI-10) and the HEXACO Personality Inventory-60 (HEXACO-PI-R). We trained 40 multi-layer neural networks on this data to predict individuals’ scores on the Big-5-personality dimensions as well as facet scores from a 30-item version of the IPIP. A neural network based short-test version, IPIP30-NNet, predicted Big-5 dimensions from IPIP-300 as well as its facets with high accuracy. The correlations with the long-test scores (IPIP-300) were significantly higher compared to short-tests using a standard averaging algorithm and a multiple regression. Particularly for the facet scores, IPIP30-NNet lead to substantial improvements in predictive validity (Δr = .04 - .17). Additionally, as a syntheses of all three personality tests we calculated Big-5-“superscores”, which could be predicted from IPIP30-NNet with high accuracy as well. Our results demonstrate that neural network based diagnostic can be used to receive a very detailed individual personality profile based on very few information. We discuss challenges, potentials and future directions for using machine learning to improve standard psychological assessment.

Consciously or unconsciously, programmes in higher education maintain a value framework about the aesthetic value of students' work, primarily based on the ability of such work to touch or move us. We consider something aesthetically valuable when it makes us feel good. In an educational environment, however, dealing with aesthetic value judgments pedagogically is complicated. After all, aesthetic judgment is a skill that cannot be taught explicitly; it can only be practised. This article discusses the underlying mechanisms of aesthetic judgment. The aim is to gain a better understanding of this skill and thus to contribute to the development of a pedagogy of aesthetic judgment. Relying on a theoretical framework developed on the basis of a literature review, we suggest that judging aesthetic value is an emotional process that requires well-formed aesthetic sentiment. Architectural education is an interesting case because it is a field in which aesthetic values occupy a central position. This study is therefore illustrated with examples from this field.