Learning, Arts, and the Brain: The Dana Consortium Report on Arts and Cognition

Reports findings from multiple neuroscientific studies on the impact of arts training on the enhancement of other cognitive capacities, such as reading acquisition, sequence learning, geometrical reasoning, and memory

The Power of Alumni Networks - Success of Startup Companies Correlates With Online Social Network Structure of Its Founders

In this paper we analyze the success of startups in Germany by looking at the social network structure of their founders on the German-language business-networking site XING. We address two related research questions. First we examine university-wide networks, constructing alumni networks of 12 German universities, with the goal of identifying the most successful founder networks among the 12 universities. Second, we also look at individual actor network structure, to find the social network attributes of the most successful founders. We automatically collected the publicly accessible portion of XING, filtering people by attributes indicative of their university, and roles as founders, entrepreneurs, and CEOs. We identified 51,976 alumni, out of which 14,854 have entrepreneurship attributes. We also manually evaluated the financial success of a subsample of 80 entrepreneurs for each university. We found that universities, which are more central in the German university network, provide a better environment for students to found more and more successful startups. University networks whose alumni have a stronger “old-boys-network”, i.e. a larger share of their links with other alumni of their alma mater, are more successful as founders of startups. On the individual level the same holds true: the more links founders have with alumni of their university, the more successful their startup is. Finally, the absolute amount of networking matters, i.e. the more links entrepreneurs have, and the higher their betweenness in the online network of university alumni, the more successful they are

A comparison of multiple intelligence preferences and performance on standardized tests

The purpose of this study was to look for correlations between levels of preference for multiple intelligences and performance rates on standardized tests, as determined by utilizing the Teele Inventory of Multiple Intelligences and standardized test scores. The research was conducted in small rural elementary school in southern New Jersey where the researcher was the art teacher. The sample consisted of the entire fifth grade class (n= 64) who provided parental consent and had the necessary test scores available. Students completed the Teele Inventory of Multiple Intelligence by viewing a series of paired images of panda bears involved in various activities. Students selected the image that best represented them and from their selections multiple intelligence levels of preference were determined on a scale from 0-8 for each of Howard Gardner\u27s seven multiple intelligence. These scaled scores were correlated with student\u27s standardized test scores on the Reading and Math sections of the New Jersey Ask-4 utilizing a Spearman-Rho nonparametric correlation. There were no correlations between any of the multiple intelligence preferences and standardized testing scores, nor between the multiple intelligences themselves. The major implication of the study was that student preference and ability are not interconnected

Innate talents: reality or myth?

Talents that selectively facilitate the acquisition of high levels of skill are said to be present in some children but not others. The evidence for this includes biological correlates of specific abilities, certain rare abilities in autistic savants, and the seemingly spontaneous emergence of exceptional abilities in young children, but there is also contrary evidence indicating an absence of early precursors of high skill levels. An analysis of positive and negative evidence and arguments suggests that differences in early experiences, preferences, opportunities, habits, training, and practice are the real determinants of excellence

Collaboration-Aware Hit Song Prediction

In a streaming-oriented era, predicting which songs will be successful is a significant challenge for the music industry. Indeed, there are many efforts in determining the driving factors that contribute to a song’s success, and one potential solution could be incorporating artistic collaborations, as it allows for a wider audience reach. Therefore, we propose a multi-perspective approach that includes collaboration between artists as a factor for hit song prediction. Specifically, by combining online data from Billboard and Spotify, we tackle the problem as both classification and hit song placement tasks, applying five different model variants. Our results show that relying only on music-related features is not enough, whereas models that also consider collaboration features produce better results

Pierre Schaeffers typo-morphology of sonic objects.

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