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Colloquially, the term university may be used to describe a phase in one's life: "When I was at university..." (in the United States and Ireland, college is often used instead: "When I was in college..."). In Australia, Canada, New Zealand, the United Kingdom, Nigeria, the Netherlands, Italy, Spain and the German-speaking countries , university is often contracted to uni . In Ghana, New Zealand, Bangladesh and in South Africa it is sometimes called "varsity" (although this has become uncommon in New Zealand in recent years). "Varsity" was also common usage in the UK in the 19th century. [ citation needed ] "Varsity" is still in common usage in Scotland.

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Comenius University in Bratislava - the largest public university in Slovakia
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In many countries, students are required to pay tuition fees. Many students look to get 'student grants' to cover the cost of university. In 2016, the average outstanding student loan balance per borrower in the United States was US$30,000. [84] In many U.S. states, costs are anticipated to rise for students as a result of decreased state funding given to public universities. [85]

There are several major exceptions on tuition fees. In many European countries, it is possible to study without tuition fees. Public universities in Nordic countries were entirely without tuition fees until around 2005. Denmark, Sweden and Finland then moved to put in place tuition fees for foreign students. Citizens of and EEA member states and citizens from Switzerland remain exempted from tuition fees, and the amounts of public grants granted to promising foreign students were increased to offset some of the impact. Sarah Chofakian Tstrap sandals pre order online NQG1uAt
The situation in Germany is similar, public universities usually do not charge tuition fees apart from a small administrative fee. For degrees of a postgraduate professional level sometimes tuition fees are levied. Private universities, however, almost always charge tuition fees.

Thus the university, as a form of social organization, was peculiar to medieval Europe. Later, it was exported to all parts of the world, including the Muslim East; and it has remained with us down to the present day. But back in the middle ages, outside of Europe, there was nothing anything quite like it anywhere.

This is the authors' version of the work. It is posted here for your personal use. Not for redistribution. The definitive version is published in the proceedings of the 43th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Vienna, Austria. Aug. 30 - Sept. 1, 2017. See: https://ieeexplore.ieee.org/abstract/document/8051322/

A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments Pavel Dmitriev, Somit Gupta, Dong Woo Kim, and Garnet Vaz, KDD 2017
Online controlled experiments (e.g., A/B tests) are now regularly used to guide product development and accelerate innovation in software. Product ideas are evaluated as scientific hypotheses, and tested in web sites, mobile applications, desktop applications, services, and operating systems. One of the key challenges for organizations that run controlled experiments is to come up with the right set of metrics. Having good metrics, however, is not enough. In our experience of running thousands of experiments with many teams across Microsoft, we observed again and again how incorrect interpretations of metric movements may lead to wrong conclusions about the experiment’s outcome, which if deployed could hurt the business by millions of dollars. Inspired by Steven Goodman’s twelve p-value misconceptions, in this paper, we share twelve common metric interpretation pitfalls which we observed repeatedly in our experiments. We illustrate each pitfall with a puzzling example from a real experiment, and describe processes, metric design principles, and guidelines that can be used to detect and avoid the pitfall. With this paper, we aim to increase the experimenters’ awareness of metric interpretation issues, leading to improved quality and trustworthiness of experiment results and better data-driven decisions.

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( slides ) Aleksander Fabijan, Pavel Dmitriev, Helena Holmström Olsson, Jan Bosch. ICSE 2017

Software development companies are increasingly aiming to become data-driven by trying to continuously experiment with the products used by their customers. Although familiar with the competitive edge that the A/B testing technology delivers, they seldom succeed in evolving and adopting the methodology. In this paper, and based on an exhaustive and collaborative case study research in a large software-intense company with highly developed experimentation culture, we present the evolution process of moving from ad-hoc customer data analysis towards continuous controlled experimentation at scale. Our main contribution is the “Experimentation Evolution Model” in which we detail three phases of evolution: technical, organizational and business evolution. With our contribution, we aim to provide guidance to practitioners on how to develop and scale continuous experimentation in software organizations with the purpose of becoming data-driven at scale.