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Converging International Cooperation Supported by Data Structures

Converging International Cooperation Supported by Data Structures
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Author(s): Gilbert Ahamer (Graz University, Austria)
Copyright: 2023
Pages: 13
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch092

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Abstract

The target of any learning is to widen the present range of mental conceptions in a person (and thus in a society). Such an approach, based on “enlarging social spaces,” is helpful in international cooperation, especially during present days of confrontation. This article presents several positive examples for mutual cooperation. The cases presented show the practical contributions of IT, especially of e-learning platforms, in facilitating the exchange of fact-based concepts for the construction of social spaces and spaces of understanding. Societal learning can enlarge and approximate spaces of understanding. Learning changes interdisciplinary realities by providing a clear mental image of what the future can bring. Actually, building consensus is the key step in international development. IT tools have promoted the dialogue in these cases of cooperative learning both in developing countries and in administrations and within academia of industrialised countries: the project Schools on Ice, the UniGIS online curriculum, the UniNet network in Kyrgyzstan, Nepal, and Tajikistan.

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