Guevara C.,National Experimental University of Guayana, Puerto Ordaz |
Aguilar J.,Technical University of Loja
Proceedings of the 2016 42nd Latin American Computing Conference, CLEI 2016 | Year: 2016
This article presents a Model of an Adaptive Learning Object (MOAA) for virtual environments from a definition of Adaptive Learning Object, and a proposition of extension of the LOM standard to specify the adaptation metadata. The MASINA methodology and the UML diagrams are used to describe it. The model specifies modularly and independently two categories of rules, of adaptation and conversion, giving it versatility and flexibility to perform different types of adaptation to the learning objects, incorporating or removing rules in each category. © 2016 IEEE.
PubMed | Des Moines University, National University of Costa Rica, National University of Colombia, Brock University and 328 more.
Type: Journal Article | Journal: Ecology and evolution | Year: 2017
The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.