Instruction may be seen as the intentional creation of conditions around learning so as to facilitate the achievement of determined goals. Normally, instruction is based upon certain educational strategies. It is well accepted in the field of educational technology that…

his dissertation analyzes how local knowledge improves environmental decisions. The premise is that controlling pollution and addressing public health disparities are not problems that professionals alone can solve. Concerned lay publics, especially low-income populations and people of color that experience…

This paper investigates and analyses the process of building a knowledge management (KM) model at Brazil‟s Embrapa (The Brazilian Agricultural Research Corporation). Embrapa is a world class knowledge organization whose mission is to provide feasible solutions for the sustainable development…

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Strategic management as an academic field of study has spanned over four decades. As an interdisciplinary subject that enjoys concurrent contributions by allied disciplines such as economics and social science, numerous industries have benefited from applying tools and concepts developed…

Knowledge acquisition for systems based on knowledge can be called knowledge engineering, according to Feigenbaum (1980) who used the term to describe the reduction of a wide context of knowledge for a set of facts and rules. The term Knowledge…

Conversational interfaces have received much attention as a promising natural communication channel between humans and computers. A typical conversational interface consists of three major systems: speech understanding, dialog management and spoken language generation. In such a conversational interface, speech recognition…

2007

In this article we examine and expose some of the results of a research work based on the appropriation of knowledge through visual expression, activity attended by teachers and students outside the schooleducational method called "Open Classroom".After the investigation, it…

Most neural network learning algorithms cannot use knowledge other than what is provided in the training data. Initialized using random weights, they cannot use prior knowledge such as knowledge stored in previously trained networks. This manuscript thesis addresses this problem.…