Sentence examples for deliberative learning from inspiring English sources

In our study sites amongst the array of criteria in the literature good public engagement boiled down to two key themes: enabling bidirectional deliberative learning and providing community benefit.

Deliberative learning is similar to reactive learning but differs in that it includes planning of learning activities.

Since learning in both self-regulation loops occured in reaction to an incidental learning need, with the long loop also involving planned learning, the short and long self-regulation loops appear to fit the types of reactive and deliberative learning as described by Eraut [ 47- 49].

According to the typology of non-formal learning proposed by Eraut (2000), these activities obviously go beyond the unintentional and unconscious implicit learning from experience, but may encompass the reactive learning that is triggered by specific events, and definitely coincides with the highest intentional level of deliberative learning in which learning is planned.

Especially if one's theory of democracy is deliberative (see section 2.4, above), the core of civic education may be learning to talk and listen with other people about public problems.

In this paper, our purpose was to consider how to meet those needs through reconceptualizing public participation as EA civics, founded on an active citizen base, deliberative in nature and orientated toward learning.

Extensive empirical evaluations of Meta-AQUA show that it performs significantly better in a deliberative, planful mode than in a reflexive mode in which learning goals are ablated and, furthermore, that the arbitrary ordering of learning algorithms can lead to worse performance than no learning at all.

In the iterative phase of the framework, the elements in the deliberative phase are folded into a sequential process of decision-making and learning.

In this paper, we summarize a framework for hybrid dynamical cognitive agents (HDCAs) that incorporates a representation of dynamical intention into both reactive and deliberative structures of a hybrid dynamical system model, and we present methods for learning in these intention-guided agents.

We discuss specific advantages of adaptive governance approaches over traditional centralised governance models related to policy-experimentation at multiple spatial scales, institutional variety and deliberative decision-making processes that establish dialogue between service providers and beneficiaries that promote group learning.

In Eraut's typology of non-formal learning (Eraut 2000), physicians' learning in medical practice can be characterised as implicit and reactive rather than deliberative.