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It is used to describe how organizations and teams develop an awareness of their own thinking, [2] learning how to learn, [3] [4] [5] where awareness of ignorance can motivate learning. [6] The organizational deutero-learning concept identified by Argyris and Schon [7] [8] defines when organizations learn how to carry out single-loop and double ...
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Double-loop learning entails the modification of goals or decision-making rules in the light of experience. In double-loop learning, individuals or organizations not only correct errors based on existing rules or assumptions (which is known as single-loop learning), but also question and modify the underlying assumptions, goals, and norms that ...
The course of his career would cover three areas in this field: 1) the learning society; 2) professional learning and effectiveness; and, 3) the reflective practitioner. [18] Together with Chris Argyris , Schön provided the foundation to much of the management thinking on descriptive and interventionist dimensions to learning research. [ 19 ]
In U.S. education, deeper learning is a set of student educational outcomes including acquisition of robust core academic content, higher-order thinking skills, ...
Self-supervised representation learning is learning features by training on the structure of unlabeled data rather than relying on explicit labels for an information signal. This approach has enabled the combined use of deep neural network architectures and larger unlabeled datasets to produce deep feature representations. [ 9 ]
Asynchronous learning is a general term used to describe forms of education, instruction, and learning that do not occur in the same place or at the same time. It uses resources that facilitate information sharing outside the constraints of time and place among a network of people. [ 1 ]
Distributed learning may be dependent on time if it includes synchronous sessions, and further time dependent if the course is paced. The oldest and most commonly used of these terms, distance education , can be used to describe distributed learning as defined above.