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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 ...
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 ...
Organizational learning is the process of creating, retaining, and transferring knowledge within an organization. An organization improves over time as it gains ...
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 ]
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.
Training and development involves improving the effectiveness of organizations and the individuals and teams within them. [1] Training may be viewed as being related to immediate changes in effectiveness via organized instruction, while development is related to the progress of longer-term organizational and employee goals.
Organizational Information Theory (OIT) is a communication theory, developed by Karl Weick, offering systemic insight into the processing and exchange of information within organizations and among its members.
The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. [34] [35] Consequently, practical decision-tree learning algorithms are based on heuristics such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot ...