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Distributed learning. Distributed learning is an instructional model that allows instructor, students, and content to be located in different, noncentralized locations so that instruction and learning can occur independent of time and place. The distributed learning model can be used in combination with traditional classroom-based courses and ...
Distributed practice. Distributed practice (also known as spaced repetition, the spacing effect, or spaced practice) is a learning strategy, where practice is broken up into a number of short sessions over a longer period of time. Humans and other animals learn items in a list more effectively when they are studied in several sessions spread ...
The Advanced Distributed Learning ( ADL) Initiative is a US government program that conducts research and development on distributed learning and coordinates related efforts broadly across public and private organizations. ADL reports to the Defense Human Resources Activity (DHRA), under the Director, DHRA.
Federated learning (also known as collaborative learning) is a sub-field of machine learning focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized. [1] This stands in contrast to machine learning settings in which data is centrally stored ...
Distance education is a technology-mediated modality and has evolved with the evolution of technologies such as video conferencing, TV, and the Internet. [4] Today, it usually involves online education and the learning is usually mediated by some form of technology. A distance learning program can either be completely a remote learning, or a ...
Distribution learning theory. The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert Schapire and Linda Sellie in 1994 [1] and it was inspired from the PAC-framework introduced by ...
Distributed cognition. Distributed cognition is an approach to cognitive science research that was developed by cognitive anthropologist Edwin Hutchins during the 1990s. [1] From cognitive ethnography, Hutchins argues that mental representations, which classical cognitive science held that are within the individual brain, are actually ...
Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets.