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There is much confusion globally over distinctions between and definitions of distributed learning, distance education, open learning, e-learning, blended learning and other related terms. Many terms are used more commonly in particular geographies. Distinctions can arise when the chosen model focuses on either or both time and geographic ...
Pavlis Korres (2010), in her instructional model (ESG Framework), [10] has proposed an expanded version of ADDIE, named ADDIE+M, where Μ=Maintenance of the Learning Community Network after the end of a course. The Maintenance of the Learning Community Network is a modern educational process that supports the continuous educational development ...
e-Learning (e.g., traditional web-based courseware) Mobile learning (m-Learning) and associated mobile performance support; Web-based virtual worlds and simulations; Learning analytics and performance modeling; Associated learning theory (e.g., pedagogy, andragogy, instructional design) Distributed learning interoperability specifications
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 Leslie Valiant .
The authors wanted to design a model which explained each component of the teaching and learning process while at the same time examining the relationship between the media and instruction. The model consists of ten elements and was constructed for teachers who both design and deliver instruction. [ 2 ]
Their work has focussed on the architecture of learning networks - aiming to identify arrangements of tasks, tools and people that contribute to successful learning networks. Some conclusions from this work have been published in The architecture of productive learning networks, which also includes a chapter on the history of networked learning ...
Design-based learning (DBL), also known as design-based instruction, is an inquiry-based form of learning, or pedagogy, that is based on integration of design thinking and the design process into the classroom at the K-12 and post-secondary levels.
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.