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Augmented learning is an on-demand learning technique where the environment adapts to the learner. By providing remediation on-demand, learners can gain greater understanding of a topic while stimulating discovery and learning. [ 1 ]
A learning augmented algorithm is an algorithm that can make use of a prediction to improve its performance. [1] Whereas in regular algorithms just the problem instance is inputted, learning augmented algorithms accept an extra parameter. This extra parameter often is a prediction of some property of the solution.
Electronic learning or e-learning is computer-enhanced learning. A specific and always more diffused e-learning is mobile learning (m-learning), which uses different mobile telecommunication equipment, such as cellular phones. When a learner interacts with the e-learning environment, it is called augmented learning. By adapting to the needs of ...
Intelligence amplification (IA) (also referred to as cognitive augmentation, machine augmented intelligence and enhanced intelligence) is the use of information technology in augmenting human intelligence. The idea was first proposed in the 1950s and 1960s by cybernetics and early computer pioneers.
English Grid began offering language learning and voice chat for language learners using Vivox in May, 2012. [52] The advent of voice chat in Second Life in 2007 was a major breakthrough. Communicating with one's voice is the sine qua non of language learning and teaching, but voice chat is not without its problems. Many Second Life users ...
We could define Augmented Learning as a new learning modality that uses some form of Augmented Reality. AR creates a semi-transparent supplemental content layer either using a mobile device (primarily smart phones) or with AR eyewear (see the Vuvix 920 ) but were initially developed to be displayed on desktops and laptops screens using a web ...
Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. [1] The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paper. [1] [2]
AlexNet, a deep learning model developed by Alex Krizhevsky, wins the ImageNet Large Scale Visual Recognition Challenge with half as many errors as the second-place winner. [108] This is a turning point in the history of AI; over the next few years dozens of other approaches to image recognition were abandoned in favor of deep learning. [109]