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Short title: Blended learning for quality higher education: selected case studies on implementation from Asia-Pacific; 2016; Author: Lim, Cher Ping; Wang Libing; UNESCO Office Bangkok and Regional Bureau for Education in Asia and the Pacific
In 2010, a scientific study found that a small percent of the population appeared to be much better at multitasking than others, and these people were subsequently labeled "supertaskers". [41] In 2015, another study supported the idea of supertaskers. This particular study showed that they tested people by making them drive on a driving ...
While multitasking is driven by a conscious desire to be productive, continuous partial attention is an automatic process motivated by the desire to constantly stay connected. Stone describes the reason for continuous partial attention as "a desire to be a live node on the network" [ 2 ] [ 3 ] [ 4 ]
Hal Pashler is Distinguished Professor of Psychology at University of California, San Diego.An experimental psychologist and cognitive scientist, Pashler is best known for his studies of human attentional limitations (his analysis of the Psychological refractory period effect concluded that the brain has discrete "processing bottlenecks" associated with specific types of cognitive operations).
Although men are doing more household chores, multitasking women still do the ‘lion’s share’, according to new research. Although men are doing more household chores, multitasking women ...
Performance on these tasks is disrupted when a switch from one task to another is required. This disruption is characterized by a slower performance and decrease in accuracy on a given task A on a trial that follows the performance of a different task B ("alternating" or "switch" trial) as opposed to performance on task A when it follows another trial of task A ("repetition" trial).
A large review of studies on driving while media multitasking showed that using a hands-free phone while driving is just as dangerous as using a hand-held version, and that both can result in many different driving mistakes including missing stop signs, forgetting to reduce speed when necessary, and following too closely, among many others.
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks.