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Published on BioPortfolio: 2018-06-15T02:13:10-0400
An e-learning module to teach how to evaluate ears in children was recently designed. The aim of this study is to measure the impact of this e-learning module on the trainees' ability to a...
In the pilot trial cardiac patients will be provided with e-learning videos, 2 à 5 times/week during the 4-week trial period.
The study aim is to determine whether simulation based learning would improve senior anesthesiology residents' patient care performance during the insertion and management of cerebrospinal...
HOPP Learning will be implemented in elementary schools in the Horten municipality and will assess the effect of a combined pedagogical approach, active learning, on a large student popula...
This study aims to compare heart rate variation, cognitive load, and learning outcomes of novel image-based virtual reality with traditional video in learning for otolaryngology. Half of p...
Selecting appropriate teaching and learning strategies within an overarching teaching philosophy is 1 way of influencing nursing students' self-directedness. We conducted research to compare the self-...
Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI d...
Implicit learning and statistical learning are two contemporary approaches to the long-standing question in psychology and cognitive science of how organisms pick up on patterned regularities in their...
Learning curves of simple perceptron were derived here. The learning curve of the perceptron learning with noisy teacher was shown to be non-monotonic, which has never appeared even though the learnin...
Peer Assisted Learning (PAL) is a well-established approach in learning and is increasingly being utilized in the medical education system. It is a process where active help of peer group members is t...
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
Change in learning in one situation due to prior learning in another situation. The transfer can be positive (with second learning improved by first) or negative (where the reverse holds).
Usually refers to the use of mathematical models in the prediction of learning to perform tasks based on the theory of probability applied to responses; it may also refer to the frequency of occurrence of the responses observed in the particular study.
A principle that learning is facilitated when the learner receives immediate evaluation of learning performance. The concept also hypothesizes that learning is facilitated when the learner is promptly informed whether a response is correct, and, if incorrect, of the direction of error.