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A learning rate schedule changes the learning rate during learning and is most often changed between epochs/iterations. This is mainly done with two parameters: decay and momentum. There are many different learning rate schedules but the most common are time-based, step-based and exponential. [4]
Each mode of practice competes with a few others within the same dimension. Modes appear in succession because their frequency is determined by four parameters: endemicity, performance rate, commitment strength, and acceptance. Transformative learning results in changing from one mode to the next.
The learning pyramid (also known as “the cone of learning”, “the learning cone”, “the cone of retention”, “the pyramid of learning”, or “the pyramid of retention”) [1] is a group of ineffective [2] learning models and representations relating different degrees of retention induced from various types of learning.
Visible learning is a meta-study that analyzes effect sizes of measurable influences on learning outcomes in educational settings. [1] It was published by John Hattie in 2008 and draws upon results from 815 other Meta-analyses. The Times Educational Supplement described Hattie's meta-study as "teaching's holy grail". [2]
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).
In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. [1]
Dr. Wolfgang Greller and Dr. Hendrik Drachsler defined learning analytics holistically as a framework. They proposed that it is a generic design framework that can act as a useful guide for setting up analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency.
An example of experience curve effects: Swanson's law states that solar module prices have dropped about 20% for each doubling of installed capacity. [1] [2]In industry, models of the learning or experience curve effect express the relationship between experience producing a good and the efficiency of that production, specifically, efficiency gains that follow investment in the effort.