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Interactive learning is a pedagogical approach that incorporates social networking and urban computing into course design. In interactive learning, people collaborate to share information. In interactive learning, people collaborate to share information.
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks.Their creation was inspired by biological neural circuitry. [1] [a] While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who developed the perceptron. [1]
The effective delivery of that content is measured, monitored, and maintained with an array of assessment and management tools that may also be part of that system. Integrated learning systems are generally associated with educational/academic environments, but are also deployed within private industry, for example, as a way to introduce ...
Self-learning in neural networks was introduced in 1982 along with a neural network capable of self-learning named crossbar adaptive array (CAA). [139] It is a system with only one input, situation s, and only one output, action (or behavior) a. It has neither external advice input nor external reinforcement input from the environment.
Instructional design (ID), also known as instructional systems design and originally known as instructional systems development (ISD), is the practice of systematically designing, developing and delivering instructional materials and experiences, both digital and physical, in a consistent and reliable fashion toward an efficient, effective, appealing, engaging and inspiring acquisition of ...
A Virtual Learning Environment (VLE) is a system specifically designed to facilitate the management of educational courses by teachers for their students. It predominantly relies on computer hardware and software, enabling distance learning. In North America, this concept is commonly denoted as a "Learning Management System" (LMS).
Landmark learning is a meta-learning approach that seeks to solve this problem. It involves training only the fast (but imprecise) algorithms in the bucket, and then using the performance of these algorithms to help determine which slow (but accurate) algorithm is most likely to do best.
High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...