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Flex model of learning is a method of teaching for non-traditional learners who work online and on-site with a mentor. Learn about its advantages, disadvantages, and related concepts such as blended learning and self-blended learning.
It differs from blended learning models in which teachers assign online content and resources, since in self-blended learning it is the students who direct their own online learning. It is a highly flexible model that allows for advanced students to take classes with increased specialization, and for students who have fallen behind in subjects ...
This model helps the students to receive differentiated experiences from face-to-face learning to online learning along with independent and collaborative practice.This model helps students to learn on their own pace and researchers have shown that these students out performs the students who have exposure only to one type of instruction.
Blended learning or hybrid learning is an approach to education that combines online and face-to-face methods. Learn about its terminology, history, delivery, and different models of blended learning.
Universal Design for Learning (UDL) is an educational framework that guides the development of flexible learning environments and learning spaces that can accommodate individual learning differences. UDL is based on research in learning theory, neuroscience, and progressive education, and calls for providing multiple means of representation, expression, and engagement.
Learning styles are theories that aim to account for differences in individuals' learning preferences and methods. Learn about Kolb's, Honey and Mumford's, and Barbe's models, and the evidence and challenges of using learning styles in education.
Cognitive flexibility is the ability to adjust one's thinking and attention in response to changes in rules or demands. Learn about the different definitions, methods, and factors that influence cognitive flexibility, such as executive functioning, multiple classification, and fluid intelligence.
Error-driven learning is a reinforcement learning method that adjusts model parameters based on prediction errors. It has applications in cognitive science, computer vision, and natural language processing.