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Digital learning. Digital learning is learning that is supported by technology. [1] It encompasses any type of learning that is accompanied by technology or by instructional practice that makes effective use of technology. It includes a wide array of practices, including blended and virtual learning. A variety of names began to be used to ...
Blended learning or hybrid learning, also known as technology-mediated instruction, web-enhanced instruction, or mixed-mode instruction, is an approach to education that combines online educational materials and opportunities for interaction online with physical place-based classroom methods. Blended learning requires the physical presence of ...
Microlearning. Microlearning refers to a set of compact e-learning modules that are designed to reduce learner fatigue. The modules can be educational, professional, or skill based, and are usually designed to be less than 20 minutes long, with a single learning objective or topic. [1] The name originates from the Greek word 'micro' meaning ...
Project-based learning ( PBL) is a student-centered pedagogy that involves a dynamic classroom approach in which it is believed that students acquire a deeper knowledge through active exploration of real-world challenges and problems. [1] Students learn about a subject by working for an extended period of time to investigate and respond to a ...
A PBL group at Sydney Dental Hospital. Problem-based learning ( PBL) is a student-centered pedagogy in which students learn about a subject through the experience of solving an open-ended problem found in trigger material. The PBL process does not focus on problem solving with a defined solution, but it allows for the development of other ...
Mastery learning (or, as it was initially called, " learning for mastery "; also known as "mastery-based learning") is an instructional strategy and educational philosophy, first formally proposed by Benjamin Bloom in 1968. [1]
Supervised learning. Supervised learning ( SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. [1]
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