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Problem-based learning (PBL) is a teaching method 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 desirable skills and attributes.
A child explores the flight behavior of a toy aircraft from Styrofoam. Discovery learning is a technique of inquiry-based learning and is considered a constructivist based approach to education.
The first is challenge-based learning/problem-based learning, the second is place-based education, and the third is activity-based learning. Challenge-based learning is "an engaging multidisciplinary approach to teaching and learning that encourages students to leverage the technology they use in their daily lives to solve real-world problems ...
PhBL forges connections across content and subject areas within the limits of the particular focus. [2] It can be a used as part of teacher-centered passive learning although in practice it is used more in student-centered active learning environments, including inquiry-based learning, problem-based learning, or project-based learning.
Learning styles refer to a range of theories that aim to account for differences in individuals' learning. [1] Although there is ample evidence that individuals express personal preferences on how they prefer to receive information, [2]: 108 few studies have found validity in using learning styles in education.
As practiced in K-12 education in the United States, practice-based professional learning develops and integrates a school's use of curriculum and assessment, instructional leadership, and professional learning communities (PLCs) to create a system-wide shift in day-to-day classroom instruction. [8]
Collaborative learning is a situation in which two or more people learn or attempt to learn something together. [1] Unlike individual learning, people engaged in collaborative learning capitalize on one another's resources and skills (asking one another for information, evaluating one another's ideas, monitoring one another's work, etc.).
Explanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory (i.e. a formal theory of an application domain akin to a domain model in ontology engineering, not to be confused with Scott's domain theory) in order to make generalizations or form concepts from training examples. [1]