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Thematic learning is closely related to interdisciplinary or integrated instruction, topic-, project- or phenomenon-based learning. Thematic teaching is commonly associated with elementary classrooms and middle schools using a team-based approach, but this pedagogy is equally relevant in secondary schools and with adult learners.
Domain-specific learning theories of development hold that we have many independent, specialised knowledge structures (domains), rather than one cohesive knowledge structure. Thus, training in one domain may not impact another independent domain. [ 1 ]
Bloom's taxonomy has become a widely adopted tool in education, influencing instructional design, assessment strategies, and learning outcomes across various disciplines. Despite its broad application, the taxonomy has also faced criticism, particularly regarding the hierarchical structure of cognitive skills and its implications for teaching ...
In the research field of evolutionary psychology it is believed that some cognitive modules are inherited and some are created by learning, but the creation of new modules by learning is often guided by inherited modules. [37] For example, the ability to drive a car or throw a basketball are certainly learned and not inherited modules, but they ...
Programmed learning (or programmed instruction) is a research-based system which helps learners work successfully. The method is guided by research done by a variety of applied psychologists and educators. [1] The learning material is in a kind of textbook or teaching machine or computer. The medium presents the material in a logical and tested ...
Perceptual learning is learning better perception skills such as differentiating two musical tones from one another or categorizations of spatial and temporal patterns relevant to real-world expertise. Examples of this may include reading, seeing relations among chess pieces, and knowing whether or not an X-ray image shows a tumor.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
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 ...