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Design-based learning (DBL), also known as design-based instruction, is an inquiry-based form of learning, or pedagogy, that is based on integration of design thinking and the design process into the classroom at the K-12 and post-secondary levels.
Thematic teaching (also known as thematic instruction) is the selecting and highlighting of a theme through an instructional unit or module, course, or multiple courses.It is often interdisciplinary, highlighting the relationship of knowledge across academic disciplines and everyday life.
The audio-lingual method or Army Method is a method used in teaching foreign languages.It is based on behaviorist theory, which postulates that certain traits of living things, and in this case humans, could be trained through a system of reinforcement.
Ralph W. Tyler introduced the idea of "backward design" (without using this particular term) in 1949 when referring to a statement of objectives.A statement of objectives is used to indicate the kinds of changes in the student to be brought about so that instructional activities can be planned and developed in a way likely to attain these objectives.
SEL Framework identified by CASEL as the "CASEL Wheel" Social and emotional learning (SEL) is an educational method that aims to foster social and emotional skills within school curricula.
A wide range of supervised learning algorithms are available, each with its strengths and weaknesses. There is no single learning algorithm that works best on all supervised learning problems (see the No free lunch theorem).
Holistic education is a movement in education that seeks to engage all aspects of the learner, including mind, body, and spirit. [1] Its philosophy, which is also identified as holistic learning theory, [2] is based on the premise that each person finds identity, meaning, and purpose in life through connections to their local community, to the natural world, and to humanitarian values such as ...
Ensemble learning, including both regression and classification tasks, can be explained using a geometric framework. [15] Within this framework, the output of each individual classifier or regressor for the entire dataset can be viewed as a point in a multi-dimensional space.