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A learning object is "a collection of content items, practice items, and assessment items that are combined based on a single learning objective". [1] The term is credited to Wayne Hodgins, and dates from a working group in 1994 bearing the name. [ 2 ]
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
Learning goals - A teacher-developed description of what the student will know and be able to do at the end of a course based upon an overarching idea for the academic or elective discipline. A teacher will know that they have an effective learning goal when the knowledge or skill can be applied to life outside the classroom.
The generator aims to minimize the objective, and the discriminator aims to maximize the objective. The generator's task is to approach μ G ≈ μ ref {\displaystyle \mu _{G}\approx \mu _{\text{ref}}} , that is, to match its own output distribution as closely as possible to the reference distribution.
The taxonomy divides learning objectives into three broad domains: cognitive (knowledge-based), affective (emotion-based), and psychomotor (action-based), each with a hierarchy of skills and abilities. These domains are used by educators to structure curricula, assessments, and teaching methods to foster different types of learning.
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