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Open-space Learning, or OSL, is a pedagogic methodology. OSL is a transdisciplinary pedagogy that is dependent on the use of physically open spaces - in the sense that tables and chairs are absent - and an open approach to intellectual content and the role of the tutor. Participants in OSL, typically but not exclusively, learn in an 'embodied' way.
At the top right corner is a monocular for assisting the alignment of the two heads. Free-space optical communication (FSO) is an optical communication technology that uses light propagating in free space to wirelessly transmit data for telecommunications or computer networking. "Free space" means air, outer space, vacuum, or something similar.
Spaced learning. Spaced learning is a learning method in which highly condensed learning content is repeated three times, with two 10-minute breaks during which distractor activities such as physical activities are performed by the students.
The intermediate (thin) rectangles represent the hypotheses in the version space. Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis space is a disjunction ...
Free-space path loss. In telecommunications, the free-space path loss (FSPL) (also known as free-space loss, FSL) is the attenuation of radio energy between the feedpoints of two antennas that results from the combination of the receiving antenna's capture area plus the obstacle-free, line-of-sight (LoS) path through free space (usually air). [1]
Learning Commons inside the library of Tec de Monterrey, Mexico City. Learning commons, also known as scholars' commons, information commons or digital commons, are learning spaces, [1] [2] similar to libraries and classrooms that share space for information technology, remote or online education, tutoring, [3] [4] collaboration, content creation, meetings, socialization, playing games and ...
Machine learningand data mining. Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. [1]
The selection and training of astronauts are integrated processes to ensure the crew members are qualified for space missions. [6] The training is categorized into five objectives to train the astronauts on the general and specific aspects: basic training, advanced training, mission-specific training, onboard training, and proficiency maintenance training. [7]
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