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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 ...
Deeper learning is based on the premise that the nature of work, civic, and everyday life is changing and therefore increasingly requires that formal education provides young people with mastery of skills like analytic reasoning, complex problem solving, and teamwork.
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.
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.
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Journals, portfolios, and electronic portfolios are examples of authentic learning tasks designed to showcase the student's work as well as give the student a means to reflect back on his/her learning over time. [8] [13] Project-Based Learning: Begins with a problem or question that is the starting point for inquiry and which all products are ...
Various techniques exist to train policies to solve tasks with deep reinforcement learning algorithms, each having their own benefits. At the highest level, there is a distinction between model-based and model-free reinforcement learning, which refers to whether the algorithm attempts to learn a forward model of the environment dynamics.