Learning theory (education)

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Related lectures (32)

Creating a Learning Community in Online Teaching

Emphasizes preparing students for virtual classrooms and fostering a sense of community through active learning strategies.

Learning in Artificial Neural Networks

Discusses the evolution of artificial neural networks, challenges in supervised learning, and the role of innate behaviors in shaping behavior.

Designing Instruction Activities

Emphasizes the importance of detailing design processes and consolidating learning through engaging activities and critical feature analysis.

Bias and Stereotypes in Learning Environments

Delves into biases and stereotypes in learning environments, emphasizing strategies to reduce biases and enhance learning outcomes.

Making an Education: Hands-On Workshop Experience

Offers a hands-on workshop experience in soldering and circuit building, exploring the maker movement and STEM education.

Learning Models for Belief-Driven Mobile Manipulation Tasks

Delves into learning models for belief-driven mobile manipulation tasks in open environments, covering actions like leaping, grasping, and stacking.

Memory Integration and Priming

Delves into memory integration, priming, and transfer of learning concepts.

How People Learn II: Course Introduction and Overview

Introduces the course 'How People Learn II', focusing on educational concepts and practical applications in project-based learning.

Visual Intelligence and Learning: Insights and Applications

Explores visual intelligence, robotics perception, and multi-task learning techniques in computer vision.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

Teaching Problem Solving

Explores teaching problem solving in higher education, emphasizing the importance of analytical skills and transfer of knowledge.

Pretraining Sequence-to-Sequence Models: BART + T5

Explores pretraining sequence-to-sequence models with BART and T5, discussing transfer learning, fine-tuning, model architectures, tasks, performance comparison, summarization results, and references.

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