ABOUT THIS COURSE
This course gives you easy access to the invaluable learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. We’ll learn about how the brain uses two very different learning modes and how it encapsulates (“chunks”) information. We’ll also cover illusions of learning, memory techniques, dealing with procrastination, and best practices shown by research to be most effective in helping you master tough subjects.
Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give you ideas for turbocharging successful learning, including counter-intuitive test-taking tips and insights that will help you make the best use of your time on homework and problem sets. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide.
This course can be taken independent of, concurrent with, or prior to, its companion course, Mindshift. (Learning How to Learn is more learning-focused, and Mindshift is more career-focused.) A related course by the same instructors is Uncommon Sense Teaching.
To join the fully translated Portuguese version of the course, visit: https://www.coursera.org/learn/aprender
To join the fully translated Spanish version of the course, visit: https://www.coursera.org/learn/aprendiendo-a-aprender
To join the fully translated Chinese version of the course, visit: https://www.coursera.org/learn/ruhe-xuexi
To join the fully translated French version of the course, visit : http://www.coursera.org/learn/apprendre-comment-apprendre
Difficulty Level: BEGINNER
Estimated Learning Time: 15 hours
SKILLS YOU WILL GAIN:
Professor of Engineering
Industrial & Systems Engineering, Oakland University
Dr. Terrence Sejnowski
Francis Crick Professor at the Salk Institute for Biological Studies
Computational Neurobiology Laboratory