- Jul 2024
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www.youtube.com www.youtube.com
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Nishant says: 2x Output for 1x input...
His formula for mastery: 1. Learn (input -- focus, singletasking) 2. Reflect (output, pause... what is the main takeaway, how to use?) 3. Implement (output, apply) 4. Share (output, teach the material)
These principles are great... Obviously they are not comprehensive as they do not necessarily reflect higher order learning. See Bloom's and Solo's, nor take foundation of Cognitive Load Theory for example... It's understandable though since you can't mention everything in a 20 minute talk XD.
The argument I'd make is that the 3 subsequent steps are a part of learning. So the first step should not be called learn but rather encode, since that is literally the process of forming the initial cognitive schemas and putting them into long-term memory...
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- Jun 2024
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jaredhenderson.substack.com jaredhenderson.substack.com
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Testing culture also discourages deep reading, critics say, because it emphasizes close reading of excerpts, for example, to study a particular literary technique, rather than reading entire works.
Indeed. But testing in general, as it is done currently, in modern formal education, discourages deep learning as opposed to shallow learning.
Why? Because tests with marks implore students to start learning at max 3 days before the test, thus getting knowledge into short-term memory and not long term memory. Rendering the process of learning virtually useless even though they "pass" the curriculum.
I know this because I was such a student, and saw it all around me with virtually every other student I met, and I was in HAVO, a level not considered "low".
It does not help that teachers, or the system, expect students to know how to learn (efficiently) without it ever being taught to them.
My message to the system: start teaching students how to learn the moment they enter high school
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- Jul 2023
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arxiv.org arxiv.org
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Liang, Machado, Talvite, Bowling - AAMAS 2016 "State of the Art Control of Atari Games Using Shallow Reinforcement Learning"
Response paper to DQN showing that well designed Value Function Approximations can also do well at these complex tasks without the use of Deep Learning
A great paper showing how to think differently about the latest advances in Deep RL. All is not always what it seems!
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- Jun 2023
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www.fandm.edu www.fandm.edu
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Liang, Machado, Talvite, Bowling - AAMAS 2016 "State of the Art Control of Atari Games Using Shallow Reinforcement Learning"
A great paper showing how to think differently about the latest advances in Deep RL. All is not always what it seems!
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