Spaced Repetition for Efficient Learning

“Spaced repetition is a centuries-old psychological technique for efficient memorization & practice of skills where instead of attempting to memorize by ‘cramming’, memorization can be done far more efficiently by instead spacing out each review, with increasing durations as one learns the item, with the scheduling done by software. Because of the greater efficiency of its slow but steady approach, spaced repetition can scale to memorizing hundreds of thousands of items (while crammed items are almost immediately forgotten) and is especially useful for foreign languages & medical studies.

I review what this technique is useful for, some of the large research literature on it and the testing effect (up to ~2013, primarily), the available software tools and use patterns, and miscellaneous ideas & observations on it.”

-Gwern Branwen, “Spaced Repetition for Efficient Learning.” Gwern.net. March 11, 2009.

The obvious application for spaced repetition is learning vocabulary.

OKIDO Magazine

“OKIDO’s philosophy is a simple one: every child is a creative scientist.

The OKIDO world immerses young children in a spectrum of playful activities and media, all intelligently designed by science and education experts. 

Whether watching the TV show ‘Messy goes to OKIDO’, engaging in family events and school workshops, or reading high quality publications and products, OKIDO children learn through play.

At the heart of it all lies STEAM learning (that’s science, technology, engineering, the arts and mathematics). Everything in the OKIDO world is designed by science and education experts to encourage collaboration, curiosity, exploration, discovery, creativity and critical thinking.

WHERE DID IT ALL START?

Messy grew up on the pages of OKIDO Magazine. An independent publication started by parents from a kitchen table in Brixton in 2007, it was designed to fire up young imaginations and spark a life-long love of art and science. Today its founders, scientist Dr Sophie Dauvois (PhD BSc PG Dip.) and artist Rachel Ortas, are still every bit as passionate about engaging young kids in the scientific world around them using play, art and fun.

FOR WHO? EVERYONE, OF COURSE!

OKIDO’s fun and games are for all genders. The OKIDO world is a stereotype-free zone, because we believe in promoting equality for all children.

OKIDO

Noba

“Noba is a free online platform that provides high-quality, flexibly structured textbooks and educational materials. These textbooks and materials are licensed under the Creative Commons CC BY-NC-SA 4.0 International License. Users may reuse, redistribute, and remix the content to suit their needs.

The goals of Noba are three-fold:

* To reduce financial burden on students by providing access to free educational content

* To provide instructors with a platform to customize educational content to better suit their curriculum

* To present free, high-quality material written by a collection of experts and authorities in the field of psychology

Noba

The 85% Rule of Learning

“…we learn best when we aim to grasp something just outside the bounds of our existing knowledge. When a challenge is too simple, we don’t learn anything new; likewise, we don’t enhance our knowledge when a challenge is so difficult that we fail entirely or give up. We learn best when we aim to grasp something just outside the bounds of our existing knowledge. When a challenge is too simple, we don’t learn anything new; likewise, we don’t enhance our knowledge when a challenge is so difficult that we fail entirely or give up.”

—Alexis Blue, “15% failure is the learning ‘sweet spot’.” Futurity. November 11, 2019.

Brahmin Left vs. Merchant Right: Rising Inequality and the Changing Structure of Political Conflict

Conclusions

Globalisation/migration (domestic vs external inequality) and educational expansion (education vs property inequality) have created new multi-dimensional conflicts about inequality, leading to the collapse of the postwar left-vs-right party system.

Why didn’t democracy reduce inequality?

Because multi-dimensional coalitions are complicated.

Without a strong egalitarian-internationalist platform, it’s difficult to have the low-education, low-income voters from all origins vote for the same party.

Racism/nativism & higher education = powerful forces dividing the poor if there’s no strong uniting platform.

-Thomas Piketty, “Brahmin Left vs. Merchant Right: Rising Inequality and the Changing Structure of Political Conflict.” EHESS & Paris School of Economics Sciences Po, January 24 2019

h/t Boing Boing, which provides a nice overview. Open Question: Am I a member of the Brahmin Left?

Liberatory Education | The Hedgehog Review

“In a psychic context, racial reparations may be less obviously needed, but needed nonetheless, for those of us from white families and communities that practice racial thoughtlessness. We, too, need to study race and racism in order to understand ourselves, because the implicit attitudes about race that we have consciously and unconsciously created and accepted are poisoning us and have imprisoned our minds. We are literally dying from our inability to break free of our prejudice, our unexamined opinions. There are direct correlations to be made between white attitudes about race, lack and fear of education, hopelessness and despair, and addiction. It is just not possible to think freely when you are also desperate to cling to your biases.”

—Leslie W. Lewis, “Liberatory Education.” Hedgehog Review. 21.2 (Summer 2019).

Read this essay. It is absolute fire, and equally applicable to gender, class, sexual orientation, disability and other prejudices.

Open Syllabus Project, Visualized

“A new interactive visualization from Open Syllabus turns this trove of data into a color-coded stippling of different-sized dots, each one representing a particular text. Float over each dot and a box appears in the corner of the screen, showing the number of syllabi that have assigned the text, and a link to a profile page with more detailed analysis. Called the “Co-Assignment Galaxy,” the infographic does what a list cannot: draws connections between all these works and their respective fields of study.

-Josh Jones, “A New Interactive Visualization of the 165,000 Most-Frequently Assigned Texts in College Courses.” openculture.com. July 22, 2019.

A list of the top 50 works is available from the Open Syllabus Project website.

Goodhart’s Law & The Ivy League

“Two years ago I was at an event in Boston and I happened to sit at a dinner table across from a guy widely recognized to be one of the most brilliant people in the world.  We talked mostly about AI but at one point the conversation turned to hiring, and he told me that for 2 decades he has tracked the performance of everyone who worked for him. Based on that performance tracking, he had stopped hiring from Harvard and Stanford. He said that historically, his best employees came from Harvard, Stanford, and MIT, but that starting 5 or 6 years prior, the people coming out of Harvard and Stanford started to really slip in their performance. I asked why, and he said that he didn’t know, but had a hypothesis. He said “I believe that ivy league college admissions has become so competitive that it rewards people who are good at the admissions process, not people who are good.”

This is a form of Goodhart’s Law, which says “when a measure becomes a target, it ceases to be a good measure.” You can see where this is going, and that there may be a similar law for machine learning.  We are in a phase of AI where we are using data sets that were created for other purposes, not with AI in mind. What happens when you know all your data is going to be fed into an AI?  Does it change the data you create?

-Rob May, “Goodhart’s Law and AI Data Sets.” Inside AI. June 3, 2019.

AI For Everyone

“AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone–especially your non-technical colleagues–to take. In this course, you will learn: – The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science – What AI realistically can–and cannot–do – How to spot opportunities to apply AI to problems in your own organization – What it feels like to build machine learning and data science projects – How to work with an AI team and build an AI strategy in your company – How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.”

A.I. for Everyone.

Audit the course for free. Takes 6-8 hours to complete.

h/t, Open Culture.