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The Universal Language of Memes

Meme culture has given rise to a language that transcends boundaries, both real and imaginary. Memes are part of a globally shared pool of memory we dip into when we want to convey an idea with nuances beyond text. They call upon a sense of familiarity and shared meaning that exists despite our many differences. 

A meme entails within it, a complex relationship between objects that make the meme, one that can be applied to explain a wide variety of phenomena and evoke appropriate reactions. Perhaps there is a deeper theory here of humor as a universal language that acts as firmware for humans.

Since memes allow for easy explanations and higher information density even for complex issues, I wanted to experiment with creating a memeified version of an article that dealt with a complex idea to see if the format was appealing and useful to a broad audience.

I recently finished reading this masterpiece called The Uruk Series by Lou Keep. It is a 12-part series that talks about a number of important big-picture themes. Read the introduction here. You should read it, if you find yourself interested in any of the following questions:

Why do state-backed schemes tend to fail? 
Why do we have discontent despite growing economic prosperity?
What do we mean when we say capitalism?
Why do mass movements become popular? What ends do they serve?
Why do we see the rise of narcissism in the modern world?

Of these articles, Part 2, titled “The Meridian of Her Greatness”, talks about the in vogue questions surrounding capitalism and its discontents. This is an important question and most of us seem to be missing the point entirely, falling squarely under the category of not-even-wrong. The article is a first step in creating the right scaffolding so we can have productive conversations around the topic. 

A memeified version of this article can be found here

You can find a tweetstorm style summary of this article here. I used some of the memes in conjunction with text to make explanations easier. 

Let me know if you have any thoughts on the format! Enjoy!

 

 

 

 

 

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Lambda School: A Case Study on How to Scale Effective Learning

“Can we devise teaching-learning conditions that will enable the majority of students under group instruction to attain levels of achievement that can at present be reached only under good tutoring conditions?”

This is the 2 sigma problem as defined by Benjamin Bloom, an educational psychologist who pioneered mastery learning.

This article is inspired from a Tweetstorm I did six months ago. You can find the original thread here.

One year ago, I found Lambda School on Twitter. While researching them online, I came across a thread from Austen talking about introducing Mastery Based Progression to their cohorts. This led me to look into Bloom’s two sigma problem, why it wasn’t solved yet and how technology can help scale effective learning.

Bloom’s 2 sigma Problem

Benjamin Bloom’s research found that one on one tutoring could help achieve 2 sigma improvement over conventional teaching. His objective was to find practical, yet scalable methods of attaining two sigma improvement since tutoring wasn’t scalable in its present form as defined by physical constraints.

A brief overview of the problem.

bloom

Why is Bloom’s 2 sigma problem important?

Effectiveness of one on one tutoring indicates that aptitude achievement correlation can be reduced from 0.6 to 0.25, i.e. effective learning levels the playing field for all those who wish to learn irrespective of skill level. It would represent an order of magnitude higher impact as compared to traditional methods.

This is how the achievement distribution looks like for the three cases mentioned in the overview.

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Finding Solutions for Bloom’s 2 sigma problem

Looking for solutions to the 2 sigma problem is a four step process.

  1. Look at the entire teaching/learning process
  2. Break down into mutually independent pieces
  3. Find intervention variables and their effect sizes
  4. Combine variables to achieve intended two sigma improvement

Bloom used this approach to look for viable solutions.

bloom2.PNG

The most important variables with high effect sizes were: Feedback and corrective measures, Cooperative learning, Classroom participation, Tutorial instruction Reinforcement and Graded homework. A combination of these variables enabled by the introduction of suitable tech seemed like a viable solution.

The Evolution of Ed Tech

Education is one of the toughest markets to crack for startups. Regulation, long sales cycles, fragmented markets and illegibility to technology (until now) make ed tech startups difficult to scale and largely outside the purview of venture funding. Until now.

Traditional education was ripe for disruption by MOOCs but this failed miserably. Only the most motivated students could finish courses and completion rates were less than 5%. Asynchronous, do it yourself modes of learning are not effective in their current form. This quote describes why: “The big MOOCs mostly employed smooth-functioning but basic video recording of lectures, multiple-choice quizzes, and unruly discussion forums. They were big, but they did not break new ground in pedagogy.” A richer discussion on the topic can be found here.

Given the advances in tech enabled solutions for various aspects of the learning process such as lesson delivery, feedback mechanisms, ability to replay videos, instant communication, TA support via Zoom/Slack, many people predicted the end of the search for a solution to the 2 sigma problem. They were proven incorrect. Instead of the predicted need of fewer teachers, we require more to achieve 2 sigma improvement with the following caveat: teachers should utilized in an effective manner using tech as leverage.

It takes a few iterations before people find a way to execute correctly. The same pattern can be seen here. The most common startups in ed tech focused on getting scale and distribution with little consideration for summative achievement. Getting courses online wasn’t enough. This article looks at the bastardization of the 2 sigma problem. The following quote summarizes the mistake succinctly:

“So here’s where the bastardization comes in to play.  I think that there has been so much focus on the tutoring part, that we’ve lost sight of the learning part.  “Will it scale?” is arguably the most important question that an ed tech investor will ask.  That’s great, and that’s an absolutely justifiable question.  But if you read through Bloom’s paper, there are two parts to the question.  Will it scale, AND, will it improve learning levels over the conventional baseline.  Like with many “shiny object” technologies, we tend to focus on the scale part and gloss over the improvement part (or worse yet, just “assume” the learning will happen).”

Lambda School’s Foray into Ed Tech

A brief description of Lambda School for the uninitiated: A live, fully online school that trains people to become software engineers, data scientists and designers which is free until you get a job. Instead, students pay a percentage of their income each year after they’re employed, the maximum of which is capped at $30k.

A combination of multiple factors enabled Lambda school’s success at finding a product-market fit: rising  student debt and default rates, reliable communication tools like Slack and Zoom and an innovative business model based on Income Share Agreements to name a few.

Lambda managed to crack the problem by innovating on pedagogy and identifying an arbitrage opportunity in the labor market (supply demand gap for CS grads to start with). The latter is important because it allows lambda to do what they do. Traditional education has no skin in the game and perverse incentives. Lambda is forced to act in the students best interests because it has aligned incentives. It earns only when students get high paying jobs.

A few reasons why lambda school works so well:
1. Small class sizes – each cohort is divided into sections with 8-10 students.
2. World class teachers – practitioners who devised OG curricula at places like Apple
3. Cooperative learning – group learning, capstone projects and build weeks
4. Hungry students – gritty students who will do the work (full time for 9 months)
5. Mastery learning – repeat parts of the course until students demonstrate mastery
6. Extensive TA support – feedback and corrective measures

These variables have high effect sizes as indicated by Bloom’s research. This innovation on the pedagogy front is the moat and is scalable across verticals. More from Caleb, VP of Learning at Lambda here and here. By reverse engineering the job market requirements and using their pedagogical infrastructure Lambda can provide effective education in almost any domain legible to online learning.

Lambda School works. The outcomes and demand are proof. The Series B they raised is going to help them scale. On a recent podcast, Austen mentioned their plans to have 3000 students in 2019 and a few cohorts in the EU. Scaling to millions will be an operational challenge and it will be interesting to see how Lambda tackles this.

Thanks to Anisha, Leon, Vidy and Vijay for helping out!
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Thoughts on Information Dissemination and Attention Spans in Today’s World

Whether it is Donald Trump being elected as the Republican nominee, Brexit, or any other serious topic in today’s world, there is a common link that I feel exists behind such instances. In this blog post I’ll try to explain what it has got to do with psychology, biases, and more importantly, thinking.

(Critical?)Thinking

Forget about critical thinking, even thinking in today’s world is underrated. It is too easy to make some believe in a notion, given that you have the apparatus for it. People fall for all kinds of bullshit, all the time. How many of us think before making decisions, whether it is choosing a school, a major, a political party or even a referendum to leave the EU or not? In my opinion, very few of us do that. Even when few actually think, it’s not good enough. That in itself is not surprising since the law of averages works everywhere, meaning most people are average at best when it comes to thinking. Combine this with the fact that people only listen to what they like, add a pinch of ignorance, and use this with short attention spans that we have today, and you can probably explain these occurrences which appear baffling to at least a section of society.

Information Dissemination and Thinking

Here’s a rough infographic I made to highlight what I think is a major issue when it comes to thinking in today’s world. Information penetrates at different levels within the population thereby making the judgement process skewed and in most cases driven by superficial evidence which might be misleading to say the least.

Information Penetration

I might be overestimating the number of people who actually think or I might just be wrong (in which case I would be happy to correct myself), but what I feel is clear is that thinking is a dwindling phenomenon. I discuss this further under Attention Spans.

There is just too much information and not enough time or interest among people to think about everything before taking a decision. I feel this can explain why masses are easily swayed by slogans and why politicians, celebrities, etc feel necessary to pander to the public every once in a while.

The Confirmation Bias

The essence of the confirmation bias is that you see what you want to see. Even when presented with evidence contrary to your beliefs or alternative to your thinking you choose to ignore those, sticking to what you think is correct.

Consider the ramifications this has over the events that I’ve mentioned at the start. Donald Trump supporters cannot see why he’s wrong, they can only hear sentences they want to.

While I’m no expert on the US elections or their mentality, the political polarization is there for everyone to see. Here’s an infographic on Political Polarization from Pew Research Center.

Political Polarization

This polarization I believe is in part created by the media. My dissatisfaction with the media in general is probably worth a blog post and something I might work on later. But just to connect the dots between the confirmation bias, not thinking and the effect of media on public sentiment here’s another infographic from Pew Research Center on Political Polarization and Media Habits.

Political Polarization and Media Habits

Some other interesting finds from this study (I’m summarizing them):

  • Consistent conservatives are tightly clustered around one news channel, meaning they receive most of their information from a single source thereby increasing the chance of bias creeping in. Also, they are more distrustful of news outlets in general. They are also more likely than other ideological groups to hear political opinions in line with their own views. They are also more likely to have friends who share the same views. See anything that strikes you ?
  • Consistent liberals rely on a greater number of sources for information and are in general more trusting of news outlets. They are more likely to unfriend or block people on social media because of political reasons, meaning they don’t want to hear the other side of the story?

It is clear that both sides of the political spectrum have biases which have now led to the creation of the biggest ideological gap in decades.

This article is not meant to discuss political ideologies, nor am I commenting on or comparing the two. I just happened to take this up as an example to explain my views on why these things could have happened.

Attention Spans

With the world becoming more and more “social” and the constant and unending flow of information, our attention spans have reduced to those of gnats. There is not enough time to put in the effort to understand and process information, but only enough to consume.

Here are some statistics from National Center for Biotechnology Information, U.S. National Library of Medicine and The Associated Press on Attention Spans and how they’ve reduced over time.

Attention Spans

Here’s a very click-baity article on the same: http://www.telegraph.co.uk/science/2016/03/12/humans-have-shorter-attention-span-than-goldfish-thanks-to-smart/

There’s even a rebuttal saying Millennials are accused of having shorter attention spans and that is just because they have much better stuff to do. While that is very subjective and may be true to an extent, I disagree and find that a convenient excuse to ditch critical thinking.

Just to summarize this piece: Humans are prone to biases; not many people are up for thinking; the media has a role to play in this; shorter attention spans are making the problem worse; all of this can lead to instances that are high impact and may even be disastrous.

Just jotting down a list of topics that we considered important and now are as good as forgotten:

  • NSA spying scandal
  • Panama Papers
  • Every other mass shooting that is happening
  • Russia’s annexation of Crimea

The list is probably bigger and even I might have missed many but you get the gist. Issues, even important ones that don’t stay in the limelight are easily forgotten, gone, poof.

Bonus Trivia: The newest and fastest growing social media platform Snapchat allows you to post snaps or video stories with an upper cap of, well as you might have guessed, 10 seconds. Coincidence much? I think not.

Also, if on Snapchat, you can follow me on rrahul30. 😛

Disclaimer: All views expressed are personal. As is with humans, I might also have certain biases or pieces of information that I might have missed; although I have tried to present a very fact-based opinion piece, any comments to ensure correctness are appreciated.

References:

  1. Infogr.am used to create infographics.
  2. Pew Research Center
  3. Thinking, Fast and Slow by Daniel Kahneman
  4. Predictably Irrational by Dan Ariely

P.S. If you liked this, please give this article a heart (recommend) so that it can reach more people! Thanks ☺