Keynote

I arrived on time, but there was a queue to get in and so by the time I got in, the keynote had already started. I made my way there and sat through the rest of the keynote. I made some notes, but in broad strokes, it was a pretty well put together “hey, look here at the cool stuff we do”, including a pre-recorded conversation between the CEO and Satya Nadella, the CEO of Microsoft. Then an interview with a VP at Astra Zeneca about how they plan to port all of their data infrastructure to Snowflake, which sounds like a very large, pretty scary project to be in charge of, exciting too. This was followed by a talk by one of the founders of Snowflake talking about the fact that despite it being 2025, many organisations still struggle to benefit from the data they collect in the course of doing business because of siloes, quality issues in the data collection, or legacy architecture that makes using the data difficult. Not all these things are easy to solve: if your data is trash, you’re done… But, at least, the potential of Snowflake to reduce siloes and dependence on legacy architecture is appealing.

Breakout sessions

After the keynote, there were breakout sessions. I did pick which sessions I attended purely on their titles, so there was a fair bit of randomness in the process.

I went to a session on no code agents which was interesting but very introductory. Then I attended a presentation on Launch Darkly that enables experiments from within Snowflake. I think this has a lot of potential but it was unclear how that would integrate with your deployed code or application (fair enough, there is only so much one can cover in 30 minutes). I then went to a session about how Hargreaves Lansdown used Snowflake to move some of its reporting out of excel spreadsheets. This highlighted the fact that while everyone is raving about AI changing everything, many companies just struggle finding the data they need because it is hidden somewhere on an employee’s laptop or in someone’s email as an attachment. Finally, I attended another session on clean rooms which is a feature that enables secure data sharing to allow a third party to run analysis on your data without them actually seeing the data, removing the risk that they would be able to copy the data itself.

Posit

During one of the break, I had time to go visit the Posit booth, which allowed me to get a nice selection of stickers that I am very pleased about.

Overall, it was very interesting and it hammered home that there seem to be a lot more work in data engineering than in data analytics, as many organisations are struggling to just make their data available to their employees and that this is the first pain point to resolve before they can hope to gain value from sophisticated analyses. It highlighted to me that Snowflake, by promising to make data more accessible has a lot of potential to help organisations move from the step at which data engineering provides most of the value, to a position where they can start deploying analytics effectively.

Reflecting on that day, the most interesting thing was to realise that if a lot of organisations are on Snowflake (or a similar platform) and tools like clean rooms become widespread, providing data analytics services to an organisation might become a lot easier and faster. An analytics team would just need access to the clients data through an appropriate clean room to be able to get started.

The problem: the slide tech stack from hell

A few years ago, in a bid to improve student engagement in the classroom, I started using Mentimeter to ask in-class questions from my students. This is all well and good, but Mentimeter has limited options when it comes to slide design. Using it means giving up a lot of control. At the time I first started using Mentimeter for the classroom (2020), my experiment with linking mentimeter questions from within Powerpoint were disappointing and I could not get mentimeter questions to behave how I was hoping them to within powerpoint. Then I made a terrible mistake. Having concluded that making slides directly on Mentimeter was as attractive as playing a level 1 character in D&D,³ and that embedding Mentimeter questions directly into Powerpoint exposed me to technical failures in the classroom, I decided to make my slides in Powerpoint as usual, upload them to Mentimeter as images (you do not get a choice), and interleave Mentimeter questions in between these slides. It worked. Kind of. Until it did not. Things did not break suddenly, rather, it was a slow rot that at first went unnoticed.

The obvious problem is that once your slides are converted into images, you cannot edit them easily. You have to go back to Powerpoint, make the changes, and re-upload them to Mentimeter (who turns them into images in the process). Then you have to move the Mentimeter questions back to where they are supposed to go. Needless to say, this is way more faff than I can bear, and it means that for small things, like a typo, I would not go through the process to fix it. For larger changes, I would eventually get around to them, but it would make me grumpy. In short, it made it too hard to iterate on the slides.

This would just be unfortunate if I was not so unable to let go and therefore not getting increasingly frustrated by my slides over time. In addition, I am old enough to know that consistent small improvements is the only way things get better.⁴

The solution: revealjs slides

Fast forward to now, and after years of frustration, I decided to take the slides out of Mentimeter. It was time to think about what technology to use to avoid being in the same place in 5 years or so.⁵ Enters Quarto. I use quarto all the time to write documents, and while working on research to document my work from cleaning the data to reporting. And I used R Markdown before that. Quarto has a revealjs format for slides, and the more I experimented with it, the more I thought that it would be suitable.

Of course, I could not live with the defaults.⁶⁷⁸⁹ I had the idea after reading the docs that using the vertical navigation would be a good idea to be able to have a main narrative and arbitrary numbers of extra slides drilling down into specific details¹⁰. So I poked around, and also asked silly questions (see below), which lead me to figure out that I could probably get what I was hoping to achieve with a bit of scss. With the help of chatGPT, I managed to put together a new style for section slides that gives them the appearance of content slides.

The style exploits the fact that revealjs organises content slides as vertical stacks under section slides that are organised horizontally. You can visualise this by pressing o. With no option set in your yaml for navigation, you go through your rendered deck linearly by pressing either ↓ or →. If you set navigation-mode: vertical in the yaml, you will navigate between section slides with →, but also be able to navigate down each content slide underneath each section with a ↓ press.

With the modified section slides that look like content slides, you can have a main story unfold linearly within your section slides, and then have an arbitrary number of drill-down slides to add details with standard content slides. For teaching, it allows you to manage your time better by having slides with extra information that you can chose to cover or not depending on time and interest from the audience. While presenting a paper, you could have your main results on a section slide, and details about the results, such as your full regression table, on content slides underneath. Giving you the option to cover these in case the audience asks.

One thing I have not solved is what presentation clicker to use with this, as most of them only have two buttons for navigating the slides.

Below is a short demo of what the slides look like.

Too good to be true?

But this apparent breakthrough does not withstand scrutiny. I have reservation about the approach for two main reasons.

First, the tool is trained on specific introductory sections of chemistry papers which could reduce its effectiveness in other contexts. There is a risk of overfitting, the tool does great on very specific narrow types of texts and badly on all other input.

If this critic has legs, the tool would struggle with generalization. And guess what? It does. When given articles from university newspapers, it couldn’t recognize them as human-written. If it can’t adapt to different types of academic writing, its utility becomes pretty limited.

But, you might say, it is still useful for detecting AI written chemistry introductions. It may be so, but I doubt that even this would be true. I am also not convinced that this is even something we should want to do. Let me explain.

The tool detected text generated by AI that was unmodified. If you have used LLMs for a while, you know that the raw output is often badly flawed, and cannot be used as is, but it can be a very useful starting point for refinement. So most users will refine and modify AI outputs, making the detector’s effectiveness lower, or negating it completely.

Another, maybe more important, question is should we try to detect AI writing? The assumption behind this new detector seems to be that AI-assisted science is inferior to human-only science. Surely, the only thing that should matter is the quality of the knowledge created, not how it has been created. In addition, the evidence that is starting to accumulate suggest that LLMs are productivity and creativity boosters (see Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality). If that is the case, wouldn’t we be better off focusing on how we can use AI to enhance our productivity and do better science, rather that going on a fool’s errand trying to develop AI detectors?

I think this underscores why critical thinking and data literacy are essential in our AI-driven world. Reading past the headline, understanding the nuances, and questioning the capabilities and limitations of technologies are key to enable you to understand the world and make good decisions.

In conclusion, while AI detectors like this might seem promising on the surface, they still do not work. And even if they worked, using them still seems to be the wrong thing to do. So, once again, do not use AI detectors. Ever!

What are your thoughts on the use of AI detection tools in academic research? Let me know in the comments below.

Thanks to Ethan Mollick for tweeting about the news article in Nature that led to this rant. 🙂

References

‘ChatGPT detector’ catches AI-generated papers with unprecedented accuracy https://www.nature.com/articles/d41586-023-03479-4

Accurately detecting AI text when ChatGPT is told to write like a chemist. https://www.sciencedirect.com/science/article/pii/S2666386423005015?via%3Dihub

Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321

Introduction

1. Do not sit down to write

2. Writing is not about writing

In this video, I mention advice from Stephen King and Deirdre McCloskey. You can get these books there:

Stephen King’s On Writing.

Deirdre McCloskey’s Economical Writing.

3. 2 tips about simplicity

In this video, I mention advice from Mortimer Adler and Charles Van Doren How to read a book. You can get the book there:

Mortimer Adler and Charles Van Doren How to read a book.

4. 3 Tricks to help with your writing

5. How to outline

6. Transform your writing with these simple but powerful paragraph hacks

7. Engage you audience

In this video, I mention advice from Mortimer Adler and Charles Van Doren How to read a book.

You can get the book there:

Mortimer Adler and Charles Van Doren How to read a book.

8. Crafting a Strong Argument: The Three Layers of Effective Writing

9. The Importance of a Strong Lead: Writing Advice for Beginnings

10. Writing Hacks: How Reading Aloud Can Help You Edit Your Work

In this video, I mention advice from Deirdre McCloskey’s Economical Writing and from William Zinsser’s On Writing Well. You can get the books there:

Deirdre McCloskey’s Economical Writing.

William Zinsser’s On Writing Well.

Thank you for watching. I hope that you find these tips useful.

References

Adler, M. J., Van Doren, C. L. 2014 [1972]. How to read a book. New York: Touchstone, A Division of Simon & Schuster.

King, S. 2012. On writing: a memoir of the craft. London: Hodder.

McCloskey, D. N. 2019. Economical writing: thirty-five rules for clear and persuasive prose. Chicago: University of Chicago Press.

Zinsser, W. 2006. On writing well: the classic guide to writing nonfiction [30th anniversary ed.]. New York: HarperCollins.

Note: Some of the links on this page are affiliate links, which means that, at no extra cost to you, I might get a small commission if you click them and make a qualifying purchase.

References

Bergstrom, C. T., & West, J. D. 2021. Calling bullshit : the art of scepticism in a data-driven world. London: Penguin Books.

Cunningham, S. 2021. Causal inference : the mixtape. New Haven, Connecticut: Yale University Press.

Harford, T. 2020. How to make the world add up: ten rules for thinking differently about numbers. London: The Bridge Street Press.

Huntington-Klein, N. 2022. The effect : an introduction to research design and causality. Boca Raton: CRC Press.

Pearl, J., & Mackenzie, D. 2018. The book of why : the new science of cause and effect. London: Allen Lane.

I generated the title of this article in part with GPT-3, OpenAI’s large-scale language-generation model. Upon generating draft language, I reviewed, edited, and revised the language and I take ultimate responsibility for the content of this publication.

Note: some of the links to books are affiliate links, this means that if you use them to make a purchase, I might receive a small commission.

References

Hand, D. J. 2016. Measurement : a very short introduction. Oxford, United Kingdom: Oxford University Press.

Note: some of the links to books are affiliate links, this means that if you use them to make a purchase, I might receive a small commission.

Inflation Figures:

• While the drop in inflation to 4.6% is significant, it’s crucial to understand the temporary factors contributing to this decrease, like the slowdown in energy prices.
• Headlines might not fully capture ongoing economic challenges or the fact that other areas might still be experiencing price hikes.
• In fact, when looking into the details of the inflation number, there are several areas where inflation has not reduced, or has even increased. For example, Food and beverage went from 12.1% to 10.1%, down yes, but still very high.
• The all services figure is 6.6 down from 6.9, but again very high (compared to a 2% target) and showing little signs of going down yet.
• Most of the fall in inflation is due to fall in energy prices, but last year, people in the UK received a £400 subsidy to help with energy bills, no such thing this year. So the actual price paid by household for energy is very similar this year to what it was last year with the subsidy.
• Finally, 4.6% is still more than twice the 2% target of the Bank of England.

While the recent fall in inflation is a positive sign, it’s important to keep in perspective that inflation rates remain high. There is no cause for celebration. Though salary growth has overtaken headline inflation, offering some hope, we must remember that wages have lagged behind inflation for a while, squeezing household budgets. And wage growth is still lower than inflation on some types of goods and services, for example, food. Additionally, as unemployment begins to tick upwards, wage growth will decelerate. This means it could take considerable time for wages to recover to their level from before this inflationary period in real terms (adjusted for inflation).

Housing Market Data:

• The reported 0.1% annual fall in house prices masks the regional variations and doesn’t necessarily indicate a nationwide trend.
• The ONS House Price Index is based on completed transactions, meaning the data is lagged (the lag varies by the time it takes for transactions to be completed which makes correcting for it harder) and might not reflect the current market conditions.

For those currently in the process of buying or selling a house, relying solely on lagged indicators like the ONS House Price Index can be misleading. While these indicators show only a slight annual fall in house prices, more current data suggests that prices are falling further. This discrepancy underscores the importance of considering up-to-date market information in your decision-making process. In a changing market, basing decisions on outdated data could lead you to overpay for a house.

On both inflation, and house prices, my comments are only some of the reason to be critical of the headlines that you have seen, there are many other things that make interpreting headline figures harder, and mean that we often see misleading headlines. One example of other factor to look at would be seasonal variation.

There is a lot of good commentary out there (for example, on YouTube) if you want to go deeper into inflation or housing data.

If you are wanting to go deeper on measurement, David Hand excellent very short introduction has a chapter on economic measurement that is well worth a read.

These examples highlight the importance of critically analyzing news. Headlines, while informative, are just the tip of the iceberg and they are written to make you click on them (just like the title of this article, sorry not sorry). The real story often lies beneath the surface, in the details and context that are not immediately apparent.

This brief exercise in data literacy shows us how vital it is to look beyond the headlines to avoid misconceptions. In a world overflowing with information, a little data literacy goes a long way in helping us understand the world around us. In the context of inflation slowing, looking under the hood makes it clear that people are going to continue struggling financially for a while. And looking at house prices, it might save you a lot of money to deconstruct the figure because you will avoid overpaying for a house in a falling market.

Note: Some of the links are affiliate links, which means that, at no extra cost to you, I might get a small commission if you click them and make a qualifying purchase.

Use good verbs

One of the best advice on verbs is found in McCloskey’s Economical Writing. McCloskey warns against feeble verbs such as implement, or comprise for include, analyze for discuss or examine, the list goes on, and on. The mistaken belief that repetition is bad leads to too much elegant variation, which, in turn, leaves one with no choice but to use verbs that are less clear, or worse, pretentious.

The active voice is better

Zinsser prefers using active verbs. He further suggests to avoid concept nouns which lead to the passive voice, instead “get people doing stuff.” McCloskey agrees: you should write with verbs and nouns and the verbs you use should be active verbs. It is not that the passive voice is wrong (it isn’t), but it hides the subject who is acting, this makes it harder for the reader to visualise what is going on and risk reducing comprehension. Her advice is to circle every “is” on the page and replace them with a different verb that describe the action.

Simplicity is key

Both Zinsser and McCloskey agree that simplicity is key. This means avoiding jargon, and generally getting rid of clutter. One can see here the influence of Strunk and White The Elements of Style’s famous rule 17: “Omit needless words.” McCloskey suggests to avoid using words of Latin origin when simpler Anglo-Saxon words will do. Overall, we should always avoid jargon as much as is possible.

Concrete details help comprehension

McCloskey suggests that being specific helps comprehension. You want to make it easy for your reader to parse your text. According to her, too much of social science requires its readers to be “code breakers”. Overall, both Zinsser and McCloskey advocate clarity above all: your role as a writer is to make sure your audience can understand you and follow your reasoning.

References

McCloskey, D. N. 2019. Economical writing: thirty-five rules for clear and persuasive prose. Chicago: University of Chicago Press.

Strunk, W., & White, E. B. 2000. The Elements of Style (Fourth Edition ed.): Pearson.

Zinsser, W. 2006. On writing well: the classic guide to writing nonfiction [30th anniversary ed.]. New York: HarperCollins.

Note: some of the links to books are affiliate links, this means that if you use them to make a purchase, I might receive a small commission.

Some limitations of Newport’s argument

While the idea of deep work is attractive, there is also a certain naïveté in Newport’s presentation. He makes the assumption that deep work is rewarded because it is valuable. For some valuable activity to be rewarded, it needs first to be valued. While the intrinsic value of deep work is not in question, whether your deep work is rewarded will depend heavily on whether is is valued by the right people (organisations, managers, etc.). In other words, there are plenty of valuable work that is devalued and badly rewarded or not rewarded at all — childcare and early years education comes to mind.

Another mildly annoying passage — this will be particularly funny for the economists among you — has Newport quoting from a Stata marketing document as if it was impressive to be able to use this software: “Stata 13 adds many new features such as treatment effects, multilevel GLM, power and sample size […]”. Newport is a Computer Science professor, he knows that most of what he is quoting is not difficult to master, nor is it complex to use in a software like Stata.

Finally, Newport sometimes exhibits conceptual fuzziness. For example, the first chapter ends with a story about Jack Dorsey (the former CEO of Twitter) as a counter example, someone who does not do deep work but is still successful. Jack Dorsey might not do deep work in Newport’s sense, but he is also not doing shallow work. Going back to my earlier critique, this example also illustrate the fact that what is valued is not always valued because it is “valuable”. The rewards given to Jack Dorsey comes from his ownership of part of the company, he is not compensated for the value of his work, but for his ownership of capital. While I do not think that this example invalidates Newport’s thesis, it is an example of fuzziness that does not help his point.

What it made me want to read next

There is always scope to get better at organising oneself, two of the books that I added to my reading list while reading Deep Work, were Atomic Habits by James Clear, and How to Change by Katy Milkman.

I also found a video about the book by Ali Abdaal, which is worth checking out. He suggests that blocking full day rather than 2–3 hours blocks might be the best way to go to achieve the maximum amount of deep work. While this might be the case for you, for me, I find that there are diminishing return to productivity/focus, and that I cannot do much more than a few hours of consecutive deep work. It is quite possible that we cannot focus adequately on deep work past the 4 hour mark in a single day (Ericsson, Krampe, & Tesch-Römer, 1993). Both Ali in his video, and Newport in the book, acknowledge this limit to how much time we can spend focusing.

References

Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. 1993. The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3): 363–406.

Giurge, L. M., & Bohns, V. 2021. Be Intentional About How You Spend Your Time Off, Harvard Business Review.

Tonietto, G. N., & Malkoc, S. A. 2016. The Calendar Mindset: Scheduling Takes the Fun Out and Puts the Work In. Journal of Marketing Research, 53(6): 922–936. Note: some of the links to books are affiliate links, this means that if you use them to make a purchase, I might receive a small commission.

Rehearse

You have to rehearse your presentation. You do not need to know it by heart, but you need to be familiar with it. Running through your slides and what you will say a couple of times will greatly enhance your presentation: you will be more comfortable and this will show and make it easier for you to connect with your audience.

Define your audience

Before you can speak effectively to your audience, you need to know your audience. It sounds obvious, but you will not address experts in the same way you do lay people. If you know your target audience, you can tailor your message so it addresses their needs.

One way to connect to your audience is to find common ground: what interestes and experiences do you share with your audience? This is easier to do for audiences you know well (i.e. close colleagues) than for broader audiences, but finding things that both you and the audience can relate to, will help you keep your audience engaged.

What is the big idea? Why should we care?

Again, this sounds obvious, but do not assume everyone in your audience knows what you are talking about, or why it is important. Even if the audience members are the ones who ask you to speak to them. Be more explicit than you think you need to be. This does not mean that you should repeat yourself again and again, but that you should make your main message very explicit, and tell the audience why they should care.

Why they should care will depend on your audience, so be sure to know who you are addressing and why what you are talking about might help them solve real problems they face.

Keep time

If you have rehearsed your presentation, keeping time should be easy. Nobody is going to be annoyed if you need an extra thirty seconds to finish your conclusion when you run out of time, but you seriously undermine your credibility if you are only halfway through when the clock runs out.

Rehearsing is the best way to know whether you have too much or too little material.

Build tension

Use contrasting elements to build tension, your solution can then resolve this tension. For example, you can build tension between different approaches to a problem, then resolving that tension by offering a solution that combines both approaches.

Organise your thoughts

Software for creating slides are great tools, but they also offer you many opportunities to stray from the message you are trying to convey. One way to help keep you on message is to focus first on the order of your ideas. One way to do this is to first only write titles for your slides and look at these titles in a sequence to check that they make sense in the order they are presented in. This will also lead you to write more precise titles. Specific titles are important because they are the only thing that most of your audience will read.

Make slides people can “get”

Your slides should be short. Put as little text on them as you can get away with. Your goal is that your audience listens to you, not that they read your slides. The slides support the points you are making, they are not the main medium to transmit your message.

Think about your body and voice

Face your audience, not your slides. Make gestures to emphasise important points. Use your voice to convey different emotions and to highlight key piece of your argument.

What it made me want to read next

Super Courses pointed me, either directly or indirectly, to a number of articles on learning science and a number of books on pedagogy which joined my long reading list (which grows faster than I can get through it, as all good reading lists do).

It led me to get Geeky Pedagogy by Jessamyn Neuhaus, Make It Stick by Peter Brown, Henry Roedinger, and Mark McDaniel, both of which I plan to review in the future.

Bain mentions Minds on Fire by Mark Carnes which presents an approach to learning based on role-playing activities. I am quite curious to learn more, but have not looked into it yet.

In terms of more direct impact on my own practice, the book led me to adopt social annotation for the readings in my classes (the practice is discussed at length in chapter 9, and while I am using a different tool to the one described in the book, the principle is the same). My first classes using social annotation will be in the second term of this year, and I am looking forward to it. I am excited to see if that helps my students engage with the readings more, and if it gives me insight into what they find challenging so I can focus on this material during the class sessions.

Overall, I found Super courses insightful and stimulating. It is a good complement to The Craft of College Teaching. I highly recommend it.

References

Chávez, K., & Mitchell, K. (2020). Exploring Bias in Student Evaluations: Gender, Race, and Ethnicity. PS: Political Science & Politics, 53(2), 270-274. doi:10.1017/S1049096519001744

Uttl, B., White, C. A., Gonzalez, D. W. (2017). Meta-analysis of faculty’s teaching effectiveness: Student evaluation of teaching ratings and student learning are not related. Studies in Educational Evaluation, 54, 22-42. doi:10.1016/j.stueduc.2016.08.007.

Note: some of the links to books are affiliate links, this means that if you use them to make a purchase, I might receive a small commission.

Overly broad statements

Overly broad statements are common in introductions and in the topic sentence of paragraphs. They are attractive to us when we are writing because they give us a way to get started. They give us something to prime the writing pump and get us going. However, very often it is better to edit them out, or make them precise and to the point during the revision process. Examples of such statements are sentences starting with “over the years”, “in the 21st century”, which usually do not add much but do little harm. Edit them out.

Even broader statement are less common, but worse. Statements like “since the dawn of time”, or “as the world evolves rapidly”; these do not say anything. There are very few things so universal about the world that they would have been true since the dawn of time. Similarly, the world might be changing fast, but you are probably talking about a very specific facet of the world that is relevant to your essay or dissertation or article, then focus on this!

Statements like “this happens because of a variety of causes” do not tell us anything. You should be able to describe at least some categories in which these causes fall.

Overly definitive statements

Statements such as “X is a universal phenomenon”, “society is X”, “no one has done this before” prompt the reader to think of any counter example to prove you wrong. And often, whether the statement itself is true has little impact on your argument. A more nuanced view is often a better bet. Very few things are universal, but if something is truly widespread, you can just use the word “widespread”, ideally accompanied with a qualifier such as a statistic that shows just how widespread the phenomenon is.

Inaccurate statements

Beware of inaccurate or confusing statements, “In the last century” is too often used to speak of the 19th century when it refers to the 20th century (or at least has if you were writing at any point in the past 20 years). Other statement of the form “X and Y have dominated our thinking for the past Z years” better be ironclad. “X has become a hot topic” might be true, but you can probably write a more precise statement that does not rely on someone having to figure out what counts as “hot”.

Plain annoying statements

Just avoid statements that are doing nothing beyond annoying the reader: “Once upon a time”, “It’s no secret”, “it is obvious”. While the first of these three statements is seldom seen in essays and dissertations, the other two are far too commonly found in students prose. If you have to stipulate that something is obvious, it often indicates that it is not obvious to everyone.

I hope this is useful in pointing out some of the things to keep an eye on while writing and editing your essays or dissertations.

Check out the Purdue writing lab

https://owl.purdue.edu

What did it make want to read next?

I use Economical Writing by Deirdre McCloskey (my review) as the source of a lot of the writing advice I give students, but I was looking for something with more explicit advice about structuring paragraphs. As this book wasn’t it, I looked further. I am currently reading How to Write a Sentence and How to Read One by Stanley Fish, which is very good so far. Next on my list is The Reader over Your Shoulder by Robert Graves and Alan Hodge (recommended by McCloskey). I am also reading Writing for Social Scientists by Howard Becker, but it is much more about the process of writing than the craft itself. I am considering looking into books aimed at secondary school students to learn more about paragraphs. I hope to find in them what I am looking for there.

In the meantime, through Jenny Richmond’s very interesting Twitter thread, I found this good blog post by Raul Pacheco-Vega on strategies to craft paragraphs. This points to a wealth of resources on writing and structuring paragraphs that is well worth exploring. I might not need a book on paragraphs after all!

Note: some of the links to books are affiliate links, this means that if you use them to make a purchase, I might receive a small commission.

1 - Chunk Your Time

You probably need to plan 1 to 3 hours of independent work per hour you spend in the classroom. Depending on the class. Undergraduate classes often requires investment on the lower end of this, while graduate classes are on the higher end of this. PhD classes usually require even more preparation time per hour of classroom. So commit to a number of hours you will work on a class every week.

2 - Block It On Your Calendar

You have just committed to spending a certain number of hours a week on a class, block that time in your calendar. This will make it easier to remember when you had decided to do something, it will also make it less likely that you will accept other commitments that will crowd out your study time.

It is often more effective to block smaller slots in your calendar rather than 1 large slot a week. We have limited cognitive capacity and few people can study intensely for 2 hours straight, but most people can do so for 20, 30 or 45 minutes. Take breaks often and repeat several shorter intense periods of study rather than trying to do one very long one.

3 - Use Techniques Like Spaced Repetition and Plan These in Your Calendar as Well.

Another reason why planning several smaller study periods rather than one long one yields results is because we know that spaced repetition increases long-term retention. This is essential in order for you to be able to mobilise your knowledge later, especially when it comes to understanding more advanced material that builds upon the material you are learning now. Spaced repetition is one of the most powerful way to enhance your learning, and while it requires planning, it is fairly easy to use. I wrote more about spaced repetition in this article about studying tips and in my review of Ultralearning. You might want to consider using flashcards to help with spaced repetition.

4 - Make Notes and Organise them.

Making notes is only half the job. You also need to organise them. Experiment with the way you take notes to find what works for you, it might be that you prefer writing by hand, or on a device; it might be that structure, like the Cornell Notes method—you reserve space in a margin for keywords and questions and at the bottom of the page for a summary—works well for you. Books like Take Great Notes (my review) can help you figure out what type of note taking method works for you. Take Note by Roy Tabor is another good book on making notes.

Once you have made notes, you need to organise them. Passive review is not very effective (Dunlosky et al., 2013), but reorganising and expanding on your notes is more fruitful. The Cornell Note method is an easy way to do this, but you should experiment with what works for you. Maybe you want to keep your notes in one of the numerous note taking apps available on the market, some are very good for organising your material.

5 - Work in A Study Group

Study groups promote active learning by promoting discussion around the class. You can use your study group to work side-by-side with others and keep yourself motivated, but you can also (and probably more effectively) use it to discuss and question the course material. This can easily be done in pairs. Select a specific concept, pick roles (explainer/questioner) and explain the concept, then try to answer the questions of your colleague. Then you both verify your understanding in the course material. After that, you can move on to another point of the course.

This approach combines several activities: it forces you to articulate what you know to explain it to someone, it also forces you to think on your feet to answer their questions and, finally, it prompts your brain for learning when you review the material, as we retain better when we do a closed book recall before reviewing material (Karpicke and Roediger, 2007).

A study group will also help you develop ties to your fellow students and stay connected to your cohort. Cooperative learning like the activities I just described has been showed to increase both achievement and enjoyment (Yamarik, 2007; Kyndt et al, 2013).

Not all of these will work equally well for you, but experimenting with the way you work will offer you an opportunity to better understand how you learn and make you better at making the most of your learning.

References

Dunlosky, J., Rawson, K.A., Marsh, E.J., Nathan, M.J. and Willingham, D.T., 2013. Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), pp.4-58.

Karpicke, J.D. and Roediger III, H.L., 2007. Repeated retrieval during learning is the key to long-term retention. Journal of Memory and Language, 57(2), 151-162.

Kyndt, E., Raes, E., Lismont, B., Timmers, F., Cascallar, E., Dochy, F., 2013. A meta-analysis of the effects of face-to-face cooperative learning. Do recent studies falsify or verify earlier findings?, Educational Research Review, 10, 133-149, https://doi.org/10.1016/j.edurev.2013.02.002.

Yamarik, S.,2007. Does Cooperative Learning Improve Student Learning Outcomes?, The Journal of Economic Education, 38(3), 259-277, DOI: 10.3200/JECE.38.3.259-277

Note: some of the links to books are affiliate links, this means that if you use them to make a purchase, I might receive a small commission.

What else did it make me want to read?

McCloskey recommends her 3 favourite books on writing in one of the rules. I have already read and used Strunk and White, The Elements of Style. I will get a copy of Graves and Hodge, The Reader Over Your Shoulder: A Handbook for Writers of English Prose; and of Williams, Style: Lessons in Clarity and Grace.

She also mentions Becker’s Writing for Social Scientists which I read a long time ago in the French translation and started rereading in English.

Note: some of the links to books are affiliate links, this means that if you use them to make a purchase, I might receive a small commission.

Teach principles, not software

Students need to be taught principles that apply whatever software they use. In the context of data analysis and scientific programming, this means teaching about data structures and programming concepts, such as loops or vectors.

These concepts will transfer to other contexts (such as other software and programming languages) and allow a student proficient with the concept to become proficient in another context much quicker. This is particularly important because which programming language or software platform dominate a specific application domain changes over time. The tool we will use in 5, 10 or 20 years will be different to the one we use today, but the principles will still apply. A student who makes a career in data analytics will have to retool several times over their career and a strong grasp of principles will make this easier.

For these reasons, it is important to teach principles, however, the software you choose still matters, because switching costs still exist. It makes sense to attempt to use the tools that will minimise the switching costs to the students in their transition from education to their professional lives.

What is used in industry?

One way to minimise switching costs is to teach using tools that are widespread in the industries your students go on to work in. In theory, this means students can directly use what they learned in the classroom in their first job.

In addition, their ability to signal to an employer that they are already familiar with specific tools in use in the company has immediate benefits for their employability.

I believe that using what the industry uses is important but less so than teaching general principles of data analysis, fortunately, one might be able to do both.

What does the user community look like?

Considering the user community provides important information when deciding what tools to use.

How large is the community? Bigger is not always better, but it often helps. A larger community means more opportunities for both permanent employment and freelance engagements. It often translates to easier access to training material and help, for example through Q&A sites and online courses providers. The bigger the community the most likely that someone has encountered and solved the most common issues that arise with the software or any module within it.

How welcoming is the community? This is a difficult one as communities will not be similarly welcoming to everyone. As an instructor, it is important to think about where to direct the students for help. Some sub-communities within the larger community will be more welcoming and that is where one should direct students.

How accessible is the software?

The last thing to consider is accessibility. It is an easy one for instructors to overlook as we often have good access to software through our institutions. But this is not the case for many students, especially for those of us teaching diverse cohorts coming from a wide range of countries and diverse class backgrounds.

Is the software proprietary? How costly is a licence? How long does the licence students obtain through the University last after they finish the course? Answering these questions help decide which tool is appropriate. For example, Stata is a popular statistical software with a large user base. Stata also costs money. The cheapest plan as a student is $94 per year, but this only applies while enrolled at University. A single-user licence otherwise costs $765 per year (or more for the multicore version). While $94 might not be much for some students, it is a lot for others. And $765 is out of reach of the majority of students or unlikely to be worth the investment, unless data analysis is central to the positions they plan to apply to. This means that all but a few students will lose access to the software outside of the classroom, limiting their ability to practice and progress, and their ability to build a portfolio of work that they can showcase to potential employers.

Software that is free, and, even better, open source allows students to have a copy of the software in perpetuity and use it as much as they need. This availability usually helps the community grow and, in many cases, leads to positive externalities: larger communities and more contributed libraries being the two most evident ones.

Focusing on principles and on answering the three questions I described—what do people in industry use? How large is the community? How accessible is the software?—will help you decide what is the most appropriate software to use in your teaching.

While I weight accessibility (as in Free and Open Source) heavily, this might not be as relevant for you or your field and you might weigh the size of the community or use in industry more than I do.

What are flashcards good for?

Flashcards are a simple and effective tool to help memorise information. However, they are often used to memorise simple relationships, such as pairs of words, or concept definitions. This makes them well suited in cases where this is what is likely to be tested.

How can one use flashcards effectively? There are two important things to keep in mind: how you practice, and the type of cards that you use.

How to make the most of flashcards

In summary, it seems that to make the most of flashcards, one should do the following:

• Make conceptual cards rather than factual ones. These cards should draw links between the different concepts that are being learned. While traditionally used for learning facts, the best way to use flashcards is by focusing on concepts and relationships between these concepts.
• Spaced repetition and successful recall are both essential. Using a tool that will keep presenting a card until successful recall is achieved and will then present it again after a short while has passed can be an effective way of maximising the return to using flashcards (for example Anki, a free and open-source tool). In addition, you should schedule flashcards recall sessions several days apart over a period of several weeks to take full advantage of the benefits of spaced repetition.

How have you used flashcards? What has worked for you?

References

Kornell, N. 2009. Optimising learning using flashcards: Spacing is more effective than cramming. Applied Cognitive Psychology, 23(9): 1297-1317.

Kornell, N., & Bjork, R. A. 2008. Optimising self-regulated study: the benefits - and costs - of dropping flashcards. Memory, 16(2): 125-136.

Lin, C., McDaniel, M. A., & Miyatsu, T. 2018. Effects of Flashcards on Learning Authentic Materials: The Role of Detailed Versus Conceptual Flashcards and Individual Differences in Structure-Building Ability. Journal of Applied Research in Memory and Cognition, 7(4): 529-539.

Wissman, K. T., Rawson, K. A., & Pyc, M. A. 2012. How and when do students use flashcards? Memory, 20(6): 568-579.

What I liked

The book is well rounded and the structure makes it easy to find information about a specific topic in the book. It gives a pretty complete overview of what goes into designing a class as well as a lot of advice for a successful delivery.

It is well written and generally as concise as one can wish (I hate it when books take 5 pages to say something that can be said in 1).

The chapter on active learning and the one experiential learning are very helpful for those wanting to familiarize themselves with these concepts.

What I liked less

A lot of the references provided are books. This is not a huge issue in itself, but it might make it more difficult for the intended audience to access sources. Indeed, faculty and PGTA might have good access to scientific papers through their institutions, but budgets for buying books will vary widely across seniority levels, institutions and countries.

Some of the interludes are very good complements to the chapter that precedes them, while others seem to come out of nowhere. They might be interesting in themselves (group work, knowledge and information), but they sometimes appear detached from the larger arch of the book.

How does it relate to other books that I have read

Reading Ultralearning by Scott Young (my review) led me to create a short reading list about pedagogy that I am currently making my way through. The Craft of College Teaching is targeting a different audience and is more structured to appeal to college teachers, but there is some overlap in the topics covered.

What did it make me want to read next

Next on my list are Geeky Pedagogy by Jessamyn Neuhaus and two books mentioned in the epilogue of the book: Mastermind: How to Think Like Sherlock Holmes by Maria Konnikova and Learn Better by Ulrich Boser.

Conclusion

The Craft of College Teaching is a useful resource for someone interested in reading about pedagogy or for someone preparing to deliver a new class. It is also a good resource for someone preparing a submission for a fellowship of the higher education academy^1.

Endnotes

1. The fellowship of the higher education academy (FHEA) is a teaching certification that most lecturers in the UK have to obtain in as part of their probation.

Note: some of the links to books are affiliate links, this means that if you use them to make a purchase, I might receive a small commission.

Free-riders

Those not enthusiastic about group work often mention concerns about free-riding. In other words, you are worried about one or several member of the group being rewarded for the group’s efforts to which they contributed less than their fair share. This sometimes leads to the sucker effect where members of the group respond to free-riding by others (real or perceived) by reducing their own effort (Davies, 2009).

This obviously can lead to the group performing worse, as the overall effort goes down as a result on both the initial free-riding and the sucker effect.

Group composition

Often group composition, or the way it is arrived at is a source of complaint. If groups are assigned, you might feel that you were unlucky in not being paired with your friends or in being paired with someone that you perceive as less well-performing.

However, leaving you to come up with groups on your own can be unfair to the students that are less well socialized into the cohort. In addition, it often reduces diversity within groups. Group diversity is important, because it can increase creativity in work groups (Vezzali et al., 2016). However, diverse groups also need more time to reach consensus and develop routines, which might explain that you sometimes experience more frustration in a diverse group than in an homogenous one.

Better learning

Groupwork is an active learning activity that forces you to use the material seen in class and mobilize it in the context of the group to produce some form of output. This implies discussing the material with the rest of the group and negotiating to reach consensus.

This helps you to engage with the material in an active way and there is evidence that active learning favors better learning and outcomes (Freeman et al., 2014)

Developing teamwork skills

One of the main benefits of group work is that it helps you to develop teamwork skills (Burke, 2011). Dealing with free-riding and its effects is part of the learning as well. There is some evidence that explicitly discussing rules of engagement within the team reduces free-riding and improves the functioning of the team (Burke, 2011).

Teamwork skills are valued by employers and being able to evidence your experience working in teams is an advantage in the workplace.

In summary

This was a very short introduction to why working in group is important even if it sometimes feels uncomfortable and leads to free-riding. I would argue that the two main complaints about group work: free-riding behavior and the composition of the group can be at the root of crucial skills arising from group work. Learning to manage free-riders and getting accustomed to working in diverse groups are two skills that will be very useful in the workplace. Group work while studying is a good way for you to start acquiring them in a controlled environment.

Let me know which important aspects of group work I have not covered. What else do you like or dislike about working in a group?

This article on group work was partially inspired by the Interlude 6 on group work in The Craft of College Teaching by Robert DiYanni and Anton Borst.

Endnotes

References

Burke, A. 2011. Group Work: How to Use Groups Effectively. The Journal of Effective Teaching, 11(2): 87-95.

Davies, W. M. 2009. Groupwork as a form of assessment: common problems and recommended solutions. Higher Education, 58(4): 563-584.

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. 2014. Active learning increases student performance in science, engineering, and mathematics. Proc Natl Acad Sci U S A, 111(23): 8410-8415.

Vezzali, L., Gocłowska, M. A., Crisp, R. J., & Stathi, S. 2016. On the relationship between cultural diversity and creativity in education: The moderating role of communal versus divisional mindset. Thinking Skills and Creativity, 21: 152-157.

Note: some of the links to books are affiliate links, this means that if you use them to make a purchase, I might receive a small commission.

Summary

The premise of the book is that one can learn vast amounts, and master subjects reputed difficult through intense practice. The book presents some of the principles, tools and techniques to achieve this. It is written in an accessible style and is well-paced.

While the book might first appear as a charge against higher education, this is not the case. Rather, it is a celebration of learning and what makes learning fun and rewarding. What Young argues is important for successful self-directed learning efforts, is also important for structured learning efforts, for example when one is enrolled in a degree programme. This means the book should appeal to anyone who is either engaged in a learning project or interacts often with learners.

I first picked up the book for myself as an entertaining read. I was also intrigued because, thanks to my job, I get to do a lot of individual independent learning and I hoped would give me a couple of ideas on how to organise some of my own learning (specifically to improve my skills in natural language processing). It turns out, however, that I found the book more relevant to educators than I expected. As a professor, I am always looking for ideas to help my students learn better. And there are quite a few things in the book that you can use to nudge students to practice.

The book introduces the ultralearning framework (I guess mega-learning was already taken?). The first three chapters set the scene: chapter one suggests one can get a college education without going to college (and facing the huge cost this represents in the US). The second chapter explores why ultralearning matters. The third chapter covers what one needs to do to become an ultralearner.

The rest of the book is then organised around 9 principles of ultralearning: metalearning, focus, directness, drill, retrieval, feedback, retention, intuition and experimentation. Each of the “principles” chapters are introduced with an anecdote. Some are very compelling (Ramanujan, chapter 8: Retrieval), some a lot less so, or even counter-productive (Richards, chapter 10: Retention).

The chapter on metalearning explores the importance of planning your learning project. The chapter on focus explores ways one can resist distractions and the temptation to procrastinate. The chapter on directness considers the difficulties with transfer: the fact that we are often not able to apply what we learned to problems that are only very slightly different from those we trained on. Drill insists on the importance of breaking down what you are learning to focus practice on specific areas of weakness. Retrieval suggests tricks to remember what you have learned effectively. Feedback is about getting feedback early and often, but also about when one should stop requesting feedback. Retention is focused on how memories decay over time and what we can do to stave off this decay. Intuition focuses on how we can build intuition for what we have learned. This chapter insists on the importance of difficulty and overcoming the difficulty. And finally, experimentation is concerned with how tweaking the way one learns can help refine one’s ability to learn.

What I liked about the book

Overall, I believe the value of ultralearning comes from the fact that in an increasing number of positions, people need to be able to learn and adapt. Therefore, one could argue that the capacity to learn and adapt is essential to stay relevant, whether you are an employee or an entrepreneur. In addition, in a world increasingly flooded with information, the ability to prioritize what one needs to know seem an obviously beneficial skill to have.

The emphasis on meta-learning both before and throughout a learning effort is something that I believe is very important. And this is often a part that students in degree programmes overlook because the structure of the programme seems to be a substitute for individual meta-learning, but this is only partially true. The book made me realize that I could do a better job of emphasizing this to students; and I could find ways to encourage their meta-learning efforts.

The book is full of solid studying advice, helpfully split across the principle chapters, which makes it easy to find specific advice when you are looking for it. For this advice alone, the book is worth it.

What I liked less

The first chapter is a collection of anecdotes about a number of ‘ultra learners’. While the chapter is entertaining, to me, it mostly illustrates the massive heterogeneity of this new category. And this is what makes it difficult for me to recognize it as a category: it has low coherence.

In the second chapter, the author explores why ultralearning matters. I find this chapter mostly unconvincing. While I think that ultralearning does matter, I don’t think it is for the reasons the author puts forward. The economic argument that being an ultralearner will help outcomes on the labor market, does not seem to hold much water as it assumes that if you learn hard enough, you will naturally come out in the group that benefits from changes in labor markets. There is little evidence of this being the case offered by the author. The education argument, which revolves around the cost of education, is incredibly US centric: there is a whole world where quality tuition is much cheaper than in the US. But even in the US, there is plenty of evidence there are good returns to education (Tamborini, Kim, Sakamoto, 2015; Hout, 2012). Overall, I am not sure that I am convinced by the argument that ultralearning matters because of the economic returns that might be associated with it.

But, overall, these issues are minor and I still recommend the book as an interesting read, especially if you are looking for ideas on how to structure your learning efforts.

How does it relate to other books I have read?

Ultralearning came back front of mind recently because I was reading The Craft of College Teaching by Robert DiYanni and Anton Borst, which is structured like an how-to for various part of a university-level course. There is some overlap between the two books despite their apparently very different audiences.

It also reminded me of some of the literature I read a few years ago while preparing my application for a FHEA^1.

What did it make me want to read next?

The book made me read a number of scientific articles on various aspect of learning. I was particularly interested in how memory works.

Ultralearning also led me to add a number of books on pedagogy to my reading list, either because they were cited or because I simply stumbled upon them browsing pedagogy titles. The first book I read from that list was The Craft of College Teaching which I already mentioned, but it also made me order Super Courses by Ken Bain, which I plan to read shortly.

The other books I added to my reading list are Geeky Pedagogy by Jessamyn Neuhaus and Make it Stick by Peter C. Brown, Henry L. Roedinger III and Mark A. McDaniel.

It also led me to start writing a short series of posts aimed at students on learning tips and tricks.

Conclusion

While the book is quite entertaining and pretty good at relying on scientific evidence for the claims made, I found the over reliance on anecdotes at the beginning of each chapter unnecessary.

Overall, I liked book for the plethora of good studying advice and some of the digression into cognition and how we learn.

Endnotes

1. FHEA stands for Fellowship of the Higher Education Academy. This is a certification that is required in most UK Universities as part of a lecturer’s probation.

References

Hout, M. Social and Economic Returns to College Education in the United States. Annual Review of Sociology 38:1, 379-400 (2012).

Tamborini, C.R., Kim, C. & Sakamoto, A. Education and Lifetime Earnings in the United States. Demography 52, 1383–1407 (2015). https://doi.org/10.1007/s13524-015-0407-0

Note: some of the links to books are affiliate links, this means that if you use them to make a purchase, I might receive a small commission.

Spaced repetition

One common practice for learning is cramming. Essentially, you are running out of time and working yourself into exhaustion to try to master some lessons or a specific skill before a test.

Cramming can work reasonably well for short-term retention. In other words, you might pass! But it is ineffective for long-term retention (Cousins et al. 2019). This is problematic for two reasons. First, you overestimate your mastery of the material and you might struggle to mobilise this knowledge in the next class, which requires you to have mastered it. This is made worse if the time between the two classes is greater, because our knowledge decays over time (unless we use it). For example, if you cram to pass a foundational probability class, you might not realise that you have not assimilated all the concepts and you might struggle in the econometrics class that follows.

Spaced repetition is not rocket science. One way to achieve it, is to schedule review times throughout the duration of a class. Maybe 30 minutes for every 2-hour class session.

Now, what to do with that time? You could just re-read your notes and maybe highlight them, but we know that this is not very effective (Dunlosky et al., 2013).

More effective are activities that force you to put the material into practice, test yourself and struggle through the points that you are less comfortable with. The following three tips, used in conjunction with spaced repetition, can make your studying very effective.

Cornell Note System

The Cornell Note System was developed by Walter Pauk in the 1940s. The idea behind the Cornell Note System is simple. Separate the page you take notes on into different areas. Specifically, reserve a margin of about 1/3 of the width of the page for marginal notes. The other 2/3 are for your notes. You can also leave a bit of space at the bottom of the page for a summary.

Write your notes during the class as you would usually do. After the class, while you review your notes, write questions about the notes in the margin. If you have left space at the bottom of the page for a summary, you can also write this the first time you review your note.

Writing questions and a summary is enough to turn the ineffective passive reviewing of notes into an active exercise of reflecting on the material in order to produce both a summary and questions. It also prepares you to review the material more effectively in the future. To make future reviews more effective, you will cover the notes and use the questions you have generated to test yourself.

This brings us to our next tip, which is an extension of covering your notes: closed book learning.

Closed book learning

Trying to recall what you learned before reviewing any notes has been shown to increase retention (Karpicke and Roediger, 2007).

You can practice closed book learning with your notes, by recalling what is in the assigned readings or trying to recall any material relevant to your learning. One way to do this is to try to write down a summary of the notes or the readings before allowing yourself to look at the notes or the text again.

Trying to recall first prepares you to retain information more effectively once you review the material.

The next tip builds on the idea that testing enhances your ability to retain information for longer and suggests that you design challenges for yourself.

Challenges

Building challenges is one way to force yourself to use the material or practice the skill you are learning.

Challenges need to be adapted both to what you are learning, but also to your current level of mastery. In short, you want to struggle, but not so much that you give up.

Challenges are particularly effective in classes with a practical element such as data analytics or programming.

Select a topic from the class and try to come up with an exercise as a challenge. You can, of course, find existing exercises, especially when you first get started; however, coming with the exercise yourself forces you to reflect on the material and helps you learn better.

Putting it into practice

Those tips are more effective when you use them in combination.

Spaced repetition is better than cramming even if you only do passive review. However, if you combine spaced repetition with the Cornell Note System and closed book learning, the amount of the material that you will retain will go up dramatically, you will retain it for longer, too.

One practical way to do this is to review the material once a week. During the first review, you write the summary of your notes and questions in the margins. In the second week, you test yourself on the notes of the first week, using the question you have generated, before allowing yourself to review the notes to check your answers. Then you write a summary and margin questions for your notes from the second week. And so on and so forth.

As you get closer to the time of the exam/assignment, you can increase the amount of time you spend doing trying to recall information through closed book learning and challenges.

What other tricks do you use to study effectively?

References

Cousins, J.N., Wong, K.F., Raghunath, B.L., Look, C., Chee, M.W.L., 2019. The long-term memory benefits of a daytime nap compared with cramming, Sleep, 42(1), https://doi.org/10.1093/sleep/zsy207

Dunlosky, J., Rawson, K.A., Marsh, E.J., Nathan, M.J. and Willingham, D.T., 2013. Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), pp.4-58.

Karpicke, J.D. and Roediger III, H.L., 2007. Repeated retrieval during learning is the key to long-term retention. Journal of Memory and Language, 57(2), pp.151-162.

Kornell, N., 2009. Optimising learning using flashcards: Spacing is more effective than cramming. Applied Cognitive Psychology, 23, pp.1297-1317. https://doi.org/10.1002/acp.1537

The strength of weak ties (Granovetter, 1973, 56508 citations on google scholar as of 7/06/2020)

Weak ties are those we have with acquaintances. They are activated less often than strong ties, or we might not be close to the person at the other end. But because these ties are less often mobilised, they tend to provide novel information when compared with strong ties that are activated several times a week (like family, work colleagues, close friends). For example, they might provide information about job opportunities that we were not already aware of.

How can one take advantage of weak ties? Think about who are relevant acquaintances and when you interact with them, make sure that you have your ear pricked for relevant information. Do not hesitate to ask questions, but be mindful to ask for something reasonable by asking yourself this question: what am I ready to do for an acquaintance without feeling like I am doing them a big favour? This probably includes passing on information about your area of expertise, giving a referral, or something similar that might get you closer to achieving your goal.

Small World (Watts & Strogatz, 1998, 42604 citations on google scholar as of 7/06/2020)

The idea of a small world is that of a network where dense communities are connected to one another through sparse “long-distance” ties. In small worlds, information diffuses through the long-distance ties and then pollinates the dense communities through the many ties between the community members. If a network is a small world, information diffusion will be fast and efficient. It seems that many human networks exhibit small-world properties, but measuring small world properties in the wild is not straightforward.

It is hard for individuals to know if they are in a small-world without engaging in costly and difficult data collection. Organisations, on the other hand, collect most of the necessary data in the course of doing business (such as email metadata or event attendance). You can create value for your firm by better understanding how your employees communicate with one another or which business units are more or less siloed. Obviously, not everyone needs to communicate across communities (or business units) but identifying who is might help you design better way to spread information across your business.

Brokerage (Burt, 1995, 27652 citations on google scholar as of 7/06/2020)

Brokers are individuals who connect otherwise unconnected others. This gives brokers an information advantage. They can recombine information they obtain from their contacts in a way that is not accessible to these contacts. They also gain some control over that information as they can choose to transmit the information or not.

If you are a broker, you probably know already. But if you are not, you can ask yourself two questions. Who are the brokers around me? And, should I be a broker? Being aware of who in your network links across communities can be useful as we have some evidence that the benefits that brokers gain from their position spill over to their contacts. Second, actively engaging with different communities as a broker might benefit you and you might be able to benefit these communities too.

How to tie it all together?

These three concepts give us clues as to how ideas spread in social networks and more importantly, what individuals and organisations can do to benefit from the social networks they are embedded in. There is still a lot more for us to learn about how social networks influence idea diffusion, but we have already made major discoveries (some over 45 years ago) that we can build on in years to come.

References