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The Lesson You Never Got Taught in School: How to Learn!
Psychological Science in the Public Interest evaluated ten techniques for improving learning, ranging from mnemonics to highlighting and came to some surprising conclusions.
A paper published in Psychological Science in the Public Interest evaluated ten techniques for improving learning, ranging from mnemonics to highlighting and came to some surprising conclusions.
The report is quite a heavy document so I’ve summarised the techniques below based on the conclusions of the report regarding effectiveness of each technique. Be aware that everyone thinks they have their own style of learning (they don't, according to the latest research), and the evidence suggests that just because a technique works or does not work for other people does not necessarily mean it will or won’t work well for you. If you want to know how to revise or learn most effectively you will still want to experiment on yourself a little with each technique before writing any of them off.
Elaborative Interrogation (Rating = moderate)
A method involving creating explanations for why stated facts are true. The method involves concentrating on why questions rather than what questions and creating questions for yourself as you are working through a task. To do this yourself, after reading a few paragraphs of text ask yourself to explain “why does x = y?” and use your answers to form your notes. This is a good method because it is simple, so anyone can apply it easily. It does however require enough prior knowledge to enable you to generate good questions for yourself, so this method may be best for learners with experience in a subject. The technique is particularly efficient with regard to time, one study found that elaborative learning took 32 mins as opposed to 28 mins simply reading.
An example of elaborative interrogation for the above paragraph could be:
Elaborative learning is useful for proficient learners because it allows them to apply their prior knowledge effectively to process new information. It is rated as effective because it is time efficient and relatively easy to perform.
“The current evidence base for elaborative learning is positive but lacking"
Self Explanation (Rating = moderate)
A technique that is useful for abstract learning. The technique involves explaining and recording how one solves or understands problems as they work and giving reasons for choices that are made. This was found to be more effective if done while learning as opposed to after learning. Self explanation has been found to be effective with learners ranging from children in kindergarten to older students working on algebraic formulas and geometric theorems. Like elaborative explanation, self explanation benefits from its simplicity. Unlike elaborative learning, self explanation was found to double the amount of time spent on a task in comparison to a reading control group.
“The core component of self-explanation involves having students explain some aspect of their processing during learning”
Summarisation (Rating = low)
An old staple, tested by having participants summarise every page of text in to a few short lines. Summarising and note taking were found to be beneficial for preparing for written exams but less useful for types of tests that do not require students to generate information – such as multiple choice tests. Summarising was rated as being likely less beneficial than other methods available but more useful than the most common methods students use – highlighting, underlining and rereading.
“It can be an effective learning strategy for learners who are already skilled at summarizing”
As you might have guessed, I personally find summarising to be very effective – my love of taking notes is probably what drove me to blogging in the first place. I love the function of being able to “ctrl-f” or search my notes folder for the fact that’s on the tip of my tounge. Since starting blogging I love that I can throw a phrase I’m after in to Google along with ‘neurobonkers’ and instantly have the relevant fact in front of my eyes. On a vaguely related note – some have suggested that the ability to Google spontaneously is destroying your memory – but based on the evidence I can’t say this is a view I agree with.
Highlighting and underlining (Rating = low)
The runaway favourite technique of students was found to perform spectacularly poorly when done on its own under controlled conditions. It seems pretty intuitive that highlighting alone is ineffective for the same reasons it is so popular – it requires no training, it takes practically no additional time and crucially, it involves very little thought above the effort taken to simply read a piece of text.
It’s worth remembering that this study only assessed research examining highlighting/underlining as a stand-alone technique. I’d be interested to discover how effective highlighting is when paired with other techniques.
The keyword mnemonic (Rating = low)
A technique for memorising information involving linking words to meanings through associations based on how a word sounds and creating imagery for specific words. Much research has found that mnemonics are useful for memorising information in the short term in a range of situations including learning foreign language, learning people’s names and occupations, learning scientific terms etc. However, it seems the keyword mnemonic is only effective in instances where keywords are important and the material includes keywords which are inherently easy to memorise. The review cites one study for example that required students to use mnemonics to memorise English definitions that were not well suited to keyword generation – the study found that the control group outperformed the group using mnemonics. More worrying – it seems that though the keyword mnemonic has been found effective for aiding short term recall, it has been demonstrated to actually have a negative effect when compared to rote learning in the long term. So, the mnemonic might be useful for remembering definitions the week before an exam but it doesn’t seem to be much use when used in any scale as a long term memory aid.
Imagery for Text Learning (Rating = low)
Experiments asking students to simply imagine clear visual images as they are reading texts have found advantages when memorising sentences, but these advantages seem much less pronounced when longer pieces of text are involved. Interestingly, visualisation was found to be more effective when students listened to a text than when they read text themselves, implying the act of reading may make it harder to focus on visualising. A major problem with imagery research is that most researchers instructed one group to visualise but did not follow up to see if they actually did. One experiment that checked afterwards found that some participants instructed to imagine did not, while some participants in the control group reported using visualisation on their own accord. It is therefore likely that imagery could be a more useful technique than this evaluation currently demonstrates – it is certainly an easy technique to use, so there is little harm in trying. Perhaps more interestingly, imagery research has found that drawing does not seem to improve comprehension and may indeed actually reverse the benefits of imagery. Finally, though imagery is reported to be more versatile than the keyword mnemonic, it has also been found useful only for certain situations. For example, imagery was not been found to be effective to help students answer questions that required inferences to be made from the text, nor was it been found useful for answering questions about a passage on the human heart.
Rereading (Rating = low)
Overall, rereading is found to be much less effective than other techniques – however the research has drawn some interesting conclusions. Massed rereading – rereading immediately after reading - has been found more effective than outlining and summarising for the same amount of time. It does seem however, that rereading spaced over a longer amount of time has a much stronger effect than massed rereading.
Practice Testing (Rating = High)
This is where things get interesting; testing is often seen as a necessary evil of education. Traditionally, testing consists of rare but massively important ‘high stakes’ assessments. There is however, an extensive literature demonstrating the benefits of testing for learning – but importantly, it does not seem necessary that testing is in the format of ‘high stakes’ assessments. All testing including ‘low stakes’ practice testing seems to result in benefits. Unlike many of the other techniques mentioned, the benefits of practice testing are not modest – studies have found that a practice test can double free recall!
Research has found that though multiple choice testing is indeed effective, practice tests that require more detailed answers to be generated are more effective. Importantly, practice testing is effective when you create the questions yourself.
So how can you apply this research? Students can create flash cards (or even use free software to do this). Alternatively students can use a system such as the Cornell note-taking system (Example PDF) which involves noting questions in a column next to their notes as they learn. This finding looks like wonderful news for MOOCS which typically use intensive practice testing as a primary method of teaching. The finding is also great news for students – as practice testing actually takes up much less time than other methods such as rereading, which practice testing far outperforms!
Try it yourself: Can you name and explain two methods of self-testing?
Distributed Practice (Rating = High)
Have you ever wondered whether it is best to do your studying in large chunks or divide your studying over a period of time? Research has found that the optimal level of distribution of sessions for learning is 10-20% of the length of time that something needs to be remembered. So if you want to remember something for a year you should study at least every month, if you want to remember something for five years you should space your learning every six to twelve months. If you want to remember something for a week you should space your learning 12-24 hours apart. It does seem however that the distributed-practice effect may work best when processing information deeply – so for best results you might want to try a distributed practice and self-testing combo.
There is however a major catch - do you ever find that the amount of studying you do massively increases before an exam? Most students fall in to the “procrastination scallop” – we are all guilty at one point of cramming all the knowledge in right before an exam, but the evidence is pretty conclusive that this is the worst way to study, certainly when it comes to remembering for the long term. What is unclear is whether cramming is so popular because students don’t understand the benefits of distributed practice or whether testing practices are to blame - probably a combination of both. One thing is for sure, if you take it upon yourself to space your learning over time you are pretty much guaranteed to see improvements.
Interleaved Practice (Rating = Moderate)
Have you ever wondered whether you are best off studying topics in blocks or “interleaving” topics – studying problems of different types in a slightly more haphazard fashion? Unlike the other methods discussed above, there is far less evidence to go on. The research that has so far been conducted seems to suggest that interleaving is useful for motor learning (learning involving physical movement) and cognitive tasks (such as maths problems) – where benefits of up to 43% have been reported. It also seems that like distributed practice; interleaved practice seems to benefit longer term retention:
“Accuracy during practice was greater during block trials but accuracy a day later was far higher for students who had received inter-leaved problems.”
So why do we use the wrong techniques and which should we use?
The review looked at a range of educational psychology textbooks and found that despite the wealth of research evidence, none of the textbooks that were reviewed covered all of the methods described above – and in those that covered one or more, the coverage was minimal. So if you happen to be an educational psychologist looking to write a textbook, you’re not in a bad position. We are all expected to be able to learn but currently we don’t ever really get taught how to learn. So next time you have something to learn why not take a second to create a schedule to distribute your practice, while you're reading – instead (or as well as) taking extensive notes why not write yourself some practice questions with a special focus on why questions; and when you are learning a new skill why not write a detailed explanation of how you answer the questions. This doesn’t mean you should rush out and bin all the highlighters, but maybe try to gradually incorporate a new technique every time you study and see which techniques work best for you!
Dunlosky, J., Rawson, K., Marsh, E., Nathan, M., & Willingham, D. (2013). Improving Students' Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology Psychological Science in the Public Interest, 14 (1), 4-58 DOI: 10.1177/1529100612453266 [PDF]
Image Credit: Slavoljub Pantelic, Sergey Nivens, Dusit, Africa Studio, Tatiana Popova, ladybirdanna, Vladgrin, Evgenyi, Digital Genetics, HomeStudio, Elena Elisseeva /Shutterstock.com
The father of all giant sea bugs was recently discovered off the coast of Java.
- A new species of isopod with a resemblance to a certain Sith lord was just discovered.
- It is the first known giant isopod from the Indian Ocean.
- The finding extends the list of giant isopods even further.
Humanity knows surprisingly little about the ocean depths. An often-repeated bit of evidence for this is the fact that humanity has done a better job mapping the surface of Mars than the bottom of the sea. The creatures we find lurking in the watery abyss often surprise even the most dedicated researchers with their unique features and bizarre behavior.
A recent expedition off the coast of Java discovered a new isopod species remarkable for its size and resemblance to Darth Vader.
The ocean depths are home to many creatures that some consider to be unnatural.
According to LiveScience, the Bathynomus genus is sometimes referred to as "Darth Vader of the Seas" because the crustaceans are shaped like the character's menacing helmet. Deemed Bathynomus raksasa ("raksasa" meaning "giant" in Indonesian), this cockroach-like creature can grow to over 30 cm (12 inches). It is one of several known species of giant ocean-going isopod. Like the other members of its order, it has compound eyes, seven body segments, two pairs of antennae, and four sets of jaws.
The incredible size of this species is likely a result of deep-sea gigantism. This is the tendency for creatures that inhabit deeper parts of the ocean to be much larger than closely related species that live in shallower waters. B. raksasa appears to make its home between 950 and 1,260 meters (3,117 and 4,134 ft) below sea level.
Perhaps fittingly for a creature so creepy looking, that is the lower sections of what is commonly called The Twilight Zone, named for the lack of light available at such depths.
It isn't the only giant isopod, far from it. Other species of ocean-going isopod can get up to 50 cm long (20 inches) and also look like they came out of a nightmare. These are the unusual ones, though. Most of the time, isopods stay at much more reasonable sizes.
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During an expedition, there are some animals which you find unexpectedly, while there are others that you hope to find. One of the animal that we hoped to find was a deep sea cockroach affectionately known as Darth Vader Isopod. The staff on our expedition team could not contain their excitement when they finally saw one, holding it triumphantly in the air! #SJADES2018
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What benefit does this find have for science? And is it as evil as it looks?
The discovery of a new species is always a cause for celebration in zoology. That this is the discovery of an animal that inhabits the deeps of the sea, one of the least explored areas humans can get to, is the icing on the cake.
Helen Wong of the National University of Singapore, who co-authored the species' description, explained the importance of the discovery:
"The identification of this new species is an indication of just how little we know about the oceans. There is certainly more for us to explore in terms of biodiversity in the deep sea of our region."
The animal's visual similarity to Darth Vader is a result of its compound eyes and the curious shape of its head. However, given the location of its discovery, the bottom of the remote seas, it may be associated with all manner of horrifically evil Elder Things and Great Old Ones.
If computers can beat us at chess, maybe they could beat us at math, too.
- Most everyone fears that they will be replaced by robots or AI someday.
- A field like mathematics, which is governed solely by rules that computers thrive on, seems to be ripe for a robot revolution.
- AI may not replace mathematicians but will instead help us ask better questions.
The following is an excerpt adapted from the book Shape. It is reprinted with permission of the author.
Will machines replace us? Since the origin of artificial intelligence (AI), people have worried that computers eventually (or even imminently!) will surpass the human cognitive capacity in every respect.
Artificial intelligence pioneer Oliver Selfridge, in a television interview from the early 1960s, said, "I am convinced that machines can and will think in our lifetime" — though with the proviso, "I don't think my daughter will ever marry a computer." (Apparently, there is no technical advance so abstract that people can't feel sexual anxiety about it.)
Let's make the relevant question more personal: will machines replace me? I'm a mathematician; my profession is often seen from the outside as a very complicated but ultimately purely mechanical game played with fixed rules, like checkers, chess, or Go. These are activities in which machines have already demonstrated superhuman ability.
Some people imagine a world where computers give us all the answers. I dream bigger. I want them to ask good questions.
But for me, math is different: it is a creative pursuit that calls on our intuition as much as our ability to compute. (To be fair, chess players probably feel the same way.) Henri Poincaré, the mathematician who re-envisioned the whole subject of geometry at the beginning of the 20th century, insisted it would be hopeless
"to attempt to replace the mathematician's free initiative by a mechanical process of any kind. In order to obtain a result having any real value, it is not enough to grind out calculations, or to have a machine for putting things in order: it is not order only, but unexpected order, that has a value. A machine can take hold of the bare fact, but the soul of the fact will always escape it."
But machines can make deep changes in mathematical practice without shouldering humans aside. Peter Scholze, winner of a 2018 Fields Medal (sometimes called the "Nobel Prize of math") is deeply involved in an ambitious program at the frontiers of algebra and geometry called "condensed mathematics" — and no, there is no chance that I'm going to try to explain what that is in this space.
Meet AI, your new research assistant
What I am going to tell you is the result of what Scholze called the "Liquid Tensor Experiment." A community called Lean, started by Leonardo de Moura of Microsoft Research and now open-source and worldwide, has the ambitious goal of developing a computer language with the expressive capacity to capture the entirety of contemporary mathematics. A proposed proof of a new theorem, formalized by translation into this language, could be checked for correctness automatically, rather than staking its reputation on fallible human referees.
Scholze asked last December whether the ideas of condensed mathematics could be formalized in this way. He also wanted to know whether it could express the ideas of a particularly knotty proof that was crucial to the project — a proof that he was pretty sure was right.
When I first heard about Lean, I thought it would probably work well for some easy problems and theorems. I underestimated it. So did Scholze. In a May 2021 blog post, he writes, "[T]he Experiment has verified the entire part of the argument that I was unsure about. I find it absolutely insane that interactive proof assistants are now at the level that within a very reasonable time span they can formally verify difficult original research."
And the contribution of the machine wasn't just to certify that Scholze was right to think his proof was sound; he reports that the work of putting the proof in a form that a machine could read improved his own human understanding of the argument!
The Liquid Tensor Experiment points to a future where machines, rather than replacing human mathematicians, become our indispensable partners. Whether or not they can take hold of the soul of the fact, they can extend our grasp as we reach for the soul.
Slicing up a knotty problem
That can take the form of "proof assistance," as it did for Scholze, or it can go deeper. In 2018, Lisa Piccirillo, then a PhD student at the University of Texas, solved a long-standing geometry problem about a shape called the Conway knot. She proved the knot was "non-slice" — this is a fact about what the knot looks like from the perspective of four-dimensional beings. (Did you get that? Probably not, but it doesn't matter.) The point is this was a famously difficult problem.
A few years before Piccirillo's breakthrough, a topologist named Mark Hughes at Brigham Young had tried to get a neural network to make good guesses about which knots were slice. He gave it a long list of knots where the answer was known, just as an image-processing neural net would be given a long list of pictures of cats and pictures of non-cats.
Hughes's neural net learned to assign a number to every knot; if the knot were slice, the number was supposed to be 0, while if the knot were non-slice, the net was supposed to return a whole number bigger than 0. In fact, the neural net predicted a value very close to 1 — that is, it predicted the knot was non-slice — for every one of the knots Hughes tested, except for one. That was the Conway knot.
For the Conway knot, Hughes's neural net returned a number very close to 1/2, its way of saying that it was deeply unsure whether to answer 0 or 1. This is fascinating! The neural net correctly identified the knot that posed a really hard and mathematically rich problem (in this case, reproducing an intuition that topologists already had).
Some people imagine a world where computers give us all the answers. I dream bigger. I want them to ask good questions.
Dr. Jordan Ellenberg is a professor of mathematics at the University of Wisconsin and a number theorist whose popular articles about mathematics have appeared in the New York Times, the Wall Street Journal, Wired, and Slate. His most recent book is Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else.
Laughing gas may be far more effective for some than antidepressants.
- Standard antidepressant medications don't work for many people who need them.
- With ketamine showing potential as an antidepressant, researchers investigate another anesthetic: nitrous oxide, commonly called "laughing gas."
- Researchers observe that just a light mixture of nitrous oxide for an hour alleviates depression symptoms for two weeks.
The usual antidepressants don't work for everyone. That's what makes a new study of the antidepressant properties of nitrous oxide so intriguing. It looks like just a single low dose of what your dentist may call "laughing gas" can help alleviate symptoms of depression for weeks afterward.
The study, from researchers at University of Chicago and Washington University-St. Louis, is published in the journal Science Translational Medicine.
Resistance to anti-depression medications
Nitrous oxide: two atoms of nitrogen, one of oxygenCredit: Big Think
According to the senior author of the study, Charles Conway, "A significant percentage — we think around 15 percent — of people who suffer from depression don't respond to standard antidepressant treatment."
"These 'treatment-resistant depression' patients," Conway says, "often suffer for years, even decades, with life-debilitating depression. We don't really know why standard treatments don't work for them, though we suspect that they may have different brain network disruptions than non-resistant depressed patients. Identifying novel treatments, such as nitrous oxide, that target alternative pathways is critical to treating these individuals."
"There is a huge unmet need," says lead author Peter Nagele. "There are millions of depressed patients who don't have good treatment options, especially those who are dealing with suicidality."
If ketamine can help, can nitrous oxide?
Credit: sudok1 / Adobe Stock
The researchers wondered if some of the anti-depression properties seen in ketamine might also apply to nitrous oxide. Nagele explains, "Like nitrous oxide, ketamine is an anesthetic, and there has been promising work using ketamine at a sub-anesthetic dose for treating depression."
The researchers conducted a one-hour session — they describe it as a "proof-of-principle" trial — in which 20 individuals with depression were administered an air mixture with 50 percent nitrous oxide. Twenty-four hours later, the researchers found a significant reduction in the participants' symptoms of depression versus a control group.
However, the individuals also suffered the unpleasant side effects that laughing gas often causes in dental patients: headache, nausea, and vomiting.
Smaller dose, longer effect
Credit: sudok1 / Adobe Stock
"We wondered if our past concentration of 50 percent had been too high," recalls Nagele. "Maybe by lowering the dose, we could find the 'Goldilocks spot' that would maximize clinical benefit and minimize negative side effects."
In a new trial, 20 people with depression were given a lighter nitrous oxide mix, just 25 percent, and the individuals tested reported a 75 percent reduction in side effects compared to the a control group given an air/oxygen placebo. This time, the researchers also tracked the effect of nitrous oxide on symptoms of depression for a far longer period, two weeks instead of just 24 hours.
"The reduction in side effects was unexpected and quite drastic," reports Nagele, "but even more excitingly, the effects after a single administration lasted for a whole two weeks. This has never been shown before. It's a very cool finding."
Nagele also notes that, despite its popular renown as laughing gas, even a light 25 percent mix of nitrous actually causes people to nod off. "They're not getting high or euphoric; they get sedated."
Delivering help to people with depression
Nagele cautions, "These have just been pilot studies. But we need acceptance by the larger medical community for this to become a treatment that's actually available to patients in the real world. Most psychiatrists are not familiar with nitrous oxide or how to administer it, so we'll have to show the community how to deliver this treatment safely and effectively. I think there will be a lot of interest in getting this into clinical practice."
After all, Nagele adds, "If we develop effective, rapid treatments that can really help someone navigate their suicidal thinking and come out on the other side — that's a very gratifying line of research."