By teaching, we learn: A retrospective on teaching about blogging

On the surface, teaching and learning have a pretty straightforward relationship: we learn something, and then we teach it, so that others can learn it (and maybe even teach it themselves). This does happen, but the learning-teaching relationship is far less linear than this might imply.

First, teachers and professors learn a topic well enough that they decide they can teach it. Sometimes they’re an expert in the topic, and other times they know the gist of a topic and (more importantly) where they can learn more.

Then they plan the course, during this phase, they often realize how much they don’t know. So they learn more. As they continue planning, they’ll put together lectures. This is another crucial part of the learning-teaching relationship, since teachers start distilling information from other sources into their own words to fit with their own course structure. Now they’re really learning.

Then comes the day of the lecture. The students might assume the professor knows all there is to know about the topic, and the professor hopefully feels prepared. During the lecture, hopefully students will ask questions. Some the professor will be able to answer — she’s already learned this stuff! But other questions might be more challenging. They might make apparent to the teacher what she doesn’t yet know. Hopefully she then tries to find the answer (if an answer exists). She learns again, and maybe communicates what she learned to the student who asked the question — so she teaches again.

This is a classroom example of how learning and teaching are inseparable — they often must happen simultaneously, since each supports the other.

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Teaching BlogSci

This quarter, I was fortunate to experience this tangle of teaching and learning for science blogging. I co-taught a seminar with Prof. Seana Coulson to introduce students to science blogging and guide them toward creating their own blog posts about Cognitive Science Research.

I’ve blogged for a few years and have paid some attention to other science blogs, implicitly gaining an understanding of the topics and strategies that make for the most engaging posts. But planning the class drove me to find and synthesize new science communication resources. Then I shared what I’ve learned with the class, and they asked great questions. Often these questions sparked the realization for me that I didn’t know the answer — and until they asked it, I didn’t know I didn’t know it.

Those moments can be unsettling (isn’t the instructor supposed to know the answer to topic-related questions?), or we can embrace them. For example, students wanted to know what makes for a good blog post title. For the final class, I asked around and looked up what other bloggers believe makes a good title, which we discussed as a class, but then we just experimented. We listed potential titles, shared them with the group, and got input on which were most compelling. We did some background research, and then we experimented.

Although I was one of the instructors, I didn’t know the answer to the post title question ahead of time. The seminar provided an opportunity for me to discover topics I didn’t know, and then work with the group to learn more. This is one example of many that show that I learned in order to teach the group, then learned while teaching the group, and in many cases, learned after formally teaching, once I realized how much was left to learn.

I’m grateful for the bright, curious students who fueled this process.

Seneca purportedly said Docendo discimus: By teaching, we learn. So my experience of learning while teaching is not novel. Instead, it’s an application of a timeless concept to a very modern one — blogging about science.

To learn more about our seminar and read the students’ polished products, check out our class blog: UCSDBlogSci.

Becoming a better teacher: Fish is Fish

This summer, I’ll be the Instructor of Record (real teacher, not Teaching Assistant) for the first time. I’m teaching Research Methods, which is a “lower level” (mainly first- and second-year undergrads) course that I’ve TAed for twice, and I really enjoy its content. Because I’m participating in UCSD’s Summer Graduate Teaching Scholar Program, I have to complete a course called Teaching + Learning at the College Level. We’re two weeks in, and I’ve picked up some interesting nuggets from the readings and class discussions, but one analogy in particular is still on my mind.

We talked about the children’s story Fish is Fish by Leo Lionni. I’m kind of glad I never encountered this story when I was a kid because its novelty had a great impact on 25-year old me. The story is about a fish and tadpole who wonder what life on land is like. Eventually the tadpole becomes a frog who can leave the water to learn about the land. He reports back to the fish, listing off features of things on land. Cows, he says, have black spots, four legs, and udders. The frog describes birds and people too, and here’s what the fish imagines:

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Image: The Eric Carle Museum

The fish and the frog are talking about the same things, and they assume that they have common concepts of cows and birds and people, but their actual mental representations — what they see in their mind’s eye — of these things are quite different. If the fish had been given a traditional paper and pencil test that asked him to define a cow, he’d be correct in writing that it has 4 legs, black spots, and udders. He’d ace the test, fooling not only the teacher, but also himself, into thinking he actually knows what a cow is.

The takeaway, of course, is to try to make sure your students aren’t fish. Find ways to lead them beyond their fishy cow concepts, which can especially be hard when they’ve never been on land and they come to class knowing only what fish are like. Students almost always need foundational knowledge in order to understand a new concept, and there’s a good chance that at least some of the students in any class are missing that foundation.  Instructors need to be mindful that there will be times when they have to step back to assess and teach prerequisite knowledge before launching into an hour-long lecture about cows (or cognition). And then once they think the students actually know what cows are, it’s important to provide assessments that actually test understanding, and not just memorization.

There are plenty of things I might not pull off perfectly when I teach for the first time this summer, but I do feel confident that I’ll at least be on a quest to help the fish in the class become frogs so they can see what cows are really like.

Teachers of all levels and subjects: I invite you to share how you make sure your students are truly understanding and not simply parroting. How do you make sure their concepts of the cow are really cow-like, and not just fish with spots and udders?

How We Learn: A Guest Review

I mentioned in a previous post that I have some stellar undergraduate Research Assistants. I neglected to mention that this summer I also have some stellar high school assistants. Juliette Hill is a rising senior whose main goal for her time in the lab was to learn what it’s like to be a cognitive science grad student. She worked on some open-ended and exploratory questions as well as some very detailed data collection. She also read and thought about cognitive science ideas beyond the specific ones we’re addressing in the lab. Here are her thoughts on How We Learn, a book by Benedict Carey:

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Like most of us, Benedict Carey grew up with the belief that in order to learn best, one had to find a quiet, designated study space. Practice was the only path to perfection. The Internet and all other electronic devices should be turned off lest they disturb your concentration. Highlighting and rereading notes, if done frequently, will improve your test scores. Forgetting is the enemy of learning.

Yet most of these adages are far from the truth.

Distractions can actually aid learning in ways that remaining focused cannot. Studying in the same spot repeatedly may weaken your grasp on the subject. After an intense study session of revising notes, we feel confident we know our subject inside out, but we still barely manage a B on the test. Why?

With the advent of modern science, we are barely able to scrape the surface of discovering the cognitive aspect of learning. In his book How We Learn, Benedict Carey walks the reader through a multitude of discoveries that may revolutionize the way we perceive the learning process. Here are some of the findings he explains:

Distraction can aid learning. While this is not an absolute (checking Facebook during a lecture does not help you learn what the teacher is presenting you), it certainly has much potential, especially in today’s society. While stuck on a difficult math problem or other similar pit, taking a study break can definitely boost your ability to solve the problem the second time around. Does this mean taking an hour-long nap will have similar effects? Absolutely! And it can possibly help even more than a simple distraction.

Sleep is your friend. Most people know that sleep can help consolidate learned facts and motor skills, but few people know when such benefits occur in the night. Each night is comprised of several cycles, alternating between a deep sleep and a more wakeful one. The times in the night when you sleep the deepest occur around the first 2 to 3 hours of sleep. This deep sleep has been found to reinforce the learning of rote facts. Yet if you are preparing for a music recital (which would involve your motor skills and learning), your peak of the night would occur slightly later.

Highlighting and rereading of notes will not carry you far. In fact, you will feel as if you know the subject manner by heart, but will be disappointed when you see an unexpected score on your test. What happened? You knew the content so well, right? The danger of highlighting and rereading is that it gives you the impression that you know the material, when you actually are only familiar with it. The best way to review content is to maintain a “desirable difficulty” (as coined by Dr. Robert Bjork) in your studying. This means that testing yourself (as opposed to just reading the content) will help you retain the material much better. So you can dig up those flashcards you never thought you’d use again. This applies to preparing a speech too, in that you will be better prepared if you practice reciting your speech instead of just rereading your notes.

Interleaving helps retain information best. If you are asked to memorize the styles of 12 different artists from different eras, do you think you would do best by studying all the works done by each artist one at a time (a method called “blocking”) or by mixing up the artists? If you are like most, you may choose to study by blocking. However, this has shown to be significantly less effective than mixing up the artists (interleaving) and studying that way. Ever noticed that when you do your math problems (by each section), you understand right away and feel like you mastered the skill, yet come time for the test, you are confused by which equations to use? This can easily be avoided with interleaving, which would mean, in this case, that you include problems from previous sections along with the night’s homework.

Your study corner is a trap. There have been several studies that looked at the effect of location on retention and found that if you studied certain information in a particular spot and were tested on it at that same location, you do better than if you studied the material in one place and tested in another location. The same is true for body states (hunger, influence of drugs, mood…) or when listening to music. You do best when these stay consistent. Yet it is often too hard to study and test in the same location, and more importantly, it becomes harder to recall the information when not in that same area. The answer is to vary your location when studying. If you only study in one location, the information will unconsciously (though not on a large scale), be tied to that location. This means that if you move to another spot, your recall will not be at its optimal. However, by altering your study spots, you can avoid this dependence on your surroundings and possibly increase your score on the next test.

These are just a few of the topics Carey explains in his book, and there are many more discovered since the book’s publishing. Therefore, I highly recommend that you look into this book and share your findings with others. It’s a shame so few people know about the science of learning, despite the fact that their lives revolve around it.

Do babies matter? A review

I was very excited to find this book by Mary Ann Mason, Nicholas Wolfinger, and Marc Goulden: Do Babies Matter? Gender and Family in the Ivory Tower. The authors deal with the complex and multi-faceted relationship between families and academia in an organized and data-driven way. They use detailed survey information to present the beliefs and career decisions of academics (especially women) at different points of the academic “pipeline,” from graduate students through tenured faculty members and how these relate to two of the most typical milestones for family formation: marriage and childbearing.

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As a married female graduate student who loves much about academia and also hopes to raise kids eventually, this book’s agenda is important to me. After reading the book, there are a few undeniable takeaway points:

  • Women, especially at the earlier stages of academic careers (PhD students and postdocs), are more likely than men to perceive raising a family and obtaining a tenure-track faculty position as incompatible goals.
  • Academic institutions lack flexibility that exist in other professional fields like law and medicine like alternating between full- and part-time work or taking maternity and paternity leave after a birth. Even when academic institutions do have these policies, people often do not know about them or are hesitant to use them because of their associated stigma.
  • Women (and especially mothers) are underrepresented at the top of the academic career ladder.

There are lots of injustices in the world, and academia is not immune. Whether we want to or not, humans have subconscious biases, and these biases take a ton of work to overcome. Bringing awareness to discrepancies is a crucial step toward eliminating them, and this book does a great job of doing just that. There are a few recurrent underlying assumptions, though, that didn’t sit right with me as I was reading this book.

  • Tenured faculty is the ultimate goal. For many grad students, this is true. In fact it is a waste of a graduate education if the recipient is not going to remain a competitive academic researcher. In a paragraph about how “Many of our best and brightest young people are rejecting careers at research universities,” the authors write that “The United States cannot afford to lose many of its best researchers and thinkers, scholars who will eventually train the next generation. And these talented young scholars should not have to forsake careers for which they have already invested many years of their lives.” If PhDs take jobs outside academia, the United States is not losing them at all. Their training isn’t going to waste, it’s just going to a different use than many people assume it is “supposed” to go to. Not to mention, many people don’t look at getting a PhD to be career training in the sense that getting a Nursing Degree or even a Master’s Degree is. You do a PhD to gain experience, thinking, communicating, innovating, and answering nearly intractable questions. Academics love to say that you don’t get a PhD to get rich (though a job is pretty universally expected at the end).
  • Correlation and causation… There are times when the authors do remind us that statistics don’t allow us to make causal claims, but other times when the authors seem to forget that crucial notion. Comments like “Marriage also leads women to leave the labor force. Compared with an unwed woman, her married counterpart is 28 percent more likely to not work.” It may be true that marriage is the reason these women leave the labor force. Or perhaps women who leave the labor force have more time for dating and get married at higher rates (that’s fairly ridiculous, but technically possible based on the statistic). Or perhaps there’s some underlying personality difference between women who choose to get married and to stop working and those who don’t, a hidden variable responsible for the different work behaviors that isn’t marriage at all, but instead tracks with marriage. What if marriage is so fulfilling and stabilizing that women decide they don’t need to keep working at jobs they’ve hated?
  • Women and men have the same career goals and desires. This follows from the assumption above. Men and women are biologically different. It’s a good thing, too, because that keeps humans on the earth. These biological differences are pronounced in parenting. I don’t doubt that dads and moms can love their kids equally, but women carry the fetus for 9 months, give birth, and often feed the baby milk from their own body. As they’re raising a human being (or multiple humans, as is often the case), women may decide that their former jobs don’t provide the same meaning that parenting does. They may cut back on work or cut it out entirely, and this might be a great thing for many women. It is a luxury to be able to make this choice. And in some families, it may be the father who makes the choice and the mother who continues to work, but I don’t think that biology has set us up for that to be the majority choice. The statistics about women who remain in R1 (top research) faculty positions and those who take less demanding roles or stop working altogether are presented as proof enough that women are underachieving because of families. If it is a genuine choice that a woman makes to prioritize family over work, isn’t that quite an achievement?

Crucially, it needs to be possible for women to be successful researchers, wives, and mothers if that’s what they want. I believe that is the authors’ motivation, and they give suggestions for ensuring this possibility. But women who leave the pipeline shouldn’t be considered failures, and their decision should not necessarily be chalked up to injustice. It’s a really messy issue, but it won’t get better unless we keep talking about it as this book has successfully prompted many to do.

A glimpse of high school science education through the lens of a science fair

Last week, I volunteered to judge the Greater San Diego Science and Engineering Fair. I found the experience interesting last year, so I participated again this year. Judges have about 3 hours to visit 12 projects and discuss the contents of the posterboard and research journal with the student who did the work. These conversations allow the students to tell us what they did, but they also give them practice at answering questions they may not have anticipated, and the conversations give judges the opportunity to gently teach and discuss things that could have made the project better. Last year, I judged middle school behavioral science projects – unique questions that 6th, 7th, and 8th graders came up with about human behavior, and often equally unique ways of testing those questions. The students put in a lot of effort and did great work, but judging the projects also made me realize that science is really hard. They still had so much progress to make before these projects could even be considered sound, let alone innovative or informative.

But these kids were on their way! I felt that if I could see their science fair projects a few years down the line, my mind would be blown by their progress. This year, I was assigned to the high school division – exactly the opportunity to see the progression of scientific thinking. I knew these students had been working on the projects since the beginning of the year, and many had dedicated class time for guidance. But after visiting just a few projects, I was let down. I wasn’t let down by the students – they were all so earnest, pleasant, and proud, and it was clear that they had put a lot of work into their projects. Instead, I was disappointed with their teachers. So much of the scientific process needs to be explicitly taught, and for some reason, these kids weren’t taught it. Either the students weren’t getting the guidance they needed, or they were actually being misguided.

Imagine that someone eats eggs for breakfast on every weekend day, and never on a weekday. That person tells you, “every time I eat eggs, I have a great day. I guess having eggs for breakfast must cause my day to be good.” You’d probably quickly object to this conclusion – what if the fact that it’s a weekend causes your day to be good? In fact, this explanation seems likely. One variable in your “experiment” is whether the day is a weekend or weekday. The other variable is whether you ate eggs, which happens to vary with the weekend vs. weekday variable. In this case, if you want to know the effect of eggs on the quality of your day, the type of day (weekend vs. weekday) is a confound. It makes it impossible to attribute the results you saw to the variable you want to attribute it to. I saw lots of these egg/weekend confounds in the students’ problems, which is alarming because they invalidate the results the students tried to convey.

For example, one student tested the effect of different font colors on reading speed. She pulled three equal-length passages from a book. One passage she left in black font, another she turned orange, and the third she made multicolored. She then had all her participants read the three passages while she timed them. She found that they were fastest to read the passage written in black ink. But wait – what if that passage just happened to be an easier passage to read? Wouldn’t that account for her results, without taking ink color into account? She thought about this, and then agreed. Together, we worked through the solution that would have avoided the confound – if some people had read passage A in black ink, others had read the same passage in orange, and still others had read that same passage in multicolor, and then we did the same with passages B and C. This way, everyone would have read each passage once and experienced each ink color once, but that the passage-ink pairings would have differed for everyone. This is counterbalancing. Counterbalancing is done specifically to avoid confounds.

These kids of errors were evident in many projects. Another pair of students presented people with songs once to see how much of the chorus they could remember. Oddly enough, they used two songs with the same lyrics in the chorus, but extremely different melodies. They presented Song A first for everyone, and had them recall the lyrics. Then they presented Song B, and had them do the same. Perhaps not surprisingly, people recalled more lyrics for Song B. The students told me this was because Song B was a more familiar genre to their participants. While that’s a possible explanation, it’s not a scientifically valid one. Their participants all had more practice by the time they got to Song B, which had the same chorus as Song A. They should have been better at the latter simply because practice improves performance.

Luckily, as I gently explained these confounds to the students, something seemed to click – they could see the logical problems in their methods and conclusions. A few mentioned to me that they didn’t counterbalance important variables because their teachers told them to keep as much constant as possible. Normally, this is true – you want to keep as much constant as possible when testing different conditions so that variations don’t make your results noisy. Noise in data makes it harder to detect real effects. But the teachers forgot to impart an important caveat of the keep-everything-constant rule: You can’t keep things constant when the constancy could explain your results – when it could become a confound!

This opens an important question for me – were the teachers not able to give guidance on these fundamental logical ideas for doing science? I realize that they have many students to oversee. Or do the teachers lack an understanding of how experiments should be designed, implemented, and interpreted? My intuition is that it might be some of both, but it seems to be pretty problematic, regardless of the source. Allowing students to carry out months-long projects that violate important rules of scientific logic seems like a very bad way for them to learn how things should be done.

But then I started to wonder, do these students actually need to understand how experiments are conducted? Do they have to know why confounds are to be avoided at all costs and how to do so? Many will pursue non-scientific fields. Others will pursue science to the extent that they might not ever need to conduct research, and will get by learning the things that other scientists have found and trusting those scientists’ conclusions. And the students that do pursue scientific research can learn from their future mentors how to conduct science (and how not to). Maybe this is all true, and maybe I can chill, but I’m still thinking about these questions almost a week after the fair, so there must be something to my concern. Shouldn’t educated citizens be able to understand the scientific process, so they can understand why scientists make claims about global warning or about how innocuous (and important!) vaccines are? I’m not sure, but these are some of the questions I’m trying to work out.

The education escalator

I spend a lot of time thinking about the problems with education and what we might be able to do to fix them. I think about it on a small scale (i.e., how can I better explain this concept to the 50 students sitting in front of me right now?) and on a larger scale (can an innovative online-based college become a competitor for traditional elite colleges?)

For these reasons, I was intrigued by this recent NYT article by Nicholas Kristoff (The American Dream Is Leaving America). The core of the article points out that America used to have unrivaled education. As a result, it was a land of opportunity. Now, other countries have improved their education systems, and the formerly stellar American system has become one that perpetuates inequality. This may all be true (the data he presents certainly suggest so).

However, he bookends the piece with a perplexing metaphor: the education escalator. He starts it by saying: “The best escalator to opportunity in America is education. But a new study underscores that the escalator is broken.” The final lines are similar: “A starting point is to embrace the ethos that was born in America but is now an expatriate: that we owe all children a fair start in life in the form of access to an education escalator. Let’s fix the escalator.”

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His point is not lost on me, but I don’t think that the American ethos is that all children should be put on an escalator that will take them to success. Usually talk of an American ethos tends to revolve around the idea of a self-made man. These two ideas are incompatible. Once you step on an escalator, you don’t have to do a single thing. Instead, you get to space out for about 30 seconds and gaze down at the ground as you slowly drift away. The most strenuous part of riding an escalator is stepping off at the right time. A self-made man, on the other hand, has to do a lot to get up to the next level. We assume that everything he does on his way up is more strenuous than stepping off an escalator.

For this reason, I don’t think our goal in America should be to fix an education escalator. Equal opportunities for all children – yes. But stepping on shouldn’t guarantee success. Maybe instead we should work on building a big, sturdy education staircase. It’ll be equipped with a handrail to guide weak and unsteady students, but they have to put in effort in order to progress.

Knitting and programming

Two things that I do almost every day are programming and knitting. Programming allows me to implement experiments and analyze the results, and knitting allows me to unwind and recharge, restoring some of the mental energy that activities like programming require for me. Programming is analytic and something that Silicon Valley geniuses do a lot; knitting is artistic and something that your grandmother does a lot. Upon deeper reflection, there are some pretty cool links between the two, though. They both require focused attention to detail and following patterns, and the end goal is usually to create something that has a functional purpose.

Image: http://www.clevelandseniors.com/images/misc/grandma-knit.jpg
Image: http://www.clevelandseniors.com/images/misc/grandma-knit.jpg

This blog post spurred my thinking about knitting and programming as related. The post talks about the benefit that handwork has on diagrammatic thinking and fine motor skills, suggesting that knitting will help children acquire analytical skills. Another post suggests even more strongly that exposing young students to more handwork might help them in computational and engineering fields down the line.

Another blog post shows a different intersection between knitting and coding. Karen Shoop, an engineer from Queen Mary University of London, writes about the complex code that knitters use to convey a pattern (to me, this can be sometimes frustrating when trying to learn a new pattern, but programming languages can be equally enigmatic). There are also some programs (both in her lab and elsewhere) that allow users to input sequences of knit stitches and purl stitches, and the generates what that sequence would look like if implemented. (This is apparently crucial for graphic artists who want to put cable-knit sweaters on their graphic people.)

After a little more searching, I found some more cool intersections between knitting and programming. One is a Japanese knitwear designer, Motohiro Tanji, who has also dabbled in fashion based on 3D geometric algorithms. There’s also a weekly meeting of computer hackers in Portland that appears to include knitters. Not many people will dispute that programming is an increasingly important skill, and kids are being exposed as early as possible in many cases… I wonder if knitting will accordingly make a comeback with younger people!

And finally, why didn’t I think of this!? A “Laptop Compubody Sock privacy, warmth, and concentration in public spaces.”

Image: http://sternlab.org/2008/04/body-technology-interfaces/
Image: http://sternlab.org/2008/04/body-technology-interfaces/

Back to school inspiration

The beginning of September marks the traditional start of a new school year, even if in reality, many start sooner or later. A few pieces of back-to-school inspiration:

The first is a blog post, How to learn anything better by tweaking your mindset. The post describes a study in which two groups were taught the exact same information, but one group was told ahead of time that they’d later need to teach the information to someone, and the other group was told they’d be tested on the material. In actuality, no one had to teach the information to someone new, and participants in both groups received the same post-learning test. Those who had been planning to teach the new info, however, did significantly better on the test than those who were planning on being tested. The bottom line is that when we learn something with the intent of teaching it, we actually synthesize the information more and mentally organize it better than when we believe we’re learning for a test.

Anecdotally, I find this true. The classes I’ve TA’ed in the past year have been outside my realm of knowledge, but I knew I’d have to get up in front of a group of students just a few days after hearing the professor’s lecture and help the students synthesize the information presented and answer questions about it. I’d never have a written test on the material, as the students would, but I’d have an oral one when leading discussion. Technically, the stakes were low for me – I wasn’t going to get a bad grade or lose my job as a TA, but learning the information in order to be a competent teacher seemed crucial. As a result, I went into sponge mode right before every lecture, and I believe that I sopped up much more information and made stronger connections among the things being taught than if I had been a student expecting to be tested on it later.

On a related note, Khan Academy reminds us that You can learn anything.  Even though we often have to fail before we can succeed, “thankfully, we’re built to learn.” Screen Shot 2014-08-28 at 12.38.48 PM

Exponential Learning

We toss around the  phrase, “learn something new everyday” jokingly, but in reality, we learn so much more than one thing per day. Many of these things are implicit, so we don’t realize we’re learning, but each experience we have is making its mark on our cognition. Many other things we learn, though, are explicit – we’re consciously learning in an effort to get better at something. Before we can master a skill or knowledge set, we often have to learn how to learn that thing. What strategies facilitate optimal learning? Which are ineffective? A recent NYT column by David Brooks highlights some overarching differences in the learning processes in different domains.

In some domains, progress is logarithmic. This means that for every small increase in x (input, or effort), there is a disproportionately large increase in y (output, or skill) early on. Over time, the same increases in x will no longer yield the same return, and progress will slow. Running and learning a language are two examples of skills that show logarithmic learning processes.

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Other domains have exponential learning processes. Early on, large increases in effort are needed to see even minimal progress. Eventually, though, progress accelerates and might continue to do so without substantial additional effort.

Mastering an academic discipline is an exponential domain. You have to learn the basics over years of graduate school before you internalize the structures of the field and can begin to play creatively with the concepts.

My advisor has also told me a version of this story. She’s said that working hard in grad school (specifically I think she phrased it as “tipping the work-life balance in favor of work”) is an investment in my career. Just as monetary investments become exponentially more valuable over time, intense work early in my career will be exponentially more valuable in the long run than trying to compensate by working extra later on.

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Even in my first year of grad school, I developed a clear sense that even learning how the field works and what are good questions to ask takes time. When I wrote my progress report for my first year, I concluded that most of what I learned this year has been implicit. I can’t point to much technical knowledge that I’ve acquired, but I can say that I’ve gained a much better idea of what cognitive science is about as a field. I’ve gained this by talking (and especially by listening) to others’ ideas, by attending talks, and by reading as much as I could. This implicit knowledge doesn’t necessarily advance my “PhD Progress Meter” (a meter that exists only in my mind), but it is also necessary to at least start to acquire before I’ll see any real progress on that meter. Once the PhD meter is complete, I will merely have built the foundation for my career, but will probably still have much learning to do before I reach the steepest and most gratifying part of the learning curve.

Brooks points out that many people quit the exponential domains early on. He uses the word “bullheaded” as a requirement for someone who wants to stick with one of these domains, since you must be able to continually put in work while receiving no glory. I think that understanding where you are on the curve at any given time is crucial for sticking with one of these fields, so that you can recognize that eventually, the return on effort will accelerate, and the many hours (tears, complaints, whatever) that went into mastering the domain early on were not in vain. Where I stand right now, progress is pretty flat… so I must be doing something right.

What teaching has taught me

In some way or another, I have loved teaching since I was young. As a first grader, I went with my mom to parent-teacher conferences so I could read to the parents waiting for their appointments. I bribed my younger sisters to play school with me, so that I could teach them the skills I was learning in school and assign them homework (for the record, they did not complete the homework). As soon as I was old enough, I established my own tutoring business and learned how rewarding teaching can be. As an undergraduate my job was to hold office for cog sci students struggling with assignments.

This quarter, I gained a lot more experience in conventional teaching: standing up in front of a group (of 10 high school students, in the case of my SAT class, or 50 undergraduates in the case of my Teaching Assistant position), lecturing, and doing whatever I could think of to get them to voluntarily participate. Especially towards the beginning of the quarter, standing in front of the class gave me (literally) cold feet and sweaty armpits. But I dealt with these consequences, and after reflecting, have some new ideas about teaching.

On the broadest level, teaching is such a unique form of human interaction. It has similarities to parenting: there is an established hierarchy, often based on age and experience, and welcomed by both parties. A teacher wants to be more knowledgable than his student, and a student wants her teacher to be more knowledgeable than she. If successful, a teacher-student relationship brings positive feelings to both people involved. The student feels accomplished by learning, and the teacher by teaching. When a student is successful, he and his teacher likely feel similarly to how a child and parent feel when the child is successful. Unlike in a parent-child relationship, though, interactions between teachers and students are almost always centered on one topic. Thus, they’re deep and focused interactions, as opposed to a parent’s varied and broad interactions with a child. The similarities are even more interesting to me in light of this difference.

I had both positive and negative experiences in the classroom. When a college student was unhappy with a quiz grade, she e-attacked me. The contraction “y’all” appeared 4 times (though one of those times it was in the form of “y’all’s,” an entirely new form to me) alongside a handful of spelling and grammatical errors and an accusation that the teaching team doesn’t want our students to succeed. And of course the icing on the cake: “Sent from my iPhone.” Luckily this message was comical enough that it wasn’t upsetting, but I’d prefer this sort of quasi-aggression if possible.

But on the other hand, plenty of students expressed positive experiences in my classes. One student wrote to me, “I was not expecting to learn this much, and I’m kind of sad that it is almost over.” Don’t worry, I want to assure her, I won’t tell anyone that you seem to kind of like our SAT classes. But I might tell them how much I do.