education – Lindsay K. Pino

CHAPTER NINE: LIMINAL

The purgatory of being in-between naive beginner excitement and rational experienced master is basically where I live for most skills, just right there in the Trough of Disillusionment on the Gartner hype cycle. It feels like there’s a ton of support for “getting started”, and a ton of support for highly niche specialization, but just not a lot that helps you get through the purgatory of “intermediate”. This goes for learning a new language, or how to code, or baking, or entrepreneurship, or writing, or managing, or whatever. There’s so much out there to support the zero-to-one initialization, and then there’s deep subject matter expertise, but the “messy middle” is really hard, seemingly endlessly wandering through a liminal space.

I don’t really feel like I’m hitting “enlightment” in anything, just maybe finding how much farther down the “disillusionment” goes, but I’m also not anywhere near “inflated expectations” for any of my skills so that seems to put me pretty solidly in the “intermediate” range. I love picking up new things – I mentioned before that I challenged myself to learn how to bake macarons, and that I wanted to learn how to code so I joined a machine learning lab – and it’s so frustrating to get the basics down then just have zero resources to get through “intermediate” to “fluent” or “advanced”. There’s the 10,000 hours rule, which says that it takes 10,000 hours to master a skill, but that seems to emphasize the point that there’s always tons of resources to help you with the first 10-100 hours, but after that it’s just supposed to be grinding until you reach near-mastery and can get into the ultra-deep niche groups, I guess.

So how do you get through “intermediate” to be considered a “master”? Although it’s the 10,000 hour rule, realistically that’s more like 5 years of fairly dedicated training, so about the average American science PhD program. The first year is structured classes like high school or college, with more in depth materials, but after the first year or two it’s all unstructured research, largely self-guided with some input from your PhD advisors and thesis committee members. In the end, when you defend and get the “PhD” letters after your name, society generally recognizes you as a “master” in that subject, which is itself a hilariously sub-sub-sub-field specific niche, a tiny drop in the vast vast ocean of human knowledge.

In the things where I’m “intermediate”, I don’t feel like I make a lot of progress after those first 1-2 years of structured learning. Maybe because the rest all needs to be self-guided? I wish there was more structure out there for intermediate anything, to at least learn more about what I need to learn.

I probably just need to learn to embrace the journey that is being “intermediate” and find ways to enjoy the process more.

CHAPTER TWO: MANAGING UP

I mentioned in PEDAGOGY that my preferred teaching style mirrors my personally preferred learning style, and that I’d write a bit more about how this probably also seen in how preferred management style mirrors how you like to be managed, how you mentor others mirrors how you prefer to be mentored yourself, etc. I never actually got to that in the EPILOGUE to that post, so maybe we tackle that today.

Much like a teacher-student relationship, mentor-mentee relationships and manager-managee(?) relationships have an inherent power dynamic and status imbalance. I think there’s quite a bit of good advice out there on how to be a better manager or mentor, and I think there’s also a lot of good advice about taking charge of your own development, like individual development plan (IDP) templates. (I love IDPs, I just don’t know if I would fit IDPs in here. Maybe later.) So instead I’m going to write about managing up – how you can manage your own supervisor and/or mentor to make the most of the relationship.

Sometimes I think “managing up” as a concept gets twisted into somehow manipulating your manager. That’s not really what I mean. Because of the power dynamic, though, I think a lot of focus is put on optimizing “top down” management/mentorship and how to improve being a manager, which is very valid and of course crucial to strong leadership. But there are also things that a mentee or direct report can do to build the relationship with their manager for the benefit of everyone involved. (This, of course, assumes your manager or mentor isn’t jealous or somehow in competition with you, their junior, or otherwise sabotaging, since that brings a whole layer of dysfunction to all of this. I’m assuming a healthy relationship, where there’s an inherent power dynamic or hierarchy, but everyone is generally a good person.)

By understanding how your own mentor or manager prefers to be mentored or managed herself, you can get some pretty powerful insights into their motivations in how they mentor or manage you. This can help color your interactions with more context and perspective. For example, I know that I myself prefer a “pacesetting” management style, where I’m generally trusted to do my own thing and make my own decisions along the way, and will proactively reach out for help when and where I need it. But that management or mentorship style can seem to my reports or mentees that I’m too aloof or hands-off, and not delegating tasks clearly. To me, it just feels like I’m avoiding being a micromanager, but to my coworkers it can create confusion and lack of clear expectations. So establishing a two-way communication stream between mentor and mentee, even in light of the power dynamic, can really help ensure that everyone is on the same page and getting what they need or want out of the relationship.

I haven’t always preferred the “pacesetting” management style. My preferred management style (the way I like to be mentored/managed) has evolved over the years, for sure. Early in my training and career, I craved a high-touch, frequent-communication style as I built confidence in my skills. I wanted a lot of feedback and I wanted it often, since I was unsure of my work. I still reach out for input more often than usual when I’m plunging into something I’ve never done before, but most often I want to be left to my own devices and be trusted to reach out with updates when I’m ready to give them.

Anticipating what my manager needs from me is helpful, too, though. A big moment for me in my career was the moment I realized that my managers were just people with their own careers and their own personal development trajectories. Basically just realizing I’m not the main character, and while I’d love for everyone around me to mold to my exact needs and desires, it was probably going to be a lot easier to meet my managers halfway. One of my first managers, for example, was working on writing some high-profile manuscript at the same time I was picking up my first independent project, and looking back it would have been an amazing learning opportunity if I had asked if I could help them, even if just by reading drafts or asking questions that would help them plug in the next block of text. I certainly couldn’t write the paper for them, even if we both wanted that, but instead of just doing my own thing, I could have reached out a bit more to see how I might help them like they helped me. As another example, you might see how your manager is gauging their own productivity to get an idea of what they might be expecting from you. If their own measure of success is submitting reports, then making sure your own reports are thorough and timely is probably going to impress them. If their measure of success is some output metric like lines of code written, then they’re probably going to be checking your commits. Stuff like that.

It’s not all altruistic. Making your manager look good usually makes you look good. Understanding where you fit in your manager’s trajectory can help your own trajectory. If your manager is gunning for a promotion, and gets it, that can in turn help you in the future by having a more senior network to pull from. Your manager doing well can mean more opportunities that you might not have gotten, especially if you’ve proven yourself trustworthy and capable of representing them. (I’m thinking especially of scientific lab settings, where your PI might get invited to give a lot of seminars or conferences and, not having enough time in their schedule or desire to travel, can pass some of those opportunities to you.)

I don’t have a real “manager” at this stage of my career, since I think of my co-founder as more of a colleague or peer than a true manager even if he’s “above” me on the HR org chart. But in the past, I’ve been able to leverage some great opportunities by taking an interest in the things my mentors and advisors have been passionate about, and figuring out how I could help them with their own professional goals. It’s also really helpful for me to get an idea about what motivates my mentors and advisors these days; sometimes, there’s opportunities to help them look good that makes me look good, too. That kind of bi-directionality in the mentor/mentee or manager/report relationship is helpful for build deeper mutual respect and I think managing “up” like that helps you to get more out of your manager, too.

ASIDE

A lingering thought on pedagogy: I meant to write more about learning objectives, since that’s the point of “Step 1: Decide on the 2-3 main things you want the participants or students to remember” since the 2-3 main things for a lecture may be the 2-3 learning objectives. Specifically, with setting learning objectives, there’s a hierarchy of subject mastery. The lower tier is rote regurgitation of facts; the higher tier is applying the materials learned to make something new. I usually try to avoid the lower tier of regurgitation, because in the real world, most of my participants will always have an “open book” to look things up, so I try instead to set learning objectives that are more applied.

CHAPTER ONE: PEDAGOGY

Top of my mind today was getting my lecture slides submitted for the ASMS Fall Workshop on Fundamentals of [Mass Spectrometer] Instrumentation. Although I first got my lecture topic assigned back in June (“Instrumentation for Quantitation of Large Numbers of Analytes”), I honestly didn’t think too deeply about it until early October when we were asked to submit a rough draft on October 14. The rabbit hole here is the theme of “procrastination”, but in an attempt to stay on topic, let’s explore my approach to building lectures.

First, a disclaimer that I’m not formally trained in education or teaching. I got a crash-course on putting together lesson plans and curricula, and general teaching/learning approaches when I first arrived in South Korea for my Fulbright scholarship to teach English, specifically, speaking/listening to English at a rural high school although I also prepped a smaller group of high schoolers for the Test of English as a Foreign Language (TOEFL). (Hello out there, Chungnam Internet High School!) This was back in 2009, so all our materials were paper print outs and chalk boards, and unfortunately I don’t have anything besides my memory to pull from, but a few things did stick with me and I keep them in mind when I’m putting together lectures today.

Second, I never have to teach from a textbook or follow a prewritten curriculum of learning objectives, so I have a lot of freedom on what exactly I deem “important”. I assume that if I had to teach specific exam materials or follow a textbook, a lot of my approach would fall apart. I usually am lucky to get even a general theme/topic to teach about, let alone have specific objectives or test materials.

Third, these days I’m always teaching professionals who are for the most part self-motivated to learn the material, so classroom management isn’t much of a concern. When I was teaching the high schoolers in Korea, classroom management was much more of a challenge. Punishments were handled primarily by their “real” teacher, who accompanied them to my classroom, but it was still expected that I make an effort to maintain the students’ attention and discipline where/when needed. These days, I might teach some undergraduates here and there, but usually I’m working with professionals who are paying good money to take my courses, so my motivation is more my own desire to make sure they get their money’s worth of material.

Finally, all of this is predicated on and influenced by my own personal learning preferences. I think, in general, preferred teaching style complements preferred learning style; similarly, your preferred management style mirrors how you like to be managed, how you mentor others mirrors how you prefer to be mentored yourself, etc. More on that in a later post, I think.

With all that established, here’s how I approach putting together a lecture or workshop.

STEP 1. Decide on the 2-3 main things you want the participants or students to remember.

Generally, starting at the end is always a good idea with any kind of presentation or communication. If you set the objective before you get carried away in the details (or in recycling old material), you can keep that “North Star” in mind in building out the background, proof, and conclusions for each of the main points you need to make. (Note: The idea of having a “North Star” as specific verbiage is something that is talked about a lot in the startup space, in the sense of keeping some guiding principle or vision and tying everything you do back to that singular goal.)

These 2-3 things become your “home slide” (a term I’m shamelessly stealing from the seminar course at the University of Washington’s Genome Sciences department), which is a slide that you’ll use as a recurring anchor point throughout your lecture or presentation. It’s a sort of agenda or syllabus, which reinforces the points that you’ve made, the points that you’re going to make, and then is a summary reminding the participants or audience what you want them to remember from your lecture. (This ties into “Step 3: Repeat everything three times”.) Sometimes I’ve seen this so that each of the points on the home slides is a question that is answered at the end of the section, which also works.

Structuring your lecture around key concepts allows you to set up multiple break points for the participants to check in with themselves and make sure they understand each point before you move on to the next. It also makes it crystal clear what you want the participants to learn so there’s no ambiguity.

STEP 2. Incorporate more than one learning style.

This whole entry is pretty poorly cited (actually, there’s not really any references) but I recall some learning styles being visual, auditory, and hands-on. (There’s probably real pedagogical terms for these, but again I’m not formally trained.) Personally, I learn best with the “hands-on” approach – I can read a text book until I have it memorized, or listen to hours of lecture, but I won’t really grok something until I have to use/apply/do it myself. For example, learning a new experimental protocol or how to calibrate an instrument. I could read about the theory and watch people do it dozens of times, but it won’t really sink in until I’ve done it myself.

It’s almost always a given that a lecture will consist of a slide deck and presenting said slide deck, so arguably there’s always at least two learning styles accommodated. However, some slide decks seem to be entirely text on slides, which I think goes against the spirit of visual learning. I try to keep text minimal on my slides and use more pictures, schematics, or even just icons to communicate the core vocabulary I’m using verbally.

And when building a workshop agenda or a lecture that’s more than 30 minutes long, getting at least one “hands on” or applied piece worked in. A lot of workshops I teach involve software usage (specifically, how to use the Skyline software for mass spectrometry data analysis) so there’s a whole slew of step-by-step tutorials and demonstrations I’ve built up over the years. But for conceptual lectures or workshops, sometimes this can be done by posing a thought question that bridges into application. For example, when I’ve taught statistical design of scientific experiments, I’ve posed some sample sets and asked participants to either think for a minute or divide into groups to talk about how they might block and randomize those samples, given a particular experimental question. When I’ve taught day-long workshops, I’ve closed out the day with a good ol’ Kahoot quiz, focusing on the 2-3 main points of each of the day’s lectures to reinforce the most important things I wanted people to remember from the day.

As a super-niche technical aside, I think this is one of the hardest barriers with (almost) all the mass spectrometry and proteomics workshops out there. I can think of only three that include a wet-lab “hands on” component: Cold Spring Harbor’s Proteomics course, MRM Proteomics workshop, and Brett Phinney’s proteomics school at UCD. To my knowledge, there’s no undergraduate, masters, or PhD program specifically for mass spectrometry or proteomics, unlike genetics, so to break into the field the only real options are to start out in analytical chemistry or biology, and pick up the mass spectrometry or proteomics through research experience.

STEP 3. Repeat everything three times.

We were drilled at Fulbright teacher training to repeat everything at least three times, ideally once in each learning style (Step 2). Thinking back, this is also how I was officially trained in assays during my internship at Wyeth Pharma (purchased by Pfizer since then) with a “watch one, do one together, do one being observed” triplicate before I was signed off as being trained. I still try to do training in the Talus lab with that in mind, and don’t expect independence on any task or protocol until we’ve done at least one iteration of “watch one, do one together, do one observed”. This also follows Step 2 of using more than one learning style, with one being visual and one being hands-on.

As a personal aside, it also follows some of the parenting books I’ve read, like Hunt Gather Parent which (although problematic in some ways, in my opinion) raises a good point that toddlers and young kids learn a lot by observing and then mimicking.

This is maybe something I took too much to heart, because some of the faculty feedback I recall receiving during my PhD training was that I repeated myself too often in my research report presentations. Habits die hard, I guess.

FAILURE MODE 1. Firehose of information.

A lot of really smart people seem to have a hard time placing lecture material in context and default to a tidal wave of information. I’m definitely guilty of the firehose approach, especially on super condensed course timelines where I’m trying to cover as much ground as possible in limited time, hoping that my Step 1 2-3 main points per lecture will be an oasis for participants to hang onto while I barrage them with references, rabbit holes, and random factoids that might be jumping off points for them to dive deeper in the future. Without having those 2-3 main points to anchor the core take-home messages and reiterate the high level objectives, getting a deck crammed full of paper citations and heavy content is disorienting and disheartening for participants, in my opinion.

FAILURE MODE 2. Overwhelming jargon.

Mass spectrometry and proteomics is hugely guilty of jargon overload. It becomes almost a second language, and teaching in a “second language” makes it even harder for participants trying to learn core concepts. Lecture material should strip away as many acronyms and jargon as possible, and ideally provide a dictionary or quick reference for participants to keep handy as they navigate the content.

FAILURE MODE 3. Failing to connect to the audience.

The exact same lecture topic should be presented completely differently if the audience changes. A lecture on “figures of merit” given to a room full of analytical chemists looks completely different from a lecture on “figures of merit” to a room full of biologists. And even more so if that room goes science-adjacent, like operations or business development. This kind of reframing is hard to do without empathy – the audience isn’t more or less smart, they just come with a different set of prior knowledge and a different motivation for learning the material. The 2-3 main points that an analytical chemist should walk away with from a lecture on “figures of merit” should be different from the 2-3 main points that a Vice President of business development walks away with. Starting from that very first Step 1, then, the entire content shifts to match the learning objectives.

SUMMARY

I wouldn’t call myself an excellent teacher, but over the last 16 years (yeesh, that math hurt to realize) since I got that initial crash-course on teaching and pedagogy, I’ve continually refined my specific lectures and my overall approach, and I’m getting pretty good course reviews these days. I’ll always be iterating and incorporating new feedback, but I think these core concepts will probably always remain a foundation in how I put together teaching material.

EPILOGUE

On my run today, I let my mind wander and spiral across the idea of “pedagogy” and teaching. Below are some of the things that spun out of the main theme as I jogged through the park on a beautiful autumn afternoon.

I struggled quite a bit in college as I worked for my bachelor’s in biochemistry and molecular biology. My degree required multiple chemistry courses: inorganic, organic, physical (thermo and quantum); however, hilariously, I did not have to take analytical chemistry, which is the one I’ve basically landed in professionally. It also required multiple physics courses: mechanics, electricity and magnetism, wave motion and quantum. My grades were, as the now-classic Chernobyl HBO series meme goes, “Not great, not terrible.” Started pretty strong but more and more C’s as I got deeper into the trenches. I wonder if part of it was that the lab portions got less and less “hands-on” and more abstract as the semesters went by. I love the inorganic and organic chem labs, where you got to “make” things; the physical chem labs were more mathematical proofs and practice problems than the hands-on stuff that helped me learn. (Not that I’m suggesting I know of any hands-on labs for quantum that would have helped me in the exams, but just reflecting back on my own failures and wondering what I could learn from them.)

While having a “hands-on” preference might suggest that I don’t learn well by reading or listening, I do love to read, although I’m not sure I really fully read every word. Instead I kind of skim over the lines and pages and get the general vibe, which is great for leisure reading but pretty terrible for studying. When I applied for the Fulbright to Korea during my senior year of undergrad, I started studying the language with two semesters of Korean classes. Nothing stuck quite as much as when I moved there and was suddenly fully immersed – my home-stay family spoke some English, but obviously used Korean with each other so I was surrounded at home, at work, and going about my day. I picked up so much more Korean with that kind of hands-on practical experience, even things I should have learned during undergrad Korean classes like simply introducing myself.

It’s no surprise then that when I went to graduate school, under the advice from my excellent post-bacc PI to learn statistics and programming, I ended up with my PhD joint-advised in a machine learning laboratory having only just learned the very first basics of coding ~6 months prior. (I did, technically, take some visual BASIC in high school and then again in college, but that’s a poster for another day.) To learn the language of statistics and computational biology, there was no better method for me, personally, than to go “full immersion”, sink-or-swim by throwing myself directly into the deep end. I wouldn’t say I ended up the strongest computational biologist that ever graduated from that machine learning lab, but I also wouldn’t say that I couldn’t hold my own. I still keep the physical print-outs of my performance reviews from that PI at my desk as a reminder to do hard things.

(PS – I may return to this topic, or branch off it at least, since I already have a few more thoughts about how it relates to science communication. Nevertheless, today I got to ~2300 words, about 3x more than yesterday! Whether it was the theme, or the run, I’ll take it.)