Fundamentals

First-semester general chemistry begins with fundamentals. What is the fundamental unit of matter? The atom – the (philosophically) indivisible particle. Until it was found to be made of smaller parts! The electron, as exemplified by Thomson’s early experiments, became the next fundamental unit – all atoms had them regardless of identity. What are the fundamental properties of the electron? It is negatively charged (1.6 x 10^-19 Coulomb) and has a measurable mass (9.1 x 10^-31 kg).

 

My students take all this information in stride, especially since many of them have had a chemistry course in high school. These fundamentals seem familiar to them, so one of my goals is to convey the surprising bits. What seems familiar to them was actually very surprising to the scientists as they made their discoveries.

 

This last couple of weeks we’ve been discussing the interaction of light and matter. I’ve talked about the surprises in the photoelectric effect and Bohr’s theory of the atom. Wave-particle duality is another of those strange concepts, and my students are having trouble accepting Heisenberg’s Uncertainty Principle. How can we fundamentally not know? Isn’t it presumptuous to say that we cannot know beyond a certain point where things get fuzzy? Won’t a future generation of scientists be able to solve such a conundrum? Maybe we’ll actually figure out the size of an electron – a seemingly fundamental property that is undetermined.

 

Concurrently, I’ve been reading Fundamentals by Nobel-prize-winning physicist Frank Wilczek. The book has ten short chapters with pithy titles that encompass a fundamental. Chapter 4 is titled “There are Very Few Ingredients”. The fundamental properties of the fundamental particles are just three: mass, charge, spin. I haven’t quite got across to the students how to think of these as labels. Because students think they’re familiar with mass (as a mishmash of weight and density), they also think that charge and spin have a similar sort of physicality to them. I haven’t spent the time in class discussing these sorts of fundamental questions.

 

But I have been using my weekly prompts to get the students thinking about fundamentals, though. This week’s prompt: “Chemistry is all about the behavior of electrons in atoms. While we know the mass and charge of an electron, it turns out there are many other things we don't know, for example its size, or where it is located in an atom (Bohr was wrong about the orbits). It's not just that we don't know, but we fundamentally CANNOT know (Heisenberg's Uncertainty Principle). We can only describe electrons in probabilistic terms. What do you think about this "fuzziness" in our knowledge of electron behavior? Is it strange that we know the mass and charge but can't know other things?”

 

As you can guess, we’re having fun talking about orbitals this week. Or maybe I’m the only one having fun discussing the strangeness of orbitals. I’m not completely sold that this topic should be in a G-Chem class, but I’ve always covered it, partly because I’m a quantum chemist, and partly because many students will encounter orbitals again in O-Chem. At least the students are appreciating the strangeness of it all: certainly when we talk about nodes! How does the electron “cross” a node? They sorta teleport. Sorta like what happens when one observes emission spectra. I’ve been using some of my older blog posts to help students think about this. There’s a fuzziness in all those sorta statements.

 

Wilczek’s chapter reminded me about the importance of fields. Chemists typically don’t think or talk much about fields; they get a brief mention when I define an electromagnetic wave but not much else. Wilczek provides a helpful analogy: “Particles are avatars of fields”. He also discusses why one might have many identical copies of fundamental particles such as the photon or electron with a field framework. In describing the weak force, he says it “supplies a kind of cosmic storage battery, allowing for the slow release of cosmic energy”. Taken out of context, these phrases might seem like new-age-y gobbledygook, but when read in context I found it illuminating as a scientist. The other definition I found useful was when he rearranged Einstein’s famous equation to m = E/c² and defines mass in that way.

 

Reading Wilczek also got me thinking about how I can connect the different topics in my G-Chem class into a more coherent story for the students. I already do this in my own understanding, but I’m reminded that the students don’t necessarily see that framework clearly. I started sketching out a topical flow chart (like a mind map) for my classes and I’m making it a point to consciously communicate this framework to the students. I should go finish up the map. Or maybe get students to do it. I could call it a road map of the fundamentals.

Noise Reduction

Daniel Kahneman’s latest book, along with co-authors Olivier Sibony and Cass Sunstein, is Noise: A Flaw in Human Judgment. What is this noise and where does it come from? Given their background and expertise, this book is about humans making judgment calls, machines or automated systems making judgment calls, and how this plays into psychology, public policy, and economics, among other things. 

 

Noise is distinguished from Bias. The authors introduce these two types of error with rifle shots at a bulls-eye. Here’s a picture from CommonCog. 

 

My introductory chemistry students learn about this on the second day of class. However, we use the words Precision and Accuracy. In this case, noise is equivalent to imprecision, bias is equivalent to accuracy. In chemistry, the judgment is a measurement – errors may come from the human observer, the measuring device, external ‘environmental’ factors, or a combination of all these.

 

The authors argue that in the context of human judgment calls, we often pay undue attention towards bias and not enough on noise. Their clarion call is to take noise more seriously. They break down the concept of noise into several types: (1) level noise is “the variability of the average judgments made by different individuals”, (2) pattern noise is “the difference in the personal, idiosyncratic responses of judges to the same case”, (3) occasion noise is “the effect of an irrelevant feature of the context on judgments”.

 

Using myself as an example to illustrate these: one of the jobs where I play a role as “judge” is as an instructor examining the quality of student work. I teach first-year college general chemistry, as do many of my colleagues. Let’s say I’m known as being a tough grader in general compared to my colleagues, and indeed students earn lower grades in my section compared to my colleagues’ sections – that’s level noise. My specialty is chemical bonding so I’m particularly sensitive to minor details in that area, and may be particularly harsh on student ‘errors’, but I’m perhaps I’m lax on stoichiometric calculations and I don’t mark students off for what I consider minor errors in calculations or significant figures – that’s pattern noise. One day, I get into a fender bender on my way to work and I’m feeling huffed and annoyed causing me to grade more harshly than I would on another day where I had a smooth drive into work with no traffic – that’s occasion noise.

 

A university administrator (perhaps a department chair) who hears student complaints might want to reduce the noise judgments of professors. Let’s get the faculty members who teach a common course together to iron out differences so we can have more uniform grade distributions – maybe we should even have common assignment questions or final exams! If you’re in academia, you know this is like herding cats. Nevertheless, it can be done (expect resistance!), for good or ill (or likely aspects of both).

 

There are many ways to potentially reduce noise in social judgment contexts and the authors provide a number of interesting examples. Some of them involve the idiosyncracy and variability among court-case judges, as you might expect. More general examples applicable to a wider swath of society may include hiring managers who interview candidates, or the admissions team at a university deciding which student applicants should receive offers. Much of the advice provided in Noise are things you’ve likely heard about, e.g., standardizing interview questions and making them less open-ended or over-dependent on personal idiosyncracy. When grading something more subjective such as essays, it’s easier to read them all and rank them before assigning grades to any one of them. Account for base rates before making your estimate. The authors summarize their advice in the following bullet points.

 

·      The goal of judgment is accuracy, not individual expression.

·      Think statistically, and take the outside view of the case.

·      Structure judgments into several independent tasks.

·      Resist premature intuitions.

·      Obtain independent judgments from multiple judges, then consider aggregating those judgments.

·      Favor relative judgments and relative scales.

 

The authors also acknowledge that there’s a trade-off between reducing noise and the effort required. Finding this balance is, well… a judgment call. And if you’re going to outsource the effort to an algorithm? Well, there are trade-offs to that too.

 

As a quantum chemist, I see nature at its base as being fuzzy. Even as we draw boundaries to distinguish one thing from another, things become fuzzy if we start to look too closely. To get geeky for a moment, I’m having an epiphany that the reason why the fundamental bits of matter (protons, electrons, neutrons, and therefore atoms) are fermions that obey the Pauli Principle, is to give some discreteness to what otherwise would be an undistinguishable goo. It’s a sort of reduction in fuzziness.

 

As an instructor, the issue of noise is challenging. Not only are we human beings with all our attendant individual quirks, the process of learning is somewhat mysterious – I’d say it is complex, not merely complicated. What does it mean to ‘know’ something? Are there levels or types of knowing, and if so, how would you distinguish them? Could we even agree on a scale? I don’t know. I don’t always agree with my colleagues on which topics are crucial in general chemistry – yes, we agree on a lot in common, but there are differences especially when you get to the finer points. Part of why we disagree comes from our different backgrounds, our relative expertise, and our relative experience in teaching.

 

There are several things I do to reduce occasion noise when grading. Not looking at the student’s name. Grading exams question-by-question rather than one student after another. Shuffling the exams (after I grade each question). Making sure I’m not in a bad mood, and I’m ready to grade fairly. When I set exams, I let a day or two pass after writing them, and then I take the exam at a particular time-of-day so that my body is roughly in the same physical state of alertness, and I time myself so I can gauge the difficulty of the exam. It’s harder to reduce pattern noise – I’d say that I grade according to what I emphasize in my class. (I expect a different instructor would emphasize different things and therefore set exam questions and grade differently.) I’m not sure how we would reduce level noise without having common exams and grading schemes. Some amount of work is involved to try and fairly assess common assignments.

 

Noise is here to stay. We should beware the siren call of algorithms to reduce noise and have the wisdom to know when it is warranted and when it is not. It’s part of what it means to be human, and we should be careful not to dehumanize in our efforts to reduce noise and error.

Evolution: Wacky Edition

I reached my goal of getting through Book 5 of the Rincewind series in Discworld, so I should be ready for its offshoot, The Science of Discworld, when my copy arrives at my local library. I must admit that after five books, I’ve found Rincewind rather tiresome and Pratchett’s over-the-top style has moved to the starting-to-grate-on-me category. Reading Book 4 (Interesting Times) had some clever sequences especially towards the end when the story comes together at the end, but reading it in 2021, it feels like Pratchett’s un-PC humorous caricature of the Far East feels strongly culturally misappropriated. But at least the game of the gods remains interesting.

 

Things do not get better in Book 5, The Lost Continent. The culturally misappropriate action moves to the equivalent of Australia. There is a god, a tinkerer of evolution, but he seems less than interesting with Pratchett’s lampooning style of writing. Rincewind is a drag in this book. And to get the inside jokes, it helps if you know culturally inappropriate tropes about Australians, some “bush” lingo, “Waltzing Matilda”, and a minor reference to “Crocodile Dundee”. I don’t know what it says about me and the age I grew up in, but I happen to know such things. If one of my students today was reading The Lost Continent, they wouldn’t get half the references. 

 

Given that biological evolution is a theme running throughout the book, there are also science references aplenty. These are somewhat more interesting, although Pratchett’s biology inside jokes are not as good as his physics ones. (There’s not much directly related to chemistry, which says something about the inaccessibility of my subject area. Humph!) One interesting idea in the book is when a group of wizards find themselves stranded on an island where the flora and fauna evolve at high speed to anticipate the needs or desires of the motley crew who find themselves stuck on the island. One gets a picture of what one might observe if adaptive ecology sped up, but played to the whims of its human visitors. There’s likely an old Star Trek episode that addresses similar questions.

 

In contrast to the wackiness of Pratchett’s fiction, I also happen to be working my way through Anticipatory Systems, a heavy theory-laden book by Robert Rosen. When one expands the notion of causality beyond the Cartesian, perhaps harking back to Aristotle’s Final Cause, things gets interesting. The scientist-brain in me is working on the problem theoretically and conceptually, but it’s hard to picture how this works in the abstract when confronted with mathematical relationships that I find difficult to grasp. In that sense, Pratchett’s ridiculous imagery is slightly helpful in getting me to put the hard theory into a wacky representation that might be easier to imagine.

 

Naturally, the physical categories of time and space come into the equation. In The Lost Continent, there’s an interesting relation between the two which might take the reader a while to figure out. It’s thought-provoking but not explored in great depth. I’d say the same for the evolutionary ideas except with hardly any depth. Following Rincewind’s story arc and the flitting in and out of time by a ghostly kangaroo (who drives Rincewind crazy) is tedious, but there is another wizard of interest – a minor character who nevertheless has ideas about science in an age of magic – Ponder Stibbons.

 

Stibbons showed up in Book 4 where the equivalent of a supercomputer A.I. (named Hex!) that he helped build teleported Rincewind to the other side of Discworld, but a glitch in the calculations botched the return journey. Hence, Rincewind gets stuck in a different time and place on the lost continent. Early in Book 5, Ponder Stibbons is pondering the hypothesis of “invisible writings”, that “all books are tenuously connected through L-space and, therefore, the content of any book every written or yet to be written may, in the right circumstances, be deduced from a sufficiently close study of books already in existence. Future books exist in potentia… but the primitive techniques used hitherto, based on ancient spells like Weezencake’s Unreliable Algorithm, had meant that it took years to put together even the ghost of a page of an unwritten book.” Yes, Pratchett still has some clever turns of phrase in this book.

 

But there’s more. Ponder’s playing around with Hex leads him to discover that “many things are not impossible until they have been tried. Like a busy government which only passes expensive laws prohibiting some new and interesting thing when people have actually found a way of doing it… When something is tried, Ponder found, it often does turn out to be impossible very quickly, but it takes a little while for this to really be the case* – in effect, for the overworked laws of causality to hurry to the scene and pretend it had been impossible all along.” Pratchett’s * references the footnote: “In the case of cold fusion, this was longer than usual.” As a bonus, Ponder’s tinkering had led to the invisible book titled “How to Dynamically Mange People for Dynamic Results in a Caring Empowering Way in Quite a Short Time Dynamically”. As an academic, who also works on high-performance computing clusters, I find this very funny and very ironic at the same time. Hex, as an evolved A.I. starts to garner the interest of wizards, even the old fusty ones who would say “In my day we used to do our own thinking”, but are now happy to let Hex do the thinking for them – for good or ill, or both at the same time.

 

When Ponder Stibbons meets the god of evolution, he gets excited at the prospect of getting involved as an apprentice creator – who now has the idea of creating organisms that are both “resourceful and adaptable”. The scientist in Ponder wants to get into the dirt and go beyond theory to practical applications. The magic of science is a bigger lure than being a wizard. There’s a punchline to this story thread, but I won’t reveal it. Perhaps it’s the ingrained scientist in me that tired of the wacky anarchic structure of Discworld. Biological evolution here on Planet Earth is more wondrous and interesting than a human-like “god” can dream up. Perhaps that’s a point that Pratchett is making, but if so, it gets lost in all the other wacky things coming one after another without much of a breather. There’s something to be said about taking a little more time to be still and to ponder.

 

P.S. Links to my blog posts of Book 1 and Book 3 in the series. The first was probably the best of the lot, as the novel was, well… novel.

Edge Cases

Technology has changed our world in unprecedented ways. Ugh. That first sentence sounds like one of those standardized ‘suggestions’ of how to begin an essay you’re writing for a school assignment. It’s true, but it’s also drab.

 

Your standard computer as an example of technology? Boring. Maybe because of its ubiquity and its eclipse by tablets and smartphones.

 

What’s more exciting? What’s cutting-edge? What’s creative? The new buzzword is machine-learning, previously better known as artificial intelligence. Or if you wanted to take the learning and intelligence out of it, these are automated algorithmic systems programmed for a specific task. That sounds more boring, but it’s the boring you have to be careful of if you worry whether you’ll be replaced by a robot. See my previous blog post about Futureproof where author Kevin Roose hammers this point home.

 

Today’s blog post is about a different book: Artificial Unintelligence by Meredith Broussard. While presently an academic in the journalism school at NYU, Broussard is also a hacker and has the skillz, both in computation and communication. Her specialty is data journalism. She’s not afraid to get into the trenches even when it means riding a ‘startup bus’ packed with too many people, junk food, and all manner of cords for one’s technology – all while trying to create an app to win kudos at a hackathon. I admire her gumption. Her experience is one interesting story among others; I also learned about self-driving cars, campaign-finance rules, the history of computing, and how our obsession with rankings has obscured the difference between the popular and the good. Broussard is an excellent storyteller even as she peppers you with data tables and lines of Python code.

 

Broussard thinks that the general A.I. portrayed in Hollywood movies and dystopian fiction is a mirage. They make for exciting stories perhaps, but are unlikely to live up to their potential. No, she doesn’t think the apocalyptic Singularity is nigh. Narrow A.I., on the other hand, deserves our attention both journalistic and otherwise. What seems boring will change our lives in ways we might not like if we don’t pay attention. Many of our twenty-first centuries problems are intertwined with issues of seemingly boring technology coupled with human greed and indifference. But like Roose, Broussard provides a positive counterweight framing to the problem: the edge cases.

 

Automation can be a good thing in many cases. Broussard write: “[It] will handle a lot of the mundane work; it won’t handle the edge cases. The edge cases require hand curation. You need to build in human effort for the edge cases, or they won’t get done. It’s also important not to expect that technology will take care of the edge cases. Effective, human-centered design requires the engineer to acknowledge that sometimes, you’ll have to finish the job by hand if you want it done.” And here Broussard refers to doing something well in a broader sense of the word.

 

What can narrow A.I. do in my area of teaching and education? Certainly, automation can take care of many mundane tasks – record keeping for example. For many introductory math and science classes, it can deliver homework problems and grade them! It will even mix up the variables so different students get different numbers, which means when they help each other they’ll need to know how to solve a problem and can’t just copy each other’s answers (at least the numerical ones). If there’s a task that many teachers would like to avoid, grading ranks very high. So-called adaptive learning is the rising star with its claims of personalizing the learning process – the ever-patient tutor who curates questions at the right level to help you advance your learning and “knows” when you’ve progressed sufficiently to move you to the next level. What does this require? In short, the ability to atomize knowledge, which I’ve argued is a questionable assumption.

 

But there are many steps in the learning process that can be atomized. My job as a teacher is to break down complex material into digestible steps for the student. After doing it for many years, I have a good idea where students get stuck, where the tricky bits are, and how to use different analogies and models to help illuminate abstract ideas. Some of this can be parameterized into an A.I. tutor. We’re still in the early days of this revolution despite the occasional grandiose claim, and I expect to see more progress in this area.

 

Can an automated tutor system handle the edge cases? Not in all cases, but it is likely to make inroads into those edges as such systems improve. How sizable are the edges? It depends on what you mean by learning and how you determine whether a student has learned or not. One danger is letting the boring technology define and determine what constitutes learning. A follow-up danger is allowing the black box machines to categorize us into boxes (the irony!) – something that’s happening at an alarming rate in so-called dynamic pricing systems be it in retail or insurance. Data is not destiny. Increasing its density does not make data necessarily better if you don’t understand its blind spots. Broussard’s many examples show us why this is so and why narrow A.I. works as well as it does. The edge might be larger than you think if you don’t stop to look at the bigger picture. The narrow A.I. cannot see the big picture, hence you might call it artificially Unintelligent. This is a good distinction to keep in mind.

 

One thing I can do well that an A.I. cannot at the moment, is answering questions from students, or eliciting the gaps in knowledge by asking follow-up questions. When you don’t know something, it’s hard to come up with a well-posed question. Human expert intelligence is particularly good at divining these cases and getting to the bottom of the root question efficiently. That’s why I’m still needed as a human educator – besides the human connection, which I think is just as if not more important. But will A.I. be able to increasingly handle that task well? It’s hard to make predictions. Especially about the future. And edge cases, by their very nature are the hardest to predict. Automated systems need predictability to be trained to work well. Real human behavior is not so predictable. Perhaps our humanity is the ultimate edge case.

Sciencery

I’m now three books into Terry Pratchett’s Discworld series involving the hapless wizard-protagonist Rincewind, who once again has to save the world. Grudgingly, of course. He’s the anti-hero and prefers a non-life-threatening life, but life seems to have different ideas. This time around, the magic of wizards will be threatened by the magic of sourcerers. No, that’s not a spelling typo. Sourcerers draw their magic directly from its source, bypassing the need for learning complicated incantations and hand-motions. I wonder if they somehow break the Law of Conservation of Reality, but sadly this is not addressed in the third book, aptly titled Sourcery. 

 

However, there is a remarkable side passage that deals with what I will dub “Sciencery”, that odd practice of its acolytes, of whom I am one of many – scientists. I will quote parts of it since paraphrasing Pratchett is nigh impossible and nowhere as fun.

 

It is a well-known and established fact throughout the many-dimensional worlds of the multiverse that most really great discoveries are owed to one brief moment of inspiration. There’s a lot of spadework first, of course, but what clinches the whole thing is the sight of, say, a falling apple or boiling kettle or the water slopping over the edge of the bath. Something goes click inside the observer’s head and then everything falls into place. The shape of DNA, it is popularly said, owes its discovery to the chance sight of a spiral staircase when the scientist’s mind was just at the right receptive temperature. Had he used the lift [elevator], the whole science of genetics might have been a good deal different.

 

Coincidentally, this week I was telling one of my research students about the tedious slog that accompanies most of research, but also the ‘high’ you get when something seems to just work out in one glorious gestalt moment. Pratchett’s prose, of course, is tongue-in-cheek. One might think of adages such as “Inspiration is 99% Perspiration”, but it is interesting how one gets these ‘aha’ moments. Chemistry has its famous iconic ones such as Kekule’s telling of his serpent-eating-its-tail dream. This past Friday, I told students about Archimedes and his eureka moment, upon which we proceeded to do calculations on gold-plated crowns and discussed the practicality of measuring water displacement when submerging said crowns.

 

But Pratchett has a twist on this story, so I’ll quote what follows.

 

This is thought of as somehow wonderful. It isn’t. It is tragic. Little particles of inspiration sleet through the universe all the time, traveling through the densest matter the way that a neutrino passes through a candyfloss haystack, and most of them miss. Even worse, most of the ones that hit the exact cerebral target hit the wrong one.

 

For example, the weird dream about a lead doughnut on a mile-high gantry, which in the right mind would have the catalyst for the invention of repressed-gravitational electricity generation (a cheap and inexhaustible and totally non-polluting form of power which the world in question had been seeking for centuries, and for the lack of which it was plunged into a terrible and pointless war) was in fact had by a small and bewildered duck.

 

Oh, well. There went our chances of a workable fusion reactor. We’ll have to wait until the real Iron Man comes along and invents his new element – a subject for one of my later classes on the periodic table. In the meantime, what are my chances of being hit by an ‘inspiration particle’? I might have had one this morning in my state of hypnagogia, just as I was waking up but still having strange thoughts. At the very least I sorta think I have a new idea to analyze some data if the current simpler approach fails. Not mind-bending or profound in any way. Too bad I don’t remember the wild dream that preceded it. Now if only the inspirational particles hit when I’m conscious and ready for it.

 

Sciencery seems like a lot more work compared to Sourcery. In that sense, it’s more like the Wizardry practiced by the denizens of Unseen University in Discworld. There’s a lot of studying by the lower echelons, and then a lot of backstabbing your way into the upper echelons. Or I should say it resembles dysfunctional academia. The strong and cunning survive, and if they bide their time, one might even become the Archchancellor of Unseen University, who as Pratchett says:

 

…was the official leader of all the Wizards on the Disc. Once upon a time it had meant that he would be the most powerful in the handling of magic, but times were a lot quieter now and, to be honest, senior wizards tended to look upon actual magic as a bit beneath them. They tended to prefer administration, which was safer and nearly as much fun, and also big dinners.

 

Perhaps more Cornelius Fudge than Albus Dumbledore? But there’s always the power-hungry wizard who is also magically powerful, and who think that might makes right. But he’s also trying to avoid his date with Death – yes, Voldermort, that’s you. There’s a sort of symmetry between the antagonist in Sourcery and that of the Harry Potter series. But this stock character is true of many other stories – perhaps telling us something about human nature and the corruptibility of power. Perhaps then it’s a good thing that Sciencery is so much more difficult than Sourcery, and that the Law of Conservation of Reality kicks in when needed.

 

P.S. Here’s a reference for actual neutrinos, those ghostly particles. How do you trap ghosts anyway?

First Week: Masked Edition

Our science building is bustling with students in the hallways, wandering around looking for their classes – not helped by the arcane numbering system of rooms and doors. One also enters the main doors on the third floor, while most of our lecture classrooms are on the first and second floors. We’re all masked, so it’s hard for me to recognize students. Thankfully, they recognize me and at least some of them are not shy about greetings. In several of these encounters with students who I’ve never seen in person but only through a Zoom box, I was able to remember the student’s name before he or she had to tell me. In other instances, my puzzled look prompted an introduction – after which, it seemed obvious when you know what to look for.

 

My main worry was teaching while masked. Microphones were hurriedly installed over the past weekend into classrooms that lacked them. They worked okay, but they’re stationary, and I’m always walk back and forth across the board as I write with occasional stops at the projection screen to explain a figure. Thankfully, the rooms aren’t that large, and the students at the back gave me the thumbs-up when I asked if they could hear me fine. My P-Chem class is a little smaller this semester, and I was pleased to walk in the first day to see them occupying the front rows. (In my larger G-Chem sections, the rooms are more full.) My masked regularly dipped while I was talking and gesticulating so my left hand was busy pushing it back up so it wouldn’t drop below by nose.

 

I was rusty in-person after the two-year hiatus. Last year we were all remote. And the year before, I was on sabbatical and not teaching. I pontificated more than I needed do, and covered less material than I had planned. But there were also additional housekeeping items we had to talk about, for example, my new office hour protocol so that students don’t crowd the narrow hallways of faculty office suites. I’ve had my first student visits and so far the protocol seems to work fine. There were also LMS problems related to first-day-access of the e-Textbook and online homework system. I had hoped the relevant folks had ironed out the problems from last semester where it was a fiasco, but no such luck. Students are frustrated through no fault of their own. That being said, the problem only seems to affect a few students and not the majority even though everyone is following the same protocol. The reasons are unclear.

 

I’m teaching three classes on MWF. P-Chem first, then an early lunch break for an hour before two back-to-back G-Chem classes. (It’s my first time teaching back-to-back classes.) After the first day of classes, I was exhausted. My body was not used to it. Teaching while masked might be a contributor. Overall, my teaching wasn’t great, but it wasn’t terrible. The next class meetings were better; I’m slowly building back the stamina. Thankfully, we have a long weekend with the U.S. Labour Day holiday so I’ll have a shorter upcoming work week.

 

This week I also trained new research students. I’ve cut down the session to just one full day by streamlining certain parts, making better handouts, and moving some other topics to when-needed sometime later in the semester. I’m excited to be rolling out a couple of new projects, and I think I’ve done a better job preparing templates to help my students organize their results. It will also help me when the time comes to write up a research article.

 

For the first time in a while, I am not an academic adviser for new incoming first-year students. This would have involved additional meetings this week and much more prep the week before. I had the luxury of spending last week focusing on class prep so that things are not as frantic when the semester begins. Still, there was a lot of running around this week with all those little tasks that add up. But I survived. Hopefully next week, I’ll thrive, even if masked.