LMS Liabilities

Many digital pixels have gone into dissecting the Canvas shutdown debacle ten days ago. What certainly emerges is that Canvas did a poor job communicating with its constituents. Whether or not they paid a ransom, or whether the hacked data is contained, or whether they have fixed their vulnerabilities, is unclear. I don’t think my campus was directly targeted; my students and I did not see a ransomware notice, only that the system was down. It was accessible again the same evening, so it was down for maybe six to eight hours. I had a P-Chem problem set due the following morning. The majority of students uploaded it to Canvas, a handful sent me a pdf directly via email, and one additional student turned in a hard copy.

 

I had not experienced using a Learning Management System (LMS) as a student. In my first couple of years teaching, everything was done hard copy just like when I was a student. I don’t recall when my campus started using WebCT, but I do remember teaching myself HTML and building a simple rudimentary website that provided my syllabus, basic information, and downloadable materials. It had a simple password protection scheme. It lived on one of the university servers that hosted faculty home pages. Few faculty had or used them. I still maintain my text-only webpages making minor updates every semester. Very low maintenance. I never bothered to learn Wordpress or other fancier tools as they showed up over the years.

 

WebCT looked clunky and I resisted using it, preferring my low-maintenance website. Blackboard bought WebCT. I used it a couple of times when I was team-teaching with colleagues who used it. The university continued to push (or “encourage”) more faculty to utilize Blackboard so that students would have a more “uniform” experience. I was one of the rogue faculty members who did not for a variety of reasons. At one point I vaguely recall being told that the university was shutting down the faculty webpage host server, but that did not come to pass. I think there were enough rogue users like me who hacked together their own sites that made sufficiently reasonable arguments. I did lose the battle over using Pine for my email and was forced to switch to my university’s Gmail.

 

Then Covid-19 came. I was on sabbatical in Spring 2020 so I did not have to make the hard pivot that my colleagues scrambled to do. I had time to think about how I was going to teach the next academic year online. My clunky website did not support video but the Zoom integration in Blackboard did. I spent part of Summer 2020 setting up all my classes in Blackboard, not just material delivery, but a Discussion Board, a wiki, online quizzes, and assignments for uploaded exams and other materials. Blackboard wasn’t too bad, and after we went back to in-person classes, I retained using Blackboard instead of my simple HTML website for managing class materials. P-Chem homework could be submitted online which worked well for students retaining their original problem set for annotations. Quizzes and exams reverted to in-person. I stopped using the Discussion Board when ChatGPT arrived.

 

Two and a half years ago, our campus made a hard switch to campus. I had winter break to make the adjustments. It was annoying because the Blackboard import did not work well for setting up my Canvas shells so I put up everything again from scratch. I don’t use the majority of the LMS features, and certainly not the gradebook, so the Canvas outage ten days ago did not affect my classes significantly. The way I set up the G-Chem online homework system and eTextbook as single embedded links to access, rather than a full Canvas integration, meant that there was an alternative way the students could access both directly from the publisher website. In a more extended outage, I could have revived the use of my HTML website and everything would have still worked.

 

Covid-19 made me think about how to set up my courses to pivot quickly without too much hassle. Although I hope it doesn’t happen before I retire, I expect a reasonable probability of another pandemic forcing us to move online. The possibility of zoonotic virus spread is very likely given how we humans live today and how biology works. But with the latest Canvas debacle, it now makes me think about how I might pivot quickly should another hack occur. I think there is an even higher probability that LMS vendors will be breached by hackers; not a question of if but when. Something will go down at a crucial time. I hope the education industry has learned something from all of this, but I’m honestly not sure if anything is going to change. In the meantime, I’ve downloaded all my Canvas materials and will rework how they are organized in case a quick pivot is needed in a future year. I just finished giving my last final exam and I go on sabbatical next academic year so I have time to think about the reorganization.

Square Integrable

I am reading about the extraordinary math and science contributions of John von Neumann in Ananyo Bhattacharya’s book The Man from the Future. I definitely get the feeling that von Neumann was indeed a rare genius. I also got the feeling that maybe I should have persevered in learning more math when I was younger. If so, not only would I have a better appreciation of von Neumann’s achievements, I would also be able to tackle some interesting problems in my research that require mathematically modeling beyond my current abilities. Feynman’s quote notwithstanding, I would like to better understand quantum mechanics since I use it heavily in my research.

 

Today’s blog post is about Chapter 3 of Bhattacharya’s engaging book. The chapter is titled “The Quantum Evangelist” and leverages the author’s physics background. While I know a number of facts about the history of the development of quantum mechanics, I learned a lot more about von Neumann’s contributions and the context surrounding his work. Reading this chapter gave me a better idea of the conceptual differences between Heisenberg’s matrix mechanics and Schrodinger’s wave mechanics. The connections to set theory in mathematics (and Hilbert’s program of systematization) helped clarify the context. Quoting the author: “An atom has an infinite number of orbits… so Heisenberg’s matrices must also be of infinite size to represent all possible transitions between them. The members of such a matrix can… be lined up with a list of the counting numbers – they are ‘countably’ infinite. Schrodinger’s formulation, on the other hand, yielded wave functions describing… an uncountably infinite number of possibilities. An electron that is not bound to an atom… could be literally anywhere.”

 

I now have a better appreciation of Dirac’s “ingenious trick to merge the ‘discrete’ space of Heisenberg’s matrices and the ‘continuous’ space of Schrodinger’s waves” with the delta function. Bhattacharya describes it as a “salami slicer, cutting up the wavefunction into ultra-thin slivers in space”. While Hilbert space still feels fuzzy to me and I don’t quite comprehend it, I can dimly see where square-integrable functions come from. When I teach quantum chemistry, I tell students about this important property and its practical uses along with Born’s probability postulate, I had never talked about their mathematical basis (because I didn’t understand it myself).

 

Where does von Neumann come into the story? Given his mathematical talents, he realized that square integrable functions “can be represented by an infinite series of orthogonal functions, sets of mathematical independent functions that can be added together to make any other… How much of each function is required is indicated by their coefficients... [which] were exactly the elements that appear in the state matrix.” In my class, I invoke orthogonality from a consequence of Hermitian operators. I discuss the importance of having linearly independent functions and spaces (e.g. Cartesian space or polar coordinates) conceptually but my students still struggle to think about it. Linear algebra is not a pre-requisite for my class and most students haven’t taken it. Neither have I for that matter. Until reading this chapter, I had not realized the connection between square integrable wavefunctions and orthogonality. In my class, when we get to multi-electron multi-atom systems, I introduce students to manipulating linear combinations of functions that sum up (invoking the principle of superposition) to get better results when solving the Schrodinger equation. They learn that the sum of the squares of the coefficients must add up to one, but I hadn’t made the connection to square-integrability.

 

There is plenty more in the chapter about the weirder aspects of quantum mechanics, wavefunction collapse, hidden variable theory, pilot waves, Bell inequalities, and Many Worlds. But what really stood out to me was where square integrable functions come from (as part of Hilbert space) and how they connected to orthogonal component wavefunctions. All these connections were a revelation to me, and I’d been teaching for a quarter of a century! How little I know. How much more to learn. This reminds me that I should get back to Beyond Weird by Philip Ball.

Overly Efficient

I was forced to get a new phone; the old one started to precipitously decline and would randomly restart. I’m a creature of habit and I’m easily bewildered by new technology (shocking for a computational chemist, I know!); I’ve only owned two smartphones in thirteen years (an iPhone5 and a first-generation SE). To minimize having to switch again in the next five years, I decided on an iPhone17. I was dreading making the switch.

 

The Apple store at my local mall was quite busy when I arrived; eventually someone was assigned to help me. I said I was interested in getting a new phone. The employee wanted to know which model, assumed I knew all the pros and cons, was curt in her responses, and I felt rushed through the process. I was nervous about moving data from my old phone to my new one, but was told it would be easy and I should “just follow the prompts”. A new phone was given to me in its box, I tapped my credit card, and I was done. The employee moved on to the next customer. Purchase completed, but anxiety heightened.

 

I stopped by the T-Mobile store for help getting the SIM and info in my old phone transferred to the new one. There were no other customers at the store, and the employee was relaxed and friendly. I said I was feeling anxious about the challenge given my old phone had a very old OS which suggested a more arduous process. But the employee helped me through the steps, which turned out to be short and easy. The phones, placed next to each other, did their info-sync dance. He then patiently showed me some basic moves for my new phone (when to use buttons and how to swipe for different options). He empathized with the challenge of switching to a new outlay, and answered my very basic questions without judgment of my ignorance. It was a very positive experience and reduced my anxiety substantially.

 

The epiphany I had after these two contrasting customer experiences is that I want my students to experience the second one when they visit me in office hours. However, more often than is warranted, they get the first one instead. The problem is me; I’m overly efficient, and sometimes the student feels rushed through a process. If the student was feeling anxious by their ignorance, I haven’t allayed the student’s (usually unspoken) concern. I expect a student to have done some background work and have their questions ready, and I answer them efficiently, especially if there is more than one student in my office. I ping-pong amongst the students so they get their answers efficiently while making sure no one student has to wait long in between. Students have busy lives, many busier than mine, and I don’t want them to experience long wait times if they visit. If there’s only one student in my office, I don’t have to ping-pong but I do multitask and work on something else in between their questions, which I suppose is me trying to be efficient with my own time. But from their point of view, the vibe I’m giving out might well be “I’m a busy person, so get on with it.” Overly efficient.

 

In my student evaluations, I get the occasional comment from a student that feels intimidated about asking questions in office hours and feels I was being dismissive in my answers. It’s true I expect the student to come into office prepared with their questions after working through the material; that’s something they should learn to do as they prepare for the working world. I also have a philosophy of not spoon-feeding; I sometimes respond with a question or ask a student to look at their notes to see the definitions or examples we covered in class. I want the student to understand that they have to put in the time and work to chew over what we’ve covered and not just say “I didn’t understand anything in class” and try to get me to go over it all again a second time. The vibe I’m giving off? Like the Apple employee. Quick, curt, efficient, and expected me to know what I was looking for before I came in.

 

Not all students feel this way. Some like the way I do things and say so in the student evaluations. They felt their questions were answered, that I was helpful in office hours, and felt that I cared that they were successful in the class. They said I was accessible, even when other students were in my office. But these are likely the students who were not struggling excessively with the material, had a reasonably good background coming into class, and were stronger academically. They liked the depth, my varied explanations, and felt that the way I organized the class set them up for success. In contrast, the students who had negative experiences comment that I go over the basics too quickly, I’m not good at teaching beginners, and that I assume prior knowledge they don’t have. How I felt at the Apple store likely mirrors how some of them were feeling. So while I have good reasons for the way I structure my class and office hours, my recent customer service epiphany tells me that I need to make some changes so that the anxious students who feel they are clueless in class feel more welcome to ask questions and feel like they were actually aided.

 

My mantra to myself this semester is “don’t be overly efficient”. When you’re old and set in your ways, it’s harder to change. But that’s not an excuse; rather it’s a challenge I need to overcome. I know that I will revert to my efficiency (hopefully not too often) during office hour visits or during my interactions in class, but I need to keep making the effort to help the struggling students feel welcome. They’re the ones that need the most help, and if they’re not coming to my office, that’s a problem and I’m not helping them. I don’t need to be overly efficient with my own time, and maybe my time-log (which was likely excellent to have in my first several years as a professor) has slowly compounded my overly efficient behavior. I’ve been successful partly because I was efficient, and I want students to learn to be efficient in managing their time and learning. But more importantly, they are students, and still learning. I need to make accommodations and at least not give out an “impatient” vibe in my interactions with them. I think I’m being efficient. They think I’m impatient and that they’re not worth my time. I need to work on this because the whole reason I became a professor at a liberal arts college (and not a research-intensive university) is because I want to spend most of my time teaching students.

 

I tell students that office hours (I’ve called them “Drop-in hours” for the last five years) are my favorite time in my workday. It’s when I feel I actually help them individually because it’s harder to do so in the classroom with many students. I tell them I look forward to seeing them, but it’s clear some don’t believe me and my over-efficiency vibe dissuades them. I feel that fewer students come to my office in the age of A.I., where a friendly chatbot is always there to answer their questions as many times as necessary and makes them feel good while doing so. I’m no chatbot; I should be better than a chatbot (I’m certainly more accurate and I actually care about their learning as a human being). My goal this semester is for more students to feel that I am accessible, especially the ones that should be coming to my office to ask questions. Maybe my new phone can be a visible reminder to be more like the helpful T-Mobile employee and not be overly efficient.

Biochem Round 2

Teaching biochemistry for the second time this past semester was not as time-consuming as the first time two years ago. I spent 3-5 hours per week on class prep and updating the materials which was three times less than my first run. This was significantly more manageable given I had two larger sections of G-Chem 1 using a new textbook. For Biochem, I did not make large-scale changes to the course. The topical flow was similar and I mainly updated the slides and study guides. I made some changes to the in-class computational activities, exclusively using the Molstar/PDB viewer (and skipping Pymol). I added a protein-folding-prediction exercise given the ubiquity of AlphaFold-like tools. It also has a nice wow-factor!

 

This second pass, I was able to clear up some errors I made and confusion on my part about some of the more complex enzyme regulation involving kinases, in particular FBPases. I streamlined the enzyme kinetics so it would be less heavy math-wise, and I think I did a better job with carbohydrate nomenclature without getting stuck in the weeds. Those are the positives. The negatives are that I likely went faster and had a little more information on my slides when I should likely have done the opposite. I also went into more chemical detail because a third of my class were chemistry or biochemistry majors; in contrast I had less than fifteen percent of them the first time I taught it.

 

My class was fifty percent larger this time around, simply because there were more students enrolling in the course as numbers have rebounded post-pandemic. This probably made the largest difference because it means I help each student less individually. This was certainly true during in-class activities where the students work in pairs or small groups and I circulate. The majority of students never came to office hours, which didn’t help matters. My end-of-semester grade distribution was much wider and included some D’s and more C’s, and there was a surprising amount of nonsense answers on exams. That being said, many of the students still did well and two-thirds were in the A and B range, unlike the first time around when ninety percent earned A’s and B’s. It was an unusually small class and I was likely paying lots of attention to the students and their learning. By spending less time on the metacognitive aspects of my own teaching and focusing much of my time on G-Chem, I think I did a poorer job overall.

 

The end-of-semester course evaluations were not surprising. On the Likert scale questions, my ratings went down – as expected for a larger class with more students not doing so well in the course. There were the usual comments about the speed at which we went through the material and the density of the material. A couple of students thought the twice-a-week format (with two longer rather than three shorter classes per week) was exhausting, and I see their point even with my three-minute break mid-class. Students found the study guides the most helpful; again not surprising. The chemistry and biochemistry majors liked my chemical emphasis and details. The non-majors did not like it. One made comparisons to the other sections which had “more MCAT applications” and another felt that while the other sections “skimmed through a lot of topics”, our class “felt like we learned the whole damn book”.

 

I don’t know when I will get to teach the class again. Recent staffing changes in my department might preclude my teaching it again anytime soon. If there is a next time, I would consider not using a standard textbook now that I am more comfortable with the material. One problem with following the textbook somewhat closely (which is a reasonable thing to do when you’re teaching something for the first time or two) is that you can get lost in the details and forget the big picture. A couple of students commented that this is how they felt about my class. I think instead of opening with review of G-Chem concepts and launching into amino acids and proteins, maybe I can start with some big picture metabolism (not the weeds) before getting into the building block molecules. There’s a logic to biochemistry and I’d like the students to see this. I thought I was trying to emphasize this, but many students found these details bewildering possibly because I had not spent enough time on the big picture or I was too abstract.

 

My self-rating for Round 2 is that I was overall mediocre; I’m not sure I did a better job teaching the second time around even though I was clearly more comfortable with the material. Perhaps that was the problem; I let the curse of knowledge slip in, and spending less time on thinking about the class showed.

Student A.I. use: Fall 2025

The last two times I taught G-Chem 1, I briefly told students how a generative A.I. such as ChatGPT can be useful and what some of its limitations are. Last semester (Fall 2025), I made no mention of A.I. use in any of my classes until the last week of the term. I surveyed the students asking if they used A.I. in my class, how so, how often, and if they found it helpful. The questions were open-ended and students could answer (or decline to do so) in any way they wish. I prefaced by saying that I had no problem with A.I. use, and that their responses would help me provide guidance to my future classes. I taught two sections of G-Chem 1 and one section of Biochem. My musings on the results are mainly focused on G-Chem because of the larger class sizes.

 

In G-Chem, 12% of students said they did not use any A.I., while 88% did so. ChatGPT was by far the main source, with Gemini a distant second. (Other apps got only one or two mentions.) Only a small proportion of students said they used it a lot. Most used it sparingly or occasionally. A.I. was most often used shortly before exams (in conjunction with getting answers to my study guides) and on the stoichiometry unit where students wanted help on step-by-step calculations. From my limited tests, GPT-4o does noticeably better on stoichiometry than GPT-3 (which wasn’t very good) in providing a correct solution, although typically a verbose one.

 

Interestingly, a few students used the chatbot to recommend youtube videos to help them understand a topic. (Many students just use Google or go straight to youtube to look for such videos.) Most students said they found it helpful in “explaining” concepts or how to solve problems. Several students specifically said they used it to generate practice problems or to quiz themselves. One student said it helped them “decode” their notes and explain it in a simple way. Students said it was particularly helpful when they missed class, one even saying “I didn’t need to go to office hours… it gave me the answer from anywhere I liked.” While the majority of students said they found ChatGPT useful, a handful did not.

 

A number of students provided specific caveats in their usage. A student writes: “I would strongly recommend not heavily depending on it for homework, as it ends up being more harmful than beneficial. You must know how you obtained your answer, not just copy and paste.” Another student: “These models are constructive for learning as long as you use them productively and have them guide you instead of answering for you.” A student notes: “It was helpful, but some ideas it presented contradicted my notes, so I am not sure how accurate it is.” Another student: “While not always correct, I felt that it would usually get me started in the right direction to finish understanding the topic or solving the question on my own.” Interestingly, the students who made these types of comments were almost all students who earned A’s or B’s as their final grade. Also noteworthy, the 12% of students who did not use A.I. also earned A’s or B’s. (The average grade was in the C+ range so slightly less than 50% of the students earn A’s or B’s.) Of the students who used it sparingly or rarely, again these were the A or B students. This is perhaps not surprising. The students who knew the material felt less of a need to use A.I.

 

Since the best use of a generative A.I. is to generate test questions and study guides, I’m glad to see many students mention it in this way. Even more use it for explanations or answers which is more hit-or-miss, but I’m glad that students noticed this. Here’s one thoughtful student comment: “When it comes to studying equations, ChatGPT was very helpful because it showed me step-by-step how to solve it. I also used this model to create practice problems for me. In terms of elaborating the material from class, it was moderately helpful. It mostly gave me vague explanations.” This student also thought it was a limitation of the free version and mused that if they had used a paid version they may have had better results. One student would load the study guide in and then ask ChatGPT to provide timed quiz questions so that the student would feel like they were in an exam.

 

In Biochem, I saw similar trends: 15% of the students did not use A.I. (All three earned A’s and were among the top five.) There aren’t many math-related or calculation questions in Biochem so most of the students used it to clear up things they weren’t sure about, again usually pertaining to the study guides or my lecture slides (which I provide to the students). Since this is a smaller class, I’m not sure if any trends are significant.

 

My takeaways: Students are going to use A.I. in a chemistry class regardless of whether you have a policy or not. The majority of them already do so and feel that it is helpful, so they will keep doing so. The academically stronger students use it less, but likely because they feel they understand the material in class and are able to solve problems without outside help most of the time. Many students leverage the generative capabilities of a Large-Language-Model A.I. to generate test questions although whether they are generating sufficiently complex questions is less clear. Some students notice the weaknesses of A.I. answers yet still find it helpful as a guide. Students think A.I. helps to “simplify” some concept they are struggling with. Whether or not it is over-simplified is less clear. Students still gravitate to video explanations to supplement the text explanations of A.I., and youtube remains a key source for students.

Spontaneity, Reversibility, Equilibrium

Last week, right after my P-Chem II class, a bright student came up to ask me to help clear up and issue comparing reversible processes that actually move a process forward (you might expect a change in free energy or overall entropy) and equilibrium (you expect delta-G to be zero). Since we only have ten minutes in between when one class gets out and the next one comes in to the room, I did what many professors do – I gave a handwaving explanation. I said something about an overall system being at equilibrium macroscopically (equal rates of forward and reverse reactions), and separated that from a “reversible” process where you’re moving something along via infinitesimal steps. One’s the overall “system”, the other is looking at a series of steps for a specific process, I said. Clearly, or maybe obtusely, I was hedging.

 

Part of the issue here is that in real life you’d never run a process in infinitesimal steps because it would take an infinite amount of time. Essentially nothing is changing if each step takes eternity. I did tell students that, in practice, no process is truly reversible in this infinitesimal-step sense if in fact it actually takes place. Students understand this issue of practically. But since P-Chem II is calculus-infused, we can do the math by taking limits. In particular we go to the limit of the infinitesimal step and use d’s instead of deltas. This leads to beautifully simple calculations to calculate the mechanical work or heat transfer in idealized “reversible” cases, while comparing them to “irreversible” cases that are “less efficient”. In the reversible case, work in equals work out if you did this ideally. In the irreversible case, you get less work out compared to what you put in. Most students are satisfied by all of this, but for this student, there was a bee in her bonnet, and rightly so.

 

Actually, I don’t know if she found my hedging answer satisfactory. I should ask her. I was unimpressed by my own answer even though it was practical in the interest of time (she had to run to another class). In fact, I told her that I needed to be more careful in how I defined the terms “equilibrium” and “reversible process” and that sometimes I’m not careful enough. I provide the students with the formal definitions in our lecture notes, but I slide my way from one term to another in the “heat of the moment” when I’m trying to be dynamic and lively in class. (“Heat” is another of those tricky terms.) In class I only look sporadically at my own notes. Sometimes I remember to emphasize something to watch out for, and other times I simply forget.

 

In my quest to figure out how to not confuse future students, I started scouring the primary literature for inspiration. The article I’ve found most useful thus far is by John Norton titled “The impossible process: Thermodynamic reversibility” (Studies in History and Philosophy of Modern Physics 2016, 55, 43-61). It provides a historical slant and includes many examples where much more famous physicists have also elided their way through. Now I don’t feel so bad about my quick handwave. I think I can tighten up my definitions a little better. And I need to spend a little time talking about the issue of using calculus to take the limit for infinitesimal steps. Many chemistry students are a little calculus-phobic so I try not to emphasize the mechanics of calculus and instead concentrate on the chemistry. But I’m reminded that I need to be more careful in this regard. Norton also points out that on the molecular scale, thermal fluctuations make this ideal-calculus-limit taking a problem. In a big-picture Mack view, all this might be okay, but tiny Mike would protest that there’s a problem! (Mack and Mike represent macroscopic and microscopic views.)

 

I also need to be very clear when I use each of these terms: reversible, equilibrium, and spontaneous. I make a very big deal (multiple times) in both my G-Chem and P-Chem classes that thermodynamic spontaneity has nothing to do with how fast a reaction might take place. All it tells you is which way the reaction is likely to proceed absent any external intrusions on the system. Thankfully, we’re on the verge of changing our G-Chem textbook to, in my opinion, a superior one that excises the confusing term “spontaneity” and instead uses “thermodynamic favorability”. I’m all in favor of that change. I’ll just have to remind students to be careful when they encounter “spontaneity” on the internet because, sadly, that’s where many of them go to look up things rather than their textbook.

 

I think I should stop using the term “reversible” in G-Chem as a thermodynamic definition. I should limit myself to discussing forward and reverse reactions (in the kinetic sense), that both occur, and that if one waits long enough eventually the rates of the forward and reverse reactions are equal. That’s when dynamic equilibrium is reached. If the change in system free energy or the change in the entropy of the thermodynamic universe is zero, then the system is overall at equilibrium. That’s it. No need to belabor the point.

 

In P-Chem I’m considering using the term quasi-reversible to emphasize that taking the infinitesimal limit is actually an impossible situation at the molecular level. Perhaps I should always say quasi-reversible process. This may help emphasize the distinction between the macroscopic system as a whole (which may or may not be at equilibrium) and considering a specific process in getting from one state to another state. I’m not sure I want to go into the language of “a series of connected equilibrium states” since this muddies the waters. Since I don’t use a textbook in P-Chem, I can just change all the notes that I provide students to tighten up these definitions. I will restrict using the word equilibrium to the usage I mentioned above in G-Chem. When I get to the stat mech version of discussing equilibrium, I will focus it on the equilibrium constant as a ratio of the number of product molecules versus reactant molecules, while reminding the students that the state of being at equilibrium is a macroscopic description. There will be a tricky part when I get to transition-state theory in thinking about the transition state as a quasi-equilibrium state; not sure how to handle that terminology-wise. We’ll see how this all works out the next time I teach P-Chem II.

Animals as Gardeners

Yesterday, in my General Chemistry class, we discussed using bond energies to calculate the change in enthalpy of a chemical reaction. Breaking bonds is endothermic and requires energy input into the system. Conversely, making bonds is exothermic and energy is released from the system. Chemical reactions almost always involve both the making and breaking of bonds. Therefore, whether the overall chemical reaction will be endothermic or exothermic will depend on whether the bonds being broken are stronger (or weaker) than the bonds being formed.

 

One example I showed was ATP hydrolysis. The reaction is marginally exothermic. Even though the same types of bonds were being made and broken, the bond energies are slightly different in different chemical structures. That’s the beauty of chemistry – a subtle interplay between structure and energetics! The purpose of this example was to counter the conceptually wrong mind-worm students acquire where they tell me that “breaking bonds releases energy”. This usually comes from a simplified misunderstanding of something they hear in a biology class.

 

Towards the end of class, I couldn’t resist connecting bond energies to the origin of carbon-based life on Earth. The students had previously worked a problem on the strength of the O–H bond in water and the corresponding wavelength of a photon that matched the bond energy. Referring to the solar spectrum and ultraviolet light, I speculated about how adenine may have been important as a photon absorber prior to its role in the universal energy transduction of living systems. I mused about water-splitting, the invention of photosynthesis and suitable molecular pigments (conjugated pi-systems!) that may have arisen through chemical evolution. I didn’t say anything about such pigments dissipating thermal energy and seemingly “wasting” it.

 

This brings us to today’s question: Why do animals exist on Planet Earth?

 

This morning, I went down a rabbit-hole reading several articles by Karo Michaelian. It all started with “The Pigment World: Life’s Origins as Photo-Dissipating Pigments” (Life 2024, 14, 912). He makes the provocative claim that animals essentially “provide a specialized gardening service to the plants and cyanobacteria, catalyzing their absorption and dissipation of sunlight in the presence of water, promoting photon dissipation, the water cycle, and entropy production.” That’s a mouthful. We’ll break it down momentarily, but essentially the claim is that animals help to move molecules around, spreading them far and wide so that more and more photons can be absorbed and that energy dissipated. It’s the second law of thermodynamics in action at the level of the biosphere. And what’s the stuff we’re moving around? Pigment molecules!

 

It's an interesting argument. He begins with the argument that many leaves absorb photons in the ultraviolet and visible range before dropping off significantly at the infrared boundary. Leaves look green to us because red and blue light are absorbed more than green. Photosynthesis however only makes use of a narrow regime of red light, yet leaves strongly absorb in the ultraviolet and in the (blue) visible range. Plants evolved to absorb photons which are hardly absorbed by water, and apparently “fill even small photon niches left by water over all incident wavelengths”. That’s rather curious. Also, the albedo in life-rich ecosystems (jungles and forests) is considerably lower than in sandy deserts which reflect much more of the incident light. Additionally, “the albedo of water bodies is also reduced by a concentrated surface microlayer of cyanobacteria”. What happens to this absorbed energy? It is converted to heat – essentially chopping up a smaller number of high-energy photons into a large number of low-energy photons. It’s the second law of thermodynamics: energy is being dissipated and entropy increases mightily!

 

The evolution of these absorbing pigments in plants may have been primarily to increase transpiration. Photosynthesis is a secondary player in this regard. That’s a shocker to me. I’ve always considered the oxygenating of the atmosphere via photosynthesis to be a driver for the complexity of life – which it is – but I hadn’t thought of it as a byproduct to mostly increase heat dissipation via transpiration. In the first week of class, I told students about water’s high heat capacity and its suitability as a calorimeter. In a couple of weeks after we get through entropy, we’ll be looking at the change in enthalpy and entropy of vaporization as liquid water turns into gas. Water is an excellent dissipator that helps drive the second law of thermodynamics, but does so if there’s more of it in Earth’s water cycle. Transpiration puts more water into the cycle!

 

What do animals do? They help disperse the pollen or seed of plants. They help bring nutrients to plants through poo or death. As heterotrophs disperse organic matter, they disperse the pigment molecules. More opportunities for absorbing photons. More dissipation to high entropy heat. We animals are the gardeners, helping the second law to roll along. Humans in particular have come up with alternative ways to tap photons with inorganic materials, but that’s a recent phenomenon. The organic pigments have been at it far longer than we have. All this makes me wonder if the reason why photosynthesis is so inefficient is because life isn’t optimizing for capturing energy from photons in that way; rather it is optimizing for seemingly wasteful heat dissipation. The second law rules!

 

The tropics are rife with life. Is it because they receive the most photons? Why are there so many insects there? They’re a key part of the gardening crew. Why are there larger animals further away from the equator? The gardening crew is mostly about seed dispersal and larger creatures roam far and wide to stay alive in a less energy-rich environment. Michaelian argues that his proposal cuts to the heart of the source of evolution – the second law, a physical imperative. It cuts through the Gordian knot of biological relativity. It gets around the problem of extending the ecosystem to include more and more of its environment until it becomes an organism of sample size one where Darwinian evolution becomes nonsense. It’s an intriguing argument.

 

A linchpin of the argument is the chemical evolution of pigment molecules that absorb well in the UV-C range eventually transforming into the “broadband pigment world of today”. A specific detailed example looks at the oligomerization of HCN into adenine (C₅H₅N₅) and relies on physics-based arguments about the dissipative process after a UV-C photon is absorbed. In particular, it hinges on the photoexcited pigment rapidly reach the conical intersection that shunts it towards a particular product. There is some hand-waving about how this opens up producing a broader spectrum of molecules capable of absorbing a larger range of wavelengths in the uv-vis range. Analogies are made to how thermal convection cells arise as forces come into “balance”. Stationary states, autocatalytic cycles, and other such features are invoked. And finally, once the ozone layer built up, access to UV-C is now much reduced and therefore we’re unlikely to see life originate again from scratch on our planet. (Also, the heterotrophs will chomp up anything they can!)

 

The final kicker? If UV-C is crucial to the origin and evolution of carbon-based life, then you’re unlikely to see life evolve on systems powered by M-type red dwarf stars. That’s not good news for astrobiologists who have become increasingly interested in such systems as providing suitable cradles for life. UV-C, primarily thought of as destroyer now also takes on the role of creator – Brahma and Shiva, two-in-one, with Vishnu in between as preserver while the photon flux from our sun lasts.

 

The bottom-line of how the second law and chemistry intersect? In Michaelian’s words (from a different article): “All material will dissipatively structure, depending on the strength of the atomic bonding and appropriate wavelength region.” Funny how a first-day class exercise of connecting bond energies to photon energies might turn out to be the foundation of everything we see in our solar system be it on our living planet or our seemingly dead neighbors. I haven’t yet wrapped my head around all of this. In the meantime, I’ll just keep on being a gardener and cultivator of my students’ understanding of chemistry and its wonders. And I won’t look at a plant in the same way again!

Stoichiometry Blues

I don’t know why my G-Chem 1 students, on average, did much more poorly than expected on the most recent midterm that covered stoichiometry. While the midterm exam average for stoichiometry is typically lower than the other midterms, this year it was substantially lower, far outside the norm.

 

Last year, my G-Chem 1 exam averages were similar in the first three midterms, so I think the range of academic ability in chemistry is similar between the two classes. I also have all four midterms at the same point during the semester (although there was minor moving around of topics). So, it shouldn’t have been Thanksgiving break that caused students to forget everything they learned. And some students still aced the exam. I even made sure to cover the last bit of stoichiometry the Friday before Thanksgiving break so that the many students who chose to miss Monday’s class wouldn’t miss the last section on redox reactions.

 

What’s different? The main change I made in my G-Chem class was to ditch the online homework system and its accompanying textbook. Instead, I assign some homework and “collect” a subset of it to grade. What I collect is clearly less than what I had previously assigned in an auto-graded online homework system. It’s possible that students are not doing the other suggested problems that I don’t collect. (Some certainly do, when they come in with office hour questions or turn some of it in even if I didn’t assign those as part of what I collected. But others might not.) But the questions I do assign are written the way I would write an exam question, so I felt that was helpful to students. This is unlike the auto-graded online homework system that often phrases questions differently.

 

None of this seemed to be a problem through the first three midterms. Students were doing similarly as they did in the past. I even asked for feedback from the students about how they felt about the changes I made and the majority seemed to like them and thought my study guides were helpful to learning the material. My current hypothesis for the difference is that when it comes to stoichiometry, the students need much more practice problem solving, and the changes I introduced caused at least half (or more) of the class to practice less compared to previous cohorts. This wasn’t as big a deal in earlier topics. Even though there were calculation type questions earlier in the semester, they weren’t as concentrated as when we covered stoichiometry.

 

I think I need to assign more problems or provide more time in class to work through them if I don’t want to be doing more grading. And given that I’ve jettisoned the textbook, I should move stoichiometry earlier. (We’ve been using “atoms-first” textbooks for many years that shift stoichiometry to the last third of the semester. I didn’t want to make too many major changes compared to what I did last year.) I also think the Thanksgiving break causes students to forget what they learned, but I suspect this wreaks more havoc for stoichiometry than other topics, and this year there was a double whammy when the students didn’t practice enough.

 

Thankfully for the students, I drop the lowest of the four midterm scores, and that will be the case for the majority of students in my G-Chem class this semester. That was also true last year (stoichiometry always has the lowest average), but the average scores were nowhere as low as this year. So the overall student grade hasn’t been impacted yet, but it may mean that many students need to beef up their stoichiometry problem-solving skills before the final exam. While the final exam is cumulative, so stoichiometry might be 20-25%, that’s still a substantial portion. Some students have come by to talk, now that they realize what they missed so that’s a good sign. Hopefully more do so.

 

There are two other possibilities for the lower-than-expected exam scores. It’s possible the exam was harder this year. I don’t actually think so, but since instructors are inflicted with the curse of knowledge, I can’t say for sure. I’ve been writing exams for many years and I’m confident that the exam I wrote was about right, but it could have been a tad harder – certainly not so much more difficult to cause the substantial drop in scores. It’s possible I have an academically weaker class this year when it comes to stoichiometry and math-related chemistry problems. My G-Chem classes are small, often less than 30 students (although it can be as high as 40) so there can be substantial differences from one group of students to another.

 

In any case, I need to think about some changes I’d like to make to my G-Chem 2 class next semester that is certainly more math-heavy. I am teaching the Honors section and the students who register for that class are self-selecting so I might not run into the same issue. Certainly I need to make changes to my G-Chem 1 class next year if I continue not to use an online homework system and textbook.

Learning by Imitation

Chunks of time in my chemistry classes (G-Chem and P-Chem) are spent working through examples on the white board. Students write along with me as the solution unfolds. Essentially, they are copying or imitating my answers. I think this is a key way for students to learn chemistry efficiently. When they haven’t yet acquired the experience of solving many, many different kinds of chemistry problems, the entry point is to go through worked examples of increasing difficulty. Then I let them loose on homework problems for further practice, but this usually takes place outside of class. There’s only so much time available in the classroom, and I prefer to spend it going through the nuances of worked examples while answering questions synchronously in real time.

 

I suspect this process of imitation is how I learned most of what I know today, be it chemistry or cooking or useless trivia. Scientists are starting to notice more examples of learning by tool-making in other animals, particularly primates. And it seems that they are learning by imitating each other too. We humans are particularly adept at imitation. Is this how we became so much more technologically advanced compared to our animal cousins? In the boardgame Origins: How We Became Human, the most important action in the Age of Instinct is Imitation. After that first age, this action is now called Education, but is essentially the same thing – you can take cards played into the discard pile by other players.

 

The idea of copying gets a bad rep in education. It’s often associated with mindless imitation without learning. I have no doubt this happens sometimes in my classes, especially if students didn’t do the assigned reading before coming to class. Then they’re lost and sometimes they copy what’s on the whiteboard without understanding. But even for students who are following along, they don’t fully understand why the protocol I am leading them through works. I talk my way through why I do each step, not just how, but the reality is that until they see a lot more examples and do the homework, they won’t necessarily appreciate the efficient process I’m taking them through. But that’s okay, because imitation and comprehension support each other.

 

Pondering the importance of imitation, I’m wondering if I should go back to providing full worked-out solutions to homework problems in G-Chem. (I’ve always provided full solutions in P-Chem.) I used to do this in my early days of teaching G-Chem. Then I stopped doing it because students would just look at the answers without struggling through the problems and then do badly on the exams. They deceived themselves into thinking they knew the material by short-circuiting the learning process. At present I provide answers to numerical problems without the full-worked out solution, but I’m starting to think I need to provide more guidance in how to explain conceptual answers. They see it in class. They imitate it in class discussion. They are partially found in my Class Notes handouts. The most capable and well-prepared students do very well on the exam with these, but there’s a large gulf compared to the least-prepared students who essentially write nonsense or leave chunks of the exam blank because they have no idea.

 

Finding the right balance will be tricky. I don’t collect student answers to all the questions I recommend they try after each class. It would be nigh impossible for me to grade them all. (Right now, I collect a subset.) Do students try everything I assigned them to practice if I don’t grade it? Some do, and they benefit from it. Others don’t. The adage of leading the horse to water may apply here, and maybe I shouldn’t worry so much and let the students choose if they want to do the work or not. Since I’ve moved away from the online homework system that gives immediate feedback to the student (although not always the best worked solution), I’m grappling with what to do about providing solutions. For some of the easier and more straightforward questions, ChatGPT does decently at providing a worked solution, but not for more challenging ones. And nuance is important in chemistry which present A.I.’s fail to capture.

 

Learning is still a mysterious thing. But I’m beginning to think that imitation is surprisingly effective. How best can I leverage it? That’s the question.

Exam Readiness

Augustine of Hippo (circa 400 A.D.), in a remarkable chapter on the nature of time in Confessions, confesses the following:  If no one asks me, I know: if I wish to explain it to one that asketh, I know not”.

 

This is why I tell students that it’s very important to try and explain aloud their answers to conceptual questions. You think you know it in your head, but you actually don’t know what you don’t know until you try to verbalize it or write it out in full. Several years ago, I revamped my daily study guides to phrase what students needed to know in question form, and I also added “test yourself” questions to each of the study guides. Whether or not students use them effectively is an open question, but this semester I have assignments requiring students to turn in a subset of their answers. (The students get full credit for the attempt regardless if they got the answers right, wrong, or something in between.)

 

In my course materials, I have a section on “how to be successful in this class” that informs students what they should be doing from the get-go. I also provide detailed information on what students should read before class, and the main things we will cover in class. It’s short and pithy. Students follow it to varying degrees, or at least they claim to do so. But if they really wanted to be successful in any class, they should read Daniel Willingham’s new book, Outsmart Your Brain. It doesn’t just provide strategies; it explains the why behind them. It also explains why your brain’s instinct is to resort to less optimal strategies that require less effort but give you a false sense of thinking you’ve learned when you haven’t. I’ve read many of Willingham’s research articles over the years but these were not aimed primarily at students. Now there’s a good book I can recommend to students!

 

Each chapter of the book also ends with notes to instructors on how to facilitate student learning. A number of those are things I already do in my classes, but there were others I had forgotten or not thought as deeply about. This week I’ve been making a better effort to not assume students know how and why I have organized the material for each class in a particular way; I’ve been making more statements about what we’re doing, why we’re doing it, and how we will proceed. I have not been putting these up on a slide, because as Willingham says, students just copy things from a slide regardless of whether it’s useful to them or not instead of paying attention to what I am saying.

 

So how should students prepare for exams? They should prepare a study guide, according to Willingham. Turns out I already help them with this by posing questions in my guides. But Willingham has helpful tips of how to pose questions bidirectionally and at multiple levels. And he tells students you need to memorize some of your answers, not necessarily word-for-word but meaningfully. Forcing oneself to recall in different places and at different times works best in solidifying the material. And saying it aloud, of course! While I encourage students to study together, Willingham explains why this is useful, and how to do it effectively. When students discuss with each other their fragmented knowledge, it introduces variation to how questions and answers are posed. Different individuals help notice things others have missed. And students can test each other! Being tested is one of the best ways to prepare for exams.

 

Chapter 7 (“How to Judge Whether You’re Ready for an Exam”) had some particularly good reminders. It’s not enough to ‘understand’ something when someone else explains it, you have to try and explain it yourself – and not just in your head. Rereading can mislead you into thinking you know something you don’t really know it; rather you just have a passing familiarity and unless you’re forced to recall (without looking) you won’t know if you actually know. (Willingham recommends letting at least thirty minutes pass between reading and testing yourself.) I’ve stopped giving previous year’s exams to students because they fail to utilize it effectively, either giving themselves a false sense of readiness or going into a panic. (Read Willingham’s book for the explanation!) In distinguishing learning from performance, Willingham recommends overlearning – essentially “study until you know it, and then keep studying… It protects against forgetting [even though] it feels as though it’s not working.” He has a great quote from a friend when he was in college who said: “When leaves blowing around on the quad look like organic compounds to me, I know I’m ready.”

 

There’s also a chapter on how to take exams including what’s effective and what’s not. This mirrors some of what I highlighted from Barbara Oakley’s book. But I liked the early chapters on the importance of active listening in lectures, how to prepare for a lecture class, how to take notes, and how to reorganize one’s notes. Willingham thinks students should take their own notes in class, regardless of whether the instructor provides notes or slides or recordings. He also explains why being in class and engaging your mind right there and then is more effective than missing class and getting notes later from a friend. There’s an interesting section on whether one should do the reading before or after the lecture – that’s dependent on how the instructor organizes the class. And he gives good advice on how to ask good questions – ones that don’t annoy your instructors or your classmates.

 

Willingham reminded me that most students don’t take good lecture notes. It’s for a variety of reasons and he provides tips to students on how to improve, but I particularly appreciated his reminders to instructors. These are things I need to pay more attention to:

·      Talk more slowly. (I talk too fast sometimes, okay, maybe most of the time in class.)

·      Signal when something should be written and pause to allow time to write it down. (I’m getting better at this.)

·      Distribute copies of figures/visuals; let students know which ones they don’t need to copy. (I think I’m good at this. Maybe, maybe not.)

·      Students copy what’s on slides, whether doing so makes sense or not. (I’ve become much more judicious in the way I use slides.)

I liked Willingham’s suggestion of how students should work together and share lecture notes. Very importantly, it is not by dividing the effort among group members, but rather that everyone should take as complete notes as they can, and then once-a-week get together so each person can fill in any missing gaps and compare how different people organized their notes to see if any improvements can be made.

 

One thing that is emphasized in Outsmart Your Brain is how effortful learning actually is. Our brain would prefer to conserve energy and get away with quick-and-easy pattern recognition, maybe. I can’t speak to other fields, but the natural sciences are full of phenomena that are counter-intuitive, conceptually challenging, and theoretically abstract. Yet, they are crucial to understanding the field. Chemistry is hard to learn. I know it from my own experience as a learner, and I certainly see the same for my students. I hope to convey some of the tips and explanations from this book to my students.

First Week: Fall 2024 Edition

I just have two classes this semester, my usual General Chemistry 1 and Physical Chemistry 1. Once again, I am teaching the Honors preceptorial, but with the smaller incoming class at my university (probably impacted by FAFSA woes), I only have fifteen new academic advisees instead of the usual twenty in my G-Chem 1 class. This morning, I recited all of their names correctly in class. Hooray! Like last year, I’m having each of them visit me during my drop-in (office) hours so I can learn a little more about each of them as individuals. I’m always delighted to get to know new students and they seem like a good bunch!

 

The one major change I’ve made to my G-Chem class is ditching the textbook. While I have chosen a decent online textbook as a reading reference that also has relevant practice exercises, I am supplementing the reading by providing Class Notes for every lecture. These do not recap the lecture in detail, but rather note the key concepts. Students still have to come to class to see the examples and engage with the explanations of the conceptual material. I will also continue to employ Study Guides. I’ve made some minor rearrangements in content. Nuclear chemistry has been moved back to early in the semester. I chopped up the standard chapter on liquids and will sprinkle it throughout the semester, and intermolecular forces has moved up earlier. I’m also making some minor moves in the chemical bonding section and stuff related to wave-particle duality. So there’s some amount of work on my part, but it’s not a major overhaul. The big question is whether the students are sufficiently prepared for the exam without the usual online auto-graded homework. First exam is at the end of the third week, so we’ll see.

 

I am not making many changes to P-Chem, just minor tweaks to most of the material with some minor rearrangements to the last two weeks where I go into different chemical bonding theories. Last year, I did expand Valence Bond Theory and I did culminate the class by examining why triplet molecular oxygen is such a strange molecule. But it didn’t feel as streamlined so I need to figure out how to do this better. I only have eight students, and I had five of them in G-Chem 1, G-Chem 2 or both. Most of them know each other, and that’s a good thing because I tell them that P-Chem is a team-sport. I’m encouraging them to work with each other on problem sets, visit me often to ask questions, and study together for exams.

 

On the first day of P-Chem, I couldn’t get through the material I had prepared. On the first day of G-Chem, I barely got through but rushed a little at the end. It could be that both classes were a little more interactive – perhaps students are more willing to speak up in smaller classes? But the other thing I’ve noticed over the years is that I tend to spend a little more time pontificating early-on about the nature of chemistry, science, and model-building to understand the natural world. I think this approach pays dividends later, or at least I hope so.

 

It was a busy week because in addition to a bunch of first-week administrative meetings, I also trained two new research students. Because of differing class schedules, I wasn’t able to train them together, but that’s okay. I did spend time last week putting together tutorials for the new research software we’ll be using. I think I did a relatively good job because there weren’t many typos and the two students (when asked) said they thought the guides were clear. I have two returning students (from summer research), one of whom has learned the new software and the other one will do so this afternoon. The semester will be less productive than the summer research-wise, but I’m looking forward to writing up the good work that my students did this past summer. I also have a potential new collaboration but I’ll need to delve in a little more to see if it will be a good fit.

 

Whew! It’s been a great first week. Tiring, but it’s so nice to feel the energy of being back on campus with students who are excited to embark on their first semester of college or coming back from the summer and reconnecting with their friends.