Developing Concepts in General Chemistry; Symposium on Chemistry MOOCs

Last week I attended the national ACS meeting in Denver. It was great to catch up with old friends and network with vendors and publishers, but the highlight for me was the symposium at which I presented: Online Courses and the Effect on the On-campus Classroom. Don’t feel like I’m tooting my own horn here, though—there were some amazing folks in the room. The symposium organizer said it best: preparing a MOOC can be a very lonely experience. Even though thousands of people might be watching your videos and hundreds (if you’re lucky) may be posting in course forums, the act of putting the course together is generally a solo venture. To some degree, all of us at the symposium were commiserating with one another.

John Hutchinson‘s talk (from Rice University) was one that stuck out to me. His approach to teaching general chemistry deserves to be spread to all corners of the globe. He emphasized that in addition to bringing education to those who want or need it, MOOCs can act as a vehicle for publishing teaching—not publishing research about teaching, or work in the domain of chemistry, but publishing teaching itself. Naturally, as someone who advocates for the publishing of teaching per se, he’s developed an excellent system for teaching general chemistry through Concept Development Studies.

The idea of the CDS approach is to reveal chemistry concepts in a mostly inductive manner through experimental results. Results of relevant experiments or observations are presented first (say, the gas laws), and a conceptual model is built around these results (say, the kinetic molecular theory of gases), mirroring the way scientific concepts are developed in practice. He argued that most general chemistry is taught backwards, using a deductive model: here are the concepts; now let’s use the concepts to solve deductive problems.

It’s delightful when hearing a speaker rekindles interest in something you haven’t thought about in forever. One of the earliest questions Hutchinson poses in his CDS text is: how do we know atoms exist? He displays an image of a single atom taken with an STM, but then throws a curve ball: the image doesn’t really help us much. After all—and here’s the kicker—to develop the technology to even build the microscope that made the image, we already had to know that atoms exist! The real question is, how do we know atoms exist given only macroscopic observations? That’s where the CDS approach comes in, as he uses mass data to inductively reveal the Laws of Definite and Multiple Proportions.

It’s easy for students and instructors both to take atoms and molecules for granted, but this can be problematic if it means stoichiometry turns into a simple game of dimensional analysis. I also think there’s a good argument to be made that grounding chemical models and theories in data makes them “stickier”—especially when the data runs counter to what we might expect based on a simple model.

Hutchinson has a MOOC through Coursera available here; from the URL, I’m pretty sure it was the first general chemistry MOOC on Coursera. Other online courses/content I’ve checked out since the symposium are Canelas’s Introduction to Chemistry, Sorensen et al.’s Science and Cooking, and John Suchocki’s Conceptual Chemistry. Beautiful production value in the last one, although it seems to be targeted at a lower level.

Organic Chemistry Curriculum: A Step in the Right Direction

Alison Flynn’s latest in the Journal of Chemical Education is an instant classic. She describes a redesign of the organic chemistry curriculum at the University of Ottawa that tackles head on the issue of “curved arrows as decorations” that has been well documented by Cooper, Bhattacharyya, and others.

Her approach begins with four units on the basics of organic structure and physical properties, which is standard stuff. An entire unit on reaction mechanisms that precedes the first reaction covered comes next, and this is really the pièce de résistance of the design. Acid-base reactions come next (pretty standard), followed by nucleophilic additions to π electrophiles and electrophilic addition to π electrophiles, including reactions of alkenes and arenes. That’s organic 1. Note the complete absence of substitution and elimination—a huge plus in my opinion!

Organic 2 begins with eliminations and oxidations—love how these two are grouped together, as many oxidations are glorified eliminations. Next come activated π nucleophiles (enols and enolates), π electrophiles with a leaving group (e.g., acid chlorides), and π electrophiles with a “hidden” leaving group (e.g., imine formation). Seems a little odd to loop back to carbonyl chemistry at the end of organic 2 after hitting nucleophilic addition to carbonyls near the beginning of organic 1, but let’s not allow “we’ve always done it this way” to rationalize away the change. Continue reading →

What Does “Inquiry” Mean?

The phrase “inquiry-based labs” has been buzzing around my department for a while now. If it’s possible to crown a king of buzzwords in the realm of chemistry laboratories, “inquiry” is probably it.

On the surface, the idea of inquiry-based laboratories seems straightforward. The idea is to design and implement experiments that require students to engage in the process of scientific inquiry—exploring questions using the scientific method and making claims based on empirical evidence. To some degree, inquiry-based experiments have to “take the training wheels off” and throw students into a situation whose outcome is unknown. The catch is that the extent to which students should be left to explore on their own is by no means clear. Some great work has been done to clarify the continuum of inquiry labs.

Dirty little secret: these kinds of experiments make professors uncomfortable too! When a student makes a mistake during a prescriptive (procedural) experiment, it’s often easy to point to what they did and say “you made a mistake in step x.” The egregiousness of the mistake is related to how far the student is from the expected outcome. But when the outcome and procedure become uncertain, how can students or faculty know when a mistake is made? Anyone who has engaged in scientific research knows that this is a constant theme: did I make a mistake, or am I really observing something new? (Personal aside: I found this tension soul crushing during my early years in graduate school.)

Eventually, every professional scientist has to look this issue square in the face and become comfortable—on an emotional level—with the difference between sloppy technique and novel results. Much of that comes with experience learning and practicing science professionally. However, there’s a great argument to be made that the affective side to inquiry—the cosmic comfort one develops with uncertainty—can be developed through inquiry-based experiments in college.

So what keeps many faculty from implementing inquiry-based labs? You rarely see the other side of the coin in the chemical education literature, of course. Some have raised the point that students don’t learn as much from open-ended experiments, which could yield problematic results. On a more fundamental level, what students learn changes drastically when they work through inquiry-based labs. I don’t agree with the claim that students learn less from well designed inquiry-based labs, but I will admit that what they learn changes drastically. The focus shifts from verifying existing knowledge to constructing arguments based on data and observations.

I’m excited to get into the business of running inquiry-based experiments at large scale—I’ve always enjoyed shaking things up!

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Percent Yield, Movie Times, and the “Science Unseen”

It’s that time of year again: labs are gearing up. Drawers are being filled with new glassware, students donning lab coats are beginning to fill the halls…the ol’ machine is revving up to roll again. For me, this time of the semester means emphasizing good practices when working up data and results. I’ve written in past semesters about significant figures and some of the interesting issues that come up when teaching them—it’s about the mindset, not the rules, I swear…!

Take percent yield, a measure that has been reported with false precision by countless numbers of students across the generations. Percent yield is really interesting because the balance is one of the most precise instruments that exist in general chemistry laboratories—depending on the range and precision of the balance, measurements with five, six, and even seven significant figures are possible. Thus, it seems like percent yields (which are really just ratios of masses) should in turn have five, six, or seven significant digits. The measured mass of product points to this level of precision.

Students often struggle to understand that the precision of the balance is irrelevant—inevitable variations between runs of the reaction introduce massive uncertainty into yields. Such variations are gargantuan compared to imprecision in balance measurements and essentially render the precision of the balance meaningless. Continue reading →

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Yin and Yang: Electrophilic and Nucleophilic Reactions

In my opinion, the most awkwardly named reaction in all of chemistry is electrophilic aromatic substitution (and all of its three-worded cousins). This name suffers from the same problem as other named reactions: it is deceptively uninformative. I still recall raising an eyebrow in undergrad when I found out that the aromatic involved in this reaction is not the electrophile—the other reagents combine to generate the electrophile. The aromatic is the nucleophile. “Why the heck is the word ‘electrophilic’ stuffed before ‘aromatic’ in the name, then?!” When you really get down to it, the name doesn’t tell you much and has the potential to feed a novice a lot of incorrect information:

“So the reaction mixture is electrophilic, then?”
“Well no, the reaction involves a nucleophile and an electrophile, just like all polar organic reactions…”

“So the aromatic is electrophilic?”
“No, the aromatic is the nucleophile in these reactions.”
“But the name says electrophilic aromatic…!”
[Professor places face in palms]

Only once the student has seen copious examples of other electrophilic substitutions does s/he realize that the adjective refers to the conditions surrounding the substrate, not the substrate itself. The naming convention makes sense to a synthetic chemist interested in “decorating” a given substrate: the substrate is what it is, and we treat it with electrophilic or nucleophilic conditions to add groups to it. The names of substitution reactions clarify the reactivity of whatever’s coming into contact with the substrate (the reagents). To a student without a synthetic frame of mind though, without an inkling of the primacy of the substrate or even its identity, I don’t think this naming convention comes naturally. Continue reading →

Writing About Writing About the Second Law

I recently returned from vacationing in the UK, and just spent a couple of days in the West End of Glasgow, near Kelvingrove Park. Yes, the same Kelvin of scientific fame! Seeing his statue got me thinking about the second law of thermodynamics—enough that I was inspired to jot a few thoughts down about the second law.

The second law and entropy are two of the hardest topics to write about at a general chemistry level, in my opinion. Not only has there been fierce debate over the years as to the ideal intuitive notions and analogies for these topics, but related derivations with mathematical rigor can be painfully complicated. There’s a gulf here between the theory and practice of chemical thermodynamics that is difficult to navigate.

A while back I tried just to get down on paper a rigorous derivation of the definition of entropy in terms of heat and temperature, using the second law and a hypothetical thermodynamic cycle. While the work was mathematically correct, the writing made me—the author, mind you!—want to tear my eyeballs out recently. That text will never see the light of day in a general chemistry class. At that point, I wondered if I was even capable of dispensing with rigor to write a more intuitive piece. I’ve always found it difficult to write while sacrificing rigor because I still recall craving rigor and theory in the depths of my soul as a student.

The reality, of course, is that all chemists use heuristics, shortcuts, or metaphors when confronted with certain topics. The best chemist writers can navigate rigorous theory and metaphor with finesse, presenting derivations where the mind “wants” them and metaphors elsewhere. Tro is a good example—while he makes no effort to dumb down important equations, he also presents the practical metaphors that chemists most often use.

In the edition I have, he even manages to lay out all three general interpretations of entropy: entropy as disorder, entropy as energy dispersal, and the statistical interpretation. Color me jealous!

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Why Does the Normal Distribution Work?

A simple question with a not-so-straightforward answer. Everyone learns about the infamous “bell curve” in one way or another—but why does randomly distributed data work this way? After all, we could imagine all kinds of wonky shapes for probability distributions. The strangeness of quantum chemistry even shows us that odd-looking probability distributions can occur “naturally.” Yet there’s something deeply intuitive about the bell shape.

For example, the normal distribution is consistent with the intuition that randomly distributed data should cluster symmetrically about a mean. Probability decays to zero as we move away from the mean in a kind of sigmoidal way: the drop is slow at first, picks up steam about halfway to the first standard deviation, and slows down again as p inches towards zero. Yet correspondence with our intuitions doesn’t give the normal distribution theoretical legitimacy: the hydrogenic 1s orbital has similar properties, after all. What’s so special about the normal distribution?

Although this is a question I’ve had for many years, I stumbled into the answer recently in an unexpected context: random diffusion. The answer gets right to the heart of what we mean by the word random, particularly with respect to the behavior of data (or little diffusing particles, in a diffusion context). If we imagine data points jiggling like little particles in a fluid, then random errors “nudge” the points to either the left or right with equal probability. What we really mean by “random” is that it’s impossible to predict which way the data points will move: they may go to the left or to the right with equal probability (50%). Randomly distributed data behaves just like little diffusing particles engaging in a random walk. Continue reading →