Models and Metaphors

I’m reading two books about models and metaphors in science. One is straightforward with many interesting vignettes, aimed at highlighting the role of models in describing abstract concepts in science. The other is difficult, aimed at telling a single story, and uses abstraction to conceptualize what scientists do when creating a model. Both highlight the role of metaphor, in different ways, to parse inherent problems and uncertainties in science and our understanding of the natural world.

 

The first book, published in 2003, is Making Truth: Metaphor in Science by Theodore Brown, a retired chemistry professor at the University of Illinois (Urbana-Champaign). His name is familiar to many students from his chemistry textbook, now in its fourteenth edition. You don’t have to be a chemist to enjoy and learn from Making Truth. As an educator, he builds his case step-by-step with many examples. As a chemistry instructor, I particular enjoyed the two chapters devoted to the concept of the atom, both classical and modern.

 

Atomic theory is the foundation of chemistry. This seems obvious to students today, steeped in images and metaphors that assume all matter is made up of tiny entities called atoms, and that molecules (the combination of two or more atoms), give rise to all the interesting stuff you can see and touch and some which you can’t see. Atoms are tiny, tiny, tiny. Nanoscopic tiny. And they’re actually rather strange. My first semester general chemistry course has been themed around the idea of making visible the invisible; discovering the molecular basis for matter in its unity and diversity!

 

I am familiar with most of the examples used by Brown, several of which also make an appearance in my classes. I emphasize the use of models and their limitations, and the important role they play. We use various visual aids, and discuss their pros and cons. Brown also talks about these. But I have not emphasized the metaphors that I use except when I tell the Happy Atoms story. More recently I’ve also used teleportation (Apparition in Harry Potter) to describe electrons changing energy levels in an atom. Brown’s book reminded me to pay attention to metaphors; especially since a metaphor that’s clear to me may not be as clear to my students, a generation removed from my own touchstones and experiences. I’ve now encountered students who’ve never read the Harry Potter books or seen the movies, believe it or not, and would not understand why my blog is called Potions for Muggles. (This proved interesting in one class session.)

 

Brown devotes a chapter to molecular models in chemistry and biology. The story of Watson and Crick’s elucidation of DNA structure is the crowning vignette; no surprises there. But what I enjoyed most from that chapter was the story of van’t Hoff trying to make sense of optical activity, and coming up with the idea of a tetrahedral carbon. Optical isomerism isn’t usually in our G-Chem syllabus although I’ve included it more often in recent years. Instead the tetrahedral shape is discussed through VSEPR theory. I like the van’t Hoff story, and will try to incorporate it into my G-Chem classes next semester. Brown also reminded me of the power of visuals. While I already use such visuals in my classes, I feel motivated to think more carefully about how I can include more interesting and relevant images, in the context of the metaphors I use, when discussing chemistry.

 

There’s a chapter on the protein folding problem (how the ‘primary’ polypeptide structure turns into its ‘tertiary’ functional structure) and its associated models and metaphors. Brown uses the metaphor of language, employing the distinction between syntax and semantics, something I’ve been thinking about recently. If you misspell a word, sometimes you change the entire meaning of a sentence, and other times the reader recognizes it as a typo and the meaning is unchanged. Using this example, Brown gets to the crux of the matter, and I’ll quote him here.

 

The gestalt that consists of the complex of associations and ideas that make up our understanding and use of the written language maps onto the molecular domain of protein sequence. Notice that this mapping does not involve a directly emergent physical experience but rather a human artifact in the social domain. This is an early example of an important and interesting aspect of metaphors in science: As the scientist attempts to understand systems of increasing complexity, metaphors based solely on embodied physical experiences no longer suffice.

 

Brown goes on to illuminate the metaphors we use regularly in our classrooms, sometimes not giving a second thought to our use of them. Energy is one of those nebulous things; we use metaphors such as the waterfall with its attendant directionality of a downward flow. Higher energy is UP. Lower energy is DOWN. Like Hermione, I’ve even had an epiphany about Pipes, although my metaphor was linked to a research problem rather than to finding a fantastic beast. Brown examines the word ‘folding’ as a metaphor that “evokes the notion of bringing into contact various parts of the object, as in folding clothing or card table chairs.” My example would be origami. Extending this image to ‘solving’ the Levinthal Paradox, creasing or pre-folding your origami is key to obtaining the end goal of a beautiful intricate structure.

 

But let’s not forget Brown’s point that the metaphor is a mapping that is also an artifact. He splits the physical domain from the human social domain, but how one imposes this artificial separation is a conundrum. That’s why we have trouble answering the question “What is Life?” as my students have encountered.

 

This brings us to the second book, Life Itself by Robert Rosen, published in 1991. Rosen, a theoretical biologist, was a student of Nicholas Rashevsky at the University of Chicago. Both names are likely unfamiliar to most biologists. The book’s subtitle, A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life, sounds grandiose. Rosen will attempt to answer the question “What is Life?” by distinguishing machines and organisms. It’s a very challenging book and not for the faint-of-heart. Background knowledge is assumed, and I especially struggled through the mathematical abstractions. Who would have thought that set theory and algebraic topology should be employed for such an endeavor?

 

I will honestly say that I don’t understand large chunks of the book. I have a glimpse of where Rosen is going; I suspect he’s on the right track; I can partially follow the arguments made; but there’s this feeling of swirling in a fog as I grope my way through, partially blind. Perhaps this is how my students feel in P-Chem. Rosen, now deceased, isn’t available to answer my questions, and he doesn’t have any disciples that I know of – although I have alluded to Mikulecky’s work, and to Rosen’s follow-up book. I’ve also mentioned Noble’s concept of Biological Relativity, that no level in biology is privileged when discussing causation – Rosen and Rashevsky’s version is called Relational Biology. But where Noble stays with qualitative examples that illustrate the problems with reductionism in biology, Rosen delves deep into the source of reductionism. And it has to do with models and metaphors.

 

If I had any reasonable grasp of Rosen’s work, I would try to explain it, but I don’t – and so you’ll have to make do with some quotes from the book and some ill-defined rambling from me. In the first half of the book, Rosen defines something called the Modeling Relation (see diagram below). In a nutshell: The real world is complex. To discover something about nature, we simplify by utilizing a formal system. This requires two additional steps: encoding and decoding, loosely associated with the activities of observation and prediction in science. And if the process 1 commutes with the sum of the processes 2+3+4, we might have a reasonably good model for what is going on in nature. I’m significantly oversimplifying things here.

 

Here’s a quote from Rosen on models and their role in causation (what he calls entailment).

 

Modeling… Is the art of bringing entailment structures into congruence. It is indeed an art, just as surely as poetry, music, and painting are. Indeed, the essence of art is that, at root, it rests on the unentailed, on the intuitive leap. I have stressed repeatedly that the encodings and decodings on which modeling relations depend are themselves unentailed. Thus theoretical scientists must be more artist than craftsmen; Natural Law assures them only that their art is not in vain, but it in itself provides not the slightest clue how to go about it.

 

Um, that’re reassuring. But Rosen has good reasons for this. So let me quote him some more.

 

Modeling relations can be thought of as transductions, in which one kind of entailment can be replaced by, or converted into, another, in an invariant way. We convert causal entailments to inferential ones for a very simple and basic reason; we can hope to understand what goes on in a formal system. The concrete manifestation of such understanding lies mainly in our capacity to predict and (though this is a quite different order of question) perhaps to control. It would be nice if could pull the modeling process itself inside a formal system, where we can see it whole. We cannot do this directly, but we can do it metaphorically…

 

In probing the natural world, we are rather limited (yes, even with state-of-the-art technology) with what we can actually pull out as actionable data, i.e., Step 2 is very difficult and often incomplete. Nevertheless, we try. Then using some inferential rules (Step 3), we eventually use Metaphor to decode (Step 4). In a sense, science is always speaking in metaphors. Brown’s examples of how we probed the atom and drew ‘conclusions’ expressing them in metaphors are aplenty.

 

Rosen will subject his own framework to intense scrutiny, at least that’s what if feels like to me, the foggy reader. He will carefully define a simulation and how that differs from a model. There will be a distinction between analytic and synthetic models, the former deriving from (mathematical) Cartesian products (and not easy to decompose into its parts) and the latter from direct summands which will correspond to the reductionist approach in science championed by physics. Reductionism is very useful, and Rosen acknowledges this. But it is limiting. Much too limiting when it comes to the wider world of biology. Synthetic models can give us machines with hardware and software, but they can’t give us organisms. Rosen has a solution, but I don’t understand it well enough to summarize.

 

With reductionism, one is stuck with the “Cartesian metaphor of organism as machine”, at least formally, and with as much rigor as the physicist can muster. When we talk about the limits of reductionism and hint at emergence, it’s mostly hand-waving in the fog. Rosen’s goal is to give it rigor through abstraction in the world of mathematics and graph theory. Since I’m still in the fog, I’ll simply quote Rosen again on the (philosophical) Cartesian metaphor.

 

It succeeds in likening organisms to machines, to the extent that both classes of systems admit relational descriptions. But beyond that, it is fundamentally incorrect; it inherently inverts the notions of what is general and what is special. On balance, [it] has proved to be a good idea. Ideas do not have to be correct to be good; it is only necessary that, if they do fail, they do so in an interesting way.

 

Rosen will use the same analogy as Brown, comparing the machine-organism distinction to the syntax-semantic distinction. I’ll leave this to the astute reader of his book to figure out how he does it. In fact, maddeningly, Rosen does this all over the book with “I leave it to the reader…” statements where something is clearly obvious to him and so he skips over steps. I try very, very hard not to do this when teaching P-Chem. Like Brown, Rosen also uses protein folding as an example, although writing in 1991 he is not sanguine about the prospects of solving the problem. Interestingly, in 2021 we’ve gotten much better at the prediction part of the problem, but with a significant loss in understanding, as the number-crunching parts have moved to an A.I. black box. We don’t know the details of Step 3 any longer. What does that tell us about Step 1? I’m no longer sure.

 

Why do proteins fold into their specific structures to carry out their specific functions? Rosen uses the metaphor of a scaffold.

 

Sequence pertains to scaffolding… held together, not by any direct intersymbol bonds, but by being suspended in a larger structure. Conversely, any larger structure that maintains their configuration would create the sequence; its exact nature, its chemistry, if you will, is otherwise irrelevant. If we perchance interpret the elements of such configurations to be… chemical groups… then such scaffolded configurations may themselves act like conventional chemical species. If so, they are in fact much more general than conventional molecules… only “exist” when scaffolded together… If the scaffolding as a whole is perturbed, or disrupted, they disappear, they cease to exist, they denature. But they do not “decompose” in any conventional sense, and they reappear when the scaffolding is restored.

 

This is a metaphor I find very helpful as I’m puzzling over my present origin-of-life projects. I might even come up with a model to go with the metaphor. Perhaps that will help concretize my foggy understanding of this whole business. It’s no wonder many folks think science is difficult. Rosen certainly hasn’t made it any easier. But he might have made it more profound.

Assumptions and Beliefs

I’ve been exploring the intersection of educational and technology through Neil Selwyn’s book in my two previous posts. The conclusion? Selwyn writes: “… the claims made for education technologies are highly symbolic and often ideologically driven in nature… by people’s wider beliefs, values and agendas… ‘educational technology’ is used as a site for wider debates, contests and struggles over education.” I’m inclined to agree with this sentiment.

 

What are some of these assumptions and beliefs? Selywn lists several, and I will comment briefly on each of these.

 

On the interplay of technology and learning:

 

·      Valuing “individual-driven learning” over “institutional-directed instruction”: Constructivist theories of education have seen a resurgence in recent years, for both good and ill, in my opinion. Yes, I agree that learning is not just moving chunks of content from teacher to student, and that something both subtle and mysterious happens in each individual mind-brain. But I’m not sure that individuals are necessarily the best drivers of their education especially at earlier stages in life, or in introductory-level courses.

 

·      Valuing “exploration and experimentation” over “pre-determined instruction”: We should continue to explore and experiment in education. But over the years we’ve learned a lot about what works (and what doesn’t) when it comes to how humans learn. The latest educational fads are often old ideas wrapped in new clothing. Perhaps I’m just conservative, but I think that education is not ripe for disruption in a major overhaul, at least where learning is concerned. There are other wider societal issues that may reasonably argue for massive changes in the structures of education, but human brains haven’t evolved so quickly as to significantly change what works in helping individuals learn new things. What works may be different depending on what you’re learning, but what works is well established.

 

·      Valuing “social and communal” learning environments: I’m in favor of this shift, because I think there is great value in students learning from each other in a wider social space. However, I think we should be cognizant not to idolize this approach. My modus operandi is to utilize different pedagogical approaches depending on what we’re going to learn in a particular class meeting. And even within the session, several approaches may be employed. Outside of class, students working together on problem sets especially in P-Chem is highly encouraged; while during class it often works better for the instructor to be explaining things and working through examples.

 

On the relevance of teachers, as valuing the authority of expertise decreases: I suppose much depends on the subject material and the level at which it is being learned or taught. If I want to learn something well, and efficiently, having someone with expertise as a teacher is extremely helpful. So I think teachers will continue to be relevant. What might change is the formal teacher-student relationship; it may become less formal, it may become more asynchronous, and I certainly hope it won’t be replaced by a bot – which I think will deepen disparities between the haves and have-nots.

 

On the relevance of schools, valuing the “efficiency” of markets over government: It’s hard for me to thread my way through this murky debate. I simply don’t have enough information to have a strong opinion leaning one way or the other. Perhaps because I’m based in the diverse and messy U.S. educational landscape, I see value in having a variety of options. Before coming to the U.S., I was in a government-only education system and had little notion that a private sector existed, not counting the problematic private test-prep market which exists whenever standardized exams of some import are a factor.

 

Like many other hot topics, there are a small number of loud voices and a mostly silent majority. Selwyn writes that “few people are overly concerned with the topic of education and technology beyond a vague notion that digital tools and applications are ‘desirable’ and ‘probably a good thing’.” That’s a problem, especially when assumptions and beliefs are not explored and debated carefully and pro-actively. If all we’re doing is responding re-actively, it will be difficult to escape the Groundhog Day cycle we seem to be stuck in.

Atomizing Knowledge

Paper gold stars, pinned next to your name, signaling your achievement at a task, may have constituted your first encounter in kindergarten with a leaderboard. It kept track of your successes, directed you towards the next task, and perhaps gave you bragging rights within your local community. That gold star may or may not mean much to the rival kindergarten down the street.

 

The present day high-tech equivalent? Digital badges. Micro-credentials. And if you string enough of these gold stars together, you might be awarded a nano-degree. At a scale of 10^-9, you’d need a British billion of these to reach a degree. We’re reaching the atomic scale of knowledge, broken down into its bits and bites. And to store it somewhere, we’ll need bytes.

 

While the culture of assessment is certainly a contributor to this trend, and the reductionism of ‘being scientific’ plays its part, my post today is to muse about how educational technology has influenced the atomization of knowledge. Some of this is discussed more eruditely in Neil Selwyn’s Education and Technology that I’ve been reading. I highly recommend his balanced and thoughtful book to the reader interested in pursuing these topics. But on to my musing.

 

Atomizing knowledge in ‘factory’-like schools was seen as a good thing at the tail-end of the industrial revolution in the early twentieth century. Instead of patchwork and varied curricula of one-room schoolhouses, the ‘modern’ school was considered standardized and efficient, just like its factory counterpart. In the present-day, the factory-like aspects are a routine punching bag for pundits of education, technology, and of course, politicians. Technology is heralded as savior, breaking the strictures of school, and having the power to unleash your creativity through freedom of exploration. Personalized education is the new watchword – tailored, crafted – God forbid it be industrially produced.

 

Teachers are often blamed for their ‘resistance’ to this evolution. Or revolution. They should move away from being ‘sage on the stage’ to ‘guide by the side’ or perhaps even ‘peer at the rear’. No, I didn’t come up with that last one on my own. Selwyn mentions it in his book, which suggests it has some widespread use. I’d never heard it before and hope it doesn’t perpetuate. I realize my blogging about it seems antithetical to my hope. Such is the power of sticky, funny-sounding, buzz-phrases.

 

But teachers might have good reason to be suspicious of the technology that claims to assist them but is not-so-secretly attempting to replace them, despite protestations of the entrepreneurs seeking the killer app holy grail of education. Some of us educators are being recruited in that effort. Sometimes there is the offer to elevate myself from an expert to a rock-star expert. Other times, it seeks my ‘valuable’ contribution to the Robot, the Singularity of A.I. For educational purposes, of course. I received yet another e-mail this morning about such an opportunity. In the past, I used to send them directly to spam without looking. But nowadays I take a brief glance to see what the public face of the edtech startup is purporting to deliver to its clients. Then I send it to spam.

 

How do you train a robot to do a human’s job with precision and efficiency? By atomizing the tasks. Since I’m in the education business, it’s the atomization of knowledge. That’s the basis of how educational adaptive systems work, at least in the present paradigm. (I’m not smart enough to predict the future paradigm.) There is learning and cognitive science to back-up some of the A.I. approaches. I personally find Cognitive Load Theory to be a useful framework when designing my course and its activities. There are many helpful practices we’ve learned to help make things stick for students. Duolingo even sent me a message earlier this year explaining its ‘techniques’, for example why it occasionally uses funny and amusing phrases.

 

When you’re teaching something that’s new and challenging – chemistry for example! – it really helps to break things down into bite-sized pieces. Some of it needs to be pre-digested. Others need to have the texture for students to chew on for a while. I am in the business of atomizing knowledge, although in my case the pun is also clearly intended because conceptual chemistry aims at the scale of atoms and molecules. This reductionist analytical approach is useful, but I pair it with building-up synthetic approaches that are not so easy to describe. I’m not just hedging. There’s a good reason for this. I think learning is a complex process, not just a complicated one, which means it cannot be reduced to the sum of its parts. The parts are the things most easily measured. It’s the ‘science’, if you will.

 

How does the Robot adaptively figure out when you’ve learned something and awards you the digital badge? By asking questions. And if you answer them ‘correctly’ then it assumes you have learned. Those correct answers are based on the programmers’ putting together the ideas of subject-matter experts, then run through test-users (students), and the data is analyzed. Rinse. Repeat. This is how the machine learns. A beta-release has now codified some of the ‘best practices’, subject to tweaking with more machine-learning data. Sounds like a factory operation to me. Tailored and crafted to what end? A standardized credential that can be exchanged for other factory goods. Gold star trading.

 

I won’t pretend to know what my students have learned with the precision of a machine. What I can offer them is an ongoing conversation. I do ask them questions and I try to elicit responses. Some knowledge will likely be passed. Hopefully their thinking will be challenged and expanded. And ideally, good decisions will be made in their lives based on wisdom derived from knowledge and understanding. None of this will pass muster in the assessment reports. For that, they’re looking for the atomization of skills. Not even knowledge.

Edutech Groundhog Day

A year of remote teaching motivated to think more about the relationship between education and technology. So what do I do when I want to learn about something? I start by reading a book. In this instance, it was Neil Selwyn’s Education and Technology: Key Issues and Debates, 2^nd edition, 2017. Today I will focus on Chapter 3, “A Short History of Education and Technology”, but let me first set the stage with some Chapter 2 quotes that advise caution in our thinking, and to avoid being too adoring or vilifying of today’s push for digital technology use in education. 

 

Anyone who is studying education and technology therefore needs to steer clear of assuming that digital technology has the ability to change things for the better. History reminds us that technical fixes tend to produce uneven results, very rarely resulting in similar outcomes across the population and often just replacing one social problem with another. Even when [it] is seen to ‘work’, it can be difficult to ascertain why… Often, [it] will only deal with the surface manifestations of a problem rather than its roots… In particular, some of the most misleading assumptions about education and technology are the deterministic claims of technologies possessing inherent qualities and being capable of having predictable ‘impacts’ or ‘effects’ on students, teachers and educational institutions if used in a correct manner.

 

Selwyn goes on to state the dangers of technological determinism and simplistic ‘cause-and-effect’ instrumentalist viewpoints. It’s not just a matter of figuring out “the impediments that are delaying the march of technological process”, but worse, it blinkers one’s view and reduces adaptability to the ensuing unpredictable outcomes. Selwyn briefly discusses the assumption (prevalent from 1980-2000) that computers and subsequently e-mail would lead to a paperless office. But in fact, paper used increased. In surveying his brief history of education and technology, Selwyn will approach the subject by looking at how society shapes and uses technology, in contrast to marching through each development as an upward evolutionary arc of increasing technology.

 

Chapter 3 focuses on four technologies of the 20^th century: film, radio, TV, and the (micro)computer. Unlike the staying power of the textbook (17^th century) and the chalkboard (19^th century), these four have had mixed success in sustained widespread use. I will provide just brief highlights along with some of my thoughts on each of these, and if you’re interested in the details, I highly recommend Selwyn’s very readable and thoughtful book for the full story.

 

The famous inventor Thomas Edison was a champion of educational film in the early 1900s. He thought it would displace textbooks and completely revolutionize the educational system. He put in money and resources, commissioning educational films related to relaying the wonders of science and the natural world. There was plenty of enthusiasm for this venture, and historians looking back found that “early ‘experimental’ studies… found that groups of students using film were ‘greatly superior in learning information and concepts’ when compared to students using traditional methods.” Does this sound familiar? The same language is adopted today arguing that study X has shown result Y which enhances student learning of Z, which is then extrapolated as a general methodology for learning anything and everything.

 

But then the use of that technology declines over time. Subsequent studies question the positive results from the earlier studies. Enthusiasm is curbed. Blame is assigned. The costs were too high. The implementation was wrong. Teachers were Luddites resisting the new technology. It didn’t fit with lesson plans and other institutional requirements. The list goes on.

 

In the mid-1900s, Groundhog Day replays the situation but now the new technology is radio. I didn’t know much about this and found it interesting to learn that World Radio University (established in 1937) “broadcasted classes in 24 languages to 31 countries”, and that there were many “Schools of the Air” which offered supplemental instruction reaching over a million students in the U.S. in its heyday. Rise and Fall. Repeat. Supplanted by the next emerging technology: television. Enthusiasm reached new heights. What could be better than a medium so engaging with access to the very best teachers surpassing anything you could experience in your local school classroom. Sounds like a MOOC to me. Why, we should bring it into the classroom! And in American Samoa, 80% of students were “spending between one-quarter and one-third of their class time watching televised lessons, which were then supplemented by follow-up exercises and question periods led by teachers. Similar ‘immersive’ projects in US states suggested that television-viewing students could improve their position in league tables of test scores when compared to national norms.” Active-learning flipped classroom, anyone?

 

And then there’s the computer. Selwyn describes a 1966 study where students from a ‘deprived’ school found themselves immersed in “computer terminal, light pens and screens to teach reading and arithmetic” and apparently loved it. New studies trumpeted the successes of computer-assisted learning to reduce the drop-out rate, not to mention freeing up teachers to do other important things, or even giving them the axe and saving money on manpower. Quoting a 1985 study by Stonier and Conlin: “Not the least of the successes was the testimony of a girl who stated that the computer was the first math teacher who had never yelled at her.”

 

When I was going to the equivalent of grade school in the 1980s, computer-assisted education had not permeated my country’s education system. I did find computers fascinating, not least because you could play cool games. I would visit friends who would have home computers (Apple II clones were in vogue) and I borrowed books from the library to teach myself BASIC. I would write out programs on paper and when I had a chance to visit a friend and we had played a few games on it, I would type in my program to see if it worked the way I had anticipated. These were my “educational” excuses for why my parents should let me visit those friends, preferably more often. Looking back on those experiences, I wonder if that subtly moved me towards computational chemistry mainly by getting me comfortable with using the command line.

 

Computers are certainly ubiquitous in education today, but perhaps not in the same way envisioned by its early prophets. They have certainly made data analysis a whole lot easier in class but also when students are writing up their lab reports or working on their problem sets. Access to data repositories in real-time thanks to the Internet allows me to design small group in-class work that, I think, promotes useful learning of the material. Without the technology and bandwidth of today, classes may have ground to a halt during the Covid pandemic. As a teacher, I’m very grateful to the Internet as a resource helping me put together what I hope are engaging classes for my students. There is reason to think that some of our 21^st century digital technologies are significantly transforming education and that we might not see a replay of Groundhog Day. But we should also learn the lessons of the past. I’ll quote Selwyn again (from the end of Chapter 3).

 

We have seen… how successive introductions of film, radio, television and micro-computing into education were accompanied by considerable hyperbole and hucksterism. Many claims were made about the enhanced nature of technology-based learning and the resulting improvements to learning, as well as the establishment of ‘fairer’ conditions for ‘rich’ and ‘poor’ students and schools. We also saw how research ‘evidence’ was produced quickly to ‘prove’ the ‘effect’ of these technologies, especially in terms of learning gains – regardless of the fact that this evidence was inconclusive and equivocal… it is notable how many of the ‘educational’ rationales for these technologies were based on ambitions towards the mechanisation of the teacher’s work, increased efficiency and economies of scale…

 

In general, the twentieth century was a period where many people were keen to proclaim the ‘power’ of various technologies to affect substantial societal change… The flaw in this reasoning… was that ‘social problems are much more complex than are technological problems’ [quoting a Manhattan Project physicist]… [there is] a clear ‘cycle’ of events that is more or less repeated with each ‘wave’ of technology development. This cycle is seen to begin with substantial promises for the transformative potential of the technology backed by research evidence and other instances of scientific credibility. Yet… educators go on to make inconsistent use of the new technologies for a variety of technical, professional and personal reasons. Perhaps most importantly, few changes appear to occur in the arrangements of educational institutions. A number of rationales are then proposed to explain this ‘lack of impact’…

 

It’s difficult to see where things are headed when you’re in the thick of it. The historical perspective is useful to consider when one has the benefit of hindsight. Even if our present ‘wave’ proves transformative, it will likely unfold in a way differently from that predicted by its present champions. I suspect thinking about the relationship between technical and social problems is important, but I haven’t quite wrapped my head around what this means. Also, Selwyn’s caution against educational hyperbole and hucksterism still stands. And there will be a lot of it. Separating the wheat from the chaff will not be easy; it might be even more difficult with the so-called information explosion. But even that’s an old story, repeated for a new generation with different technology. To quote from the Matrix movies that also feature cycles: “some things never change… but some things do.”

 

Stay tuned for more on Selywn’s book in future posts!

Biological Relativity

The last two decades have signaled a shift away from the reductionist paradigm in the sciences, notably with the rise in systems-approaches. Ecology and environmental science has been doing this for some time; but for a long time, many biologists and chemists preferred to stick with reductionist projects – simpler, cleaner, and very successful throughout the twentieth century in advancing our understanding of nature. The limitations of the reductionist paradigm have become more apparent, and I expect it to slowly die from a thousand cuts. 

 

The latest book-length stab at reductionism is Denis Noble’s Dance to the Tune of Life, published in 2017. Noble is a biologist at Oxford (since the 1960s), noted for his advances to our understanding to the physiology of the heart, and is often credited as a pioneer in systems biology. He’s been arguing against the reductionist paradigm of biology’s Central Dogma and Modern Synthesis for decades, while pushing for an alternative view he calls Biological Relativity. But he started out as a reductionist, and in a brief account of his personal scientific journey, he tells the tale of how mathematics and computing, coupled with experimentally studying protein channels in the Hodgkin Cycle, led to his ‘conversion’ into a systems biologist.

 

What is Biological Relativity? In a nutshell, there is no “privileged level of causation” in biology. The book’s postscript sums his argument in a series of pithy statements:

·      Organisms, including their genetic material, are necessarily open systems.

·      Open systems are necessarily influenced by processes at larger scales.

·      Meaning and function are natural features of larger-scale phenomena in biology, not of individual molecules.

·      The reason is that physico-chemical processes at smaller scales are necessarily constrained by higher scales. Even a molecular determinist has to admit this.

·      What is ordered and functional at higher scales can appear stochastic (random) and non-functional at lower scales.

·      Lack of purpose at the molecular scale does not therefore entail lack of purpose at other scales.

 

Noble marshals a series of arguments through his book, beginning with principles of relativity in physics. He quickly gets into biology by introducing the difference between scales and levels: “Scale refers to the dimensions and boundaries of a chosen subset of nature. Level refers to (often roughly) distinct forms of organisation… Scale is a more neutral description than level since it does not depend on organisation, even though different forms of organisation occur at the different scales. By contrast, the conept of level depends precisely on what we identify as forms of organisation. The level of cells depends on the form of organisation we call a cell, which is viewed as being above the level of molecules and, in multicellular organisms, below that of the whole organism.”

 

Hence, ‘level’ is a metaphysical concept. Noble elaborates this with examples from biology and philosophy. But his take-home message is that “a cell is vastly more than its DNA, and an organism is vastly more than a collection of cells. All of that ‘vastly more’ is passed on to subsequent generations…” with profound consequences when you look at things from a systems point of view. Noble does not shy away from language about ‘natural purposiveness’, sometimes referred to as teleology or teleonomy (depending on how you split the differences). Noble credits the gifted writing skills of Richard Dawkins in The Selfish Gene, but argues that Dawkins’ reductionist view has led biologists and the general public astray in the range of ‘nature’ versus ‘nurture’ debates that are widespread today.

 

Here’s what Noble has to say: “… it is combinations of genes, or rather combinatorial total interactions between large numbers of their products, RNAs and proteins, that are important functionally… the functional pattern may not be visible at a molecular level... one of the reasons why Neo-Darwinists use a gene-centric view to claim that all variation is random with respect to function. The non-randomness may only be evident if one takes a high[er] level perspective… most single genes contribute very little to complex functions, which is why the correlations between genes and complex diseases have been found to be a matter of large numbers of very small effects… The atomistic view was never going to be of much use in physiology and pathology.”

 

In my field of computational chemistry, the dictum is to use the “right level of theory (methodology) for the right problem”. At the quantum level, we cannot solve the Schrodinger equation exactly for anything beyond a one-electron system, so we make approximations. The larger our system, the more approximations we make. Error-cancellation is our friend. What seem like simplistic models can in some cases give you very good predictions (when compared to ‘clean’ experimental results, often unavailable for interesting systems). So I’m in sympathy with Noble when he argues that “we should ascribe functions and purposes to the level at which they make sense, which is the level at which they constrain the interactions of the system at lower levels. This constraint is also what canalises those interactions to serve the natural purposiveness of organisms.”

 

The nub of the problem, according to Noble is a clash of conceptual categories between the mechanical view and the “functionally purposive” view. Noble categorizes the former as “the unjustified assumption is that organisms are closed, determinate systems” and that “demonstrating pure ‘blind’ mechanism at one level does not guarantee the absence of function at a higher level.” The problem with the reductionist view, and one that as a chemist I’m used to taking, is that it privileges the atomistic or molecular level. I call as witness the huge shift in grant funding opportunities towards the molecular sciences in the last half century, quite prominent in molecular biology. Organismal level biologists are getting shut out. Without denigrating the amazing advances in molecular biology, Noble would argue that it’s a huge mistake to privilege a particular level when arguing about causation. Noble also reminds his readers that DNA isn’t the all-important director of cell affairs. It’s more like a very useful data-storage-bank that the organism utilizes as it goes through the motions of life.

 

As I moved into studying self-assembly and complex systems, and puzzled over origin-of-life chemistry, I’ve gone through a similar journey. My initial focus was on prebiotic chemistry pathways to make particular molecules that extant life now uses. How and where those molecules were used in an ‘open’ living system were less of a concern. Why? Because it’s easier to reduce a problem into something bite-sized and more manageable, especially if you exclusively work with undergraduates in your research program. I was relatively successful at this, but I now think it’s somewhat of a dead-end approach. I’m moving on to messier systems, thinking about non-equilibrium thermodynamics, and puzzling over how to appropriately choose boundary conditions in multi-level modeling.

 

I’m using the phrase ‘multi-level’ for the first time; previously I used ‘multi-scale’, but Noble’s distinction has made me more aware that I need to think more about functional relationships and less about molecular structure. The chemist’s dictum that “(molecular) structure dictates function” is one I’m moving away from. When taking on larger-scale multi-level systems, a Chemical Relativity seems in order! I’m feeling just a tad more ready to study of Life Itself. 

Remote Reflections

I made it through a year of remote teaching, research, and service as a faculty member. My institution is planning on full in-person classes for the upcoming fall semester. I hope there won’t be a Covid surge this summer here in the U.S., and while I’d like to say this will be my one and only year of fully remote work, I think the likelihood is high we will see another zoonotic epidemic within the next ten years. Whether that spills over into a global pandemic is less clear, but I certainly hope it won’t.

 

So how was working remotely? Overall, not as bad as I anticipated. The physical barrier separating work from non-work dissolved, but I did not find myself getting distracted by non-work activities while being home. And except for some online evening seminars, I also maintained my discipline of not working on evenings and weekends. Attending committee meetings, department meetings, and the like, was simplified, and I thought things ran well for the most part except discussions are a little slower. Folks (including me) occasionally still forget to turn on the mic when starting to speak. And I got used to following and utilizing the Zoom chat as an aid to the discussions.

 

Since I’m a computational chemist, my research was relatively unaffected by remote work. There was the inconvenience of using VPN on my Mac laptop, and not having my own Linux workstation setup at home, but I adapted quickly after writing some scripts and procedures for moving and analyzing files. Discussions with my research students was a less efficient, but still manageable. Lots of screen-sharing flipping back and forth. And analyzing data together just takes longer over Zoom. I think I did a poorer job mentoring my students through the research process overall. However, I did meet each of them once in person early on for research training, albeit in a less ideal truncated form.

 

Teaching, as expected, was the biggest adjustment. I’ve written several blog posts about preparing for the process (here and here and here), some of my frustrations (here), but also some of the milestones (here). Having gotten over the learning curve the previous fall semester, things went more smoothly this past spring semester. I was better at pacing myself, figuring out how and when to use breakout rooms, more detailed in organizing how I structured the course, and even tried one or two new things. I covered a little less material than I normally would in-person – I expected this and planned for it – but I feel that in some places, I covered things with a little less “meat” in them, and students may not have grasped some topics as well because of my limitations as a teacher doing things via Zoom for the second time.

 

A number of students enjoyed the more focused weekly prompts that I introduced this past semester. I wasn’t happy with my overly open-ended approach in the fall semester, so I tightened things up this semester. Several students commented that their favorite prompt was one that also encouraged a dose of creativity. While, I had some logic to connecting my prompts from week-to-week to what we were learning in class, I did not have an overarching coherent plan – a weakness, and something I need to improve on in its next iteration. The students also liked the detailed instructions of how to prepare for each class. I beefed them up this past year compared to the very brief version I had pre-pandemic. This is something I will keep doing.

 

While I continued to employ annotated self-graded midterms in my honors G-Chem class, I decided to do a mix of annotated weekend self-tests and two in-class midterms for my regular (larger) G-Chem section given the wider mix of academic abilities. I also think this worked well overall but I need to tweak how I allocate my grade bands since I grade on an absolute scale and I tell the students upfront what these are. There’s always the spectre of potential cheating during the exam even though Zoom cameras are all on. I don’t stare at the students nor do I employ any privacy-invading proctoring software. Overall final exam scores were a tad lower than previous semesters, but not by much, so even if there was some cheating, it wasn’t rampant. My exams are also written such that trying to look things up doesn’t help you all that much in the given time frame of the exam if you didn’t already know the material well. This was already true pre-pandemic, but I tweaked questions a bit to take this into account.

 

Student perceptions of the class and my teaching via their evaluations was overall positive. That was also expected. While my rapport with the students was not as good as it would have been in-person, I made an extra effort and some of the students noticed this. Those who came to office hours certainly appreciated it and I was able to give them more time and attention. Students liked that I wrote a lot on the whiteboard instead of talking my way through slides. Several of them said I was enthusiastic and enjoyed the connections I made to other classes (especially biology), and a number mentioned thought I cared about their learning. Not everyone was happy with their experience, and this is also typical. As in a non-pandemic year some students thought the class was too hard, and that I went too fast, but interestingly not as many said this as in previous years. I made an effort to slow down this pandemic year, and that was likely a good thing. One student did say that I cut off students when they were speaking. While I think this was because I would nod and make verbal noises (in an effort to communicate I was following along), I think that in a number of instances I was too hasty in thinking I understood the question and answered too quickly. I’m glad the student made me more aware of this.

 

What did I learn from remote teaching that I will carry over to my in-person classes? I think I will continue to use the LMS for its Discussion Board, detailed preparatory material for each class, and for students to submit annotated self-tests. I might consider a mix of both in-person and online office hours (they’re certainly more convenient for students instead of having to trudge across campus to my office). I think I should keep the pace I used during remote learning (clearly better for the students overall) which means talking and writing a little slower, and introducing pauses (which automatically happened remotely when I would flip between the camera focusing on the whiteboard versus screen-sharing). I think I should also be more actively conscious about connecting with my students and building rapport. Thinking about all this is making me excited about teaching the upcoming semester!