On Thursday, November 9, 2023 CE, at 10:16 EST (by whatever network server my phone syncs with), I finished the last lecture of my university career. It wasn’t actually “my” lecture, with respect to content: my eldest offspring, Carolyn, was at a convention in Barcelona, and needed me to cover two of their lectures this week. But it was “my” lecture in terms of delivery.
You see, it’s never a good idea to just read the slides all the time. You do need to make sure you cover the material on the slide; students can benefit from the combination of visual (the information on the slide) and the verbal (the words someone speaks about the slides). So simply reading the slides still has some benefit, but so does rephrasing the material; some ways of explaining things might click with students, or some students, that other ways.
But what made it “my” lecture was what wasn’t on the slide, or even a paraphrase of it. It was things I could say, from my own experience and knowledge, that were related (at least tangentially), and which might enrich the learning experience of some subset of the students.
I am passionate about leading students to go beyond what they are focusing on in the moment, and open themselves up to a broader set of associations with other material they might already know, or what they might want to learn about once they realize there is a connection. One of my favourite technical examples is that when I teach about the Linux make program (which I have done in several software development courses), I point out the connection with what students learned in their discrete structures class: make performs a topological sort of a bipartite dependency graph and carries out instructions that annotate one of the two distinct kinds of nodes in the graph.
The subject of the lecture in COGS 100 (Introduction to Cognitive Science) was social cognition: how connections with other people affect how we think. This is something I’ve learned tiny bits about by “osmosis” – things I’ve read over the years but never studied in detail. I read the lectures in advance and asked Carolyn for clarification of a few points; I made notes on my phone with the clarifications, and with other things it occurred to me to mention. I told the students I could answer simple questions (though they never asked any, other than whether the slides would be on the learning management system eventually) and that they would have to wait until Carolyn was back for more complex questions. So, I was about as well prepared as a non-expert could be – which to me is the minimal level of competence anyone should expect of a substitute lecturer.
But what I really enjoyed was drawing connections. I don’t recall all of them, since several were things that occurred to me in the moment, but several came from my long experience as an academic. Here are a few of them; afterwards I’ll talk about how I feel about the lecturing experience.
Being a Scholar
In the Tuesday lecture I had alluded to an old article, “Artificial Intelligence Meets Natural Stupidity,” by Drew McDermott, which among other things talked about how, at the time of writing, it was common for researchers to name fairly ordinary data structures after complex pre-existing concepts that the data structure was trying to represent. But there’s a “fundamental principle of data modelling:” the only fully accurate model of the real world is the real world itself. Representing things in any model requires omitting details the creator of the model deems not relevant enough to the purpose of the model. So i cautioned the students to think carefully about how well computing terminology (not just in Cognitive Science) actually matches the pre-existing concepts with similar names. I told them how to find the article with Google Scholar.
In the lecture, I asked how many had looked it up. I didn’t want an answer and told them so, but I led into discussing life-long learning: how, when they left university, they wouldn’t have textbooks and instructors to guide them, and needed to develop the intellectual skills that would help them learn on their own. In particular, they needed to learn to go beyond the content of Wikipedia articles, to the original material on which those articles are based.
Personalizing the Topic
One set of material wasn’t an aside from me directly: Carolyn had, at my request, included a couple of slides about how most research on cognition was for neurotypicals, and autistics like me are considered abnormal. For example, supposedly we don’t have empathy – but more careful research has shown that autistics understand other autistics better than neurotypicals do, and neurotypicals understand other neurotypicals better than autistics do. Well, duh: it makes sense (and should have occurred to the researchers, if not for their bias against autistics) that people of any sort understand people similar to themselves better than people who are dissimilar.
I have become quite willing to tell people I’m autistic (which is not always a good idea), and was very happy to be able to relate something personal to the material of the lecture.
Right And Wrong Answers
There were a couple of places where technology could have made the lecture more interactive. There are systems (a mix of hardware and software) that can present a list of choices, gather answers from the audience, and show how many people picked each answer. Then the instructor can ask people to discuss with their neighbours, whether that convinces anyone wants to change their answer, and take a second poll. I mentioned the technology and said that it is useful to take the position that there are no wrong answers: that “wrong” answer is often a sign of a misconception that can be discovered, leading to a change of learning materials to guide people away from the misconception. And sometimes it isn’t even a misconception: when I teach the part of the Universal Modelling Language (UML) about relationships between classes, then ask people to group together and create a poster for a model of a specific problem, I commonly get some (perhaps most) add attributes to classes that actually represent one choice for a representation of a relationship. The real issue is that learning UML involves taking programmers at a particular level of learning to a new level requiring more abstract thinking. I needed to change my teaching approach to point that out, show an example of the ‘error,” and show them how the community of people using UML expects to represent that situation.
I have alexithymia, common among autistics: difficulty recognizing emotions, both our own and those of others. So introspection about how I feel is difficult, and has a strong element of trying to interpret physical feelings and glimmers of mental ones, substituting analysis for intuition. But I’m fairly sure about a few of my reactions during and after the lecture.
I felt fairly comfortable and confident in the second lecture; in the first I had been a bit rusty. My lecturing skills have never been great but rarely bad. For most of my career I expect my lectures would have been described as fairly easy to understand but a little boring. There may have been a few flashes of brilliance – a few former students have told me of some – but for the most part I wasn’t as inspiring a lecturer as some of my colleagues (though perhaps a little better than a very few of them). It felt good that I hadn’t declined much in skill over the COVID gap.
I felt a little sad about leaving lecturing, but not a lot. I’ve always enjoyed finding good ways of explaining things, and I can keep doing that, in textual form. I can write short blog entries, and may be able to write long books now that I’m freer to do so.
I’m glad Carolyn gave me this one last opportunity.