The other class I’m teaching this semester is introduction to cognitive science (hereby referred to as CogSci 101), which I co-teach with two other professors. This is the second time I’ve joined a co-teaching team – I was also one of three instructors this past string, teaching an introductory computer science course (hereby referred to as EECS 183). Co-teaching is a fairly superficial way of clustering courses, but the teams are organized differently enough to warrant some thought. It’s obvious in retrospect that organization of the team depends on the structure of the course, but somehow I’ve never thought about how one influences the other.
To start, the two courses could not be more different. EECS 183, being an introductory course at a large state university, had close to 700 students, who were split into four parallel lectures of hundred-plus students each. In contrast, CogSci 101 has maybe 50 students, who all fit into one medium-sized lecture hall.
These enrollment numbers themselves dictate the organization of the co-teaching teams. With 700 students in parallel lectures, each instructor covers the same material, and must therefore coordinate their presentations; for EECS 183, this was done by rotating who prepares the slides, which then go through edits by the other instructors. The University of Michigan, and the computer science department in particular, is fond of using undergraduates as teaching assistants (TAs), and for EECS 183 we had over twenty of them, who not only held office hours (in addition to the ones the instructors held, of course), but also helped prepare upcoming assignments. Aside from the infeasibility for only three instructors to deal with 700 students, the class size also meant that projects need to be thoroughly proofread and tested before dissemination. It is not an exaggeration to say that without the TAs the course would have fallen apart.
CogSci 101, on the other hand, doesn’t face any of these challenges. The class is co-taught not because there are too many students, but because we each bring a different perspective on cognitive science. (I enjoy this tremendously; it’s fun to dig into parts of cognitive science and philosophy that I haven’t touched in a while.) This also means that we co-teach in serial instead of in parallel, with each class covering a different topic. It’s the fourth week of the semester, but I have only given one lecture – although, to be fair, I will have given two more by the end of the week.
One major difference that initially surprised me is the lack of any regular course planning meeting. Compare this to the weekly meetings in EECS 183, where all the instructors and TAs would talk about what is happening in the class and what needs to be done in the future. At first I thought it was a consequence of me no longer being a graduate student, but even the full-time lecturers for EECS 183 had to attend the meetings. I now understand why this difference exists – with 700 students this kind of check-in is crucial, especially since different instructors must teach the same material (including the TAs during discussion). For 50 students, with each instructor teaching something different, the only coordination is at the level of how one topic flows into another – which was all decided in a single meeting at the beginning of the semester. I have no doubt that when homeworks and exams come around we’ll have more meetings, but this level of coordination is sufficient for just teaching.
One problem that I do have with the current arrangement with CogSci 101 is that I have yet to know any of the students really well. Only teaching one in three classes makes it difficult to build up rapport, and because the course does not yet have any work due, no students have come to office hours yet either. I don’t think the distribution of students coming to office hours is different from the beginning of EECS 183, but with 700 students I at least got to know some of them.
Which brings me to my last point. A class of 50 students is in the awkward range where you can’t interact with every student every class, but also can’t justify students buying and using remote clickers (which we did in EECS 183). The only work we get from students on a weekly basis are what we call “thought questions”, which are usually about applying a theory to some real world phenomena or to a case study. This is in line with the course goal of introducing cognitive science ideas, as opposed to having students memorize the specific results of specific experiments (or researchers).
What I realized is that this is almost never the case in computer science – there is usually some technical component that students have to learn, which can be adapted for in-class concept questions. It struck me that most of my pedagogy education is in STEM (and especially computer science), and that I’m not sure what low-stake, formative assessment techniques are used in the social sciences or the humanities. I think the problem that the subject is less stringent on the correct answer, which makes it harder to show mastery in a short period of time. But there must be some way of finding out whether students are on the right track, beyond having them self-report what they understand and what they still find confusing.
Do people have any ideas?