Of course, institutions with more money have lower teaching loads. I have a specific hypothesis: Endowment size predicts teaching loads, like this:
But is this true? If it’s true, how true is it? How much variance is there? Does enrollment matter, such that it’s endowment-per-student? I’d like an answer to these questions, in the realm of non-PhD-granting institutions. If you have a minute, and you’re faculty at one of these places, could you humor me by filling out this quiz? If you have another minute, maybe pass it to colleagues at other institutions? The reward will be a post with the findings. It’s embedded below, but if you want a direct link, here you go.
Stay tuned, probably at some point in January I’ll have fund putting the results together for you. Have a great holiday!
5 thoughts on “Why do some places have high teaching loads and others have low teaching loads? [poll]”
I’ll be interested to see what you find. There’s also variation in teaching loads among PhD-granting institutions, but my guess is that differences there correlate better with total federal dollars received.
Doesn’t this also vary by discipline?
I know the calculation of teaching load that you’ve suggested is meant to be representative but it’s really not because sometimes we teach more lectures with labs in a year and sometimes more lecture only classes. The best way to calculate this would be number of hours in the classroom teaching. This would be easily calculated and comparable. And at my institution the requirement for teaching hours varies by humanities/sciences (unfortunately, because we all know that teaching labs at a SLAC is usually time and labor intensive, not a ‘half’ class).
Meanwhile, our base teaching load on my campus is somewhere between 12 (all lecture load) or 18 (all lab load), because many institutions don’t track contact hours and give different credit for lectures and labs. There’s no single measuring stick that can be used to represent base load among institutions, because campuses use different units. This is an approach in the middle designed to make the variance smaller.