T-minus three days to online teaching

Today, enough pieces clicked that I have a picture of what the rest of the semester will look like.

One challenge to this transition is that one of my courses is a new offering. I had a plan for the course, but I didn’t have a bank of entirely complete materials to fall back on. But I got the detailed project document for the second half of the term done and posted with its deadlines set, and things are falling into place now that I have that as a framework.

I’m still structuring my content around “class days” because it helps me make sure I’m parceling out work at a reasonable pace. Otherwise, I could see myself pushing in just one more activity or reading each week. For students, it doesn’t particularly matter if Monday’s work happens Monday, or Sunday, or Tuesday, or sometime roughly around Monday.  But for some of them, I also think it might help them to have the work parceled out in bundles of a size they are used to thinking of as “what I do in the next day or two”.

I have got used enough to lecturing into my monitor/webcam that I can hear myself getting my “teaching voice” back. I’m resisting the urge to go back and look at my first couple of videos I filmed and seeing how bad they are.

I’m guessing that as a mass, the faculty all have enough content and information starting to flow out that students are starting to test out their technology setups and use the second half of this week to figure out what online learning will look for them. So support for student technology is ramping up. This included working with the College to make sure that students who need a computer to complete our coursework and have been relying on the computer labs have loaner machines they can use now that the campus is in essential-services only mode. It seems like the gaps are getting plugged, but I worry about the gaps I don’t know about yet.

One of today’s lectures focused on getting students set up to build a machine learning classifier using the classic Iris dataset – it’s a nice, simple starting point that is small enough you can actually look at the data as well as train the system to predict what species an Iris is based on measurements of its flower. I introduced a personal touch by going out front and taking a picture of the irises growing in my yard to illustrate where the petal and sepal are and acknowledge that I, in fact, could use a machine learning system like we are building because I have idea what species of iris I planted in my yard two years ago.


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