Thanks to Mark Guzdial for pointing this out to me via his great computing education blog, but this post gave me pause -- we intend (or at least I intend) for technology and computing to provide more access, and thus more diversity, but might the tool(s) "bake in" (or exacerbate?) existing biases and inequities?
I think this is the most troubling question posed in the blog post: "What is the difference between the pattern recognition afforded by big data, and profiling on the basis of gender, race or class?"
My initial response involves how we use this profiling/pattern recognition information -- computer programs recognize, people profile (whether they admit it or not). Our choice is how we use this information, and it seems we need to tread with caution.
What's yours?
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2 comments:
John,
I would have to agree with mooc thing. I have started quite a few as a student myself, and never finished.
With that said, I do not think it is the same with online courses that are smaller and more personal. I have been teaching online at Bucks County Community College since 1994.
I could pull the class lists with grades, but I am going to guess that I have had as many successful women as men.
Right now, I am teaching 2 online sections right now.
Why?
At BCCC we have same class size for online as the F2F classes. Class size is max of 20.
Instructor in present in the "online class" on a regular basis.
Students are contacted if they do not participate on a regular basis.
Often the subject line of the email is "I am concerned about your progress in this course" and in the content, I just say. "contact me asap."
Those that do, usually succeed. Those that don't are gone.
Connection is the most important thing.
Most people who teach online know that it is bymodal. 30% at top, 30% at bottom (fail or drop course).
Sounds like this calls for a research project.
SIGCE Research project?
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