Just about a year ago I asked a few people what seemed like an easy question at the time : If we think of a university as a business, what’s our “product”? Are we in the business of “selling courses”? Or “converting non-educated students into educated students”?
The most compelling answer that came back was that universities, at their best, are in the business of “knowledge creation”.
huh… It wasn’t the answer I was expecting nor was it aligned with anything I measure (as the person in charge of measuring and KPIs). It was though the only way that the combined interest in teaching and research really made sense. It also seemed to be a worthy and useful goal.
Throughout the year, I bounced the idea around with a few more people. They seemed to agree that universities ought to be in the business of knowledge generation.
OK, so if my job is (or at least was when I undertook this project) to measure the “things that matter” at my university, it seemed that I should at least be asking the question: How would we measure knowledge creation ?
Enter the SPLOT. Thanks to Brian Lamb and Alan Levine, we’ve got these relatively small class blogs in which anyone can post without creating an account. Not in the class? No problem. Want to stay completely anonymous? Yup.
So here’s what I’m thinking: What if we used data from a SPLOT or other open content as a starting point to measure something of value. There’d have no author names, no click measurements, no page views data. What would be left? What traces of data might be in the file? Could those traces show any evidence that a class has generated new knowledge?
And if we are going to go that fat, why not move to process of analyzing that data into the open as well? What would analysis of learning data in the open look like? How could the privacy issues of students be addressed?
The open content and the ability to contribute without an account may address the privacy issues. The analysis could be completed using open tools and is shared (as in a Jupyter notebook). Others could poke holes in the assumptions, research questions, analysis, code (or anything else that could be improved.
Could something useful come from this exercise? At the beginning, I had my doubts.
I’ve started looking at the data from one SPLOT (though I’m not yet able to share) and I’ve now reviewed and cleaned it. There enough data there to support analysis, so at this point I have full confidence that it could be useful.
My next steps and going to get into content analysis and looking at the connections between the ideas in the posts and comments. (Not my area if expertise, so thoughts are welcome.)
If nothing else I’m spending a lot of time twisting my brain and challenging all of my foundational assumptions about learning, assessment, analysis and sharing which feels like it just might have value.
Interested (or not)? Let me know.