Project Puppy: radically transparent engineering education research begins

February 1, 2012 – 2:26 pm

“Quick, Joi — I need a codename so I can blog about our project. What’s your favorite baby animal?”

And so begins the public chronicle of Project Puppy. I must apologize for the obscurity; we’re still waiting to get consent from our interviewees to be 100% transparent about the whole project, but the short version: Project Puppy is radically transparent engineering education research. What would it mean to run an engineering education project like an open source one – if you assumed an abundance rather than a scarcity  mentality, if you shared rather than hoarded your data, if you welcomed surprises and “uninvited” contributions instead of carefully curating access? What would happen?

At least that’s the lens I’m bringing to it – my other collaborators all come from different backgrounds and perspectives. Linda Vanasupa from Cal Poly started the whole story; some time ago, Linda recorded 8 fascinating interviews, but didn’t have time to go through and analyze them. So she poked Robin Adams at Purdue, who brought up the idea to a few grad students: Joi-Lynn Mondisa (doing mentoring research), Junaid Siddiqui (doing transformation research), Dana Dennick (using conceptual change frameworks), myself (radical transparency and the open source way). And off we ran.

We’ve poked around the data a bit, but today was the first conversation we had about what it might mean to make this an “open research project.” What if we asked our interviewees – there are only 8, after all – for permission to release their transcripts under a Creative Commons license (Linda’s idea) and then did our coding process in public so that people could see what it looked like to do engineering education research?

Sure, there are concerns to deal with (for instance, Junaid brought up the question of what happens if someone does a writeup on “our” data that is a distorted misrepresentation – how would we deal with that?) but this opens up some interesting possibilities. For instance, not only would we have (we hope) some papers, but we’d get out a manageable-sized dataset for coding practice in a qualitative methods research course for anyone who wanted it. We’d be working with questions of open access – which Dana, with her library science background, intellectually understands but doesn’t practice, and which I, with my open source background, practice but may not be fully conscious of. We’d be looking at an interesting model of research mentorship and resource allocation; Robin already mentioned that it was refreshing for her to not be “the boss” of the project, and to not have the pressures of funding breathing down our necks!

In short, it’s an adventure. Now, Project Puppy isn’t about radical transparency per se. It’s a “normal” engineering education research project (on a topic I can’t yet reveal, but Linda’s working on that) we’re trying to conduct in a radically transparent way. My job, in part, is to model this radical transparency for the rest of the group – and so here comes this blog post. More stories to come!

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  1. 6 Responses to “Project Puppy: radically transparent engineering education research begins”

  2. Mel, wow! You don’t mess around. Thank you so much for your insights today. It’s my feeling that we are witnessing the weak signals of the genuine collapse of our “normal” way of doing things, which is based on a scarcity mentality. The open source people have been amazing in this regard, really demonstrating the reality of abundance. For myself, I want to live an assumed (natural) abundance; I often find myself habitually living in a narrative of scarcity, however. Thank you for reminding me today of this important disposition of abundance!

    By Linda Vanasupa on Feb 1, 2012

  3. In Machine Learning there’s a long tradition of releasing data sets, although in our case its less as a demonstration of the research process and more about peer review, the scientific method, and providing a way to compare different analytical methods. It will be interesting to see whether releasing interview data will function in the same way, or if differently, how!

    By Katie Rivard on Feb 1, 2012

  4. @Linda: I think it’s wonderful and brave that you and Robin are willing to let us head in this direction (and actually, I’m in awe that the rest of the group is cool with it too!) I realize that it’s difficult to let others bring you into unknown terrain you can’t yet see.

    I’m actually pretty bad at that myself – I definitely saw myself dragging my feet yesterday when the other students and I were talking about the FIE abstract (because those kinds of things are my blind spot) so I can only hope I’ll match you all for courage throughout the process.

    @Katie: See, you would think that this would hold true for other fields as well, but nooooo. What you mentioned about doing open data for scientific method peer review is one of the biggest and most damning critiques of academia as “open and replicable” I’ve heard so far. I know it’s not all gloom and doom, so it’s good to know there are certain subfields in academia who’ve done it before, so we can emulate you folks. (And I’ll pass this on to Dana, one of the folks on the Project Puppy research team, who is looking for open data usage in STEM for another project of hers.)

    Regarding the whole “emulation” bit — is there any particular way you have to work around releasing datasets with regards to IRB (I imagine this may not even be an issue for machine learning?) and/or things like blind review of grant proposals?

    By Mel on Feb 1, 2012

  5. Kickass! Hope it goes forward as planned, Mel!

    By Jodi Schneider on Feb 1, 2012

  6. Hey, Mel! I started chatting with Linda about this radically transparent engineering education research project last week, and I just learned that you’re on the team. Awesome! It sounds like a fantastically radical approach, I’m very much looking forward to how the project progresses.

    By Jon Stolk on Feb 4, 2012

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