Part of the QualMIP series, introduced here.

Today was our first "research meeting" in studio -- last week we wrapped up the technique-focused exercises and plunged full-steam-ahead into data collection, so this week was the first hurtling-back from that and was entirely driven by... what the group brought in. Sharing and co-analyzing qualitative data is a skill, and over the next few weeks we'll be practicing getting "better" at "this." I deliberately haven't defined what "better" is, nor what "this" is -- looking at the emergent definitions for those two terms will be part of our praxis for the data collection phase of QualMIP.

It's a messy process, and a self-revising one, and that's part of what makes these discussions so difficult; as the famous quote goes, "if we knew what we were doing, it wouldn't be called research." Everyone selected a subset of their data, fieldnotes, or memoes to copy and share. Artifacts analyzed this week included slide decks, photos of signs in the studied environment, and fieldnotes. Cesar and Emily were initially worried they wouldn't be able to gather data because of their transport limitations in getting off campus, but found that the internet provided them with a wealth of complex documents to analyze.

The discussion was difficult but good, and one of my thought is "wow, how am I going to scale this up for more students, when I can't be with every group?" I feel a tension between trying to be a good model vs. having students actively engage and drive. It's something I'll be watchful for as we go along, because I know it's not a binary opposition (I can do both at the same time!) but I'm not sure how to actualize it yet.

From my perspective, one temptation that came up within the group today is the tendency to present "too much" data for the group to engage with. (I kinda expected this.) Giving others context for analysis, scaffolding discussion, and choosing which pieces to select for sharing are all sub-skills that take practice. We... will practice them. I'm pretty sure this blog post will prompt me to build more scaffolding for this activity for the later, larger run of the class next time around (that's part of why I'm taking these notes now).

Another challenge is continuing to seek alignment between research question(s), dataset(s), and analysis methodolog(y/ies) as we journey through the mess. Technically, I should put paradigm in there as well, but last week's discussion seemed to indicate everyone wanted to pursue a mostly-interpretative paradigm this time around (as opposed to critical, postmodern, etc). In future iterations of this course, I'd like to play more with the different paradigms, but interpretative is as good a place as any to begin.

Next week's assignment is to continue converging on the data bounds (topic/site/type), research questions, analysis plans, and their alignment. The data collection schedule for the remaining two weeks should be crystal-clear, and the team will be bringing in another round of artifacts and memos and scaffolding for everyone to review "at home" so we have more time to look over and dive into one another's work. We'll also be revisiting the question of what "better" and "this" are in terms of getting "better" at "this" (whatever we're doing in our studio meetings). We'll be revisiting that question a lot.

That means that next week's studio time will largely be occupied by... my data. This wasn't planned from the beginning of the semester, but now that the team has gone through their first round of scaffolding others through engaging with their in-progress data and analysis... now it's my turn. Part of the motivation is to give us a brief look at what a more experienced researcher does (I've already told them I'm not the best at this yet, but I've at least had more practice), part of it is to offer my practice up for critique because I'm not perfect either and that's important to know, and part of it is to give us time to look over the project data "at home."

I've offered the team a choice among some of my projects (at various stages in the process of completion) and will be modeling being on the other end of the co-analysis/critique/review/etc. process next week, and then we'll step back and compare. I will not do it perfectly, and that's the point; I'm hoping they'll spot the weak spots I already know of, and also guessing they'll catch things I have not yet seen. I'm looking forward to that last one.