I’m wrapping up my doctoral dissertation and have been presenting the findings of my study (here and here). But as I reflect on my graduate school experience, I’m realizing that the most important thing I learned in graduate school wasn’t quantitative methods, educational theory, or empirical literature in learning analytics. It was how I produce my best work. And what I learned runs counter to my notions of productivity and efficiency.
I found that my best ideas don’t come immediately, but they take time to cultivate, mature, and develop. Once launched, they seem to grow and mature on their own. To write something good (e.g. original and interesting) I needed to pre-write, brainstorm, and outline my chapters. But once I started writing, I deviated from that outline. It turned out to be less a script than a warmup exercise. The process seemed hopelessly inefficient at times, especially compared to cohort-mates who would write everything at one shot. But once I started writing, the words would flow. And I even found the writing process a little bit fun.
This approach was encouraged by my graduate school program. One of our lead professors (Dr. Heckman) used his trademark New York style to give compelling terms for this process — encouraging us to continue “noodling” about our ideas and “thinking about thinking”. I have to admit that at the time, as a part-time student with a full-time job and family responsibilities I just wanted to get to my end goal. But about midway through my dissertation the benefits of this process became clear.
This realization made me wonder … about how we work, write, and analyze data for decision-making outside of the protected space of academic research programs.
Rebecca Solnit speaks to this process in “A Field Guide to Getting Lost.” She traces the etymology of the word “lost” to the Old Norse word los, which means “the disbanding of an army … suggests soldiers falling out of formation to go home, a truce with the wide world.” (P.7). She distinguishes getting lost — operating without a mental map or predictive sense of where you are, from losing something — not being able to locate something that you already know. I love to hike. What I love best about getting into the wild is precisely this aspect of getting lost in the complexity of nature’s beauty. I’ve been trying to get lost lately in suburbia and my work life to discover more of what’s around me.
With low staffing and reduced funding, we’ve got way more questions and formulated hypotheses than we have time to investigate. But with big data (and even small data), we have the opportunity to get lost and see something new. I revised my research questions three times, which might be considered taboo. But I was able to ask better questions once I get familiar with the data. Beyond refuting or validating a hypothesis, we can discover something that we didn’t know before; discover questions that we didn’t have. This process takes more time, thought, and energy. It is also less “productive” in the tasks we check off. But the results have incomparably greater potential.
As we develop Learning Analytics for decision making, I hope we’ll give ourselves a chance to get lost in the data, to disband our conceptual armies and discover new patterns to help us improve student success.
OK, now I’d better get to writing that summary and checking off the next to-do item.
Thanks to Phil Hill (@PhilOnEdTech), Michael Feldstein (@mfeldstein67) and Kimberley Hayworth for their feedback and comments on this post.