I recently had the chance to listen to a talk from Dr. Claire Meaders about her intervention science work in scaffolding better and more accessible pathways into biology, a colleague-turned-friend who teaches at UC San Diego.
Claire Meaders is remarkable in a couple of ways that are both illustrated by this story she told at the beginning of her talk: she flew halfway around the world from UCSD on a recent vacation and still ran into a former student who came up, excitedly, to say hi. The couple of remarkable things I think about are breadth and depth. Breadth: her introductory biology courses are so immense, take in so many students, that out of all the teachers at UCSD I know she's the one with the odds of running into students in other countries. Depth: she's such a caring teacher that those students still feel like saying hi.
Throughout the talk, we learnt about several of the ways she's approached creating interventions that help students imagine themselves as scientists. We should care about this for a lot of reasons, including but not limited to the fact that there aren't necessarily enough scientists in the world, and not everyone gets an equal shot at becoming one. I knew all that before but what really sat with me about Dr. Claire's work here is a focus on context as intervention. By asking when do people hear key messages, you can shift your attention to maximizing the limited space of communication we have with people. One example that sat with me was a large-scale campus audit of whether or not faculty mentioned other campus resources during their office hours with students -- and the large beneficial impact of reminding faculty to do this. It's such a lightweight and relatively simple thing to change, yet for students who are information impoverished (which is many newcomers to both a university campus or a software team), someone taking the step to close this gap is not just about the content of the information (hey, you didn't know this resource existed and now you do) but also about when you get it. Time thinking is a very useful angle on interventions in my opinion; it's the way you have to think when you're doing applied work. Maybe we have good content or good information but we haven't thought enough about where and when to deliver it. For example, what happens on the first day of class. There are salient effects to our introduction to environments, but not everyone capitalizes on this as a moment to set norms and provide key messages. I thought immediately about my own work and questions I've asked engineers about onboarding to unfamiliar codebases, and the expectation that folks will "just figure it out" in a dynamic, constantly changing environment with secret rules that were built on and deal with other people's secret rules. Dr. Claire's studied this across multiple "first days", and the variance in how instructors use this time is an important opportunity space. Another interesting example of Dr. Claire's work is around collaborative two-stage exams, which benefitted learners and created a very positive experience around a not particularly positive event. This was such a neat model, from classroom learning, that I think also could apply more broadly. Despite all of the furor around the dangers of measuring individual developer productivity I have heard very little in software conversations about how to reward collaborative performance even though group assessment and collaborative measurement models have been a rich field in achievement research for a long time. I loved this work thinking about and then measuring what it really looks like to take an assessment and create a group-level, multistage form of it, and I think that as we work to understand our teams and our own work we could learn a lot from paying attention to better and more creative assessment practices around this.
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