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Short abstract:

As ML & AI encroach into education and research practices in the natural sciences, near-complete digitization and automation in analytical processes serve to distance us from alternative ways of knowing. We explore teaching interventions and foster situated, and embodied thinking in data science.

Long abstract:

As machine learning and artificial intelligence encroach further into education and research practices in the natural sciences, near-complete digitization and automation in analytical processes serve to distance us from alternative ways of knowing. For example, students no longer peer through the eyepiece of a telescope to ponder the night sky, they instead munge “downstream data'' processed and decontextualized by computers. Data pipelines replace complex sensorial field notes and train students to be consumers of data products rather than foragers for natural phenomena. During this talk we ask, how can educators collaborate across disciplines to design and implement a “green crossing,” between the power of data sets and the pleasures (or pain points) of the field? As educators, how do we privilege student learning over machine learning?

During this multimedia-rich presentation we explore two teaching interventions meant to foster divergent, situated, and embodied thinking in data science students. We begin our talk with a story of fieldwork unfolding on a small island in the Atlantic ocean. Here we show how real-world observations of wildlife can lead not only to unpredictable research findings, but can also spark unanticipated artistic endeavors. Then, we visit the forested edges of a heavily-used freeway on the west coast of the US, situating students of machine learning in the landscapes of wildlife crossings. What, we ask them to consider, can an embodied researcher accomplish that a fieldcam alone cannot? Why should we study the world through our full sensory apparatus?