Exploring Student Sensemaking When Engaging with Anomalous Data

In undergraduate research settings, students are likely to encounter anomalous data, that is, data that do not meet their expectations. Most of the research that directly or indirectly captures the role of anomalous data in research settings uses post-hoc reflective interviews or surveys. These data collection approaches focus on recall of past reasoning, rather than analyzing reasoning about anomalous data as it happens. We use the frameworks of sensemaking and epistemological resources to explore in-the-moment how students identify, generate ideas about the cause of, and determine what to do with anomalies. Students participated in think-aloud interviews where they interacted with anomalous data within larger datasets. Interviews were qualitatively analyzed to identify epistemological resources students used when interacting with anomalous data, and how students' reasoning influenced later choices with the data. Results found that students use a variety of resources as they sensemake about anomalous data and determine what to do with the anomalies. Furthermore, the explanation that students generate about the cause of an anomaly impacts whether the student chooses to keep, remove, recollect, or mitigate the anomalous data. Findings highlight the need to understand students' complex reasoning around anomalous data to support students in lab settings.