PERC 2021 Abstract Detail Page
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| Abstract Title: | Investigating Students' Process of Data Cleaning |
|---|---|
| Abstract: | Inquiry-based physics labs often include a focus on collecting and analyzing scientific data. During the process of making sense of the data, students often engage in data cleaning, even if not explicitly instructed to. Data cleaning is the process of identifying and mitigating instances in the data that are misaligned with expectations or hypotheses of the experiment, often due to equipment or software bugs. Our research analyzes think-aloud interviews from thirteen students and observational data in a first-semester, undergraduate IPLS lab. Qualitative coding focused on students' decision-making processes while engaging in data cleaning in a think-aloud interview setting and in observation data of students working in the lab. Results show that students typically follow a process of noticing an anomaly, reasoning about its cause, and then either keeping or discarding the anomaly. However, nuances in this pattern differed in the lab environment as teaching and learning assistants had a significant impact on students' methods of data cleaning. Although determining the source of outliers in data is complicated, unpacking students' decision-making process is important for lab instructors to better support students in learning how to work with and interpret data. |
| Abstract Type: | Contributed Poster Presentation |
| Session Time: | Poster Session 1 Room C |
| Poster Number: | 1C-21 |
Author/Organizer Information | |
| Primary Contact: |
Adrian Adams Department of Educational Psychology, University of Utah Salt Lake City, UT 84112 |
| Co-Author(s) and Co-Presenter(s) |
Dr. Lauren Barth-Cohen, Department of Educational Psychology, University of Utah |
