As educators and researchers, we often enjoy enlivening classroom discussions by including examples of cutting-edge high-throughput (HT) technologies that propelled scientific discovery and created repositories of new information. We also call for the use of evidence-based teaching practices to engage students in ways that promote equity and learning. The complex datasets produced by HT approaches can open the doors to discovery of novel genes, drugs, and regulatory networks, so students need experience with the effective design, implementation, and analysis of HT research. Nevertheless, we miss opportunities to contextualize, define, and explain the potential and limitations of HT methods. One evidence-based approach is to engage students in realistic HT case studies. HT cases immerse students with messy data, asking them to critically consider data analysis, experimental design, ethical implications, and HT technologies.The NSF HITS (High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students) Research Coordination Network in Undergraduate Biology Education seeks to improve student quantitative skills and participation in HT discovery. Researchers and instructors in the network learn about case pedagogy, HT technologies, publicly available datasets, and computational tools. Leveraging this training and interdisciplinary teamwork, HITS participants then create and implement HT cases. Our initial case collection has been used in >15 different courses at a variety of institutions engaging >600 students in HT discovery. We share here our rationale for engaging students in HT science, our HT cases, and network model to encourage other life science educators to join us and further develop and integrate HT complex datasets into curricula.
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Using Messy, Authentic Data to Promote Data Literacy & Reveal the Nature of Science
Authentic, “messy data” contain variability that comes from many sources, such as natural variation in nature, chance occurrences during research, and human error. It is this messiness that both deters potential users of authentic data and gives data the power to create unique learning opportunities that reveal the nature of science itself. While the value of bringing contemporary research and messy data into the classroom is recognized, implementation can seem overwhelming. We discuss the importance of frequent interactions with messy data throughout K–16 science education as a mechanism for students to engage in the practices of science, such as visualizing, analyzing, and interpreting data. Next, we describe strategies to help facilitate the use of messy data in the classroom while building complexity over time. Finally, we outline one potential sequence of activities, with specific examples, to highlight how various activity types can be used to scaffold students' interactions with messy data.
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- NSF-PAR ID:
- 10205875
- Date Published:
- Journal Name:
- The American Biology Teacher
- Volume:
- 82
- Issue:
- 7
- ISSN:
- 0002-7685
- Page Range / eLocation ID:
- 439 to 446
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1525/abt.2020.82.7.439
