Abstract
Integrating the use of large datasets into our teaching provides critical and unique opportunities to build students' skills and conceptual knowledge. Here, we discuss the core components needed to develop effective activities based on large datasets, which align with the 5E learning cycle. Data-based activities should be structured around a relevant question, use authentic publicly accessible data, be scaffolded to include choice, and involve discussion of the results. It is important that the software that is used to manipulate, analyze and/or visualize the data is accessible for students. There are a range of strategies to reduce the barriers of working with large datasets through pre-organizing and pre-scripting code for analyses, using online cloud-based versions of software, and reducing opportunities for error in syntax. Resources exist for learning open-source software (e.g., Data Carpentry) as well as for support and professional development in teaching with large datasets (Project EDDIE).
| Original language | English (US) |
|---|---|
| Pages (from-to) | 466-472 |
| Number of pages | 7 |
| Journal | Biochemistry and Molecular Biology Education |
| Volume | 50 |
| Issue number | 5 |
| DOIs | |
| State | Published - Sep 1 2022 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2022 The Authors. Biochemistry and Molecular Biology Education published by Wiley Periodicals LLC on behalf of International Union of Biochemistry and Molecular Biology.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 4 Quality Education
Keywords
- RStudio
- big data
- professional development
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