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Abstract There is a clear demand for quantitative literacy in the life sciences, necessitating competent instructors in higher education. However, not all instructors are versed in data science skills or research-based teaching practices. We surveyed biological and environmental science instructors (n = 106) about the teaching of data science in higher education, identifying instructor needs and illuminating barriers to instruction. Our results indicate that instructors use, teach, and view data management, analysis, and visualization as important data science skills. Coding, modeling, and reproducibility were less valued by the instructors, although this differed according to institution type and career stage. The greatest barriers were instructor and student background and space in the curriculum. The instructors were most interested in training on how to teach coding and data analysis. Our study provides an important window into how data science is taught in higher education biology programs and how we can best move forward to empower instructors across disciplines.
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This content will become publicly available on March 7, 2026
Reproducibility in the Classroom
Difficulties in reproducing results from scientific studies have lately been referred to as a reproducibility crisis. Scientific practice depends heavily on scientific training. What gets taught in the classroom is often practiced in labs, fieldwork, and data analysis. The importance of reproducibility in the classroom has gained momentum in statistics education in recent years. In this article, we review the existing literature on reproducibility education. We delve into the relationship between computing tools and reproducibility through visiting historical developments in this area. We share examples for teaching reproducibility and reproducible teaching while discussing the pedagogical opportunities created by these examples as well as challenges that the instructors should be aware of. We detail the use of teaching reproducibility and reproducible teaching practices in an introductory data science course. Lastly, we provide recommendations on reproducibility education for instructors, administrators, and other members of the scientific community.
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- Award ID(s):
- 2123366
- PAR ID:
- 10644837
- Publisher / Repository:
- Reproducibility Education
- Date Published:
- Journal Name:
- Annual Review of Statistics and Its Application
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2326-8298
- Page Range / eLocation ID:
- 89 to 105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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