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Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training
Nathan C Emery1, Erika Crispo2, Sarah R Supp3
1Michigan State University, East Lansing, Michigan, United States.
Bioscience
|December 6, 2021
Summary
Life science instructors value data management, analysis, and visualization skills. Key barriers to teaching data science include instructor/student backgrounds and curriculum space, with a strong interest in training for coding and data analysis instruction.
Area of Science:
- Life sciences education
- Higher education
- Quantitative literacy
Background:
- Growing demand for quantitative literacy in life sciences.
- Many higher education instructors lack data science expertise and research-based teaching methods.
- Need to understand current data science instruction practices and challenges.
Purpose of the Study:
- To survey biological and environmental science instructors on data science teaching in higher education.
- To identify instructor needs and barriers related to data science instruction.
- To inform strategies for empowering instructors in data science across disciplines.
Main Methods:
- Survey of 106 biological and environmental science instructors.
- Assessed perceptions and practices regarding data management, analysis, visualization, coding, modeling, and reproducibility.
- Investigated barriers to data science instruction and preferred training areas.
Main Results:
- Instructors widely use, teach, and value data management, analysis, and visualization.
- Coding, modeling, and reproducibility were less emphasized, varying by institution type and career stage.
- Primary barriers included instructor/student backgrounds and curriculum limitations; training interest focused on teaching coding and data analysis.
Conclusions:
- Data science instruction in higher education biology programs is evolving.
- Addressing instructor and student backgrounds and curriculum constraints is crucial.
- Targeted training in coding and data analysis can empower instructors to enhance quantitative literacy.
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