Bringing computational data sciences to your undergraduate ecology classroom ESA 2019 BEDENet Workshop
Synopsis
The biological and environmental sciences have been rapidly and fundamentally reshaped by recent technological advances, including increased computational power, sensor technologies, publicly available software and data, and Internet connectivity. These advances, together with the demands that we provide our students with technical skills to navigate data and technology in the 21st century, necessitate the integration of computational data sciences into our undergraduate and graduate classrooms. However, many instructors do not feel qualified or prepared to teach such materials, limiting the usefulness of already developed computational course or lab modules. This “train the teachers” workshop will include both technical training for fundamental data science skills, including R, Github, and Markdown, as well as pedagogical training for communicating those skills in the classroom and lab. Working through data science examples in-real time, participants will experience the material as a learner and gain strategies toward including and being able to teach these skills in their own courses. While our focus will primarily be on the undergraduate classroom, content will likely be relevant for many mixed-level or graduate classrooms as well. The goal is to equip instructors with technical tools and instructional strategies that they can then confidently customize to their own curricular goals and institutional needs.
Organizers
- Dr. Sarah R. Supp
- Dr. Andrew J. Kerkhoff
- Dr. Matthew E. Aiello-Lammens
- Dr. Naupaka Zimmerman
Logistics
- Date: Sunday, August 11, 2019
- Time: 8:00 AM to 5:00 PM (there will be a 1-hour lunch break)
- Place: Kentucky International Convention Center - Room L007
Schedule
Times are approximate.
Settle in and meet one another (8:00 AM to 8:30 AM)
- Participant introductions
Introduction to the workshop
- Brief Introduction to BEDE Network: Rationale and Goals
- Context: The need for data science education in Biology and Environmental Science
- Method: Backwards Design
- Rationale: Data science, Inclusivity, and Empowerment
Data science for undergraduate biology and environmental science (9:00 AM to 9:30 AM)
- Activity: What is Data Science?
- Short Presentation: Data Science Fundamentals
Methods and tools for teaching data science in your classroom (9:30 AM to 12:00 Noon)
- Activity: Speed Data Science
- Lesson 1: Introduction to R/RStudio for Instructors
- Lesson 2: Using Markdown/RMarkdown in the classroom
Lunch Break (12:00 Noon to 1:00 PM)
Development of data science lessons (1:00 PM to 2:30 PM)
- Our materials are now your materials too
- Lesson 3: Basics of GitHub for reproducibility and sharing
Hands-on lesson development (2:30 PM to 4:30 PM)
- Start developing a lesson of your own
- Individually or in small groups - use the tools you want to practice
Wrap-up (4:30 PM to 5:00 PM)
Additional Resources
- DataOne best practices https://www.dataone.org/best-practices
- Lessons learned from teaching Data Science https://www.dataschool.io/teaching-data-science/
- A guide to teaching data science https://arxiv.org/ftp/arxiv/papers/1612/1612.07140.pdf
- 10 steps to teaching data science well https://towardsdatascience.com/10-steps-to-teaching-data-science-well-322966188323