Using Project EDDIE modules in Data Science for Life Sciences

Derek Sollberger, University of California-Merced

About this Course

Data Science for Life Sciences

Laboratory Course

Introductory Undergraduate

Majors

60

students in the course

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This semester's adventures will take us through analytical and computational methods for statistical analysis of data. Descriptive statistics, graphical representations of data, correlation, regression, causation, experiment design, introductory probability, random variables, sampling distributions, inference, and significance.

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As aligned with the mission of the biological sciences department, Bio 18 meets the following PLOs:
1. (not covered)
2. An ability to develop and critique hypotheses and to design experiments, models, and/or calculations to address these hypotheses.
3. The ability to use appropriate instrumentation and computational tools to collect, analyze, and interpret data.
4. The ability to read, evaluate, interpret, and apply numerical and general scientific information.
5. (not covered)
6. An ability to communicate biological science topics in written, oral, and visual formats.
7. An understanding of the relationship of biological sciences to society.

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Bio 018 Syllabus (Microsoft Word 2007 (.docx) 5.8MB Jun1 22)

EDDIE Module(s) Adopted and/or Adapted

Hypoxia in Coastal Marine Ecosystems

This hypoxia case study was implemented toward the end of the semester in my Data Analysis for Life Sciences course. As such, I wanted this to simultaneously be an in-depth exploration and a practice run for how the students were then shifted to their own semester-end projects. In addition to modifying the materials to run in an R programming environment, I split the material by ecological themes—Day 1: physical factors, Day 2: chemical factors—to encourage synthesis of concepts.

Jump to: Course Context | Teaching Details | How It Went | Future Use

Relationship of EDDIE Module(s) to my Course

Implemented toward the end of the semester, students will be tackling most of the materials independently from the lecture slides with their peer groups in the form of in-class lab sessions. This environment promotes a lot of communication among the students.

Teaching Details

In addition to switching the computer processes from Excel to R, I have rearranged the materials to be more modular. Students encounter the introduction and definitions on their own in pre-lecture assignments. After the in-class labs with the coding and calculations, students synthesize their findings in brief writing tasks to finish the week.

How did the activity go?

At this point in the semester, the students handled the R programming well and were able to debug their errors among their peer groups. However, these sophomores had little interest in connecting the data and evidence with their general knowledge of biology. Perhaps spending more time on introducing concepts and definitions (such as the location of the Chesapeake Bay) could prove fruitful.

Perhaps due to the bulk of math and programming exercises in my course, students struggle with reading assignments and connecting the ideas with the data visualizations that we build in class.

Future Use

This instructor story and adaption materials were developed during a Project EDDIE Faculty Mentoring Network in partnership with QUBES in the Spring of 2022.

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I am looking forward to running this case study again in the Fall semester where, by the time I submit these adapted materials, I will have reordered the examples and exercises so that:

• Day 1 (physical causes): students encounter the temperature data on their own and hopefully think about global warming
• Day 2 (chemical causes): students encounter the nitrates data on their own and hopefully think about usage of fertilizers