Course Syllabus

Below is all the information on the course.

Table of contents

  1. Computational Pedagogy for Biology Educators
    1. When: summer 2025
    2. Cost:
    3. Supplies needed:
    4. Location:
    5. Schedule:
      1. 1-Week Intensive (In-person only)
      2. Follow up meetings (Remote optional)
    6. Program Overview
      1. Program Goals
    7. Learning Objectives
    8. Program Structure
  2. Point of Contact
  3. Parking
  4. Example Material

Computational Pedagogy for Biology Educators

When: summer 2025

Initial session will be June 23rd, based on attendee availability.

Cost:

Completely Free.

Supplies needed:

A laptop and a lunch.

Location:

Room 360, Shields Library, UC Davis

Schedule:

1-Week Intensive (In-person only)

10 am to 3pm from Monday (the 23rd) to Friday (the 27th)

Follow up meetings (Remote optional)

Week day and time to be determined during the 1-week intensive

Program Overview

We will introduce a complete computational workflow for basic microbial genome analysis to attendees, scaled for use in a high school classroom. The workflow will introduce fundamental data analysis and processing techniques in support of answering questions about global microbial presence and absence in various environments.

Attendees will gain the necessary computational skills and pedagogical skills to prepare them to teach this workflow in their classrooms. The learning outcomes based on attending this workshop will be bolstered by a module collaboratively written and updated during the workshop.

This is intended to be an engaging, interactive, and generative learning experience at the intersection of pedagogy and hands-on digital exploration

Program Goals

  • Multiply the educational impact of each instructor attending this course.
  • Explore computational skills and pedagogy with a TA and Professorate cohort of instructors.
  • Teach a small cohort of high school biology teachers computational practices, methodologies, and pedagogy.
  • Facilitate discussions about differences in teaching styles and pedagogical skills across the instructors of this workshop.
  • Develop a well-rounded module to teach high school students computational literate skills for exploration of biological questions.

Learning Objectives

The members of this workshop will systematically explore computational space through active live-coding sessions. While the cohort is gaining the fundamental knowledge of computational practices, they will also learn how to teach the skills they are learning actively and effectively.

  • Fundamental Computational Skills
    • Effectively navigate and use local and remote computing platforms
    • Develop intermediate steps in a “hit to lead” pipeline, generating and refining hypotheses based on intermediate results
    • Learn and employ common data manipulations on large spreadsheets
    • Use and understand standard plotting and data visualization approaches
    • Facilitate exploratory data analysis and systematic step-wise analysis
    • Discuss patterns observed in data and develop cross-cutting analyses
    • Explore big data handling and approaches to allow application testing (subsetting and indexing the data)
    • Emphasize the importance of version control and open-source architecture

Program Structure

  • First week: Intensive, week-long training focusing on establishing the breadth of foundational skills we will discuss over the remaining summer sessions
    • The approximate schedule will take place Monday through Friday during a morning and afternoon 3-hour session.
    • Transition from petabyte search of genetic information overlap in the public sequence archive
    • To a geospatial metadata analysis of the initial search results
    • Update: the dates for this week have been set to June 23rd to June 27th.
  • Remaining summer: Weekly meetings discussing a focused, in-depth examination of concepts and coding paradigms, followed by co-working sessions. (Via zoom as necessary)

    A response to inquires on the nature of these follow-up meetings:

    I would like to clarify that we are planning to flexibly approach the problems, interests, and learning desires of the teacher cohort of this workshop. We will be focused not on a rote curriculum, but a dynamic, focused set of skills and scripts defined by the teachers. Our goal is that after attending this workshop, they can bring back to their classrooms, clubs, and campuses engaging ways to foster learning of logic and hypotheses, data science, coding skills with their students. We want them to be empowered to explore the things that interest them and their students in new ways.

  • Cohort size: 10-15 participants

  • Cohort members: Local area highschool biology teachers capable of commuting to University of Califonia, Davis for the week-long workshop.

  • Tools & Topics:
    • Primary language for analysis (R)
    • Bash scripting (Logical control flow loops, Sed, Awk, Grep)
    • Command line experience (Bash, R, Python)
    • Version Control (Git/GitHub) (SSH setup)
    • Workflow manager (Snakemake, Nextflow)
    • Environment Management (Conda, pixi)
  • Infrastructure
    • Understanding the local environment of the computer
    • Use of temporary high performance cluster or cloud computing resources
      • Digital Ocean ($10 per 10 students for 1 month)

Point of Contact

Colton Baumler, humble TA and graduate student

ccbaumler@ucdavis.edu

Parking

I am in communication with the parking authorities on campus and will provide registrants with a affilated parking over the summer. (Meaning you can park in more lots than visitor-only)

While the rate are available here. Dr Whitehead has offered to pay the parking costs for the cohort.

Also, these are perhaps the best locations to park to walk to the Shields library:

https://maps.app.goo.gl/YaLALNqk33RjpPSLA

https://maps.app.goo.gl/Xx4dEoSwEQsQYwsn9

https://maps.app.goo.gl/a3YrTvqRdijksg5e7

Example Material

computational pedagogy

  • https://hackmd.io/eJ4-gTTuT8CnEFQMyeozRA
  • https://hackmd.io/zGu3bvHeQXmjx_yj6GSvEg

https://ucdavisdatalab.github.io/adventures_in_data_science/

Discussion feedback and slides