Years of experience, but i’m still learning…
Today i’ll talk about the Earth and environmental data science corps, a program that I designed with colleague, Jenny Palomino in 2019. This program which targeted students and faculty at schools that serve communities traditionally underrepresented in STEM was taught fully online.
For me, this program pulled together a solid 15 years of experience working on and creating different education programs combined with work that targeted under-served communities. It was funded by the National Science foundation in September 2019. A large proportion of the funds went to faculty and students at partner schools.
DISCLAIMER: This post represents my thoughts and experiences. Please know that work in this space of DEIA requires continued learning. I am continuing to learn every day.
About the Earth Data Science Corps
The Earth and Environmental Data Science Corps (EDSC) built capacity at schools that serve historically under-represented groups to teach and to learn open and reproducible earth and environmental data science skills.
The program had several goals
- Build capacity at schools serving historically underrepresented communities to teach earth and environmental data science to students.
- Train undergraduate students at these schools with technical data skills to support their career development.
- Increase awareness of job potential in the sciences if you have data science skills
Components of the program
- Weekly data skills workshops (training)
- Student-driven project based learning (internship style)
- Faculty support to add technical data skills elements to their courses
- Lots of evaluation surrounding both participant satisfaction with the program, sentiment around belonging to the science community and around learning.
IMPORTANT: Students and faculty were financially supported to participate in this program.
What is earth and environmental data science?
I define earth and environmental data science as skills at the intersection of science and data science including:
- technical data science skills
- the ability to work with different types of data
- communication skills
- collaboration skills
- and finally the ability to create workflows using open reproducible approaches.
Our Partner Institutions
In addition to CU Boulder which was where I was working at the time and thus our home institution, we had 3 other partner institutions:
- Oglala Lakota College
- United Tribes Technical College
- Metro State University
Support For Institutions Serving students Traditionally Underrepresented in STEM
The program was designed to support our partner institutions. While there is huge job market demand in the earth and environmental data science field, these smaller schools often do not have the resources to teach these skills.
Why don’t smaller schools offer earth and environmental data science programs?
There are many often complex reasons why schools like this do not always offer data science programs including:
- Lack of faculty and instructors with skills needed to teach earth and environmental data science.
- Faculty who might be interested in such skills are often don’t have resources to learn them.
- Lack of institutional funding to develop new programs and curriculum.
Students don’t see themselves as data scientists and thus don’t know to pursue such skills
One reason that smaller school often lack programs related to earth and environmental data science is particularly profound. Often students are unaware of career paths available to them if they gain these skills in part because they lack of role models that are similar to them. In short many tribal students are unaware of career opportunities because they don’t see their colleagues pursuing this path.
In this case there is low demand for courses that offer these data skills; school thus don’t invest in the curriculum. Talk about systemic issues and cyclic challenges.
These are just a few of the challenges that we grew to better understand as we worked with faculty and students. But that last one was the most profound for me to learn about. There are so many well-paying jobs for those with domain specific data science skills.
Building earth and environmental data science capacity at smaller schools
Our program goal was to build capacity at these schools to teach earth and environmental data intensive science. We also wanted to build awareness of job potential in this space.
While I think we did the former well, I am not convinced about the latter… more on that in a bit.
EDSC program components
Our EDSC program can be broader broken up into three components.
1. Provide training to faculty and students in in-job-market demand earth and
The idea behind our leading the initial training sessions was was that, while we were delivering it, it faculty could learn as we taught and slowly begin to teach components themselves in their courses.
2. Supporting faculty in adding earth and environmental data science curriculum to their courses
This involved faculty-specific mentoring sessions where they developed curriculum in small groups that they could teach in their programs.
3. Build career awareness
The third activity involved actually career awareness webinars. The idea was that we could connect students to people in industry and other students who were early career and pursuing earth and environmental data science related jobs. We quickly learned that the impact of these activities wasn’t as powerful as we imagined. Why?
Because our panelists often had PhDs. Our panelists came from very different backgrounds. It was difficult for our students to connect with those in the webinars. I’d design this much differently if I were to do it again. But i’ll save this for another blog post…
Publishing lesson content as open education resources
All of the content used in the program was either already on or published on an open education portal that I spent a few years working on to support our professional graduate program: earthdatascience.org. This portal has has hundreds of lessons available for anyone to use in any course. The idea was that if the teaching and learning content was online, anyone including faculty from our partner schools, could use the content in their courses.
Compute environment - JupyterHub
We originally setup a JupyterHub for students to work. A JupyterHub is an online cloud based environment that provides Jupyter notebooks and memory and processing resources that you allocate for students to use. The benefit of this was that it removed the barrier of a student needing to have a particular type of computer, and needing to install software on that computer. In fact the Jupyter hub works on a tablet or any laptop with internet access.
We had been using this environment for a few years to support our professional graduate program and it worked well there. However I quickly realized this was not a good long term solution for this program.
Well we were maintaining it. And there was no way for the schools to take the environment home with them to use in their curruclum. (they were dependent on us). This platform certainly did not build capacity to teach at other schools!
In the end we moved to Google Colab. I’ll share the details of compute environments for teaching - specifically as it relates to supporting smaller schools in another blog.
The program as designed in the original proposal was greatly modified during implementation.
- First, the program began in April 2020. The COVID 19 pandemic quickly pushed us into a fully online environment. We went to a go to tool - Zoom to teach content (more on why this wasn’t ideal in a separate blog).
- Second, students were heavily stressed out given the pandemic which hit under-served groups even harder than others.
We modified the program to be a summer program and quickly learned that Zoom is not an ideal way to mimic a classic classroom teaching environment. These students needed to work in small groups and to have 1:1 interactions with faculty to feel supported. We lost a few students in the first year because of this.
In years two and three we adjusted the program in many ways. Lessons learned are discussed below.
Core Lessons Learned From This Program
Working with under-served institutions, students and faculty requires care, time and consideration. We learned a tremendous amount from the faculty and students who participated in this program.
1. Design & Build the Program With the Participants From the Beginning
I made the mistake of starting work on the proposal only weeks before it was due. It was my first NSF proposal and I didn’t account for the time needed to truly build relationships with our partners and include them in the core design. Because of this, I had to redesign the program once we began actual work because it was important to us that the faculty and students felt invested in the program and gained a large return from it.
Lesson learned: Don’t try to design a training program without involving the students and faculty. While you might have considerable expertise in teaching, program design, timing of activities, how content is delivered may all need to be adjusted depending upon your target audience.
2. Allow for considerable amount of extra time when working with these communities
This time is critical to building trust with these communities. Spend time with students individually and in small groups. Get to know the issues they are dealing with that may get in the way of learning and participation. Accommodate them in every way possible. This all takes time to do well.
3. Be Flexible to Adapt when things don’t work
I am a strong believer in adaptive program design and development. What this means is develop surveys for participants and collect data often and frequently. Then adapt and change based upon their needs and feedback. Finally let them know when you are making changes and why (because of their helpful feedback). This not only improves your program but also helps participants feel more a part of the entire process rather than feeling “left behind”.
4. Mentorship and Check-ins Are Critical
Set up a support system for students is critical for project success. This support system could be multi-tier. In our case we invited students from previous years of the program to mentor in following years. While student can look up to their instructors, it’s even more powerful to learn from and receive support from their peers - particularly if they identify with the background of their peers.
Also ensure that student have structure. Schedule check-ins with faculty and instructors regularly.
5. Working Online is Hard - Zoom Doesn’t Cut It
When I talk with others about programs that have DEIA goals, the immediate response is often “oh, online won’t work”.
While I do think there is a place for in person interactions. And they are important. I think that immediate response to online learning does not take into consideration, one thing:
teaching online requires a redesign of your curriculum and how you teach.
I will spend more time talking about this in another blog. In our case, we had to move our entire program online with a few weeks notice due to the pandemic. So we learned the hardway what worked and what didn’t work.
However, it helped us that in our professional program, I was already teaching in a hybrid in person / online setting. Thus I had adapted a lot of my content to support online participants.
In year two of the program we moved from Zoom to an interactive online platform called Spatial Chat. I had started using this platform in my Earth Data Analytics professional program courses and found it to be a great way to recreate the classroom environment where ou can check in with students individually or small groups. The platform also allows student to work together dynamically during class and share screens.
We all learned a lot during the beginning of the pandemic. But spatial chat or something like it, is one tool i’ll take with me and use for the forseeable future.
6. Make Sure You Have A (Relatively) Fail-Proof Compute Environment
In the early days of teaching earth and environmental data science in our professional graduate program, I created a Jupyter Hub (Many thanks to Tim Head and Karen Cranston for help with that effort!) that we hosted on Google CLoud for students. What I loved about this platform is I was able to skip over troubleshooting student issue with their compute environment during class. The platform just worked. However, the problem with this environment in a non-paid environment such as our work with the faculty at these smaller schools, is they are left without a compute environment when the program ends.
Because of this we moved all of our programs to Google Collab which allows students and faculty to take all of the resources with them. Since we were using Python, that meant Jupyter Notebooks that were stored in participants Google Drive (now Google Workspace). This is a huge win for everyone - especially faculty who wish to adapt these content into their own courses.
Wrapping This Up
So the above is in a nut shell some things I learned from this program. While I pointed out a lot of the pain points above, we had some great successes too. Several of our tribal students went on to Graduate school and were incredible mentors and advocates during their time in and after the program. I will never forget my time designing and launching this program. All of the amazing people I got to know and work with and how much I learned from them all.
Some of the faculty and students that we worked with on this project are listed below (because I left CU and was unable to work on the project as a lead PI after the departure due to challenges with CU policy, some names are missing from this list as leads at each school changed!! These are people whom I worked closely with while there):
- Elisha Yellow Thunder (A true leader and rock star, Oglala Lakota College)
- Jim Sanovia (Oglala Lakota College)
- David Parr (Metro State University)
- Jeremy Guinn (United Tribes Technical College - now at NSF)
- Emily Biggane (United Tribes Technical College),
- Nate Quarderer (CU Boulder, who ran and taught a significant portion of the program in years 2 and 3 ),
- Bill Travis (CU Boulder faculty representative) and
- All of Earth Lab who created space for the proposal to be written and supported.