How we run an inclusive & online coding program for biology and chem undergrads in 2020 

7 May

By: Nicole Adelstein, Pleuni Pennings, Rori Rohlfs

Coding summer program (BDSP) in 2018, when students were in the same room for 8 hours a week.

In 2018 this team (led by Chinomnso Okorie) met in the “yellow room” for 8 hours a week to learn R.  

We have been running combined coding/research summer programs for several years, with a  focus on undergraduate students, women, and students from historically underrepresented racial and ethnic groups. This summer, we will run our 9-week program as an online program. We think that others may be interested in doing this too, so we’ll share here how we plan to  do it. 

Some of the information below will also be published as a “ten rules paper” in Plos Computational Biology*, but we wanted to share this sooner and focus on doing things online vs in person. 

TL; DR version

  1. Have students work in teams of 4 or 5, for 2 hours per day, 4 days a week. Learning to code should be done part-time, even if your program is full time. 
  2. Use near-peer mentors to facilitate the team meetings (not to teach, but to facilitate). 
  3. Use existing online courses – we’ll share a few that we like. Don’t try to make your own curriculum last minute. There are good online courses available. 
  4. Give the students a simple (repeat: simple!) research project to work on together. 

1. Have students work in teams for two hours a day – with pre-set times. 

Learning to code is stressful and tiring. Even though many students may not have jobs this summer – it doesn’t mean that they can code for 8 hours a day. First, because they have other stuff to do (like taking care of family members) and second because there’s a limit to how long you can be an effective learner. 

Our program is 10 hours per week (8 hours of coding, 2 hours of “all-hands” meeting). We make it clear that no work is expected outside of these hours. For example, a team may meet from 10am to 12pm four days a week for coding. 

Check-ins, quiet working, shared problem solving. 

During the coding hours, the near-peer mentor is always present (on Zoom, of course!) and facilitates the meeting. The very first day should be all about introductions and expectations. After that, we suggest that every day, there is time for check-ins (everybody shares how they are doing, what they’re excited about or struggling with, or what music they’re listening to), quiet working (mute all microphones, set a timer, everybody works on the online class by themselves) and shared problem solving (for example, let’s talk about the assignment X from the online class). One of the mentors last year was successful with starting every meeting with a guided meditation. 

Each team has a faculty mentor in our program (this could be a postdoc or faculty member). Once a week, the faculty mentor joins the meeting for about 1 hour. This hour could consist of introductions / check-ins, a short presentation or story by the faculty mentor, and the opportunity for the team to ask questions. It’s great if the near-peer mentor and the team prepare questions beforehand. 

1B. Add a non-coding meeting (if you can/want)

In addition to the 8 coding hours per week, our students also meet for 2 hours per week in an “all hands meeting”. Such an all-hands meeting is not absolutely necessary, but if you have the bandwidth, it may be nice to meet once a week to do something other than coding. Maybe to read a paper together or meet with someone online (an alum who is now somewhere else? A faculty member or grad student?). 

If your program is full time (like an REU program), we suggest to still only do about 8-15 hours of coding per week. Fill up the rest with more standard things such as lectures, reading etc (and don’t make anyone do Zoom 40 hours a week!). If students are enjoying themselves with coding and getting more confident, they may do more coding by themselves, but in our program it is not the expectation. 

2. Mentors and teams are key 

When working alone, we’ve often seen students get stuck on technical problems, leaving many feeling lost and inadequate and wanting to discontinue learning this new skill. Working in a mentored team, however, students have access to immediate support from their peers and mentor. This helps them learn technical skills more efficiently, develop relationships with each other, and cultivate a shared sense of belonging in computational research (Kephart et al. 2008). We recommend that each participant in a coding summer program be assigned to a team of 4 to 5 students with similar technical skill levels led by a near-peer mentor. 

Mentors in our program are typically a year or two ahead of participants but belong to similar demographic groups and come from similar academic backgrounds. The mentor facilitates the meetings and leads the team in learning skills and applying them to a research question (without doing the work themselves). 

Each team also has a faculty advisor, who comes up with a research project that is likely to be completed in the available time and that is of interest to the students (Harackiewicz et al. 2008). The faculty advisor meets with the whole team at least once per week to guide learning and research. Of note, acting as a mentor improves students’ retention and success in STEM (Trujillo et al. 2015) therefore, this setup benefits mentors as well as mentees. 

2B. Who can be mentors? 

Over the years, we have found that near-peer mentors are incredibly useful for a number of reasons including 1) student participants are more likely to ask for help from a near-peer mentor than from a faculty advisor, 2) near-peer mentors serve as role models, giving participants an idea of what they can aim for in the next year or two, and 3) the use of mentors allows the program to serve many more participants than it could if it relied on a few time-pressed faculty advisors. Our selection criteria for mentors include essential knowledge (for example, the mentor for a team doing an advanced chemistry research project should have taken physical chemistry), mentoring experience or potential, logistical availability, and having a similar demographic background as the participants. Mentors don’t need experience with the specific coding language or research topic they will work on with their team. Rather than being the expert in the room, they are expected to help team members work together to find solutions or formulate questions for the faculty advisor. 

Mentors are crucial for the success of the program and need to be paid well for their work. Each week of the program, we pay our mentors a competitive wage for 8 contact hours with their team, a 2-hour all hands lunch meeting, a 2-hour mentor meeting, and 3-4 additional hours to account for preparation. However, we realize that this summer, things may be different for many! You may find that PhD students or Master’s students who can not work in the lab (but are still paid / on a fellowship) could be excellent near-peer mentors. Just make sure that the mentors know that this is a real commitment that will eat up a significant chunk of time each week. 

3. Identify an appropriate online course for each team

We have found that when learning basic coding skills, interactive online classes to learn computer programming (for example, from Datacamp, Udacity or Coursera) motivate and engage students better than books or online texts. Yet, when working individually, most students – especially beginners and historically underrepresented students – don’t finish online classes (Ihsen et al. 2013; Jordan 2015). As a solution, we have found that in teams, where students can work together and support each other, they learn a great deal from an online class. 

Each team’s faculty advisor picks a free, clearly structured online class with videos and assignments to teach participants coding skills. We have had good experiences with Udacity’s Exploratory Data Analysis course because this class is suitable for beginners. It does a good job motivating students to think about data and learn R. In early team meetings, participants spend time quietly working on the online class with their headphones on, followed by a team discussion or collaborative problem-solving session. If students encounter difficulty with any of the material, mentors may develop mini-lectures or create their own exercises to facilitate learning. Note, the students’ goal is not necessarily to finish the online course, but to learn enough to perform their research project. 

3B. Suggested classes:

Udacity Exploratory Data Analysis with R https://www.udacity.com/course/data-analysis-with-r–ud651

CodeHS https://codehs.com/ (the faculty mentor or the near-peer-mentor needs to create a section on Code HS, we use the introduction to python (rainforest).  

Coursera https://www.coursera.org/learn/r-programming (this one is a tip from our UCSF colleague Dr Kala Mehta)

4. Assign each team a simple and engaging research project 

Learning to code without a specific application in mind can feel boring and irrelevant, sometimes leading students to abandon the effort. In our summer program, teams carry out a research project to motivate them to learn coding skills, improve their sense of belonging in science (Jones, Barlow, and Villarejo 2010) and cultivate their team work and time/project management skills. Faculty advisors assign each team a research project early in the program. These projects should answer real questions so that participants feel their work is valuable (Woodin, Carter, and Fletcher 2017). The projects should also be relatively simple. Small and self contained projects that can be completed within a three week time frame are ideal to ensure completion and make participants feel that their efforts have been successful. For example, past research projects in our program, which reflect the interests of faculty advisors and the students, include writing computer simulations to model the evolution of gene expression, analyzing bee observations from a large citizen science project, examining trends in google search term data with respect to teen birth outcomes, and building an app for finding parking spots on or near campus. 

For 2020, we’d like to encourage you to pick a project that appears extremely simple if you normally use R or Python to make your plots / do stats, but that would be quite challenging if you’re new to coding. We also suggest that – unless the students are already quite advanced – you don’t give them a project that you want to publish on quickly. Nobody needs more pressure this summer.  

Here are some suggestions for simple research projects

  1. Let students plot the number of COVID19 cases in their county over time using R. Let them plot the number of cases in 5 different counties on the same figure. Add an arrow for when a stay-at-home order was implemented or terminated. Easy to download data are here: https://github.com/nytimes/covid-19-data 
  2. Let students keep track of how many steps they take each day for 10 days using their phone or watch. Let them plot the number of steps per day using R. Let them add a line for the mean. Collect data from 6 people and create a pdf with 6 plots in different colors. 
  3. If you have any data from your lab, let the students plot those data. Try making 4 different plots with the same data (scatter, box, histogram, etc). 
  4. Let students recreate an existing plot from a publication when the data are available. 
  5. Let students analyze (anonymized) data from your class. How strong is the correlation between midterm grades and final exam grades? Do students who hand in homework regularly do better on the test? 

* reference: Pleuni Pennings, Mayra M. Banuelos, Francisca L. Catalan, Victoria R. Caudill, Bozhidar Chakalov, Selena Hernandez, Jeanice Jones, Chinomnso Okorie, Sepideh Modrek, Rori Rohlfs, Nicole Adelstein Ten simple rules for an inclusive summer coding program for non-CS undergraduates, accepted for publication in Plos Computational Biology.

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