Interview: Technology as a tool in teaching quantitative biology at the secondary and undergraduate levels
Interview with Shelby Scott and Miranda Chen of the University of Tennessee Knoxville
Shelby Scott and Miranda Chen recently published the article Technology as a tool in teaching quantitative biology at the secondary and undergraduate levels. You can find the paper here. In this interview, the authors describe what they see as the next steps for integrating quantitative methods into biological curriculum and tons of online resources to get you started. This information will help anyone involved in education, from kindergartners to graduate students! You can listen to the audio here!
If you had to pick, which quantitative method would you say is the most important for high schoolers or undergraduates to be exposed to? (Ie, bioinformatics, programming, math biology, biostatistics, data visualization, big data, etc).
M. Chen: Big Data and Data Visualization—like in the paper I talk about the post-secondary RNA-seq data (Markarevich et al. 2015) or using Breeding Bird Survey (BBS) data to practice statistics, data visualization, etc. MathBench in the K-16 world have modules for data visualization, and helps with data interpretation. Data Nuggets (K-12) too!
Data Nuggets are free classroom activities, co-designed by scientists and teachers. When using Data Nuggets students are provided with the details of authentic science research projects, and then get to work through an activity that gives them practice looking for patterns and developing explanations about natural phenomena using the scientific data from the study. The goals of the Data Nuggets project are to (1) help scientists increase the broader impacts of their research by sharing their “science story” and data with the public, and (2) to engage students in the practices of science through an innovative approach that combines scientific content from authentic research with key concepts in quantitative reasoning.
S. Scott: I think it differs for high schoolers and undergraduates. For high schoolers, I think it is important for them to be exposed to mathematical biology. Connecting two historically separate disciplines helps them to think critically and to be able to infuse creativity into academics. We combine social studies and English regularly in high school curriculum, why do we not more regularly and rigorously do the same with mathematics and science?
At the undergraduate level, an exposure to computer programming is most important. In the current workforce, having coding experience makes an employee far more valuable. If they have a background in programming, then their options in the working world open up. And this, combined with high school exposure to mathematical biology, makes students well rounded and prepared to move into positions both within and outside of STEM.
Do you have any suggestions for how scientists can help combat the secondary and post-secondary educational problems you mention in the paper?
M. Chen: Some of the challenges: buy-in of administrators and students, monetary costs, lack of knowledge of these resources. How to combat barriers:
- Be up for challenge and rally people around you, especially the administrators if possible!
- Convince administrators that this is important—show them the calls to actions and the evidence that it works using these tools (though we need more instruments that are vetted to measure impact).
- Share the resources you know about with instructors.
- Find support—QUBES’ Faculty Mentoring Networks (FMNs)
Resources from Mathematical Association of America (MAA), the Mathematical Biosciences Institutes (MBI), the National Institute for Mathematical and Biological Synthesis (NIMB),the National Computational Science Institute (NCSI), the National Institute for Mathematical and Biological Synthesis (NIMBioS), and QUBES.
S. Scott: For individual scientists at the secondary level, outreach and effective science communication are the best ways to address some of the educational problems. Graduate students are especially well equipped to go into high school classrooms and give short lessons on any of the topics listed in the above section. This also helps high school students to become more aware of potential future careers. Most students think that if they are good in STEM fields, they will have to be in the health professions. By showing students what a scientist looks like, it will allow them to be more comfortable considering science as a lifelong career and growing their passion for pursuing it.
For individual scientists at the post-secondary level, it is most important to incorporate some of the topics into class curriculum. As an example, rather than having students point and click to find the “line of best fit” in a regression for a lab, it is important to discuss why we believe a linear model is best, the importance of a p-value, and the assumptions made using normal/linear models. It is also important to teach students to critically and appropriately read scientific papers.
This is where effective science communication comes into play. In many cases, scientists write papers that are inaccessible to undergraduates or secondary students due to content. And that is very necessary due to the complexity of our work. But if there is more acceptance for writing blog posts or creating quick videos that accompany scientific papers and reduce the topics presented to easier to understand chunks, then educators can use these to give their students access to what they read or write.
Finally, it is important to humanize what it means to be a scientist or a professor to students. Talk about your experiences, find promising students and mentor them. Make science seem more reasonable in the way of, “if I can do it, so can you.”
At the broader university and foundation level, there needs to be more funding towards these programs. Universities should fund more summer research experiences in quantitative methods, invest in computational tools and software licenses, and hire more quantitative-minded faculty members. Foundations should make donations to secondary schools with the request that the funds be used to improve quantitative education at the secondary level. And they should not be limited to upper-middle class and middle-class schools, they should funnel resources into lower income areas to improve diversity (racial, ethnic, gender, class, and experiential) in the STEM fields.
What are some teaching tools you would like to see available to educators? Are there any particularly good ones you would suggest to others?
- QUBES—faculty mentoring networks, building community, access to a slew of free tools for teachers.
- Data Nuggets (MSU)
- Book/Journal Clubs
- Support for changes in quantitative biology education.
S. Scott: There are a whole lot of tools out there at the undergraduate level and the main issue is educators not knowing that they are out there and not knowing how to access them. That comes down to publicity and sharing. If there are one or two passionate quantitative individuals in a university, it is easier for the information to spread.
At the secondary level, there are not many resources. Those that are out there are either incredibly specific (write-ups on how high schoolers were brought into a lab) or far too general (an entire curriculum). There is a need for short, accessible lessons that can be tacked on to the already existing curriculum at the secondary level. For example, if a biology teacher is on a biodiversity unit, have a 15-minute activity discussing and exploring the Allee effect or the quantitative methods associated with genetic bottlenecking. Similarly, if a math teacher is talking about difference equations, make sure there is at least one biological example.
In terms of specific resources, I am biased towards QUBES Hub because of some of my network connections. It has set resources that do allow educators to pick and choose certain lessons, but there is also a really cool community aspect to it. The discussion boards allow posters and users to communicate about the pitfalls of a tool and some of the ways it could be improved. Then there are the faculty mentoring networks, where small groups of faculty members can get together and discuss what they think are the best ways to tackle teaching certain courses or developing those courses.
What got you interested in this aspect of biology education?
M. Chen: The application of it—being interested in teaching well, having theories and ideas without the literature while teaching, and then realizing that there was a literature base to draw from. Also a great mentor from Toronto, Dr. Tamara Kelly at York University, who pushed me into going to education conferences during my Master’s.
S. Scott: We were in a class where we had to write a review paper. Miranda is in qualitative biology education research, I am a mathematical/computational biologist/statistician, and Jess is interested in bioinformatics. It made sense to look at how quantitative methods are taught. Personally, I was lucky to come from an undergraduate institution that had a Biomathematics major and attended the Annual Symposium on Biomathematics and Ecology: Education and Research starting my sophomore year of college. Since it emphasizes education, I started to think about a lot of these topics and pay more attention to the type of education I was receiving.
What was the most surprising thing you learned in conducting the review?
M. Chen: Learned so much about the secondary school world, not something I was familiar with before reading and writing pieces with my colleagues—Common Core, AP classes, the lack of instruments out there for measuring a technology impact in quant bio.
S. Scott: I was not aware of how sparse the literature is at the secondary level. There are a few papers out there, but we do not give our secondary educators the support to publish papers and work with education specialists at the doctoral level. A lot of them are doing fantastic work and it is not disseminated because there is not a real place for it.
What are you working on now?
M. Chen: I am in the midst of my dissertation examining teaching and research anxiety in graduate students. With the mental health crisis in academia, I want to dig deeper as to what factors may be contributing to these anxieties. Fingers-crossed, I get the first manuscript on this work out in the Fall!
S. Scott: I am working on some mathematical and statistical models of the spread of gun violence in Chicago, Illinois. It combines social science with mathematics, computation, public health, epidemiology, geography, and statistics. I see myself as a bit of an “academic chameleon” in that I do not fit in one department or discipline necessarily.
Do you have any advice for biologists looking to improve their computational skill set?
M. Chen: I honestly don’t have great computational skills myself, so what I’m trying to do to improve is by using online resources that I find (via social media or YouTube) or get connected to an online community to learn together via programs like QUBES or simply working/practicing with colleagues.
S. Scott: Find all the workshops you can, read all the papers you can, talk to everyone that you can. I have benefited from being proficient in multiple different coding languages so that I can help other people in any language and so that I have a better grasp of what is going on. And, of course, practice a whole lot. Find papers with the code files publicly available and see if you can replicate the results that they presented in the paper.
Do you know of any great educational tools for bringing quantitative skills to students? Are you working on implementing any of these into your classrooms? What challenges do you see to their widespread inclusion? Comment below!