Teaching

Courses Taught

Instructor on Record:

BIO652 syllabusDownload

Teaching Assistant:

  • Data+ Duke Big Data Initiative (2021): Project manager for a 10-week summer program, applying data science to analyze a 10+ year oceanographic time‐series dataset sampled near the Duke Marine Laboratory. Student team learned how to program in R, access environmental data, perform time series analysis and interpret ecological implications of their results, and prepare a manuscript for publication.  Project led by Dr. Zackary Johnson.
  • Data Analytics for Environmental Science (graduate level) (2021 & 2020)
  • Marine Geospatial Analysis (graduate level) (2019 & 2018):

Teaching philosophy

My primary teaching goal is that every student is immersed in the subject and comes away from my class having learned something valuable. This requires a supportive environment, where students with diverse backgrounds and learning styles can enjoy learning while also succeeding in their educational goals. I am dedicated to focusing on conceptual understanding rather than rote memorization and encouraging students to take an active role in their learning – supporting students in discovering their own passions in research and teaching analytical skills and innovative thinking that will help them succeed regardless of their career path.

Approach to teaching

Climate change and data analytics are universally relevant and interdisciplinary. When teaching quantitative analytical courses, students should leave my classroom being able to describe, explain, and predict the systems that they are interested in. When teaching courses on ecosystem dynamics and climate change, I focus on connecting students with core concepts and encouraging applied thinking (Barrett et. al, 2014) with the overarching goal that students will be able to think critically about the processes that shape our planet. In addition to teaching terms and theories that are important for scientific thinking, I move beyond rote memorization and teach content centered around discussions. For issues such as ecological responses to climate change, students should be exposed to a variety of viewpoints beyond the scientific perspective that can draw upon student’s own experiences. When I teach courses in ecology and climate change, I will utilize active learning techniques such as assigning students to predict, observe, and explain a phenomenon to encourage critical thinking (White and Gunstone, 1992).  

When teaching analytical courses, such as environmental statistics, remote sensing, machine learning, or geospatial analysis, I focus on critical thinking skills and problem solving. In my classes, I emphasize the importance of failing in order to succeed. As the instructor on record for BIOL562, statistics for environmental scientists at UNC, I found that many students taking their first data science course approach programming and statistics like math, where getting the wrong answer means failing, and even more, trying something that gets you to the wrong answer is also a failure. I fully believe that in data analytics you need to fail in order to succeed. In early assignments I provide the answer, and don’t tell the students how to get there (i.e., here is the figure that you need to make, now work out how to make it). Failing in as many ways as possible to make that figure will teach students much more than simply providing them the code to get there. Later, they apply the lessons from those failures to datasets and questions of their choice in a group project. These projects ensure that students can go from collecting and downloading raw data to presenting findings that will ultimately aid in management decisions.

Adaptability is especially important in quantitative ecology. I learned early on in graduate school that a career focused on modeling and analytics must be able to adapt to new methods and programs. Advances in computing allow for more complicated and computationally demanding techniques to run on local machines, boosting the field to constantly develop. When I learned remote sensing in graduate school, we downloaded datasets using specific APIs in R or python, whereas google earth engine has superseded these techniques. This past year, I made it my mission to get up-to-speed with google earth engine functionality so that I could teach students in my Duke BASS connections course how to access and analyze Lidar and imagery datasets using google earth engine in R, the platform they are most familiar with. I am excited to continue learning with my students, and fully believe that as technology advances, my courses should reflect these changes.

When students take my courses, they must learn not only how to “do” data analytics or geospatial analysis, but also to communicate and share their findings effectively. I assign group presentations and poster sessions as an opportunity for students to communicate what they have learned. Importantly, I have learned throughout my academic career that effective writing is critical for proficiently communicating findings, so I will emphasize this skill through lab reports and essays and actively seek ways to teach writing skills in my classes.

When teaching, I critically examine the content of my courses so that they do not reproduce biases or overrepresent one model of legitimate knowledge. For instance, as a part of the geospatial analysis module in my statistics for environmental scientists course at UNC, I provided students with global maps that were created using different projections, and asked them to discuss the implications of these projections and how the pervasive use of one could distort our understanding of the world. In that same module, students had a lab assignment that used geospatial analysis to map the unequal distribution of superfund sites in North Carolina using similar methodology to a study conducted in Illinois (Maranville et. al, 2009). Students were able to use geospatial analysis to explore and reveal environmental justice issues, encouraging students to think critically through place-based learning.

In my classroom and lab, I aim to create the space for students from underrepresented groups to express their perspectives and succeed in their educational goals. When I teach a course, I send out a survey to students inviting them to anonymously communicate anything they would like me to be aware of to help them learn and be comfortable in my class, thereby encouraging students from historically marginalized groups to feel more welcome in my classroom and other students to reflect on their potential biases, assumptions, and identities. When conducting field work with students, I review and agree upon fieldwork and safety plans with all researchers so that all individuals, especially those at higher risk for conflict and violence in the field, are aware of the increased risks certain populations face in the field and are provided with informed strategies to minimize those risks (Demery and Pipkin, 2020).    

Mentoring

Over the course of my schooling, I have had the privilege of learning from many great teachers and mentors. Each of them, in their own unique way, has inspired and motivated me and I am excited to similarly influence students and help them achieve their goals. Just as I value the diversity of backgrounds that have shaped my scientific career, I encourage students to learn from other research groups and mentors to broaden their perspectives. As a mentor, I focus on the “growth mindset”, whereby instead of focusing on inherent ability, I encourage students that effort and dedication create exceptional scientists. I focus on learning from our failures and instilling a love of learning with the ultimate goal of fostering a culture of inclusion and belonging for all.

To facilitate productive student-mentor relationships, I familiarize my mentees with the literature and analytical methods needed to develop research questions that they are interested in and can clearly tackle. Instead of prescribing tasks, I nurture students’ diverse interests and encourage them to focus on questions that arise from the literature, their observations, or past problems. My direct involvement wanes as students grasp core concepts and analytical methods with the ultimate goal that students develop independence and lead their own research projects so that they can become future peers and collaborators. I also encourage a healthy work-life balance and work with students and their unique situations to create a pathway to their chosen career.

Conclusion

In sum, all of my pedagogical strategies are dedicated to teaching students to think critically about the processes that shape our planet and develop analytical skills that allow students to succeed in their chosen career path. I aim to instill in students an appreciation for ecology and global change as well as an understanding that science and analytics are constantly growing and evolving. Effective teaching and mentoring require a willingness to continuously grow, thereby maintaining and improving strategies that work well and critically examining those that do not. My end goal is that students leave my class with valuable knowledge and tangible skills and apply what they have learned beyond the classroom.

Works Cited

Barrett, B.S., W.A. Swick, and D.E. Smith Jr. 2014. Assessing an undergraduate oceanography curriculum. Oceanography 27(4):13–17, http://dx.doi.org/10.5670/ oceanog.2014.99.

Demery, A.C., Pipkin, M.A. Safe fieldwork strategies for at-risk individuals, their supervisors and institutions. Nat Ecol Evol (2020). https://doi.org/10.1038/s41559-020-01328-5

Maranville, A. R., Ting, T. F., & Zhang, Y. (2009). An environmental justice analysis: superfund sites and surrounding communities in Illinois. Environmental Justice, 2(2), 49-58.

White, R.T., and R.F. Gunstone. 1992. Probing Understanding. Routledge, Great Britain, 196 pp.