Jennifer Kovacs, Ph.D. Agnes Scott College


I love to discover. It’s what drew me to biology and to become a scientist ultimately, but it’s not just the moment of discovery that thrills me. It’s all the pieces that go into it: the learning and observations that precede any discovery, that tantalizing, tip-of-the-tongue feeling that there’s something just beyond our current understanding that’s waiting for us, the work and research and tool-building and collecting that’s necessary, and finally after we’ve found it, the storytelling and teaching needed to share any discovery no matter how big or small. I love all of it, and I love empowering others to make discoveries about themselves, about their learning, about their fields & passions, and about their world.

My goal in any course, lab, research project, or workshop is for students to acquire and practice skills and tools to help them make their own discoveries. The main focus of my classes is 1) to use the scientific method to pose interesting questions, develop relevant hypotheses, and generate testable predictions and 2) to develop and advance our data analysis and visualization skills using a variety of tools, including coding language such as R. My classes also focus on exploring real-world problems. I’ve found that using real data sets and developing case studies from recent primary literature helps students better engage with the larger scientific issues, like climate change, that they will likely continue to grapple with throughout their lives and careers. So, while my courses center around important biology topics like ecology, evolution, and behavior, I have intentionally used my time with students to hone the quantitative reasoning skills and communication skills crucial to their continued success after graduation.

While our incoming students have completed the high school math courses required to graduate high school and enroll in college, very often, students are not comfortable using math and quantitative reasoning outside of the confines of a math course. Multiple studies have found that women, minorities, and minority women often self-report being academically and mathematically weaker than their Caucasian male counterparts, though they often perform similarly academically (American Association of University Women Report 2010; MacPhee et al. 2013 Analyses of Social Issues and Public Policy). Biology, like many disciplines, requires a high degree of quantitative skill, which, with basic data literacy and reasoning, is easier to achieve. Therefore, to build greater confidence and self-efficacy around quantitative skills in my students, I have intentionally built a sizeable quantitative component into all of my courses. This emphasis starts in Bio 110, the first course in the biology major sequence, where students are given real-world data sets to analyze and explain. It continues into the upper-level courses I teach in the biology sequence.

This emphasis on analysis, visualization, and communication is best exemplified by the Data Intensive Ecology course I designed and delivered for the first time in the Spring of 2021 and will teach again in Spring 2023. The course is built around a semester-long project that provides guided training on using valuable and in-demand tools like R, Python, and GIS to complete an authentic research project. Students first develop an ecological research question based on assigned preliminary readings and delve into the massive collection of long-term ecological data that is publicly available through NSF’s NEON (National Ecological Observation Network) initiative. We then work to have every student develop and then test a publishable scientific hypothesis. I then work extensively with students in small groups and one-on-one settings to help them develop the coding skills (either R or Python) necessary to wrangle, combine, and analyze these large ecological datasets to address their original research question. With these new tools, students then complete the statistical analyses and data visualizations needed to evaluate their predictions. The course culminates in every student presenting the findings of their individual semester-long research project during the annual undergraduate research day (SpARC) to the broader Agnes Scott community. For many of my students, this is one of their first research presentations and their first time using a coding language to analyze data.  My goal for the course is that students leave better equipped to make and share their own scientific discoveries.

“I knew nothing about R or Python before! I had not individually conducted my own research either! This was even my first time participating in SpARC, so I learned a lot from this course!” -from Spring 2021 student evaluations for Data Intensive Ecology

“Even though I had previous experience with coding, I learned so many new things that I otherwise would not have.” -from Spring 2021 student evaluations for Data Intensive Ecology

“I loved this course! It will be a perfect addition to my resume and it will be very helpful in my long-term career.” -from Spring 2021 student evaluations for Data Intensive Ecology

“I value the experiences and knowledge I gained from this course!” -from Spring 2021 student evaluations for Data Intensive Ecology

In all my classes, my goal is to empower my students to become agents of change, whether by learning enough R to say something meaningful about a dataset and sharing that with others or learning how to get stuck and then unstuck while learning about something new. The key to creating a space where students can genuinely become leaders of their own learning is to build high-trust/low-stress classrooms where students feel comfortable failing, struggling, and asking questions. I work to create these spaces by offering low-stakes, skill-building assignments, often with flexible deadlines, while also allowing in-class work time (even when online)  in which I am available to answer questions, offer encouragement and clarification, and help students recognize that struggling is often an essential part of learning something new.

“Dr. Kovacs clearly explained any questions I had regarding the coursework. She always reminded us to reach out to her if we had any issues and when we did she was always very helpful” – from student evaluations

“I never felt like I was a hindrance for asking questions. Dr. Kovacs treated me with respect and encourage any questions to make sure that I would be able to succeed.” – from student evaluations

“Dr. Kovacs always understood when extenuating circumstances were at play and treated all of us with dignity and respect through a very difficult online semester.” – from student evaluations

By placing a heavy emphasis on data literacy and quantitative reasoning while also providing students the support needed to successfully grapple with learning these skills, my classes can make students feel more comfortable interpreting and evaluating data presented to them, as well as provide them with the confidence to do their own analyses and make their own discoveries. Confidence starts with familiarity. With repeated exposure to data, data analysis, and data visualization, students become comfortable using the tools, including computational skills such as coding and data visualization, necessary for working with real-world data sets, answering real scientific questions, and making real discoveries.