Ph.D. Biological Sciences, Purdue University.
B.A. Molecular, Cellular, and Developmental Biology, University of Colorado at Boulder.
One of my favorite parts of my job is getting lost in data. We live in a data age, which brings up new questions, tools, and concerns for how to manage, share, and understand a rich sea of information. My goal is to help my students and colleagues to usher in data-intensive research responsibly. My path to data visualization has been circuitous. I was trained in molecular biology, worked in developmental biology laboratories, and now research undergraduate biology education. I use data visualization to conduct research in science education, empower students to work with data, and provide insights into how students learn.
I believe my role as an educator is to prepare students to tackle complex problems. To do this in the classroom, I prioritize making models, constructing explanations, and arguing claims with evidence. When I teach, I align my lessons with education research to engage students in challenging and rewarding work. To help students achieve, I take the role of partner to guide students as they learn. I use group work, projects, journaling, and activities to create a dynamic learning environment with minimal lecturing that can support all my students toward success.
Recent Courses Taught
- BIS 232 Introduction to Data Visualization
- BIS 315 Understanding Statistics
- BES 301 Scientific Methods and Practices
Research and Scholarship
My research spans three broad areas related to visualization: teaching science as it is practiced, teaching different ways to think, and mapping the scholarship that informs teaching. First, rather than emphasizing facts, I am interested in teaching scientific practices such as modeling and explaining. Teaching students to use and master these practices is a top priority for science education. I research the activities and assessments that support these practices and their impact on different student groups. Second, people have many different ways to think about the world. I study two way of thinking: mechanistic thinking and systems thinking. As part of my research, I unpack, visualize, and test these two frameworks to understand their usefulness to students as they learn. Finally, I analyze and synthesize scholarship related to science education using tools from information sciences. By using measures of document citation and co-citation, I create vivid maps of published scholarship. These maps help to visualize the relationship between impactful ideas and also illuminate missed opportunities that are ripe for future research. Through these three areas of scholarship, I strive to enhance instruction and advance student achievement and retention in data-intensive fields to support the next generation of visualizers.
Trujillo, C. M., & Long, T. M. (2018). Document co-citation analysis to enhance transdisciplinary research. Science advances, 4(1), e1701130.
Trujillo, C. M., Anderson, T. R., & Pelaez, N. J. (2015). A model of how different biology experts explain molecular and cellular mechanisms. CBE—Life Sciences Education, 14(2), ar20.