Within biology education, our research spans many different contexts and topics but is united by a core set of theoretical commitments and assumptions that shape the specific questions we ask and the methods we use.

Broadly, we assume that human cognition, behavior, and learning are dynamic and context-sensitive. That is, rather than assume people’s thinking or activity can be explained in terms of the knowledge, skills, or dispositions they have or do not have, we seek to understand what allows people to think or behave in different ways: How do different environments or situations evoke or constrain how people behave, think, or feel?

In attempting to understand these dynamics, we tend to rely on qualitative methods including observations, interviews, and analysis or writing or other artifacts. Often, our research involves designing new learning environments or experiences and trying to understand how these designs make new ways of being and learning possible. 

On this page, I’ve tried to give some examples of different projects — some new, some more developed and ongoing, and some completed or abandoned — to give a flavor for the kinds of things we do.

“Hybrid Labs”: Computational Models and Experimentation

in Introductory Biology Labs

The main idea for this project comes from the observation that in science, experimentation and modeling bootstrap one another (see e.g. MacLeod & Nersessian, 2013): Modeling can inspire a need to do experiments, and experiments can lead to model revision or expansion. We designed biology lab units in which interactions between computer simulations and experiments could drive students’ scientific practice (Gouvea & Wagh, 2019; Gouvea et al., under review).

Question we ask in this context:

  1. In what ways does this “hybrid” design support students’ leaning to engage in scientific practices?
  2. How do students understand the value of experimental data and computational output? How do they seem them as related or different?

In his dissertation work, Rob Hayes is examining how students perceive and act on their own sense of scientific agency in this context.

Students’ reasoning and argumentation in lab reports

Lab reports can be a venue for students to practice articulating and defending their own ideas. But too often lab reports are more like filling in a form (see e.g. this book review). In this line of work we ask the following questions:

  1. How can we (re)design learning environments that activate students’ abilities to engage in scientific thinking and argumentation?
  2. How can we identify evidence of reasoning in students’ writing?

We have found that by reframing intro labs around students’ ideas and by making lab experiments more complex and problematic, we can elevate the quality of students’ engagement in scientific argumentation (Gouvea et al., in preparation).

We are now investigating ways that machine learning techniques can help us discover and characterize different forms of reasoning and grappling with uncertainty in lab reports (see e.g. Jiang et al., 2020 )