Hybrid Labs: How Students Use Computer Models to Motivate and Make Meaning from Experiments

Experimentation in science is integrated with other scientific practices that give experiments purpose and meaning. Yet most students experience experiments as stand-alone activities. We argue that this makes it challenging for students to motivate and make meaning from experimentation. One way to ameliorate these challenges is to put experiments in conversation with other scientific practices. In this chapter, we describe the design of an introductory biology laboratory curriculum in which students conduct investigations using both experiments and computational models. We highlight two ways in which the interaction between these two practices can enrich students’ engagement with experimentation: (1) Comparing the assumptions and patterns that emerge from experiments and models can help students identify questions to motivate experimental design; (2) Attending to the parameters and dynamics in the computational model can help students make sense of experimental data and situate their claims in a broader conceptual context. Our purpose is to inspire educators in the life sciences to consider how putting experiments in conversation with other practices like computational modeling can enrich students’ engagement with experimentation.