Abductive reasoning in modeling biological phenomena as complex systems

Abstract

IntroductionAbductive reasoning is a type of reasoning that is applied to generate causal explanations. Modeling for inquiry is an important practice in science and science education that involves constructing models as causal explanations for scientific phenomena. Thus, abductive reasoning is applied in modeling for inquiry. Biological phenomena are often best explained as complex systems, which means that their explanations ideally include causes and mechanisms on different organizational levels. In this study, we investigate the role of abductive reasoning in modeling for inquiry and its potential for explaining biological phenomena as complex systems.MethodsEighteen pre-service science teachers were randomly assigned to model one of two biological phenomena: either a person's reddened face, for which participants knew of explanations from their everyday lives, or a clownfish changing its sex, for which participants did not know about explanations. Using the think-aloud method, we examined the presence of abductive reasoning in participants' modeling processes. We also analyzed modeling processes in terms of participants' ability to model the phenomena as complex systems.ResultsAll participants reasoned abductively when solving the modeling task. However, modeling processes differed depending on the phenomenon. For the reddened face, participants generated simple models that they were confident with. In contrast, for the clownfish, participants generated more complex models that they were insecure about. Extensive engagement in abductive reasoning alone did not lead to the generation of models that explained the phenomena as complex systems.DiscussionBased on the findings, we conclude that engagement in abductive reasoning will not suffice to explain phenomena as complex systems. We suggest examining in future studies how abductive reasoning is combined with systems thinking skills to explain phenomena as complex systems in biological model construction.