Abstract
In this study, we investigated participants’ reactions to supportive and anomalous data in the context of population dynamics. Based on previous findings on conceptions about ecosystems and responses to anomalous data, we assumed a tendency to confirm the initial prediction after dealing with contradicting data. Our aim was to integrate a product-based analysis, operationalized as prediction group changes with process-based analyses of individual data-based scientific reasoning processes to gain a deeper insight into the ongoing cognitive processes. Based on a theoretical framework describing a data-based scientific reasoning process, we developed an instrument assessing initial and subsequent predictions, confidence change toward these predictions, and the subprocesses data appraisal, data explanation, and data interpretation. We analyzed the data of twenty pre-service biology teachers applying a mixed-methods approach. Our results show that participants tend to maintain their initial prediction fully or change to predictions associated with a mix of different conceptions. Maintenance was observed even if most participants were able to use sophisticated conceptual knowledge during their processes of data-based scientific reasoning. Furthermore, our findings implicate the role of confidence changes and the influences of test wiseness.
Highlights
Developing, understanding, and critically questioning knowledge and processes of deriving knowledge in science are key aspects of scientific reasoning [1,2]
We developed a paper-and-pencil instrument in the context of population dynamics containing a set of tasks for assessing individual initial expectations and subsequently responding to anomalous and supportive data (Table 1)
Balance of Nature (BoN)‐associated data sets presented in the instrument are assumed to be perceived as supportive, while Flux of Nature (FoN)‐associated data sets are assumed to be perceived as anomalous data
Summary
Developing, understanding, and critically questioning knowledge and processes of deriving knowledge in science are key aspects of scientific reasoning [1,2]. Studies that only focus on the reaction (e.g., change of initial theory) analyze responses to anomalous data from a product-based view. Previous studies on anomalous data show that data contradicting initial expectations are discounted in different ways [8,15,17,18] Such responses to anomalous data rely on a variety of justifications [8,9] based on different aspects of conceptual, procedural, or epistemic knowledge [3]. The presentation of empirical data sets is more likely to induce theory change [39]; presenting anomalous data in the context of population dynamics in their typical representation as line graphs might give interesting insights for research on data-based scientific reasoning. Epistemic knowledge associated with meta-modeling knowledge is required during scientific reasoning in the context of population dynamics [42]
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