Analysis of Data-Based Scientific Reasoning from a Product-Based and a Process-Based Perspective

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.