Abstract
In this research project we investigate the role of responses to anomalous data during modelling processes. Modelling is seen as a comprehensive practice that encompasses various aspects of scientific thinking; hence, it is an important style of scientific thinking, especially if analysed from a process-based perspective. Therefore, it provides the opportunity to understand the role of anomalous data on scientific thinking from a broader perspective. We analysed how pre-service biology teachers (N = 11) reacted to self-generated anomalous data during modelling processes induced by investigating a water black box. The videotaped and transcribed modelling processes were analysed using qualitative content analysis. If anomalous data were recognised, a majority of explanations were based on methodical issues. This finding supports results from previous studies investigating responses to first-hand anomalous data. Furthermore, we found four response patterns to anomalous data during modelling processes: no recognition, no explanation, methodical explanation, and model-related explanation. Besides, our study indicates by trend a systematic relation between response patterns to anomalous data and modelling strategies. Consequently, the improvement of responses to anomalous data could be a promising way to foster modelling competencies. We are convinced that an integrated approach to anomalous data and modelling could lead to deeper insights into the role of data in scientific thinking processes.
Highlights
One goal of science education is that students should become scientific thinkers in order to participate in today’s science-based society
For relating pre-service biology teachers’ responses to anomalous data to their modelling strategies, the results of the previous mentioned study (Göhner and Krell 2018) are used, which focusses on the identification of modelling strategies by analysing pre-service biology
The aim of this research is to investigate the role of responses to anomalous data during modelling processes
Summary
One goal of science education is that students should become scientific thinkers in order to participate in today’s science-based society. Scientific thinking is an umbrella term including practices like asking questions, formulating hypotheses, conducting investigations, and evaluating data (Fischer et al 2014; Hetmanek et al 2018; Rönnebeck et al 2016). These practices of scientific thinking are included in national standards for science education of several countries (e.g. Australia: VCAA 2016; Germany: KMK 2005; USA: NGSS Lead States 2013). Most research in science education on responses to anomalous data is conducted with a focus on experimentation as scientific processes (Crujeiras-Pérez and Jiménez-Aleixandre 2019; Lin 2007) as well as conceptual development (Chinn and Brewer 1998; Hemmerich et al 2016). The combination of these two research perspectives offers new implications for science and science teacher education to support both, handling anomalous data and modelling
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