Humboldt-Universität zu Berlin - Department of Biology

Sabine Meister, Annette Upmeier zu Belzen

Investigating confirmatory strategies during the perception and interpretation of anomalous data

When students enter science classes, they have their individual conceptions and explanations about scientific phenomena. Sometimes these preconceptions lead to naïve thinking and are not in line with the actual scientific theory. In biology, one example is the balance of nature metaphor used by students, adults and even ecologists to explain ecosystem dynamics (Hovardas et al., 2011; Sander et al., 2006; Zimmerman & Cuddington, 2007). The presentation of anomalous data is one promising approach used in conceptual change research to create a situation in which students’ preconceptions cannot explain the anomaly, thereby inducing the need for an alternative explanation (Chinn & Brewer, 1998, 2001). However, recent studies show that students’ preconceptions are strongly held and conceptual change may be hindered by a robust tendency to confirm existing conceptions rather than to change them in the light of anomalous data (Chinn & Brewer, 2001; Hemmerich et al., 2015; Mason 2001). Those confirmatory strategies can occur during the perceptional and interpretational processes of data evaluation (Knöner, 2014). Research on responses to anomalous data and their influence on theory change mostly use written text passages as the “data” presented to participants (Chinn & Brewer, 1998; Hemmerich et al. 2015; Mason, 2001). So far, there is little research on how students would respond to anomalous data in the form of raw data sets presented in a graphical form (e.g., line graphs). The proposed study focuses on students´ use of confirmatory strategies that can influence the process of conceptual change while they evaluate data presented as line graphs. In particular, we want to investigate, referring to Hemmerich et al. (2015), how students perceive and interpret data sets depending on the amount of anomalous data sets that are given (varying the ratio between anomalous and supportive data sets). In an exploratory study eye-tracking techniques and think-aloud protocols will be used to understand the underlying processes during task processing. The results will be used as foundation for a subsequent large-scale assessment.