A Model of Scientific Data Reasoning

Abstract

Data reasoning is an essential component of scientific reasoning, as a component of evidence evaluation. In this paper, we outline a model of scientific data reasoning that describes how data sensemaking underlies data reasoning. Data sensemaking, a relatively automatic process rooted in perceptual mechanisms that summarize large quantities of information in the environment, begins early in development, and is refined with experience, knowledge, and improved strategy use. Summarizing data highlights set properties such as central tendency and variability, and these properties are used to draw inferences from data. However, both data sensemaking and data reasoning are subject to cognitive biases or heuristics that can lead to flawed conclusions. The tools of scientific reasoning, including external representations, scientific hypothesis testing, and drawing probabilistic conclusions, can help reduce the likelihood of such flaws and help improve data reasoning. Although data sensemaking and data reasoning are not supplanted by scientific data reasoning, scientific reasoning skills can be leveraged to improve learning about science and reasoning with data.

A characterization of chemistry learners’ engagement in data analysis and interpretation

“…Relevant prior knowledge is also necessary to tie patterns found within the data back to the phenomenon under study (Shah and Carpenter, 1995;Latour, 1999;Shah and Hoeffner, 2002;Lai et al, 2016). In the absence of the necessary content knowledge, many students will rely on heuristics and intuition or neglect to use reasoning entirely (Heisterkamp and Talanquer, 2015;Becker et al, 2017;Masnick and Morris, 2022). For more advanced scientists, this prior knowledge is key because with it, the scientist will contextualize their interpretations in the broader scientific context; that is, they consider hypotheses, theory, experimental design, and implications to draw conclusions (Angra and Gardner, 2017).…”

“To Be Honest, I Didn’t Even Use the Data”: Organic Chemistry Students’ Engagement in Data Analysis and Interpretation

“…The primary source of empirical data from which students have reasoned about these phenomena is spectral data, which is a unique data source in that there is substantial instructional time devoted to developing students’ competency at spectral analysis and interpretation. , Less instructional attention is typically paid to organic chemistry students’ engagement in other data analysis and interpretation and science practices, generally . It remains understudied then how organic chemistry students engage with empirical data, use empirical data to refine models, and integrate empirical data with models to construct arguments . We seek to add to this body of work by understanding specifically how students’ conceptual knowledge and engagement in modeling interacts with their use of empirical data.…”

“…Data analysis and interpretation involves deliberate and purposeful analysis of raw and transformed data to identify and extract important information, establish relevant patterns and relationships, and build or evaluate models. ,, However, students have demonstrated difficulty differentiating between relevant and irrelevant information. , Research has shown that relevant content and prior knowledge and framing plays an important role in students’ capacities to find appropriate features of the data for quality analysis. ,,− In the absence of relevant chemical knowledge or limitations in using chemical models, Heisterkamp and Talanquer found that students faced more challenges in explaining empirical data trends . In addition, Beck, Rupp, and Brandriet noticed students who expressed more confusion in their content knowledge also tended to exclude empirical data in constructing their kinetic rate models .…”

How Does the Accuracy of Children’s Number Representations Influence the Accuracy of Their Numerical Predictions?

“…Predictions make the environment more understandable by easing the burden on our limited cognitive capacities ( Bubic et al, 2010 ). Predictions are generated, in part, by summarizing relevant information and using this summary as the basis for plausible forecasts ( Kveraga et al, 2007 ; Henderson, 2017 Masnick and Morris, 2022 ). People rapidly summarize perceptual (e.g., color and position) and cognitive features (e.g., interpreting emotions from faces) in complex scenes ( Whitney and Yamanashi Leib, 2018 ), which become available for predictions ( Kveraga et al, 2007 ).…”