Challenging Postsecondary Students' Climate Change Misconceptions With Novel Data and the Data Literacy Supports

• In Event: Grappling With Socioscientific Topics During Knowledge Revision and Belief Change in Varied Social Contexts

Abstract

Objectives: Prompting people to estimate just a handful of climate change statistics before presenting them with the scientifically accepted value can shift their misconceptions to be more aligned with scientific consensus (Ranney & Clark, 2016; Thacker & Sinatra, 2022). Yet, interventions created thus far are not easily accessible to the general public and the estimation strategies that people employ when reasoning with climate change numbers are not well known. The purpose of this preregistered study was to address this research gap by developing and testing an openly accessible online intervention that presents undergraduate students with numbers about climate change after they estimate those numbers and by exploring relevant data literacy strategies that support learning in this context.

Perspectives: Theory posits that, when people are presented with novel information (e.g., climate change data), they initially pre-process the information, judge the plausibility of associated claims, and potentially restructure their knowledge and change their misconceptions as a result (Lombardi et al., 2016). Based on this framework, we designed a learning intervention that presents people with novel data and bolsters estimation strategies (Siegler, 2016) to enhance comprehensibility and plausibility of the consensus around climate change.

Methods: This study consisted of two phases. The first phase was a design-based research study in which an open-source online “estimation game” was developed over the course of 22 think-aloud interviews with undergraduate and graduate students. This intervention prompted participants to estimate 12 numbers about climate change before presenting the true value. The second phase employed the intervention in an online experimental survey setting with a national sample of undergraduate students (N=605). Students were randomly assigned to one of three experimental conditions: (a) an intervention group, (b) the intervention group modified with additional instruction on key data literacy skills, and (c) a control group that read an expository text. This study was pre-registered; all hypotheses and study materials are available on The Open Science Framework.

Results: Results revealed that individuals who were randomly assigned to engage with the estimation game had fewer climate change misconceptions at posttest than the control group (d=0.3, p<.001). Modifying the baseline intervention with data literacy instruction had no additional learning benefits compared to the baseline intervention (d=0.06, p=.167); however, qualitative reports of estimation strategies revealed that more students in the modified intervention group reported using explicit computation strategies such as making use of given information (z=3.0, p=.001), and mathematically manipulating that information (z=1.73, p=.041). They also tended to unexpectedly reflect on their inaccuracies when reporting their estimation strategies (z=3.12, p<.001).

Significance: Findings contribute to theory and practice. This study (a) tested relationships hypothesized in conceptual change models (Lombardi et al., 2016), (b) replicated and extended prior work in novel contexts, (c) explored the extent to which bolstering data literacy skills can shape problem solving strategies, and (d) resulted in an intervention that can be easily shared online with science instructors, mathematics instructors, and with the general public.

Authors

• Ian Thacker, University of Texas - San Antonio
  Presenting Author

• Rachel Broadway, University of Texas - San Antonio
  Non-Presenting Author

• Hannah French, University of Texas - San Antonio
  Non-Presenting Author