Supporting Climate Change Understanding with Novel Data Estimation Instruction, and Epistemic Prompts

Posted on by Stephen Gee

Texts presenting novel numerical data can shift learners’ attitudes and conceptions about controversial science topics. However, little is known about the mechanisms underlying this conceptual change. The purpose of this study was to investigate two potential mechanisms that underlie learning from novel data: numerical estimation skills and epistemic cognition. This research investigated combinations of two treatments—a numerical estimation and epistemic cognition intervention—that were designed to enhance people’s ability to make sense of key numbers about climate change when integrated into an existing intervention. Results indicated that undergraduate students (N = 516) who engaged with climate change data held fewer misconceptions compared with a group that read an expository text, though their judgments of climate change plausibility were similar. Results also showed that the two modifications to the central intervention did not have statistically significant effects on knowledge or plausibility when compared with the unmodified intervention. However, we found that individuals’ openness to reason with and integrate new evidence significantly moderated the knowledge effects of the intervention when the intervention was supplemented with both modifications. These findings provide emerging evidence that, among those who are open to reason with new evidence, supporting mathematical reasoning skills and reflection on discrepant information can enhance conceptual change in science.

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