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Title: Detecting Cherry-Picked Evidence in Texts: Challenges for Undergraduate Students
Authors: Oura, Hiroki
Mochizuki, Toshio
Chinn, Clark A.
Winchester, Eowyn
Yamaguchi, Etsuji
Keywords: Learning Sciences
Issue Date: 2022
Publisher: International Society of the Learning Sciences
Citation: Oura, H., Mochizuki, T., Chinn, C. A., Winchester, E., & Yamaguchi, E. (2022). Detecting cherry-picked evidence in texts: Challenges for undergraduate students. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 1257-1260). International Society of the Learning Sciences.
Abstract: Authors of digital documents often seek to mislead readers by presenting cherrypicked evidence—e.g., a single study supporting a claim when most studies support a different claim. We report results of two experiments to examine whether undergraduate students adjust their epistemic judgments to account for cherry-picked evidence when they read multiple texts with conflicting claims. In Study 1, students adjusted their epistemic judgments when cherry picking was explicitly indicated with warnings called out in texts. In Study 2, however, with a different topic and four conditions that manipulated different degrees to which cherry picking of evidence was explicit, students did not adjust their epistemic judgments, even when evidence was blatantly cherry picked. In addition, very few students mentioned cherry picked evidence in explaining the grounds for their judgments, even when cherry picking was explicit but without such warnings in Study 1. These suggest that most students attend little to whether evidence is cherry picked, except the condition in which authors call out warnings of cherrypicking in texts.
Description: Short Paper
Appears in Collections:ISLS Annual Meeting 2022

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