Detecting Cherry-Picked Evidence in Texts: Challenges for Undergraduate Students

Hiroki Oura, Toshio Mochizuki, Clark Chinn, Eowyn Winchester, Etsuji Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Authors of digital documents often seek to mislead readers by presenting cherry-picked 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 cherry-picking in texts.

Original languageEnglish (US)
Title of host publicationInternational Collaboration toward Educational Innovation for All
Subtitle of host publicationOverarching Research, Development, and Practices - 16th International Conference of the Learning Sciences, ICLS 2022
EditorsClark Chinn, Edna Tan, Carol Chan, Yael Kali
PublisherInternational Society of the Learning Sciences (ISLS)
Pages1257-1260
Number of pages4
ISBN (Electronic)9781737330653
StatePublished - 2022
Event16th International Conference of the Learning Sciences, ICLS 2022 - Virtual, Online, Japan
Duration: Jun 6 2022Jun 10 2022

Publication series

NameProceedings of International Conference of the Learning Sciences, ICLS
ISSN (Print)1814-9316

Conference

Conference16th International Conference of the Learning Sciences, ICLS 2022
Country/TerritoryJapan
CityVirtual, Online
Period6/6/226/10/22

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Education

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