Abstract

One of the main challenges of our times is the spread of misinformation and disinformation on the Internet. Manipulation of graphs is one powerful way that has been used to persuade and mislead people. We investigated how adolescents (N = 404) can read and interpret well-constructed and misleading bar graphs. A
misleading graph is based on valid data, but the visual appearance of the graph has been manipulated to distort the message of the graph. The results indicated
that most students knew how to read a single data point from a graph. However, students’ graph interpretation skills (inferring relationships in the represented
data) varied considerably. We performed latent profile analysis (LPA) and identified five profiles, which differed in students’ abilities to interpret well-constructed and misleading graphs, and their risk of being misled with visual manipulations. The students of the best performing profile (Critical graph readers; 17%) could consistently interpret well-constructed and misleading graphs correctly. The weakest performing group (17%) had considerable difficulties in graph interpretation, suggesting an overall lack of understanding of how to interpret graphs. Importantly, the students of the largest profile (Misleadable graph readers; 35%) could interpret well-constructed graphs but were misled by visual manipulations leading to faulty interpretations. We assume that these readers neglected the numerical information of the graph and based their interpretation only on the visual aspects of the graph, i.e., the height of the bars. There is a clear need to develop interventions to help students build resistance against common graph manipulation techniques.
Original languageEnglish
Pages390
Publication statusPublished - 2023
MoE publication typeNot Eligible
EventEARLI 2023 Conference : Education as a Hope in Uncertain Times - Thessaloniki, Greece
Duration: 22 Aug 202326 Aug 2023

Conference

ConferenceEARLI 2023 Conference
Country/TerritoryGreece
CityThessaloniki
Period22/08/202326/08/2023

Fields of Science

  • 113 Computer and information sciences
  • 516 Educational sciences

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