Imagine a dragon made of seaweed: How images enhance learning in Wikipedia
March 12, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Anita Silva, Maria Tracy, Katharina Reinecke, Eytan Adar, Miriam Redi
arXiv ID
2403.07613
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.MM
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Though images are ubiquitous across Wikipedia, it is not obvious that the image choices optimally support learning. When well selected, images can enhance learning by dual coding, complementing, or supporting articles. When chosen poorly, images can mislead, distract, and confuse. We developed a large dataset containing 470 questions & answers to 94 Wikipedia articles with images on a wide range of topics. Through an online experiment (n=704), we determined whether the images displayed alongside the text of the article are effective in helping readers understand and learn. For certain tasks, such as learning to identify targets visually (e.g., "which of these pictures is a gujia?"), article images significantly improve accuracy. Images did not significantly improve general knowledge questions (e.g., "where are gujia from?"). Most interestingly, only some images helped with visual knowledge questions (e.g., "what shape is a gujia?"). Using our findings, we reflect on the implications for editors and tools to support image selection.
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