Imagine a dragon made of seaweed: How images enhance learning in Wikipedia
Anita Silva, Maria Tracy, Katharina Reinecke, Eytan Adar, Miriam Redi
TL;DR
This study investigates whether images embedded in Wikipedia articles support learning. By building a dataset of 94 articles with lead images and 470 questions, and conducting a preregistered online within-subject experiment with $n=704$ participants, the authors assess learning across three question types: General Knowledge, Visual Knowledge, and Image Recognition. They find that images significantly improve performance on image-recognition tasks, while effects on general knowledge are negligible and visual-knowledge gains depend on image-text consistency; high-quality, text-consistent images yield the strongest benefits. These findings yield concrete design implications for editors and tools, suggesting targeted image addition and alignment with text, and they release a valuable dataset to benchmark Wikipedia image usefulness for learning.
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.
