Spotlights: Designs for Directing Learners' Attention in a Large-Scale Social Annotation Platform

Jumana Almahmoud, Farnaz Jahanbakhsh, Marc Facciotti, Michele Igo, Kamali Sripathi, Kobi Gal, David Karger

Research output: Contribution to journalArticlepeer-review


A new approach to online discussion, which situates student discussions in the margins of the course content, can enhance student engagement with course materials. However, in high-enrollment classes, the large number of comments can overwhelm and intimidate students. Some become frustrated by the volume of potential online interactions and by a perceived lack of immediate relevance to their studies. Likewise, instructors are disappointed when outstanding discussions, that they deem valuable for all to see, get lost in the clutter.To address these challenges, we propose visual spotlighting mechanisms for increasing the saliency of selected comments. We piloted and deployed multiple designs in two high-enrollment biology courses at a large public university in the United States. Interviews, surveys, and a controlled experiment show that spotlighting relevant comments in heavily annotated texts positively affects students' engagement, measured in terms of their attention to comments, and their reported sense of validation and pride. Students also reported their preferences for certain spotlighting designs.
Original languageEnglish
Article number540
JournalProceedings of the ACM on Human-Computer Interaction
Issue numberCSCW2
StatePublished - 1 Nov 2022


  • ux
  • social annotation
  • comments
  • spotlights
  • annotations


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