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

Author:

Almahmoud Jumana1,Jahanbakhsh Farnaz1,Facciotti Marc2,Igo Michele2,Sripathi Kamali2,Gal Kobi3,Karger David1

Affiliation:

1. Massachusetts Institute of Technology, Cambridge, MA, USA

2. University of California, Davis, Davis, CA, USA

3. Ben-Gurion University of the Negev, Beer Sheva, Israel

Abstract

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.

Funder

NSF

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Human-Computer Interaction,Social Sciences (miscellaneous)

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