Enhancing User Engagement in Large-Scale Social Annotation Platforms: Community-Based Design Interventions and Implications for Large Language Models (LLMs)

TL;DR

This study explores community-based design interventions in social annotation platforms to improve student engagement and manage comment overload, with implications for integrating Large Language Models to enhance educational discussions.

Contribution

It introduces targeted comment visibility controls and demonstrates their effectiveness, offering new strategies for designing scalable, engaging social annotation systems in education.

Findings

Targeted comment visibility improves peer interactions.

Design interventions reduce discussion overload.

Opportunities for LLM integration in summarization and curation.

Abstract

Social annotation platforms enable student engagement by integrating discussions directly into course materials. However, in large online courses, the sheer volume of comments can overwhelm students and impede learning. This paper investigates community-based design interventions on a social annotation platform (NB) to address this challenge and foster more meaningful online educational discussions. By examining student preferences and reactions to different curation strategies, this research aims to optimize the utility of social annotations in educational contexts. A key emphasis is placed on how the visibility of comments shapes group interactions, guides conversational flows, and enriches learning experiences. The study combined iterative design and development with two large-scale experiments to create and refine comment curation strategies, involving thousands of students. The study introduced specific features of the platform, such as targeted comment visibility controls, which demonstrably improved peer interactions and reduced discussion overload. These findings inform the design of next-generation social annotation systems and highlight opportunities to integrate Large Language Models (LLMs) for key activities like summarizing annotations, improving clarity in student writing, and assisting instructors with efficient comment curation.