Enhancing User Engagement in Large-Scale Social Annotation Platforms: Community-Based Design Interventions and Implications for Large Language Models (LLMs)
🤖 AI Summary
Social annotation platforms in large-scale online courses suffer from comment overload, exacerbating learner cognitive load and degrading discussion quality. Method: We propose a community-driven design intervention featuring (1) a novel tiered visibility mechanism calibrated to individual preferences and collective awareness, and (2) an LLM-augmented paradigm supporting annotation summarization, writing refinement, and intelligent filtering—both iteratively co-designed with users and rigorously evaluated via a large-scale educational A/B trial (N > 3,000). Contribution/Results: This work provides the first systematic empirical validation of how visibility controls shape conversational flow and cognitive depth in peer annotation. It significantly reduces discussion fatigue, increases high-quality interactions by 37%, and boosts cross-group collaborative engagement. The study yields actionable design principles and an implementable LLM-integrated architecture for pedagogically grounded social annotation systems.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Massive Online Courses
Annotation Platform
Learning Discussion Quality
Innovation
Methods, ideas, or system contributions that make the work stand out.
Community Design Optimization
Large Language Model Assistance
Comment Visibility Control
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