Work / Bluelearn — Community Discussions

Bluelearn — Community Discussions

Designing scalable discussion experiences to increase student engagement and participation.

Bluelearn — Community Discussions

Problem

Students were not actively participating in community discussions, limiting peer learning and engagement. The existing discussion interface felt overwhelming and didn't encourage meaningful participation.

Client

Bluelearn

Role

Product Designer

Industry

Education & Community

Type

App, Web Platform

Scope

UX Design, UI Design, User Research, Community Design

Context

Bluelearn has a large student community where peer learning and discussion are central to the platform's value. However, discussions were underutilized—students either didn't participate or posted low-quality content. The platform needed a discussion experience that: - Encouraged quality contributions - Made it easy to find relevant conversations - Scaled to thousands of active users - Maintained a supportive, educational tone Constraints included: - Large user base with varying engagement levels - Need for moderation at scale - Mobile-first student audience - Integration with existing course and career content

My role

I led the design of the community discussion experience. My responsibilities included: - User research with students to understand barriers to participation - Design of discussion interface, threading, and navigation - Design of content discovery and recommendation systems - Creation of moderation and quality control mechanisms - Collaboration with community managers and engineering team I worked closely with product managers, community managers, and frontend engineers.

Approach

I studied how students currently used (or didn't use) discussions. Many felt intimidated by long threads or didn't know how to contribute meaningfully. Key decisions: - Simplify the discussion interface—remove clutter, focus on content - Design clear visual hierarchy for questions, answers, and follow-ups - Create guided contribution flows for first-time participants - Build in discovery mechanisms to surface relevant discussions I prioritized reducing friction for participation while maintaining quality through design patterns rather than heavy moderation.

Key solutions

Simplified discussion cards

I redesigned discussion cards to clearly show the question, number of responses, and engagement level. Each card had a focused layout that made it easy to scan and decide whether to participate.

Simplified discussion cards

Guided contribution interface

I designed a contribution interface with prompts and templates for common discussion types (questions, answers, follow-ups). This lowered the barrier for students who weren't sure how to participate.

Guided contribution interface

Smart topic discovery

I integrated discovery features that surfaced discussions related to courses students were taking or topics they'd shown interest in. This helped students find relevant conversations without manual searching.

Smart topic discovery

Impact

Student participation in discussions increased by over 50% after the redesign. More importantly, the quality of contributions improved, with more substantive questions and helpful answers. Students reported that the simplified interface felt less intimidating, and the guided contribution flows helped them participate more confidently. The discussion feature became a core part of the Bluelearn learning experience, with students actively seeking peer help and sharing knowledge.

What I learned

This project reinforced that good community design is about removing barriers rather than adding features. The simpler, more focused interface actually led to better participation. I learned that guidance and structure can help users contribute better—giving students templates and prompts didn't restrict creativity; it actually helped them express their ideas more clearly. The discovery mechanisms were crucial—making relevant discussions visible without requiring active searching significantly increased engagement. I'd explore more personalized recommendation algorithms in future iterations.