FullBrain: a Social E-learning Platform

TL;DR

FullBrain is a social e-learning platform enabling students to share knowledge, ask questions, and access intelligent search and ranking features, with promising user engagement and system performance metrics.

Contribution

The paper introduces FullBrain, a novel social e-learning platform with integrated search and leaderboard modules, and provides performance analysis and user behavior insights.

Findings

Query response time below 0.11 seconds

97% of user activity concentrated in top 4 leaderboard positions

System stress tests inform future scalability improvements

Abstract

We present FullBrain, a social e-learning platform where students share and track their knowledge. FullBrain users can post notes, ask questions and share learning resources in dedicated course and concept spaces. We detail two components of FullBrain: a SIR system equipped with query autocomplete and query autosuggestion, and a Leaderboard module to improve user experience. We analyzed the day-to-day users' usage of the SIR system, measuring a time-to-complete a request below 0.11s, matching or exceeding our UX targets. Moreover, we performed stress tests which lead the way for more detailed analysis. Through a preliminary user study and log data analysis, we observe that 97% of the users' activity is directed to the top 4 positions in the leaderboard.